Romanian Distribution Committee Magazine, Volume 9, Issue 1, Year 2018

EDITORIAL BOARD

Romanian Distribution Committee Magazine

Volume: 9 Issue: 1 Year: 2018 Scientific Review of the Romanian Distribution Committee

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Ion Ababii, Chişinău

Aurel Iancu, Bucharest

Constantin Roşca, Craiova

Nicolae Albu, Brasov

Mitsuhiko Iyoda, Osaka

Analisa Romani,Turin

Ruxandra Andreea Albu, Bucharest

Mohamed Latib, Gwynedd

James Rowell, Buckingham

Levent Altinay, Oxford UK

Dong II Lee, Seoul

John Saee, Virginia Beach VA

Kathleen Andrews, Colorado Springs

Min-Sang Lee, Gyeonggi-Do

Cătălin Sfrija, Bucharest

Virgil Balaure, Bucharest

Claude Magnan, Paris

Adrian Socol, Strasbourg

Dan Barbilian, Bucharest

Radu Titus Marinescu, Bucharest

Eliot Sorel, Washington D.C.

Riccardo Beltramo, Turin

James K. McCollum, Huntsville

Mihaela-Luminița Staicu, Bucharest

Richard Beresford, Oxford Uk

Nicolae Mihăiescu, Bucharest

Radu Patru Stanciu, Bucharest

Dumitru Borţun, Bucharest

Dumitru Miron, Bucharest

John L. Stanton, Jr., Philadelphia

Leonardo Borsacchi, Turin

Dan Mischianu, Bucharest

Peter Starchon, Bratislava

Mihail Cernavca, Chişinău

John Murray, Dublin

Felicia Stăncioiu, Bucharest

Ioana Chiţu, Brasov

Alexandru Nedelea, Suceava

Marcin Waldemar Staniewski, Warsaw

Doiniţa Ciocîrlan, Bucharest

Hélène Nikolopoulou, Lille

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Tudorel Ciurea, Craiova

Olguța Anca Orzan, Bucharest

Filimon Stremţan, Alba-Iulia

Alexandru Vlad Ciurea, Bucharest

Gheorghe Orzan, Bucharest

David Stucki, Fribourg

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Elena Mihaela Pahonțu, Bucharest

Ion Voicu Sucala, Cluj-Napoca

Jean-Sébastien Desjonqueres, Colmar

Rodica Pamfilie, Bucharest

Kamil Pícha, Ceske Budejovice

Aurel Dobre, Călăraşi

Iulian Patriche, Bucharest

Laurenţiu Tăchiciu, Bucharest

Luigi Dumitrescu, Sibiu

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Emil Toescu, Birmingham

Mariana Drăguşin, Bucharest

Mircea Penescu, Bucharest

Simona Ungureanu, Bucharest

Ovidiu Folcuţ, Bucharest

William Perttula, San Francisco

Vlad Budu, Bucharest

Luigi Frati, Roma, Italy

Virgil Popa, Targoviste

Eva Waginger, Wien

Petru FILIP, Bucharest

Marius D. Pop, Cluj-Napoca

Léon F. Wegnez, Brussels

Victor Greu, Bucharest

Ana-Maria Preda, Bucharest

Răzvan Zaharia, Bucharest

Bernd Hallier, Köln

Monica Purcărea, Bucharest

Gheorghe Zaman, Bucharest

Sang-Lin Han, Seoul

Cristinel Radu, Călăraşi

Dana Zadrazilova, Prague

Florinel Radu, Fribourg

Sinisa Zaric, Belgrade

Gabriela Radulian, Bucharest

Hans Zwaga, Tornio

YOUNG EDITORIAL BOARD MEMBERS REVIEWERS

SCIENTIFIC COUNCIL

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Andreea Apetrei, Iasi

Adrian Lală, Bucharest

Adalbert Lucian Banyai, Bucharest

Irina Purcărea, Bucharest

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Roxana Codita, München

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George Cosmin Tănase, Bucharest Oana Patricia Zaharia, Bucharest

Alexandru Ionescu, Romanian-American University Adriana Bîrcă, “George Bariţiu” University Brasov Nelu Florea, “Alexandru Ioan Cuza” University Iasi Ana Ispas, Transilvania University Brasov Irena Jindrichowska, University of Economics and Management in Prague Costel Iliuţă Negricea, Romanian-American University Adina Negruşa, “Babes-Boyay” University Cluj-Napoca Anca Purcărea, Academy of Economic Studies in Bucharest Monica Paula Raţiu, Romanian-American University Gabriela L. Sabau, Memorial University, Sir Wilfred Grenfell College Andreea Săseanu, Academy of Economic Studies in Bucharest

Vlad Barbu, Bucharest Gabriel Brătucu, Brasov Ion Bulborea, Bucharest Mircea Buruian, Targu Mures Iacob Cătoiu, Bucharest Jean Constantinescu, Bucharest Beniamin Cotigaru, Bucharest Radu Diaconescu, Iasi Valeriu Dulgheru, Chişinău Constantin Floricel, Bucharest Valeriu Ioan-Franc, Bucharest

Gheorghe Ionescu, Timisoara Christophe Magnan, Montréal Pompiliu Manea, Cluj Andrei Moldovan, Bucharest Dafin Fior Muresan, Cluj Neculae Năbârjoiu, Bucharest Constantin Oprean, Sibiu Dumitru Patriche, Bucharest Florian Popa, Bucharest Dumitru Tudorache, Bucharest Ion Smedescu, Bucharest Victor Părăuşanu, Bucharest

YOUNG EDITORIAL BOARD MEMBERS REVIEWERS

SCIENTIFIC COUNCIL

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Andreea Apetrei, Iasi

Adrian Lală, Bucharest

Adalbert Lucian Banyai, Bucharest

Irina Purcărea, Bucharest

George Bobîrnac, Bucharest

Ivona Stoica, Bucharest

Roxana Codita, München

Dan Smedescu, Bucharest

Stefano Duglio, Turin

Constantin C. Stanciu, New York

Larisa-Diana Dorobat, Geneve

Radu Pătru Stanciu, Bucharest

Darius Ilincaş, London

George Cosmin Tănase, Bucharest Oana Patricia Zaharia, Bucharest

Alexandru Ionescu, Romanian-American University Adriana Bîrcă, “George Bariţiu” University Brasov Nelu Florea, “Alexandru Ioan Cuza” University Iasi Ana Ispas, Transilvania University Brasov Irena Jindrichowska, University of Economics and Management in Prague Costel Iliuţă Negricea, Romanian-American University Adina Negruşa, “Babes-Boyay” University Cluj-Napoca Anca Purcărea, Academy of Economic Studies in Bucharest Monica Paula Raţiu, Romanian-American University Gabriela L. Sabau, Memorial University, Sir Wilfred Grenfell College Andreea Săseanu, Academy of Economic Studies in Bucharest

Vlad Barbu, Bucharest Gabriel Brătucu, Brasov Ion Bulborea, Bucharest Mircea Buruian, Targu Mures Iacob Cătoiu, Bucharest Jean Constantinescu, Bucharest Beniamin Cotigaru, Bucharest Radu Diaconescu, Iasi Valeriu Dulgheru, Chişinău Constantin Floricel, Bucharest Valeriu Ioan-Franc, Bucharest

Gheorghe Ionescu, Timisoara Christophe Magnan, Montréal Pompiliu Manea, Cluj Andrei Moldovan, Bucharest Dafin Fior Muresan, Cluj Neculae Năbârjoiu, Bucharest Constantin Oprean, Sibiu Dumitru Patriche, Bucharest Florian Popa, Bucharest Dumitru Tudorache, Bucharest Ion Smedescu, Bucharest Victor Părăuşanu, Bucharest

EDITOR-IN-CHIEF Theodor Valentin Purcărea

EXECUTIVE EDITOR Victor Lorin Purcărea

ASSISTANT EDITORS Dodu Gheorghe Petrescu Cătălina Poiană Raluca Gheorghe Mihaela Luminița Staicu

PUBLISHING EDITORS Petruţ Radu Ovidiu Călin

ART DESIGNER DIRECTOR Alexandru Andrei Bejan 8

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Romanian Distribution Committee Magazine Volume: 9 Issue: 1 Year: 2018

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CONTENTS

P. 12.

Retailers’ Technology Investments, Behavioral economics, Psychographic Profiling and the Paradox of the New Technology

Theodor Valentin PURCĂREA

P. 14.

Extending Information and Communications Technologies’ Impact on Knowledge Based Society through Artificial and Collective Intelligence -Part1Victor GREU

P. 24.

Artificial Intelligence: Optimizing the Experience of Digital Marketing George Cosmin TĂNASE

P.30.

Conversational Commerce, New Marketing Tactics, CX, Loyalty and Emotions Theodor PURCĂREA

P.40.

Léon F. WEGNEZ (by courtesy of) - How supermarkets seduce shoppers, “Distribu-

tion d’aujourd’hui”, 58ème année, Novembre 2017, Brussels

P. 46.

Bernd HALLIER (by courtesy of) - EuroCIS February 2018, Vietnam, Impacts from

History, Matching store formats, Astana & Almaty, Geopolitical Context, Plekhanov University

P. 52.

Isabelle WEGNEZ (by courtesy of) - <<All about Beauty>>, the new positioning of Di,

“Distribution d’aujourd’hui”, 58ème année, Novembre 2018, Brussels

The responsibility for the contents of the scientific and the authenticity of the published materials and opinions expressed rests with the author.

RETAILERS’ TECHNOLOGY INVESTMENTS, BEHAVIORAL ECONOMICS, PSYCHOGRAPHIC PROFILING AND THE PARADOX OF THE NEW TECHNOLOGY Theodor Valentin Purcărea Consultants argues that there is a gap between what retailers understood with regard to consumer behavior’s shift and their slowly reaction, a critical role in keeping customers’ attention being played by technology driving personalized customer experiences. That is why retailers need technology investments improving Omni channel experiences, offering a seamless experience across mobile, desktop, and in-store by synchronizing the physical and the digital worlds.

Retailers need to allow their customers to share their feedback through a conversational customer dialogue, engaging them at their right moment, valuing their time, enriching their CX. Being aware of the increasingly competitive and fragmented retail marketplace, they also need to allow their customers to take control of their shopping experience, personalizing content for the mobile shoppers who are regularly interacting and engaging with retailers’ apps, addressing their customers’ individual purchasing habits and desires. But in order to discern their customers’ connections, and engage with each more personally across all touchpoints retailers need Customer Intelligence 360 capabilities which enable an interactive Omni channel experience for their customers, increase their customers’ knowledge for superior RFM (Recency, Frequency, Monetary Value) results, and manage and measure retailers’ outlets as Omni channel hubs.

As shown recently by Retail eMarketer, a BRP (Boston Retail Partners) survey of retailers in North America (“2018 POS/Customer Engagement Survey,” January 11, 2018) revealed that 62% of respondents confirmed that the leading engagement priority for 2018 is personalized CX. This was followed (54% of respondents) by customer mobile experience alignment (mobile app/website/responsive design), empowering sales associates with mobile tools (51%), real-time retail (38% - disseminating data across all channels in real-time), customer-facing technology in-store (21% digital signage, “smart” fitting rooms etc.), social media analytics (21%), and guided selling/clienteling (21%).

The “Heroes of the Mobile Age” panel that McCann Worldgroup (MWG) UK & Europe hosted at Mobile World Congress 2018 in Barcelona was surveyed by Campaign Magazine. Through the many key takeaways highlighted by Campaign Magazine within this innovative and inspiring framework allow us to give a few examples: the limitless of mobile; voice complements mobile, being the most natural and convenient user interface (according to Max Amordeluso, EU lead Evangelist, Amazon Alexa); brands are forced to have a more mobile mindset, mobile being the best channel to personalise messages, but not forgetting how important is adapting company’s approach to existing technologies (and producing adequate content) while using the new technology (Elena Alti, Head of digital marketing at Santander); the real innovation lies in what people are doing with existing platforms (Jon Carney, Chief Digital Officer, McCann Worldgroup Europe). Campaign Magazine also underlined its top trends to inspire innovation: “The democratisation of technology… Convenience makes a hypocrite of us all… The ubiquity of AI… Keeping the things you love safe… Shared experiences, not solo.” Within this context, at the fourth point, Campaign Magazine attracted our attention on the remaining paradox: “new technology can help protect us but also exposes us to new dangers.”

This made us recal that two years ago, in 2016, the Father of Modern Marketing, Philip Kotler, said that: “Economists rarely mention marketing… Ironically, the discipline of marketing was started by economists... Behavioral economists… have to study how different marketing actors actually behave. This involves collecting empirical data.

This will lead to recognizing many instances of non-rational or even irrational behavior...” Then, in 2017, Shahram Heshmat, Ph.D., an Associate Professor emeritus of health economics of addiction at the University of Illinois at Springfield remembered in “Psychology Today” that: “The field of behavioral economics blends insights of psychology and economics, and provides some valuable insights that individuals are not behaving in their own best interests…

Behavioral economics attempts to integrate psychologists’ understanding of human behavior into economic analysis…

The understanding of where people go wrong can help people go right…” While in 2018, also in “Psychology Today”, Nir Eyal, a technology entrepreneur who blogs about the intersection of psychology, technology, and business, showed that: “Our gadgets and apps are more persuasive than ever… There’s nothing wrong with building products people want to use, but the power to design user behavior ought to come with a standard of ethical limitation. The trouble is the same techniques that cross the line in certain cases lead to desirable results in others… The tech industry needs a new ethical bar… I humbly propose the “regret test.”

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And also in 2018, very recently, the Association for Psychological Science (former American Psychological Society) remembered how often is used in marketing and advertising – in order to classify people according to their attitudes, preferences, and other psychological factors – the so-called “psychographic profiling”, this being approached within the context of “The scandal involving Facebook and political data firm Cambridge Analytica”. The dimension of the debate on this subject was confirmed, for instance, by: Mark Zuckerberg in a new post on March 21: “We will learn from this experience to secure our platform further and make our community safer for everyone going forward;” David Beer, Reader in Sociology, University of York (a member of the prestigious Russell Group of researchintensive UK Universities): “…We should look beyond that to try to understand how data-led approaches are influencing our lives on lots of different fronts, especially as the tools of data analysis are taken up in numerous different sectors.

Just because the rest of the industry may not be as extreme as Cambridge Analytica, it does not mean that we should neglect to ask questions about the many ways that our data are being used to judge, rank and order our lives.”

The Best Book of the Year 2009 according to The Economist and the Financial Times was “Nudge: Improving Decisions About Health, Wealth, and Happiness”, written by Richard H. Thaler (the winner of the 2017 Nobel Prize in Economics), Professor of Behavioral Science and Economics at the University of Chicago’s Booth School of Business and Cass R. Sunstein, Professor at Harvard Law School, where he is the founder and director of the Program on Behavioral Economics and Public Policy. According to them, a nudge is “any aspect of the choice architecture that alters people’s behaviour in a predictable way without forbidding any options or significantly changing their economic incentives. To count as a mere nudge, the intervention must be easy and cheap to avoid.”

In 2016, the prestigious Harvard Business Review published an article written by Utpal M. Dholakia, a George R. Brown Professor of Marketing at Rice University’s Jesse H. Jones Graduate School of Business, and entitled “Why nudging your customers can backfire”. In this article, the author started from making reference to the impact of the above mentioned book on consecrating the nudge marketing as influencing what consumers choose (steering them toward different options or stimulating purchases; nudges originating from behavioral economics), in contrast to the motivational psychology which arms consumers to make virtuous choices on their own. Taking account of certain limitations (nudges may not achieve the ultimate goal or produce sufficient impetus to achieve the desired outcomes even when they work, or consumers may become immune to nudges once understanding how they work), Dholakia pledged for the use of nudges in tandem with effective motivational psychology tools, treating customers as equals and empowering them accordingly. While in 2018, an article published in The American Journal of Clinical Nutrition and entitled “Nudging and social marketing techniques encourage employees to make healthier food choices: a randomized controlled trial in 30 worksite cafeterias in The Netherlands” concluded, for example, that: “Strategies based on nudging and social marketing executed in a real-life setting are effective in encouraging healthier food purchases by employees and aim to remain effective over time.”

Allow us to end by making reference to an article approaching “Data Driven CX Improvements: A Retailer’s Must Have For A Successful 2018” and concluding that: “data you can understand supports improvements you can trust”. And marketing (a conversation focused on solutions) is aiming customers’ trust.

Theodor Valentin Purcărea

Editor-in-Chief References: 1.

Chippa. H. (2018). Keeping Customers Loyal: Creating Experiences Powered by Data. My Total Retail, March 7. Retrieved from http://www.mytotalretail.com/article/keeping-customers-loyal-creating-experiences-powered-by-data/#ne

2.

Turner, M. (2018). Bringing Personalization to CX. CustomerThink, March 9. Retrieved from http://customerthink.com/bringing-personalization-to-cx/?

3.

Dicso, J. (2018). Connecting With Mobile Consumers Requires a Personalized Touch. My Total Retail, March 16. Retrieved from: http://www.mytotalretail.com/article/connecting-with-todays-mobile-consumers-requires-a-personalized-touch/#ne

4.

Mullen, D. (2018). Customer Intelligence 360: Laying the Foundation for Retail Advantage. My Total Retail Webcast, March 10. Retrieved from http://www.mytotalretail.com/webinar/customer-intelligence-360-laying-foundation-retail-advantage/

5.

Garcia, K. (2018). How High-Touch Retailers are Betting on Tech. Retail eMarketer, March 12. Retrieved from https://retail.emarketer.com/article/nordstrom-bets-on-tech/5aa6c360ebd4000ac0a8ac83?

6.

McCann (2018). Posts, March 6. Retrieved from https://www.facebook.com/McCannWG/posts/1632083213544287

7.

Simpson, G. (2018). Heroes of the mobile age: industry leaders and visionaries - from brands to tech giants - reveal how to maximise mobile. Campaign Magazine, March 05, 2018. Retrieved from https://www.campaignlive.co.uk/article/heroes-mobile-age-industry-leaders-visionaries-brands-tech-giants-reveal-maximise-mobile/1458385?

8.

Kotler, Ph. (2016). Why Behavioral Economics Is Really Marketing Science. Evonomics - The Next Evolution of Economics, August 24. Retrieved from http://evonomics.com/behavioraleconomics-neglect-marketing/

9.

Heshmat, S. (2017). What Is Behavioral Economics? Psychology Today, May 03. Retrieved from https://www.psychologytoday.com/us/blog/science-choice/201705/what-is-behavioral-economics

10.

Eyal, N. (2018). Want to Design User Behavior? Pass the ‘Regret Test’ First. Psychology Today, March 22. Retrieved from https://www.psychologytoday.com/us/blog/automatic-you/201803/want-design-user-behavior-pass-the-regret-test-first

11.

Association for Psychological Science (2018). Cambridge Analytica Scandal Casts Spotlight on Psychographics. March 22. Retrieved from https://www.psychologicalscience.org/publications/observer/obsonline/cambridge-analytica-story-casts-spotlight-on-psychographics.html

12.

Zuckerberg, M. (2018). Facebook post, March 21. Retrieved from https://www.facebook.com/zuck/posts/10104712037900071

13.

Beer, D. (2018). Cambridge Analytica: the data analytics industry is already in full swing. The Conversation, March 23. Retrieved from http://theconversation.com/cambridge-analytica-the-data-analytics-industry-is-already-in-full-swing-93873

14.

Thaler, H.R.,‎ Sunstein, R.C. (2009). Nudge: Improving Decisions About Health, Wealth, and Happiness. Penguin Books; Revised & Expanded edition, February 24. Retrieved from https://www.amazon.com/Nudge-Improving-Decisions-Health-Happiness/dp/014311526X

15.

Comunigator (2016). How to use nudge theory with your marketing campaigns. gatoradmin, August 2. Retrieved from https://www.communigator.co.uk/blog/use-nudge-theory-marketing-campaigns/

16.

Dholakia, M.D. (2016). Why nudging your customers can backfire. Harvard Business Review, April, 15. Retrieved from https://hbr.org/2016/04/why-nudging-your-customers-can-backfire

17.

Velema, E. et all. (2018). The American Journal of Clinical Nutrition, Volume 107, Issue 2, 1 February 2018, Pages 236–246, https://doi.org/10.1093/ajcn/nqx045

18.

Levy, S. (2018). Data Driven CX Improvements: A Retailer’s Must Have For A Successful 2018. HappyOrNot, January 10. Retrieved from https://www.happy-or-not.com/en/2018/01/data-driven-cx-improvements-retailers-must-successful-2018/

EXTENDING INFORMATION AND COMMUNICATIONS TECHNOLOGIES’ IMPACT ON KNOWLEDGE BASED SOCIETY THROUGH ARTIFICIAL AND COLLECTIVE INTELLIGENCE -PART1Prof. Eng.Ph.D. Victor Greu

Abstract: The paper details the analysis of the digital disruption (DD), in the context of the Information and CommThe paper approaches the analysis of the role of collective intelligence in the context of Information and Communications Technologies (ICT) exponential evolution, which tends to have an important impact on the progress of the Information society (IS) toward Knowledge Based Society (KBS).

Premises for this impact refer to the most prominent advances and challenges of ICT, including Cloud, Big Data, IoT, green ICT, artificial intelligence (AI) and Digital Disruption (DD), which have to be approached in a responsible and systemic way, as their interdependent consequences reached unprecedented complexity and crucial importance for World economy, humankind life and future of Earth ecosystem. It is enough to recall the pace of ICT’s carbon footprint growing versus the totality of airplanes.

The Collective intelligence main concrete components, crowdsensing (CSENS), crowdsourcing (CSOURS) and generally crowd intelligence (CI), enable AI to create more knowledge from the real time data deluge that is generated at Earth scale, impacting IS/KBS, people’s life and Earth environment. The functional structures of crowdsourcing are analysed and classified by two main criteria, referring to the degree individuals are implied and respectively the way crowd intelligence is collected.

Machine learning (ML) is pointed as the most important, performant and productive subfield of AI due to its unprecedented capacity of self-learning algorithms, which make the difference between ML and the prior advances on AI. Going further and deeper into ML, the AI most prominent performant applications will be based on Deep Learning (DL).

On the other hand, the paper point out the fact that the further AI advances, the complexity of its jobs

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and challenges is increasing and this way new kind and complicate issues are raising. IoT and other emerging sensing systems could be more performant using highest technologies as AI - HI combinations, but this way they become more complex, especially by volunteer and generally human participation and eventually progressively changing from sensing to sourcing or including both CSENS and CSOURS. As a consequence, combining HI and AI deep learning is an emerging trend in Big Data and Collective intelligence, based on the power of AI to offer performant solutions of processing the overwhelming data volumes, along with the HI’s deepness of imagination, capacity of generalize and superiority of processing sparse data.

Practically AI could further improve its performance when approaching difficult problems for machines, which could be solved by addressing CSENS and CSOURS systems and then discovering information/ patterns in collected data, but we have to watch every time to reasonably keep the control of this complexity increasing, as remarkably also argued the genial and regretted Stephen Hawking.

Consequently, we have to watch this HI/AI combination as the natural trend of high technologies exponential progress and keep it in the secure area of KBS development.

We have to notice the need for human intervention to resolve issues and improve algorithms, but we would add the imagination and responsibility of human to design safe AI (including algorithms).

A paper’s conclusion is that AI needs high value data in order to generate information and eventually knowledge, but the data value is depending on ... crowd wisdom. In order to assure an efficient contribution of crowd, we have to carefully manage the combination of AI-HI in all typical scenarios, i.e. providing expertise for reference design tools, improve algorithms, supervising ML/DL processes and ...contributing in large volumes of data/information/knowledge by CSENS and CSOURS systems.

We have to conclude that responsibly analyzing people and information quality or refining knowledge are among the most complex, complicate and time sensitive problems to be solved, perhaps perpetually.

Keywords: artificial intelligence, collective intelligence, crowd wisdom, crowdsensing, crowdsourcing, crowd intelligence, Deep Learning, human intelligence, Big Data, machine learning, Internet of Things, information society, knowledge based society JEL Classification: L63; L86; M15; O31; O33

1. Artificial intelligence, crowdsensing and crowdsourcing - everywhere through Information and Communications Technologies “Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations” - James Surowiecki (Wisdom of crowds-2004) The amazing progress of mobile communications and computation along with sensors technology are generating a huge potential of applications and services the Information and Communications Technologies (ICT), as the main driving factor of the Information society (IS) toward Knowledge Based Society (KBS), could deliver for all human activity domains.

The exponential pace of ICT development, that Moore Law is still leveraging, determines also a continue re-evaluation of the processes ICT generate, along with their complicate consequences at Earth scale.

As we already presented [3][6][13][19], the most prominent advances and challenges of ICT, including Cloud, Big Data, IoT, green ICT, artificial intelligence (AI) and Digital Disruption (DD), have to be approached in a responsible and systemic way, as their interdependent consequences reached

unprecedented complexity and crucial importance for World economy, humankind life and future of Earth ecosystem. Here it is enough to recall the pace of ICT’s carbon footprint growing, versus the totality of airplanes [19][14][16].

In this challenging context, the ICT potential is still offering advanced solutions for all above mentioned development domains and much more, but AI has the highest power, by its increasing performances and self-developing features [3][23].

Speaking of self-developing features, it is obvious that this way AI tends to have a similar potential with human capacity of improvement, but this is just the tip of the iceberg, as the reality is a dynamic and complex process we intend to shortly analyse.

Anyway, these are the premises and reasons for a responsible approach of the complicate link between AI and human intelligence (HI), which inherently leads us to crowd wisdom.

Using crowd wisdom (CW), by crowdsensing (CSENS), crowdsourcing (CSOURS) and generally crowd intelligence (CI), enables AI to create more knowledge from the real time data deluge that is generated at Earth scale, impacting IS/KBS, people’s life and Earth environment.

The status and evolution of CSENS and CSOURS are very well expressed, as basic components, in the frame of the functional structures of Next Generation Crowdsourcing for Collective Intelligence [1]: “CrowdSensing; Wearables Crowdsourcing; Situated Crowdsourcing; Spatial Crowdsourcing”.

From the point of view of human participation, these 4 functional structures could be classified as passive (Wearable CSOURS; CSENS) or active (Situated CSOURS; Spatial CSOURS), while by the intelligence/information type, they could be individual (Wearable CSOURS; Situated CSOURS) or environmental (CSENS; Spatial SOURS).

These structures could also be represented in an intuitive diagram, as it is sketched in Fig.1.

Fig.1 Functional structures of crowdsourcing (CSOURS= crowdsourcing; CSENS= crowdsensing)

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Usually, Wearable CSOURS and CSENS provide data through ICT devices without human permanent intervention, i.e. in a semi-automated mode, while Situated CSOURS and Spatial CSOURS need a human intervention during every task/job they are involved in.

It is very interesting to point out that CSENS and Spatial CSOURS are based on information/ intelligence collected from environment, but in the case of Spatial CSOURS the environmental intelligence could be obtained only through human, i.e. not directly digitally transferred – the typical example being image labelling.

More than these, CSOURS could generally contribute in a diversity of scenarios like [18]: “Solving tasks that computers cannot easily perform without human assistance. Human computation projects include work on crowdsourcing, where sets of people jointly contribute to the solution of problems. Crowdsourcing has been applied to solve tasks such as image labelling, product categorization, and handwriting recognition” The AI status and potential are also largely confirmed in the literature, as it is crucially impacting the World [2]: “The collection of “Big Data” and the expansion of the Internet of Things (IoT), have made a perfect environment for new AI applications and services to grow. Applications based on AI are already visible in healthcare diagnostics, targeted treatment, transportation, public safety, service robots, education and entertainment, but will be applied in more fields in the coming years. Together with the Internet, AI changes the way we experience the world and has the potential to be a new engine for economic growth” Above the context and horizontal potential development, the deep mechanisms of AI applications implementation and operation are also very important for the clear picture, but also for its inherent impressive benefits and challenges [5]: “In this age of modern technology, there is one resource that we have in abundance: a large amount of structured and unstructured data. In the second half of the twentieth century, machine learning evolved as a subfield of Artificial Intelligence (AI) that involved self-learning algorithms that derived knowledge from data in order to make predictions. Instead of requiring humans to manually derive rules and build models from analyzing large amounts of data, machine learning offers a more efficient alternative for capturing the knowledge in data to gradually improve the performance of predictive models and make data-driven decisions. Not only is machine learning becoming increasingly important in computer science research, but it also plays an ever greater role in our everyday lives. Thanks to machine learning, we enjoy robust email spam filters, convenient text and voice recognition software, reliable web search engines, challenging chessplaying programs, and, hopefully soon, safe and efficient self-driving cars.”

Here, machine learning (ML) is pointed as the most important, performant and productive subfield of AI due to its unprecedented capacity of self-learning algorithms, which make the difference between ML and the prior advances on AI. As we also presented [3][6], going further and deeper into ML, the AI most prominent performant applications will be based on Deep Learning (DL)[4]: “Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind’s AlphaGo beat the World champion at Go — a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that’s why it’s at the heart of Artificial intelligence.”

Here we just arrived where our paper is intended to focus, i.e. the fact that the further AI advances, the complexity of its jobs and challenges is increasing and this way new kind and complicate issues are rising.

For instance let pass over the meaning of above remark, referring to the complexity of applications or fields where AI is expected to offer solutions, as predictive or creative problems.

It is also known and we also presented, in[3][9], the risk of developing AI out of human control, where the regretted Stephen Hawking and other prominent savants even made investment for preventing.

Besides, among the actual and emerging challenges we consider as very important the complex relation of AI with HI, including both “cooperation” and “competition” sides, so we further will analyse each of them, but focusing on cooperation.

In fact, the main goal of AI - HI combination is to extend the ICT applications performances and penetration, including the huge area of emergent fixed or mobile IoT. IoT and other emerging sensing systems could be more performant using highest technologies as AI - HI combinations, but this way they become more complex, especially by volunteer and generally human participation and eventually progressively changing from sensing to sourcing or including both CSENS and CSOURS. The usability of CSENS and CSOURS services are extending very fast in diverse domains like environment/utilities monitoring, Big Data, user experience, home working, smart city, knowledge based society applications, games/entertainment industry etc.

If we would refer only to user experience, CSENS and CSOURS with opportunistic/volunteer participants could cover a multitude of scenarios [23].

For example, stimulating participants to cooperate in tasks of usual interests like user experience of home utilities, environment, government or social assistance, a diversity of cost effective applications installed on mobile devices deployed in areas of mobility could provide incentive information/elements for both users and participants.

Similar scenarios could include IS/KBS emergent domains like business intelligence searches, human behaviour topics, health care anonymous data collecting from Body Area Network (by cooperation with medical service providers and aiming qualified advices and best practice), intelligence gathering workshops or games (oriented on selecting intelligent and imaginative persons), home working creative jobs, startups ideas, natural language processing etc.

When involving participants in collecting data, through their devices or by direct observation/ contributions, they could also benefit of informations or learn about interesting issues and this way AI and HI provide incentive elements, as participants are usually interested of commercial offers, health, jobs, utilities, entertainment, environment etc.

Far from being exhaustive, the above approach of AI, CSENS and CSOURS has been focused on their potential applications and benefits, but these represent just the tip of iceberg, as the complex issues of generating data, extracting information and refining knowledge, by human/crowd intelligence contribution need deeper further analysis.

2. The age of Big Data, the need of knowledge and the crowd wisdom

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The actual ICT development, without considering the spectacular proliferation of IoT, determines, as we also have presented [6], in all humankind activity fields, the power to generate exponential growing volumes of data [7]: “The world’s production of data grew 2,000-fold between 2000 and 2012. Its stock of data is expected to double every two years; 99 percent of it is digitized and half has an IP address. This means that half of the world’s data can now be put together, at near-zero cost, to reveal patterns previously invisible. Half of the world’s data is already, technically, a single, universally accessible document. All this data is linked by fixed and mobile communication networks and is managed by layers of modular, interoperable software …Information is comprehended and applied through fundamentally new methods of artificial intelligence that seek insights through algorithms using massive, noisy data sets. Since larger data sets yield better insights, big is beautiful.”

Here we have to notice not only the data deluge, but the unprecedented capacity to digitally process them (99%) and the most important, to instantly access and use these data (50%).

This situation is generating both opportunities and responsibilities from incumbents, specialists and ... crowd.

Combining HI and AI deep learning is an emerging trend in Big Data and Collective intelligence [21][22][23].This is based on the power of AI to offer performant solutions of processing the overwhelming data volumes, along with the HI’s deepness of imagination, capacity of generalize and superiority of processing sparse data.

Practically, AI could further improve its performance when approaching difficult problems for machines, which could be solved by addressing CSENS and CSOURS systems and then discovering information/patterns in collected data.

As the first section has focused on opportunities, now we intend to analyse how the responsibilities could be better approached, using ... ICT opportunities and the human potential.

Speaking about responsibilities any improvement has to be done carefully setting our target level, especially when facing with complex ICT realities and forecasts like “exponential pace” and ”deluge”.

We have to recall here a confirmation of this preventing approach [6][20]: “I enjoyed Samuel Arbesman’s first book, The Half-Life of Facts, which was a discussion of the exponential pace of change, as exemplified by Moore’s Law, among other things. When I saw the title of his new book, Overcomplicated, I assumed that it would be a warning that we technologists had gone too far in creating complex systems. It would advocate moving to simpler systems, just as a doctor might advise an overweight person to go on a diet. I was prepared to argue against such a conclusion, but as I discovered upon reading the book, Arbesman does not say that complexity is necessarily bad or that we should seek simplicity. Instead, he maintains that systems are now unknowably complex, that they will become even more so, and we should…just get over it. Much of the book is spent in discussing the reasons why complexity is inevitably increasing.“ Our opinion is similar, but we have to watch every time to reasonably keep the control of this complexity increasing, as remarkably and financially also argued the genial and regretted Stephen Hawking.

Consequently, we have to watch this “HI/AI combination as the natural trend of high technologies exponential progress” and keep it in the secure area of KBS development.

If still a shadow remained about “carefully setting our target level”, we have to also recall a deep suggestion resulting from[7]: <<. . . In that Empire, the Art of Cartography attained such Perfection that the map of a single Province occupied the entirety of a City, and the map of the Empire, the entirety of a Province. In time, those Unconscionable Maps no longer satisfied, and the Cartographers Guilds struck a Map of the Empire whose size was that of the Empire, and which coincided point for point with it...” On Exactitude in Science” - Jorge Luis Borges>> We believe that the Borges message is also clear today as preventing as to keep reasonable and safety limits in our ”exponential pace”, before it would not be too late.

About the map, decades ago we were telling to our students “in the future every cub inch of Earth will be digitally known”, but recently, after 9-th 11 (2001), we were telling to other students the same, but adding: “we had all the data, but how did we used them safely?”

So here we are, to design AI and HI combinations having as first priority a safer future, i.e. setting the targets levels as to provide safety before performance.

It is obvious that HI and AI cooperation is the natural way for high technologies improvement, due to the fact that AI reached unparalleled levels when closing to humankind model.

On the other hand, the human potential of imagination, lifetime experience knowledge and creativity is still unreachable, so “HI and AI are efficiently supporting each other in a symbiosis, becoming the most advanced and powerful engine for World progress toward KBS” [23].

Such opinions are largely agreed, as it is very eloquently presented in the next example [8]: <<In her presentation titled “Humans to the Rescue: Troubleshooting AI Systems with Humanin-the-Loop,” Ece Kamar, Senior Researcher at Microsoft Research AI, discussed the need for human intervention to resolve issues and improve algorithms. ...“Behind all the AI improvements has been large amounts of collected training data,” explained Kamar. “That data comes from the crowd.”....“Collaboration with human intelligence is the key for building reliable AI systems. Humans need to be kept in the loop,” said Kamar....One example Kamar highlighted was the series between World Chess Champion, Garry Kasparov, and IBM’s Deep Blue in 1997. After Kasparov lost to Deep Blue, some thought humans wouldn’t play chess anymore. Indeed, according to research, humans haven’t drastically improved their chess-playing abilities since 1980, while chess-playing bots have. However, when humans and chess software play together, it’s superior to the best chess algorithm...To advance AI, identify failures, and enhance algorithms, a crowdsourced feedback loop is essential, Kamar said. “Perfecting these complex systems doesn’t work without the human input.”>> By this consistent example, coming from Microsoft specialists, we just entered the core of generating data, extracting information and refining knowledge, by human/crowd intelligence contribution to the AI performant processes.

The mechanisms and the dimension of such processes were also recently expressed by IBM specialists [10]: “According to IBM, about 90% of data existing in the world has been generated in the last 2 years. On average, we generate about 2.5 Quintillion bytes of data every day. This massive amount of data can’t be processed and managed by humans physically. This is where Machine learning comes into the picture”

Here we observe the amazing figures of generated data, but most of all the clear recognition of the fact that ”Machine Learning and AI is Improving Lives in 2018” and how ML (and DL how we 20

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have presented in the first section) will impact practically all industries, including commerce, health care, robots, mobile devices, trains, cars, Plaines, drones etc.

Perhaps ML/DL will support the most impressive applications in the most difficult fields of improvement, as predictive analysis (Climate Changes, Weather Forecast, Data Analytics, Business Intelligence), health diagnosis, real time translation or natural language processing.

In all ML/DL applications the practice proved that the quality of data sets offered for processing is essential, but the point is that these data usually come from the crowd (as Ece Kamar said).

More than these, we have to notice “the need for human intervention to resolve issues and improve algorithms”, but we would add the imagination and responsibility of human to design safe AI (including algorithms).

This way we have arrived in the middle of the section idea, as AI needs high value data in order to generate information and eventually knowledge, but the data value is depending on ... crowd wisdom.

In order to assure an efficient contribution of crowd, we have to carefully manage the combination of AI-HI in all typical scenarios, i.e. providing expertise for reference design tools, improve algorithms, supervising ML/DL processes and ... contributing in large volumes of data/information/ knowledge by the above presented CSENS and CSOURS systems.

That is way the specialists largely agreed that quality of crowd contribution is essential for enabling AI, CSENS and CSOURS systems [12]: “One of the key aspects of crowdsourcing is to engage the right crowd. There is no point engaging members of the crowd who neither have knowledge nor the skill and motivation to partake in the initiative. If the crowd lacks critical knowledge or even a basic insight of the domain, they will only produce results that are way below the set benchmark. On the other hand, if the crowd lacks the necessary motivation, the crowdfunding initiative will fail to realize its full potential”.

Naturally, the next (complicate) issue is how to select people in CSOURS systems and eventually how to filter the collective intelligence toward refining knowledge.

Here is the point where we have to conclude, although we have partially approached these issues [3][11][15][17], that responsibly analyzing people and information quality or refining knowledge are among the most complex, complicate and time sensitive problems to be solved, perhaps perpetually, i.e. to be further continued.

3. Conclusions

The paper premises refer to the most prominent advances and challenges of ICT, including Cloud, Big Data, IoT, green ICT, artificial intelligence (AI) and Digital Disruption (DD), which have to be approached in a responsible and systemic way, as their interdependent consequences reached unprecedented complexity and crucial importance for World economy, humankind life and future on Earth ecosystem. Here it is enough to recall the pace of ICT’s carbon footprint growing versus the totality of airplanes.

An important conclusion is that using crowd wisdom (CW), by crowdsensing (CSENS), crowdsourcing (CSOURS) and generally crowd intelligence (CI), enables AI to create more knowledge from the real time data deluge that is generated at Earth scale, impacting IS/KBS, people’s life and Earth environment.

Consequently, the functional structures of crowdsourcing are analysed and classified by two main criteria, referring to the degree individuals are implied and respectively the way crowd intelligence is collected.

The AI status and potential are also largely confirmed in the literature, as it is crucially impacting the World, but machine learning (ML) is pointed as the most important, performant and productive subfield of AI, due to its unprecedented capacity of self-learning algorithms, which make the difference between ML and the prior advances on AI. On the opposite side, the paper point out the fact that the further AI advances, the complexity of its jobs and challenges is increasing and this way new kind and complicate issues are raising.

IoT and other emerging sensing systems could be more performant using highest technologies as AI - HI combinations, but this way they become more complex, especially by volunteer and generally human participation and eventually progressively changing from sensing to sourcing or including both CSENS and CSOURS. As a consequence, combining HI and AI deep learning is an emerging trend in Big Data and Collective intelligence, based on the power of AI to offer performant solutions of processing the overwhelming data volumes, along with the HI’s deepness of imagination, capacity of generalize and superiority of processing sparse data.

AI could further improve its performance when approaching difficult problems for machines, which could be solved by addressing CSENS and CSOURS systems and then discovering information/ patterns in collected data, but we have to watch every time to reasonably keep the control of this complexity increasing, as remarkably also argued the genial and regretted Stephen Hawking.

Consequently, we have to watch this “HI/AI combination as the natural trend of high technologies exponential progress” and keep it in the secure area of KBS development.

We have to notice “the need for human intervention to resolve issues and improve algorithms”, but we would add the imagination and responsibility of human to design safe AI (including algorithms).

The main conclusion is AI needs high value data in order to generate information and eventually knowledge, but the data value is depending on ... crowd wisdom. In order to assure an efficient contribution of crowd, we have to carefully manage the combination of AI-HI in all typical scenarios, i.e. providing expertise for reference design tools, improve algorithms, supervising ML/ DL processes and ... contributing in large volumes of data/Information/knowledge by the above presented CSENS and CSOURS systems. The next (complicate) issue is how to select people in CSOURS systems and eventually how to filter the collective intelligence toward refining knowledge.

Also, we have to conclude that responsibly analyzing people and information quality or refining knowledge are among the most complex, complicate and time sensitive problems to be solved, perhaps perpetually, i.e. to be further continued.

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REFERENCES

[1]JOHN PRPIĆ, Next Generation Crowdsourcing for Collective Intelligence, 2016, https://arxiv.org/abs/1702.03109 [2]***, Artificial Intelligence and Machine Learning: Policy Paper, April 2017, https://www.internetsociety.org/wp-content/uploads/2017/08/ ISOC-AI-Policy-Paper_2017-04-27_0.pdf [3]Victor Greu, Developing information and communications technologies with more artificial intelligence, using artificial intelligence, when internet of things is “intelligence everywhere”-(Part 1), Romanian Distribution Committee Magazine, Volume 7, Issue 4, Year 2016.

[4]***, Top Tutorials To Learn Deep Learning With Python, Nov 26, 2017,

https://medium.com/quick-code/top-tutorials-to-learn-deep-learning-withpython-e593d3449aca [5]Sebastian Raschka, Python Machine Learning, BIRMINGHAM – MUMBAI, 2015 Packt Publishing [6]Victor Greu, Information and communications technologies drive digital disruption from business to life on earth -(Part 1), Romanian Distribution Committee Magazine, Volume 8, Issue 2, Year 2017.

[7]Philip Evans, Patrick Forth, Navigating a World of Digital Disruption, April 22, 2015, https://www.linkedin.com/pulse/borges-map-navigating-world-digital-disruption-fabio-bottacci [8]Carole Lundgren, The Secret to Successful AI, October 26, 2017, https://appen.com/secret-successful-ai/ [9]Victor Greu, Searching the right tracks of new technologies in the earth race for a balance between progress and survival, Romanian Distribution Committee Magazine, Volume 3, Issue1, Year 2012.

[10]Bhupinder Kour, The Rise of Machine Learning and AI is Improving Lives in 2018, January 5, 2018, https://www.smartdatacollective.com/ rise-of-machine-learning-ai-improving-lives/ [11]Victor Greu, The Exponential Development of the Information and Communications Technologies – A Complex Process Which is Generating Progress Knowledge from People to People, Romanian Distribution Committee Magazine, Volume 4, Issue2, Year 2013.

[12]Anas Baig, Artificial Intelligence Can Solve The Biggest Crowdsourcing Problem, Aug 11, 2017, https://crowdsourcingweek.com/blog/artificial-intelligence-can-solve-biggest-crowdsourcing-problem/ [13]Victor Greu, The information society towards the knowledge based society driven by the information and communications technologies from the Internet of Things to the Internet of …trees (Part 1), Romanian Distribution Committee Magazine, Volume 6, Issue1, Year 2015.

[14]Prechi Patel, Building a more eco-friendly telecom industry, IEEE The Institute, Mar. 2016.

[15]Victor Greu, Context-aware communications and IT – a new paradigm for the optimization of the information society towards the knowledge based society (Part 2), Romanian Distribution Committee Magazine, Volume 5, Issue4, Year 2014.

[16]Ana Carolina Riekstin,Bruno Bastos Rodrigues,Viviane Tavares Nascimento, Claudia Bianchi Progetti,Tereza Cristina Melo de Brito Carvalho, Catalin Meirosu, Sustainability Information Model for Energy Efficiency Policies, IEEECommunications Magazine, November 2016.

[17]Victor Greu, Tomorrow’s paradox: refining knowledge by smarter information and communications technologies while humans tend to become a limited factor of performance, Romanian Distribution Committee Magazine, Volume 7, Issue1, Year 2016.

[18]Ece Kamar, Severin Hacker, Eric Horvitz, Combining Human and Machine Intelligence in Large-scale Crowdsourcing, Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012), 4-8 June 2012, Valencia, Spain

[19]Victor Greu, Information and communications technologies go greener beyond iot- behind is all the earth-Part1, Romanian Distribution Committee Magazine, Volume 7, Issue 2, Year [20]Robert W. Lucky, Cozying Up to Complexity [Reflections]: IEEE Spectrum, Volume: 54, Issue: 1, January 2017 [21]Jeffrey David Orkin, Collective Artificial Intelligence: Simulated Role-Playing from Crowdsourced Data, Dissertation, MIT, Feb. 2013.

[22]*** , Transforming data into knowledge, Collective Learning group at the MIT Media Lab, 2017, https://www.media.mit.edu/ groups/collective-learning/overview/.

[23]Victor Greu et all, Human and artificial intelligence driven incentiveoperation model and algorithms for a multi-purpose integrated crowdsensing-crowdsourcing scalable system - paper submitted to International Conference Communications 2018 (Politehnica University of Bucharest, Romania Military Technical Academy, IEEE Romania), June 2018.

TRENDS DRIVING THE FUTURE OF THE RETAIL INDUSTRY FOR THE NEXT DECADE Cosmin Tănase

ABSTRACT

The wealth of media and channels that have arisen in the past years has created a complex problem for modern marketers. Today’s advertisers must simultaneously dedicate their energy to brand storytelling, audience-based targeting, insights from customer data, personalization, and customer experience, all across a myriad of new media channels and devices. Consumers have raised their standards for personalization in marketing campaigns, and without a way to efficiently leverage the increasing amount of data they produce, brands will consistently fail to meet those standards. A combination of the advertising and marketing technology ecosystems offers hundreds of software solutions that claim to automate and simplify many different processes for marketers, from email creative optimization to the buying of digital media. In fact, there are so many of these solutions that piecing them together into a comprehensive “marketing” stack is nearly impossible. Marketing stacks today have become so complicated that operating them is just as, or even more difficult than, manually completing the tasks they’re designed to automate.

Keywords: Tech Solutions; Optimization; Vendors; Customer Data; Predictive Analytics JEL Classification:L81, L86, M31, Q55

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It is important to reiterate that when talking about a marketing stack, it means a converged ad and marketing stack - both the acquisition tools and data, along with the customer relationship marketing tools and data. This is the world brands should strive to live in if they want to truly own their customer data. One item that makes building and consolidating a marketing stack so difficult is that solutions are split between integrated suites and individual point solutions. Vendors are trying to straddle two priorities - having the best-of-breed solution to address a particular marketing need, and offering a fully integrated solution to deliver more consistent brand experiences to customers.

Trying to mix the best-of-breed and fully integrated solutions can be a logistical problem, especially when considering the work that must be done to make the resulting suite functional across all channels. There’s also the issue of ad agencies and what components get outsourced to them.

Over the past few years, the arrival of new technologies to market and a desire to own all their data in-house have caused brands to pull some tasks back from agencies, particularly on the data and execution side. Outsourcing only further complicates an already complex ecosystem, and further reinforces channel-based silos that hinder communication and make it much harder for marketers and brands to run coordinated campaigns.

DIGITAL COMPLEXITY HAS MANY FACTORS

There are a few factors that help to explain why digital technology has so far not only failed to simplify the work performed by marketers, but in many ways made it more complicated.

- Marketers, by training, are technology “light”: The average marketing education (in school) doesn’t require becoming versed in technology management, so when marketers enter the real world and have to deal with technology on the job, they either outsource or become too dependant on vendors for assistance in utilizing their technology.

- Pace of change: Thanks in part to expansive capital, the rate of innovation over the last ten years has grown exponentially. This rapid development has had two key effects: a massive increase in the amount of relevant data available for marketers to use, and the entry of hundreds of point solution technology vendors, each of them claiming to be the answer to every marketer’s problems.

It’s a constant struggle for marketers to learn how each piece of technology works in the first place, let alone figure out how of them fit together.

- Increasing consumer touchpoints: The complexity of the modern media landscape has led many companies to seek separate data/analytics and optimization/execution tools for each channel.

These tools are typically myopic in their execution, and while they may be loosely connected with some form of middleware, any bridge between these two functionalities can only work so well if it’s not inherent to the system.

A tech stack can be made vastly more simple and intuitive by consolidating the tasks performed by many of its various elements — from the analytics platforms that analyze campaign performance to the individual pieces of ad and marketing tech that execute and optimize campaigns on each respective channel — under one “execution” solution. Replacing these tools with artificial intelligence can improve communication with customers by reducing the time marketers spend on analytical work, freeing them to focus more on strategic tasks like messaging and creative strategy.

Not all AI is created equal, however. Much of the marketing-facing AI technology available today has a very particular application; for example, an AI product might be built specifically for content, or for data. As a recent eMarketer report points out, while use of the technology in marketing is on the rise, “Marketers across all industries are experimenting with ways to put AI to use.” The report cites applications as disparate as “business intelligence, customer acquisition, programmatic advertising, campaign optimization, and multichannel communication.” The diverse range of applications that qualify as AI has led to some confusion among marketers as to what it really is, with 35% of marketers claiming they “don’t know enough about” AI to adopt it — despite the fact

that most marketing efforts already rely on some form of the technology every day. Some tools use machine learning to uncover marketing insights and make automated suggestions based on them, for instance, but aren’t capable of executing on those suggestions autonomously. Other tools might help optimize content and campaign copy, but because this capability isn’t built organically into the execution and analytics platform, it reinforces silos, which creates more work for marketers. What marketers need is a central “brain” behind their tech stacks that is built to facilitate and optimize the connections between these various functions and tools. This is precisely the role that AI should play in the tech stack.

A good AI marketing platform is built from the ground up on artificial intelligence while utilizing several of the techniques that comprise the technology, including machine learning, predictive analytics, and natural language processing, among others, rather than a set of capabilities or software with machine learning bolted onto them. It should consolidate processes across channels rather than let unnecessary tech platforms segregate the activities that must be connected. And AI marketing platforms should be simplifying tech stacks to the point that it doesn’t take a technical expert to run a great campaign.

So many tech solutions have flooded the market that there are an almost infinite number of different approaches and software combinations that can make up any given marketing stack.

With so many different vendors offering various tools and functions across channels, hunting for the optimal set of solutions and vendors can quickly become dizzying. Despite the array of options available, every good marketing stack can be boiled down to four simple groups of solutions: central data repository, content management, product information, and execution/optimization software.

Artificial intelligence can play a major role in helping marketers better manage the execution, optimization, and analysis of campaigns and interactions, pulling from myriad data streams to target and autonomously identify new user groups with relevant content without the need for much human intervention.

CENTRAL REPOSITORY FOR CUSTOMER DATA/WEB ANALYTICS

Every company needs a central database where customer data is stored. Contributing to this central repository of behavioral and demographic data are the various web analytics platforms plugged into the campaigns, as well as any Internet-of- Things-connected sensors and devices the company may own. AI must connect all of these disparate data streams to identify user groups, and consolidate them into a firm understanding of what messaging is appealing to which groups.

IT professionals should ensure that data collection mechanisms are properly and automatically transferring new information into this database, but management of it should not require much effort on the part of human employees. AI-based optimization and execution software pulls from this database and acts as an administrator for the analytics accounts - first to develop insights that will influence the development of content, then to strategically place that content in front of those users who are most likely to convert.

PRODUCT INFORMATION MANAGEMENT (PIM)

Medium- to large-scale retailers may have a hard time keeping track of the specifications of their many hundreds of thousands of products. That is a problem when they are trying to reach users with just the right product to fit their specific needs and trigger a conversion, or when they are selling someone a product that it turns out isn’t currently in stock. In light of these risks, product information management software (PIM), which helps manage available and in-stock inventory, must be another key component in the marketing tech stack. Product specs must integrate with the optimization and execution software so that specific customers can be targeted with the right products.

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EXECUTION AND OPTIMIZATION SOFTWARE

A crucial part of the stack, the execution and optimization software, must work across the marketing channels to inform, coordinate, and adjust campaigns in real time, saving creative teams hours of tedious data analysis. Because this kind of capability requires analytic work at a pace and scale that is impossible for humans to accomplish on their own, the execution and optimization software should run 100% on artificial intelligence. The AI platform must also be able to integrate with every other piece of the tech stack, using information from the central repository to target users with the right campaigns; to create a one-to-one user correspondence with those campaigns, the platform must have access to wherever both content and product information are stored. While many companies today use several execution/ optimization tools at once to work with different channels and devices, this defeats the essential point of using such software in the first place: simplicity and ease-of-use. The best possible software works to optimize campaigns across channels, requiring little or no extra coordination on the part of marketing or IT staff.

Artificial intelligence enables marketers to simplify their stack. Implementing AI unilaterally across an entire stack would be a massive project and require exhaustive system changes to replace ingrained technology. It could also result in unnecessary expenses if done incorrectly. In a post for Digiday, Clear Code CEO Maciej Zawadzinski points out that “Many companies added multiple technology layers that charge additional fees on top of the inventory cost; that can add up to 50% to 70% [of the total expense].”

Adding AI systems specifically to the execution side of the stack could be a gradual process, replacing all or some of the point solutions according to the marketing department’s preference and its tolerance for change. Over time, this would simplify the tremendously complex stack of technology used by many companies and their agencies.

That is not to say that replacing any part of a stack is simple work. Addressing the myriad needs of a large marketing organization with a single suite of tools can be very frustrating, especially when working with legacy hardware and software. How an organization implements AI will depend on its needs, its existing tech stack, and its relationships with agencies and publishers. To get a sense of how it would fit into the stack, a company should consider each of AI’s capabilities and what entities or technologies it could replace or manage.

Many technology systems are implemented simply to identify the low-hanging fruit, ensuring that those customers already at the bottom of the sales funnel are given an easy path to purchase or adoption. These systems simply analyze a series of patterns that are repeated over and over again. AI can be used to quickly identify, test, and optimize the paths between those customers and a particular brand or product. Thus, many retargeting, personalization, or optimized messaging platforms can be easily replaced by the speed and efficiency that a proper cognitive-based learning system can provide.

AUTONOMOUS MEDIA BUYING

Media buying, and programmatic in particular, is an environment that is set up for making decisions based on a given set of attributes (price, placement, quality, content, etc.) at the exact nanosecond of individual transactions, which is then governed by human- inputted thresholds.

It’s a numbers game based on supply, demand, and the law of diminishing returns. The goal is

to pay the exact right price for the ideal grouping of attributes per message that is likely to lead to success. Beyond that perfect situation, it then becomes a sea of variables that involve what action the audience has most recently taken (or not taken) and the price point. Again, this is the ideal environment for AI because it is constantly evaluating every possible permutation of price and attributes for each transaction. In terms of both customer experience with marketing overall, as well as the resulting benefits in performance, some of the most important efficiencies created by AI happen in cross-channel execution.

Most buying or optimization point solutions are very good at making effective and efficient performance decisions for their designed purpose (i.e. the channel that they are deployed on).

However, there are numerous salient data streams coming from other channels running parallel to that decision engine. Each point solution is making decisions based on what is best for that medium, failing to account for the other interactions with the same audiences that are happening across a multiverse of different media channels.

By contrast, a multichannel AI system will consistently seek to deliver the ideal sequence of messages to any audience based on the sum of all data points collected across all channels.

AI systems consider content, context, price point, placement, competition, and any other useful datapoint over any given amount of time to create the optimal campaign at the optimum efficiency and scale. This is directly linked to its ability to instantly process and utilize any element of informative data about consumer behavior or interaction across all media: paid, earned, and owned.

Conclusion

Digital marketing still requires the coordination of many different technologies in order to effectively reach audiences across markets and channels. But it should not require dozens of intermediary tools and vendors/agencies that only further complicate the respective tasks that marketers are expected to manage. The artificial intelligence reduces the company’s issues by replacing, consolidating, and automating many of these channel- and device-specific systems.

The ultimate result of this leaner, smarter tech stack isn’t just reduced costs, but more time for marketers to focus on the creative material that will draw users to the brand. Marketers must abandon the mindset that their employees have to spend their time watching over technology rather than focusing on messaging and branding, especially as more channels are added to their brand’s repertoire. Today’s consumers demand more personal, more creative marketing than ever, and AI offers marketers the time and money they need to deliver it - all they need to know now is how to deploy it.

References

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[1]

Davis, B. (2016). 15 examples of artificial intelligence in marketing. [Blog] Econsultancy. Available at: https://econsultancy.com/blog/67745-15-examples-of-artificial-intelligence-in-marketing

[2]

Karjaluoto, H., Lehto, H., Leppäniemi, M. and Mustonen, T. (2007) ‘Insights into the implementation of mobile marketing campaigns’, International Journal of Mobile Marketing

[3]

Kemper, H.G., Baars, H.: Business Intelligence und Competitive Intelligence. HMD, Prax. Wirtsch.inform. 43(247), 7–20 (2006)

[4]

M. Reimann, O. Schilke, and J. S. Thomas (2010) “Customer Relationship Management and Firm Performance: The Mediating Role of Business Strategy,” Journal of the Academy of Marketing Science

[5]

Martin, K.D. and Smith, N.C. (2008), ‘Commercializing Social Interaction: The Ethics of Stealth Marketing’, Journal of Public Policy and Marketing, 27(1), 45–56.

[6]

Pickton, D. and Broderick, A. (2005), Integrated Marketing Communications. Harlow: Pearson Education.

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CONVERSATIONAL COMMERCE, NEW MARKETING TACTICS, CX, LOYALTY AND EMOTIONS Theodor Valentin Purcărea

Abstract Seven years ago we were introduced to Conversational Commerce, beginning to better understanding the growing impact of several emerging technologies supporting highly conversational interactions. Year by year after that we were witnessing how conversational commerce, as strategic business initiative through text and voice, is delivering customers convenience, personalization, and decision support, changing the way of shopping, transforming CX within the context of messaging apps, and consecrating as a priority for brand marketers and agencies building conversational channels with their prospects and customers, and trying to gain competitive advantage and increase CLV from the ongoing customer relationship-building provided by assistants in conversations beyond engagement or interactions. Now are already better understood, for instance, the reasons to implement AI-backed conversational commerce into the retail strategy, the need of delivering value to customers by creating adequate Omni channel content and experience, the further reinvention and modernizing of the retail business by new technologies, and the powerful link between emotion and CX and loyalty.

Keywords: Conversational Commerce, Marketing, and Intelligence; New Marketing Tactics; CX; Loyalty; Emotions JEL Classification: L81, L86, M31, Q55 CX in the Era of Conversational Commerce The Romanian Academician Victor Slavescu (distinguished personality of the Interwar period) underlined that the past is always full of wisdom and historic experience should never be underestimated.

The famous Romanian Sculptor Constantin Brancusi (well-known as the patriarch of modern sculpture) argued that what is real is not the exterior but the idea, the essence of things, and that things are not difficult to make, difficult being putting ourselves in the state of mind to make them, also considering that to see far is one thing, and going there is another. On the other hand, other distinguished personalities of the world showed that: there are many different elements in history (elements of human nature), history being the development of humanity (Victor Cousin); history is a starting point telling us not only where we are but also what we must be (Henrik Clarke); the course of history is shaped by ideas (John Maynard Keynes); if we look at history, innovation comes from creating environments where their ideas can connect (Steven Johnson).

Seven years ago, on February 2, 2011, on the occasion of the Conversational Commerce Conference (C3), which took place between February 2-3, 2011 in San Francisco, CA, Dan Miller, Senior Analyst, Opus Research, opened the conference with the topic “Intro to Conversational Commerce”, starting (on the basis of Opus Research 2010) from the fact that the customer is always on, and showed, among other aspects: (Miller, 2011) ▪ the channels used by customers (who have it their way) in the past six month, and that companies scramble to respond, while “social” permeates, new graphs prevailing, as shown in the figure below: 30

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Figure 1: New graphs prevail, Opus Research, C3 Source: Miller, D. (2011). Welcome to C3: Intro to Conversational Commerce, p. 8 (cited work)

▪ the spawning of new metadata, as shown in the figure below:

Figure 2: Spawning new metadata, Opus Research, C3 Source: Miller, D. (2011). Welcome to C3: Intro to Conversational Commerce, p. 9 (cited work)

▪ the heart of the matter, as shown in the figure below by Julian Gay, Orange Business Live Blog:

Figure 3: The heart of the matter, Opus Research, C3 Source: Miller, D. (2011). Welcome to C3: Intro to Conversational Commerce, p. 10 (cited work)

▪ what is going on as companies capture much more metadata (from activity among each cell in the social graph and monitoring in real time to detect “#Fail”) and are applying more analytics on archival stuff (to impute or predict intention, become more prescriptive, and support other business objectives), this being a “feel-good” tactic addressing as “social CRM” (which lends feeling of better customer service but has some shortcomings being still under control of Big “E” and Big Data (conversations, data and metadata) and the intent being inferred (too complex, little basis for trust etc.);

▪ the need for a “facilitator” working for the customer, trusted storage of personal data, trusted instructions (and simple ways to enter it), transparent monitoring/capture of dynamic info.

At the end of the same year, Miller highlighted several emerging technologies (forming the basis of products and services which define how individuals carry out everyday commerce) such as: accurate speech recognition combined with natural language processing, the smartphone+cloud paradigm, spoken words recognized as information assets, and advent of true “self” service (offered by services that adhere to the “smartphone+cloud paradigm). He also showed that only those technologies supporting highly personalized, conversational interactions which culminate in a transaction or other tangible result will survive and thrive.

(Miller, 2011) Exactly two years later, in December 2013, Miller argued that customers are putted in command of the devices they use and in charge of the relationships they have with their selected vendors (this being considered the whole point of “Conversational Commerce”), the objective of customer control being made a reality by improvements in the accuracy of speech recognition (which is augmented by Natural Language Processing (NLU), Artificial Intelligence (AI) and Conversation Management resources in the cloud). (Miller, 2013)

On January 15, 2015, the co-founder of Molly, Chris Messina (product guy, inventor of the hashtag, ex-Uber, ex-Google), attracted the attention on the fact that “conversational commerce” (which is growing) is about delivering convenience, personalization, and decision support while people are overloaded, having only partial attention to spare. (Messina, 2015) Exactly a year later, on January 15, 2016, Messina remembered this post in which he underlined the dominant trend of consumer computing apps in 2016 (a trend that he dubbed Conversational Commerce and have tracked with the hashtag #ConvComm), showing, among other aspects, that: the above mentioned trend best came to life with Uber’s integration into Facebook Messenger in 2015; the messaging apps have eclipsed social networks in monthly actives (according to Business Insider); on January 14, 2016 WhatsApp (owned by Facebook) took the unanticipated step of allowing its free use while still giving an experience without third-party ads and spam; 2016 will be the year of conversational commerce, people learning to type commands into messaging apps, users benefiting of the most utility (even of extreme personalization enabled by conversational interfaces) with the least effort and the least complexity; the conversational paradigm is more social, more accessible (“add”, “invite”, “contact”, “mute”, “block”, “message”); users’ interaction with agents and bots will make service builders to humanize the conversation, localize correctly, and provide a meaningfully useful and differentiated service; an entirely new era of lightweight experimentation is promoted by this conversational commerce paradigm etc. (Messina, 2016) Two month later, in March 2016, a Content Crafter at Shopify documented how conversational commerce is forever changing the way we shop, (Kumar, 2016) how the customer experience (CX) is transformed by this conversational commerce which is bringing commerce into the familiar and personal context of messaging apps, making it much more convenient for both businesses and customers.

Entering 2018, we find out that:

• An “Ecommerce Performance Report 2018” conducted by Econsultancy and Conversion (Garcia, 2018) revealed that – within the different priorities for brand marketers and agencies (as shown in the figure below) – conversational commerce (including comprehensively chatbots and personal assistants), AI for personalization, Digital wallets/mobile payments and Social commerce were the top choices (followed by: voice technology, beacon technology, different interfaces such as smart watches and IoT); on the other hand, a “Smart Speakers Research - Q1 2018” conducted by Delineate revealed that US consumers are turning to such devices as the smart speakers Amazon Echo or Google Home to order groceries or toiletries, while a survey conducted by Capgemini (in November 2017) underlined an increase in the use of virtual assistant in Western Europe and the US to order not only consumer products or retail items, but also some type of service (like a meal) via the platform; (King, 2018)

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Figure 4: Digital technologies that ecommerce service providers and client-side/brand marketers at ecommerce companies plan to experiment with by 2020 Source: Garcia, K. (2018). Many Brand Marketers Plan to Try Conversational Commerce. eMarketer (cited work)

• At the Conversational Commerce Conference London (8-9 May, 2018), Alex Murray, Digital Director at Lidl UK (in order to help customers select the best wine for their meal Lidl launched a fully-automated Messenger chatbot “Margot”, which utilizes Aspect Software’s NLU technology and intelligent assistant platform), will present a challenging Featured Case Study including the lessons learned in creating a meaningful CX; (Top., 2018) • In order to build conversational channels for brands and marketers with their prospects and customers an Opus Research Report entitled “Foundations of Conversational Marketing” provided a common framework and accepted terminology to use as companies evaluate their deployment options for Conversational Marketing technologies, so as to gain competitive advantage and increase customer lifetime value from the ongoing customer relationship-building provided by bots (Conversational Marketing messaging virtual assistants as the source of the most recent and accurate indicators of prospects’ and customers’ intents, preferences and instructions). (Top, 2018) It is useful to also remember that Chris Messina attracted the attention already from March 27, 2016 that better descriptors than “bots” may be “assistants in conversations” or “conversation augmentations” or “conversation decorators”; • Conversations (as highlighted by Mitch Lieberman who recently joined the team of Opus Research contributors) are something beyond engagement or interactions, (Lieberman, 2018) being the critical element in support of customers getting their jobs done (see below the channel adoption as inspired by the Gartner analyst Esteban Kolsky); Conversational Commerce (a strategic business initiative through text and voice) is seen as the intersection of messaging applications, shopping and people talking, while Conversational Marketing is seen as a superset of system-based conversations focusing on brand and product awareness, customers being helped by the brand (which is able to choose the right time to have the right conversation with the prospective customers) to understand what it is offered to them, overcoming their issues or concerns, co-creating value and considering value-in-use as an important conversation to have at the right moment before renewal. The above mentioned conversation types are brought together by the Conversational Intelligence (Relationship Intelligence), considering information, knowledge and intelligence as foundational to each conversation.

Figure 5: Conversations Source: Lieberman, M. (2018). A Defining Moment for Conversational Commerce. Opus Research (cited work)

• As the way how consumers communicate and transact with brands will be fundamentally transformed by the conversational commerce (CC), the global head of conversational strategy at LivePerson (a conversational business platform for brands), Rurik Bradbury, made reference to the “IDC FutureScape: Worldwide Retail 2018 Predictions” (Brown, 2017) and highlighted four reasons to implement AI-backed conversational commerce into the retail strategy: CC eclipses antiquated customer relationship tools (at each touchpoint of the consumer journey it will be personalized the long-homogeneous digital experience ); meeting consumers where they are is a must for retailers (AI-fueled CC letting retailers tap into messaging etc.); the low conversion rate for websites (below 3%) and app fatigue; the new truly customized web design is represented by the conversational design (CC can connect brands more organically with their customers, creating customized experiences, serving as the first universal interface and offering real advantages to retailers). (Bradbury, 2018) Delivering value to customers by creating adequate Omni channel content and experience A recent survey conducted by RetailMeNot, Inc. in conjunction with Kelton Research revealed, among other aspects, that in order: to succeed in their complex shopping journey brands need to continue the winning combination of delivering a remarkable CX across all marketing initiatives and solving for customer pain points; to positively affect sales growth and offer consumers the desired smartphone shopping experience retailers need to tackle the challenges with mobile web checkout and to ensure an improved experience for bottom-line gains. (RetailMeNot, Inc. 2018) In the figure below we can see new marketing tactics respondents (200 senior retail marketing leaders surveyed) plan to implement in 2018:

Figure 6: New marketing tactics to implement in 2018 Source: RetailMeNot, Inc. (2018). What’s Shaping the Future of Retail Marketing (work cited)

Also recently, the Vice President, content marketing, at Ansira (a data-driven marketing agency) 34

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argued that as customers are demanding to know more, see more and actively participate with brands before buying, marketers are motivated to create meaningful content (which is at the heart of CX) to promote positive CX within the current retail environment characterized by the convergence of technology and experience. It was shown that in-store and online retail is influenced not only by websites, blogs and whitepapers, but also by new forms of information such as augmented reality, user-generated content and video, retailers being challenged to adequately communicate resonating with their customers across channels, influencing accordingly the customer journey and brand sentiment. (Kinzie, 2018) While an annual report (entitled “Modern Shoppers and Their Quest for Savings”) recently released

by Valassis (a leader in activating consumers through intelligent media delivery), so-called 2K18 Coupon Intelligence Report (which examined 1,000 U.S. shoppers’ behavior in-store, online and Omni channel) revealed that: over 60% respondents constantly plan their purchases and 36% have used a shopping list app; many shoppers prefer print offers in the mail (48%), coupon books in newspapers (42%) and paperless discounts downloaded to store loyalty cards (39%); using mobile offers to save is one of the biggest growth areas (RetailMeNot and Local Flavor being, for instance, two mobile savings apps); in-store shoppers are more promotion sensitive, while online shoppers are driven by convenience, and in order to find out what is on their list Omni channel shoppers will go online or in-store. (Valassis Report, 2018) At Las Vegas retail conference Shoptalk in March this year Daniel Alegre, President, Retail and Shopping at Google (which is the top traffic driver to retailers), underlined that: customers want a deep personal experience, and that an offline and online Omni channel experience has been made necessary by those 58% of in-store sales which were influenced by a digital touch point last year (and this mainly because mobile searches for “where to buy” go up by 85%); Google is used by 81% of people aged 18 to 34 to aid their shopping experience. Alegre also said that the new launched Shopping Actions program is a part of working with retailers to develop a strong partnership (retailers being required to share the portion of a sale if one occurs), the new service allowing consumers to buy items (saving their payment information) through either Google Assistant or sponsored shopping ads that show up adjacent to search results.

(Alcántara , 2018) It is interesting to note that at the same recent Shoptalk retail conference in Las Vegas it was underlined that: by removing the scanning of SKUs from the equation AI is taking the scan-and-go concept a step further, AI being seen as the final frontier of checkout solutions, within the context in which the unchanged for decades checkout process continues to be a pain point for physical retailers given the long lines and lackluster interactions; (King, 2018) a director at Wayfair Next detailed in an interview how Wayfair’s “View in Room 3D” mobile app feature allow users to see virtual furniture and decor in the homes before they purchase. (My Total Retail, 2018) A year ago, on April 4, 2017, a Content Marketing Associate at Reflexis Systems, Inc., remembered that the always practiced concept of experiential retail is becoming more popular than ever within the competition between the brick-and-mortar retailers (the experiential retail giving them the ability to engage customers’ senses as they navigate the store, but its implementation depending of providing store managers and associates with tools that simplify their jobs) and the online pure-player (the online shopping experience being delivered by controlling what a customer sees on a screen). (St. Charles, 2017) Comin back to the current year, it is worth showing that in March 2018, Bart Mroz, CEO at SUMO Heavy (a digital commerce strategy firm), attracted the attention on the fact that the innovative use of technology to provide richer, more informative, frictionless, immersive and satisfying shopping experiences represents a key element of experiential retail, and traditional retailers, in order to create richer online and offline experiences are employing modern technologies such as: AI, VR, AR, online communities and live streaming video. In the opinion of Mroz, retail business will be further reinvented and modernized by new technologies such as chatbots, the Internet of Things, machine learning, personalization engines, blockchain, and voice shopping applications. (Mroz, 2018) But before to clarify other aspects it is useful to underline the difference between Customer Experience (CX, which is proactive, considering the overall customer journey; Forrester defines CX as the way how customers perceive their interactions with a company) and Customer Service (which is reactive, considering only a part of the overall experience), showing that very recently Arvato (a leading customer service provider) presented survey findings from a group of 500 US consumers and business leaders in the customer service space, revealing that: businesses broadly overestimate the quality of their customer service; only 9% of consumers surveyed said they always received excellent customer service; long hold times and having to repeat information are bigger complaints for consumers; for solving a customer service issue the phone is considered the most reliable channel (52%); with regard to the interaction with a chatbot

in a customer service setting 49% of consumers don’t want to be served by a chatbot at all. (Business Wire, 2018) Preoccupied to transform the customer service landscape, Ameyo (the market leader in Omnichannel CX and Contact Center Technology in cloud and on-premise) showed that: businesses can be enabled to engage with the customers better with AI powered Chatbot for customer service, which has an incremental role is improving CX; customer service departments are already impacted by chatbot (dealing with all mundane customer requests efficiently; fetching information from the database quickly; handling all predictive tasks; more time to deal with complicated tasks and responsibilities etc.). (Datt, 2018) Ameyo also showed recently that it was necessary that organizations look to merge Contact Center Infrastructure (CCI, which is relying on different entities such as frontend agents, platform CRM, ticketing system, transactional system) and Customer Engagement Center (CEC) as a single entity in order to ensure a true Omni channel CX, these entities being brought together on a single platform for a 360 degree view, (Datt, 2018) being well-known that “Single Customer View” (Dharmshaktu, 2017) means to have all the interaction history of a contact bundled into one (every interaction being mapped to a Unique Contact Identifier, and all contact interactions via all communication channels being stored in the database on a realtime basis).

Instead of conclusions

At the mid of March this year, the author of “The Conversation Manager” (Van Belleghem, 2010) and of “The Conversation Company”, (Van Belleghem, 2012) Steven Van Belleghem, highlighted three clear benefits to help customers, to save them time and to boost the overall CX: faster than real time customer service (the aim being to solve problems before they arise), hyper-personalization (sales and marketing being about the needs of the individual customer), and effortless user interfaces (being no longer any need for an instruction manual or a help function). (Van Belleghem, 2018) At the end of this approach, Van Belleghem made reference to one of his mantras (“Convenience is the new loyalty”), signaling the conclusion of Byron Sharp – Professor of Marketing Science at the University of South Australia, Director of the Ehrenberg-Bass Institute (“The home of evidence-based marketing”, Marketing Science Info), and author of the book “How Brands Grow: What Marketers Don’t Know” (first published in 2011) – after an impressive recent study that classic loyalty programmes no longer work (Van Belleghem highlighting that generic searching is more important than brand searching, and loyalty to a brand being transformed into loyalty to the most user-friendly interface).

Exactly six years ago, Van Belleghem explained how to manage a Conversation Company by using the 4 C’s: CX (the most important conversation starter, the key driver of consumer conversations being people’s love to talk about companies’ service and products); Conversation (the goal being to converse by listening, asking questions, facilitating the conversations and actively taking part in them); Content (in an authentic, positive and relevant way so as people talk about); Collaboration (involving customers in the decision making processes). (Van Belleghem, 2012) As concluded by Deloitte Canada some years ago: “To deliver a compelling brand experience, consumer-centric companies must combine reimagined brick-and-mortar locations, online engagement, easy e-commerce and multiple, effortless options for product and service delivery that creates a distinctive, differentiated experience that meets the ever-changing needs of the Canadian consumers.” (Deloitte, 2014) Much more recently, as argued by Interactions, while approaching the necessary steps to developing a customer care strategy: “A modern customer care strategy… it’s an extension of your brand and can make a difference in future sales, brand perception and customer loyalty.” (Interactions, 2018) And with regard to the customer loyalty, also recently, Synchrony Financial Loyalty attracted the attention that to build loyalty brands need to create the best experiences for its customers (who are loyal to brands because of quality products, experiences, and benefits), bringing customers in with discounts and promotions, but delivering then the right experiences to encourage their engagement, and creating long-term relationships with them and improving the bottom line. (Synchrony Financial, 2018) In February this year, the Senior & Strategic Editor at CMO.com, Giselle Abramovich, approached the emerging technologies which will transform experiences, such as: Virtual Reality, Voice, Machine Learning, Chatbots (highlighting here, for example, that Starbucks is one brand pushing the limits of chatbots, considering My Barista app etc.), Facial Recognition, and Biometrics. Within this framework she quoted the CEO of Lightwave (an emotion technology company looking at biometric data and creating a righteous representation of someone’s interaction with a brand), Rana June, who showed that measuring 36

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physiological data to understand how somebody truly feels represents one way emotion technology is being used frequently, and as consumers want experience a fundamental change in how success is assessed will be to create an emotional metric as the measure to which a person experiences things. (Abramovich, 2018) This made us recall some other significant aspects: ▪ the distinction between feelings (an impression, a perception, an awareness of something experienced, a self-state) and emotions (a feeling-driven motion in oneself); (Zvi Lothane, 2015) ▪ the importance of emotional bond between loyal customers and brands, the typical CX being more than half emotional; (Beyond Philisophy, n.d.)

▪ Forrester’s emotion-driven approach to branding built on the interrelatedness of experience, perception, and outcome; they identified three contributors to the Brand Energy (a holistic measure of the power of a brand) to be transformed into action: Emotion (almost half of Brand Energy, knowing that decisions are primarily driven by emotional markers and automatic processes in our subconscious mind); Salience (about 30%, which stems both from traditional awareness building and the longer-term impact of positive emotional experiences; Fit (20%, which is about both relevance and alignment to the consumer’s world view); (Chatterjee, 2017) ▪ emotion is established as the top driver of a positive CX and loyalty, and with this in mind brands are challenged to better design, measure and iterate on CX; (Wilkie, 2017) ▪ (as revealed by Capgemini Digital Transformation Institute survey, The Key to Loyalty; August–September 2017, N=9,213 consumers) emotions (honesty, integrity, trust, familiarity, belonging, gratitude, compassion, joy, surprise, security) are the main driver of loyalty, the correlation between emotions and loyalty being the highest, 0.75 (Emotions Index = emotions consumers feel when they think about the brands they use or visit frequently) compared to the correlations between rational elements and loyalty, 0.53 (Rational Index = consumers’ views on the importance of rational factors when deciding which brands they will be loyal to), and between brand values and loyalty, 0.49 (Values Index = consumers’ views on the importance of brand values when deciding which brands they will be loyal to); within this framework, the correlation between emotions and loyalty in Retail is the highest compared to other sectors like Financial Services, Automotive and Telecom; (Capgemini, 2017) ▪ (more than demographics, stated attitudes, or sentiment) emotion – as demonstrated by Forrester – is critical to understanding and anticipating emerging technology usage; (Lai, 2017) ▪ if user experience design (UX) and user interface design (UI) are working together to form a conversational experience, it is essential to add emotion in the messages sent to a user so as to resonate instantly with this user and to establish accordingly the desired relationship between brands and users; (Ramos, 2018) ▪ brands are being made great by the emotional seduction. (Chatterjee, 2018) The competition for emotion-driven convenience within the emerging technology usage landscape makes it necessary to improve conversational experience by adequately adding emotion in the messages sent to customers, helping shopping and people talking at the right moment wherever they are, so as brand energy to ensure highly valuable and valued personal relationships.

References Abramovich, G. (2018). Emerging Technologies That Will Transform Experiences. CMO.com, February 13. Retrieved from http://www.cmo.com/features/ articles/2017/11/2/how-emerging-tech-will-impact-customer-experiences.html#gs.sGtnuXw Alcántara , A-M. (2018). Google Wants Retailers to Help Them Turn Search Queries Into Actual Purchases. Adweek, March 21. Retrieved from http://www. adweek.com/digital/google-wants-retailers-to-help-them-turn-search-queries-into-actual-purchases/?

Bradbury, R. (2018). 4 Reasons Brands Should Focus on Conversational Commerce in 2018. My Total Retail, March 13. Retrieved from http://www.mytotalretail. com/article/4-reasons-brands-should-focus-on-conversational-commerce-in-2018/# Brown, V. et all. (2017). IDC FutureScape (2017): Worldwide Retail 2018 Predictions. IDC, Oct. Retrieved from https://www.idc.com/getdoc.jsp?

Business Wire (2018). Only 9 Percent of Consumers Say They Always Receive Excellent Customer Service. Business Wire, March 19. Retrieved from: https:// www.businesswire.com/news/home/20180319005121/en/9-Percent-Consumers-Receive-Excellent-Customer-Service/?

Capgemini (2017). Loyalty Deciphered — How Emotions Drive Genuine Engagement. December 5. Retrieved from https://www.capgemini.com/consulting/

resources/how-emotions-drive-customer-engagement/ Chatterjee, D. (2017). Introducing Forrester’s New Brand Energy Framework – Emotions Fuel Your Brand’s Energy. Forrester Blogs, July 17. Retrieved from https://go.forrester.com/blogs/17-07-18-introducing_forresters_new_brand_energy_framework_emotions_fuel_your_brands_energy/ Chatterjee, D. (2018). Consumer Marketing 2018: Build Emotion-Powered Brands With Dipanjan Chatterjee. Forrester Blogs, March 19. Retrieved from https:// go.forrester.com/blogs/cm2018brand/ Conversational Commerce Conference (C3) 2011. Opus Research, Agenda & Presentations, February 2-3. Retrieved from http://opusresearch.net/ wordpress/2011/02/03/conversational-commerce-conference-c3-2011/ Dharmshaktu, G. (2017). How to Enhance Customer Experience with Single Customer View. Ameyo, Jun 19. Retrieved from https://www.ameyo.com/blog/ enhance-customer-experience-with-single-customer-view Datt, K. (2018). How to Overcome Technology Challenges In Omnichannel CX. Ameyo, Mar 21. Retrieved from https://www.ameyo.com/blog/how-to-overcometechnology-challenges-in-omnichannel-cx Datt, K. (2018). Chabots-Ready to Overtake Customer Service in a Big Way (Infographic). Ameyo, Mar 16. Retrieved from https://www.ameyo.com/blog/chatbotready-to-overtake-customer-service-infographic Deloitte (2014). Consumer experience - The new brand imperative. Deloitte LLP, Canada. Retrieved from ca-en-consumer-experience.pdf Garcia, K. (2018). Many Brand Marketers Plan to Try Conversational Commerce. eMarketer, January 26. Retrieved from https://retail.emarketer.com/article/manybrand-marketers-plan-try-conversational-commerce/ King, J. (2018). The Future of Checkout Lies with AI, Scan and Go. Retail eMarketer, March 21. Retrieved from https://retail.emarketer.com/article/future-ofcheckout-lies-with-ai-scan-go/5ab2aa99ebd4000ac0a8aca7?

King, J. (2018). Voice Commerce Is Becoming the Norm. eMarketer, March 13. Retrieved from https://retail.emarketer.com/article/voice-commerce-becomingnorm/ Kinzie, J. (2018). The Future of Retail: Why Content Matters in Marketing. My Total Retail, March 19. Retrieved from http://www.mytotalretail.com/article/thefuture-of-retail-why-content-matters-in-marketing/#ne Kumar, B. (2016). How Conversational Commerce Is Forever Changing the Way We Shop. Shopify Blog, Mar 28. Retrieved from https://www.shopify.com/ blog/113660229-how-conversational-commerce-is-forever-changing-the-way-we-shop Interactions (2018). 4 steps to developing your customer care strategy. Retrieved from INT_EB_4StepsDevelopingCCStrategy_031918.pdf Lai, A. (2017). The Data Digest: Understand Emotion To Drive Technology Engagement. Forrester Blogs, August 17. Retrieved from https://go.forrester.com/ blogs/the-data-digest-understand-emotion-to-drive-technology-engagement/ Lieberman, M. (2018). A Defining Moment for Conversational Commerce. Opus Research, March 2. Retrieved from https://opusresearch.net/ wordpress/2018/03/02/a-defining-moment-for-conversational-commerce/ Messina, C. (2015). Messaging apps bring the point of sale to you. Medium, Jan 15. Retrieved from https://medium.com/chris-messina/conversational-commerceMessina, C. (2016). 2016 will be the year of conversational commerce. Medium, Jan 19. Retrieved from https://medium.com/chris-messina/2016-will-be-the-yearof-conversational-commerceMiller, D. (2011). Welcome to C3: Intro to Conversational Commerce. Conversational Commerce Conference (C3), February 2. Retrieved from http:// opusresearch.net/wordpress/pdfreports/C3_Day1_DMiller.pdf Miller, D. (2011). Conversational Commerce in 2012: Emphasizing the “Self” in Self Service. Opus Research, December, 15. Retrieved from http://opusresearch. net/wordpress/2011/12/15/conversational-commerce-in-2012-emphasizing-the-self-in-self-service/ Miller, D. (2013). 10 Trends to Watch: Conversational Commerce 2014. Opus Research, December, 13. Retrieved from http://opusresearch.net/ wordpress/2013/12/13/10-trends-to-watch-conversational-commerce-2014/ Mroz, B. (2018). How Retailers Can Get ‘Experiential’ With New Technologies. My Total Retail, March 20. Retrieved from http://www.mytotalretail. com/article/how-retailers-can-get-experiential-with-new-technologies/#ne=c4e4aa5148bbd545a29a454a970be8d3&utm_source=total-retail-report&utm_ medium=newsletter&utm_campaign=2018-03-20 Ramos, R. (2018). Conversational commerce is no longer a one-person show. VentureBeat, March 21. Retrieved from https://venturebeat.com/2018/03/21/ conversational-commerce-is-no-longer-a-one-person-show/ RetailMeNot, Inc. (2018). What’s Shaping the Future of Retail Marketing. Retail eMarketer, March 16, 2018. Retrieved from https://retail.emarketer.com/article/ whats-shaping-future-of-retail-marketing/5aa80a35ebd4000ac0a8ac89?

St. Charles, M. (2017). Implementing an Experiential Retail Model. Reflexis, Inc., Apr 4. Retrieved from http://www.reflexisinc.com/implementing-experientialretail-model/ Synchrony Financial (2018). Defining loyalty for your brand. Tips for building the foundation of loyalty. January. Retrieved from definingloyaltywhitepaper.pdf Top., D. (2018). How Grocery Giant Lidl’s Chatbot, Margot, Offers Consistently Great Wine Recommendations to Thousands. Opus Research, February 8.

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wordpress/2018/02/21/opus-research-report-foundations-of-conversational-marketing/ Valassis Report (2018). Valassis Research: Dynamic, Informed Shoppers Empowered to Save. Business Wire, March 20. Retrieved from https://www.businesswire. com/news/home/20180320005820/en/Valassis-Research-Dynamic-Informed-Shoppers-Empowered-Save?mkt_tok Van Belleghem, S. (2018). Three clear customer benefits in a world of artificial intelligence (AI). CustomerThink, March 14. Retrieved from http://customerthink. com/three-clear-customer-benefits-in-a-world-of-artificial-intelligence-ai/?

Van Belleghem, S. (2012). The Conversation Company: Managing The 4 Câ&#x20AC;&#x2122;s. Steven Van Belleghem Blog, March 28. Retrieved from http://stevenvanbelleghem. com/blog/the-conversation-company-managing-the-4-cs Van Belleghem, S. (2012). The Conversation Company, Kogan Page, January 1, 2012. Retrieved from https://www.amazon.com/Conversation-Company-StevenVan-Belleghem/dp/1280494921 Van Belleghem, S. (2010). The Conversation Manager, Lannoo Publishers (Acc), September 16. Retrieved from https://www.amazon.com/Conversation-ManagerSteven-Van-Belleghem/dp/9020991272 Wilkie, B. (2017). Emotion and Customer Experience: Connecting Feeling With Your Bottom Line. CustomerThink, May 7. Retrieved from http://customerthink. com/emotion-and-customer-experience-connecting-feeling-with-your-bottom-line/?

Zvi Lothane, H. (2015). Archives of Psychiatry and Psychotherapy, 2015; 2: 61â&#x20AC;&#x201C;74 DOI: 10.12740/APP/42669. Retrieved from http://www.archivespp.pl/uploads/ images/2015_17_2/61Lothane_ArchivesPP_2_2015.pdf *** Wayfair Using Augmented Reality to Help Sell Furniture. My Total Retail, March 20. Retrieved from http://www.mytotalretail.com/video/single/wayfairusing-augmented-reality-help-sell-furniture/#ne=c4e4aa5148bbd545a29a454a970be8d3&utm_source=total-retail-report&utm_medium=newsletter&utm_ campaign=2018-03-20 *** The Emotional Experience. Retrieved from http://www.beyondphilosophy.com/customer-experience/the-emotional-experience *** https://www.marketingscience.info/

LÉON F. WEGNEZ (BY COURTESY OF) – (BY COURTESY OF) – HOW SUPERMARKETS SEDUCE SHOPPERS, “DISTRIBUTION D’AUJOURD’HUI”, 58ÈME ANNÉE, NOVEMBRE 2017, BRUSSELS Léon F. Wegnez Sharing with our distinguished Readers a wellknown source of usable and useful knowledge…

Prof. Dr. h. c. Léon F. WEGNEZ is an Honorary Member of the Romanian Distribution Committee, and distinguished Member of the Editorial Board of our “Romanian Distribution Committee Magazine“. He was honored by the European Retail Academy (ERA) as the 2015 “Man of the Year” (the distinguished personalities who have been honored by ERA in the last six years were: Philip Alexander Nobel, John L. Stanton, Léon F. Wegnez, Romano Prodi, Klaus Toepfer, and Robert Aumann).

Knowing our distinguished readers’ thirst for knowledge, we offer you, by courtesy of this remarkable personality, the above mentioned article published in the prestigious “Distribution d’aujourd’hui”.

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EUROCIS FEBRUARY 2018, VIETNAM, IMPACTS FROM HISTORY, MATCHING STORE FORMATS, ASTANA & ALMATY, GEOPOLITICAL CONTEXT, PLEKHANOV UNIVERSITY Bernd Hallier

Prof. Dr. Bernd Hallier, President of the European Retail Academy (ERA), an Honorary Member of the Romanian Distribution Committee, and distinguished Member of the Editorial Board of “Romanian Distribution Committee Magazine” (he is also Honorary Member of the Romanian Scientific Society of Management - SSMAR) attracted our attention on great events happening in the first quarter 2018, and allowed us to present them. It is also worth remembering that: immediately after visiting Romania for the first time on the occasion of the 24th International Congress of the International Association for the Distributive Trade (AIDA Brussels), Prof. Dr. Bernd Hallier sent us, in May 2008, a memorable letter we have referred initially in the Journal of the Romanian Marketing Association (AROMAR), no. 5/1998, and also later, in 2010, in the first issue of the Romanian Distribution Committee Magazine; the RomanianAmerican University has awarded Prof. Dr. Bernd Hallier a “Diploma of Special Academic Merit”; the “Carol Davila” University of Medicine and Pharmacy, Bucharest, has awarded Prof. Dr. Bernd Hallier a “Diploma of Excellence”.

EuroCIS February 2018 Started in 1997 as an annual congress-show to bridge the years between the triennial EuroShop meanwhile EuroCIS in Dusseldorf/Germany is an annual hot spot for the European retail technology - during the EuroShop years as a special segment and in the two years in-between as a stand-alone exhibition.

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“Technology is the main driving-force for innovation of retail in this century” Prof. Dr. Bernd Hallier stated in expectation of the record breaking exhibition (11.000 visitors, about 450 exhibitors from 28 countries on 13.000 square meters net exhibition floor).

Vietnam Traveling to Far East since 1974 and being a member of the international Jury of the magazine Retail Asia for more than a decade, for Prof. Dr. Bernd Hallier the united Vietnam is a special case of development and innovation (see: LINK YouTube).

“In my student time Vietnam was the controversial hot-spot in society - dividing people according to political systems. Within the last 20 years the united Vietnam shows how important PEACE is for the human beings, economics and the region! The European Retail Academy is happy to help in this transformation by an exchange of students - working in an international Team Spirit”, Prof.Hallier stated thanking his two ERA-students Trang Pham and Sampsa Hyväri for the YouTube video

Impacts from History The defeat of the Germany Army at Stalingrad/Wolgograd 75 years ago was a major turning-point of World War II. In the Post-War-Era the cities of Wolgograd and Cologne/Germany have been the spearheads of the German/Russian city partnerships. Visiting Wolgograd Prof. Bernd Hallier resumed that Wolgograd is a symbol for human beings suffering by conflicts of ideologies ending in wars. The young generation has to learn from history.

Another historical focus is Yekaterinburg/Ural where 100 years ago the Romanov-family was liquidated at the start of the Sowjet-Empire. It was the courage of the Russian President Jelzin - who came from Yekaterinburg - to disclose the hidden graves of the Zar family and to transfer their bodies to St. Petersburg - and to rename Yekaterinburg again to its historical name - as in Sowjet times it was called Swerdlowsk in memory of one of the revolutionaries. The USUE, which honoured Prof. Hallier by a Doctor h.c., is releasing a Call for a Summer School to explore the history and challenges of the Ural (more: Call Letter, Flyer). The German DAAD will support this initiative.

Matching store formats Taken the Top Ten of retail formats - and giving 10 points to the number 1 and 9 points for the number 2 and so on - it accumulates to a Total of 55 points. Analyzing the 2017 data of the Kuala Lumpur Awards of the magazin Retail Asia this is an interesting indicator for country-competences.

Within the sector of Department Stores, Japan accumulates 40 credit points versus South Korea with 14 points, dominating this sector in the Asian Pacific Region. Within the Supermarket sector, the international variety is bigger: Australia leads by 26 points in front of Japan with 17 points.

Astana & Almaty The European Retail Academy for many years partipates at the Astana Forum; Prof. Dr. Bernd Hallier is member of the Club of Eurasian Scientists and was speaker in 2017 at the EXPO-event at the Nazarbayev University, together with the Korean Nobel Laureate Prof. Raekwon Chung. Hallier appreciates the high-speed international education programs in Kazakhstan like the Master Program for International Relations.

Launched in the Fall 2014, MAPSIR is the first world-class, graduate level political science and international relations program taught in English in Kazakhstan. A wide range of courses is offered by an internationally recognized faculty drawn from North America and Europe. The 21-month-long program is structured similar to top-rated Master’s programs in the universities in North America. However, unlike most top-rated MA programs, MAPSIR is fully funded, its students receive monthly scholarships of 100,000 tenge plus room on campus. (More info)

Geopolitical Context According to Prof. Dr. Bernd Hallier within politics as well as within curricula at universities often there is a knowledge-gap between connectivity of facts/views in respect of geopolitical competences: “Crises are handled as daily affairs without knowledge about the regions history and ethnic/ religious problems - further on in conflicts each side is often biased by the local flavour of education”.

The European Retail Academy therefore started, in 2017, a new special AEUC about the ancient Silk Road and in the beginning of 2018 with BUN a special about the Baltic Sea. “Those internet-platforms will be pools for info to be discussed interdisciplinary at international Conferences by lecturers and students from our global community” Hallier stated .

Plekhanov University Moscow The Academic Department of Commodity Science and Commodity Examination of the Plekhanov University/Moscow was organizing its Vth International Tserevitinov Conference .

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Invited as an international expert and speaker was Prof. Dr. B.Hallier from the European Retail Academy who explained the vision and mission of the Thematic University Network (TUN) Food. After the lecture the Plekhanov University decided to join the TUN Cooperation to promote by this action Applied Sciences in Russia. Several of the participating professors mentioned former visits to Germany to learn more about GS 1, Orgainvent and Globalgap - they would like also to exchange students as trainees or joint research for MA and PhD. Â

<<ALL ABOUT BEAUTY>>, THE NEW POSITIONING OF DI, “DISTRIBUTION Isabelle WEGNEZ

We offer you, by courtesy of the Director of Editorial, the above mentioned article published in the prestigious “Distribution d’aujourd’hui”, 58ème année, Novembre 2017, Brussels

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