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How AI, machine Transforming Purpose-driven learning will visual marketing impact DM communications

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vol. 31 • No. 5 • May 2018

The Authority on Data-Driven Engagement & Operations

Flipping your AI on-switch


Marketing is at the front line of the AI revolution (surprise!)


From Marketing Potential To Marketing Performance

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Flipping your AI on-switch

Vol. 31 | No. 5 | May 2018 EDITOR Brendan Read -

Marketing is at the front line of the AI revolution (surprise!)

PRESIDENT Steve Lloyd - DESIGN / PRODUCTION Jennifer O’Neill - Advertising Sales Mark Henry - CONTRIBUTING WRITERS Adam Mittelberg Richard Boire Michael Phillips Jerrid Grimm Paul Roehrig Braden Hoeppner Nick Sleeth Caroline Japic Paul Vincent Michael J. Martin LLOYDMEDIA INC. HEAD OFFICE / SUBSCRIPTIONS / PRODUCTION:

Customer Centricity


Retail’s future: complementary online and offline experiences

Applied AI

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How will AI and machine learning impact direct marketing?


Enabling productive B2B marketing with human-assisted AI This method can also spot revenue leaks

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Reinventing customer service with AI chatbots May 2018


Why and when to use AI-based marketing modelling ❰

Customer Centricity

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Retail’s future: complementary online and offline experiences

Michael Phillips is creative director at

Making Waves, a digital agency and consultancy. He oversees creative concepting and execution, brand development, brand management and digital and offline campaign development. He can be reached via email at Visit the prototype at



offline-only experience). She does that, while the monitor displays more information about the shoe’s attributes. She likes it but ultimately puts the shoe down and leaves the store.

Beginning the journey Our simulated customer journey begins with a mobile phone and an app (as many journeys do these days). Let’s say that our customer is in the market for a new pair of shoes. As she interacts with our brand on her mobile phone, perhaps through search, an online ad or email, we can suggest that she download our app and opt-in for exclusive products, discounts and content. She does, and we log that interaction to start her user profile, along with any other interaction she takes while viewing our products. In this case, after a few online and mobile browsing sessions, we see that our customer seems to like red running shoes. But she really needs to see them before buying. So, she visits a nearby running shoe store where we have several styles sitting on a shelf, each with a small Bluetooth beacon in the sole, including a red running shoe. As she enters the store, the app on her phone tells the beacon in the red shoe that she is near. So, the monitor above the shoe comes to life, encouraging our customer to pick up the red shoe. She notices and does indeed pick it up. The beacon notices the movement and now prompts her to interact: perhaps encouraging her to turn the shoe over and feel the tread engineered for off-road running (a tactile

What happened? We know that she showed strong interest in the red shoe. She viewed it on her mobile phone and on the app. She visited a retail location to see it in person. She picked it up and spent some time with it. She turned it over, felt the sole then put it down. Then, she left. We know that this product has a sole specifically engineered for off-road running. And once our customer interacted with the sole she ended the engagement. Maybe she’s not an off-road runner. Maybe she’s a road runner? Is it worth sending an email or a notification through the app that we have that same red running shoe with a sole engineered for road running? Let’s say we do and our customer makes a purchase. We can then follow up with products and content that make her running life even better: maybe reflective gear to stay safe, or recommended routes through her city (which we know, because we logged her location when she visited the store). Now, we’re adding value to her interaction with our brand, while adding knowledge to our marketing and product teams. Of course, the above example is oversimplified. But its purpose is to demonstrate how adding value to each step of the customer journey—from online to offline to online again—leads to a more complete and rewarding experience, and a better chance for conversation and conversion. By connecting the experiences, every little interaction gives marketers another key data point. Instead of acting on assumptions, technology like this gives us the ability to constantly refine our communications and approaches. In combination with artificial intelligence (AI) and machine learning, we can help customers get exactly what they want, down to the individual level. We can also make predictions based on the past behaviour of similar customers. Much like Waze, the crowd-sourced navigation app helps commuters find the best route to work based on people who drove to that same destination minutes earlier. Ultimately, it’s by providing value like this that consumers will continue to allow marketers into their lives. Because while technology gives us the ability to do some amazing things, we can’t do any of it without users helping us understand what they want and need. In exchange for value, the brands receive data about how customers live with the brands (or don’t) that can be used to build even more disruptive technology that helps everyone prosper.

ake a walk through a nearby mall (if it’s still open) and you’ll notice the obvious: retail is in free fall. And while there are many reasons for the crisis—online commerce, shifting consumer behaviour and poor in-store experiences, to name a few—there is also a solution. That solution, and the key for retail moving forward, is about providing complementary online and offline experiences so that consumers get what they want from those specific interactions. Marketing, both direct and in-store, will have a big role to play. Marketers and retailers must train customers to expect appropriate and valuable experiences and value exchanges, according to whichever way they have decided to interact with the brands that day. That means adding value to the customer journey every step of the way, from initial consideration to brand loyalty and from the mobile phone to the store aisle endcap. The exciting thing about being in this space is that technology is giving the marketing world a huge set of tools to build each of these individualized experiences. To help our clients start thinking about this exact issue, and the possibilities of a new era of marketing and retail, Making Waves built an experience that combines content management systems (CMS), beacons, Bluetooth and mobile apps.

May 2018

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Applied AI

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How will AI and machine learning impact direct marketing? Pre-Roll





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By Nick Sleeth


rom everything we hear and read today, it sounds like artificial intelligence (AI) and machine learning (ML) are going to make data more insightful and actionable. But the reality is that AI and ML are very complex and expensive technologies to leverage, if you include all the costs for the technology, processing power and labour. They also require very large amounts of data to be effective. In a recent Boston Consulting Group survey of over 3,000 companies, 85% believed they could gain a competitive advantage from AI, but only 15% of organizations were using AI extensively1. As a result, AI and ML are mostly used in a “black box” approach, meaning AI and ML technologies are imbedded inside other solutions, like digital journey engines, dynamic content selectors or digital ad placement to enhance their effectiveness. Accessing AI and ML through the black boxes shields us from their complexities and high direct costs while giving us the benefits for our businesses. Integrating data sources Marketers have always tried to leverage data and insights to increase the performance of the ❱

next campaign with some degree of success. Up to now, we have mainly used a single source of data based on simple spreadsheets. The reality is that there are many data sources. Using only one source with column filtering of a simple mailing or contact list is not enough to give us the insight into our customers and how to reach them with the right message. For example, to accomplish a much more targeted approach to direct marketing, we would bring together 1st party data, usually from corporate systems or CRM, 2nd party data from competitive or market information and 3rd party data, such as prospects’ demographic or psychographic information. As you can imagine, the challenge is bringing it all together to create prospect insights, but it would allow for a real targeted approach to people who are the potential of becoming our customers. If we agree on the need to integrate more data sources to build better insights, then where does your business start? ❯❯ Start small with identifying data and insight objectives driven by business needs. Don’t start with spending months building a complete data dictionary with access to all your data sources as this will be a wasted effort; and


Definitely do not start with AI and ML solutions directly as both of those approaches require a massive amount of information and knowledge to be useful. Leave this to the experts.

Instead, look at sources of data you have, or could easily acquire, to be used to help your organization make decisions. For example, adding local demographic information to a customer list give insights into why and how those people purchase your product. You may want to acquire a data analytics or management tool to bring your multiple sources of data together and allow you to run simple reporting to learn and grow. Is there a chance that you throw away this tool in a year? Absolutely, but that is the benefit of Softwareas-a-Service (SaaS)-based tools. Easy in and easy out so when you are done with it, you can graduate to a more complex tool. Using the black box As you use AL and ML-embedded tools that leverage these complex technologies based on this black box approach, you need to make sure you understand how they work and how they get results. The providers of these tools should explain in a good level of detail how they work and give you the

documentation on how these technologies come up with results. As you rely on the results from these tools to make decisions and build a strategy, you will be asked by people in your organization to explain how and why, so you need the underlying knowledge. There is no doubt that AI and ML will be part of our marketing technology stack in the future. They will most likely be used in a black box approach to keep it simple particularly if we want better insight into our customers. What matters today is to start thinking and developing better insights about our customers based on multiple sources of data to better target them and which sources of data we have to use to get there. AI and ML technologies will only be the enablers, and the strategy and tactics still need to come from marketers. Nick Sleeth is a member of the Analytics and

Insights Executive Council for the Canadian Marketing Association ( He earned a Bachelor of Science in Computer Science and Statistics from the University of Western Ontario and has held senior marketing and sales leadership roles at Myplanet Digital and Cision among others. 1 Sam Ransbotham, David Kiron, Philipp Gerbert and Martin Reeves, “Reshaping Business with Artificial Intelligence: Closing the Gap Between Ambition and Action”, MIT Sloan Management Review (MIT SMR) and The Boston Consulting Group (BCG), press release, September 6, 2017.

May 2018



Ph Da Physicality


To answer this question, Canada Post has completed extensive neuroscientific research. The results suggest an integrated marketing campaign that includes direct mail is more effective in driving consumer action. In fact, campaigns including direct mail can drive greater consumer attention, more emotional intensity, and higher brand recall than single-media digital campaigns. Read the research that confirms, what we call, the connectivity effect.

Download our whitepaper Connecting for Action at


Trademarks of Canada Post Corporation.

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Applied AI

Flipping your AI on-switch

Marketing is at the front line of the AI revolution (surprise!) By Paul Roehrig


arketers know artificial intelligence (AI) and digital technologies are impacting their work. The list of responsibilities is longer. The budgets are tighter. The opportunities are growing (but so are the risks). You have more data and a seemingly endless ability to target ads and optimize spend. Yet it doesn’t seem to be making things easier. ❱

Many marketers are left thinking, “Hey, I thought AI and digital were supposed to make my life better! What the heck just happened?” What happened is that marketers are now on the front lines of applying AI to business, making it real and offering up proof of impact: and that’s just hard. The good news is we have learned a lot over the past few years. There are some practical, “no regret” steps to take, so let’s find the on-switch.

AI is a challenge… Marketers have always been problem-solvers, fixers, the optimistic alchemists charged with converting supply and unrealized demand into gold. That’s not going to change. What is changing—and fast—is the crescendo of demand for more technology, automation, scale (for less money), personalization and content, leading to and ultimately more digital-fueled growth at the top line.

It’s no coincidence that these demands are arriving at the same time nearly every marketing “constant” is being disrupted by data, processing power, algorithms and AI. Consider the following: ❯❯ Marketing conglomerates are under immense pressure. In 2017, WPP, Omnicom, Publicis Groupe, IPG and others all suffered with little to no growth, according to Adexhanger.com1; ❯❯ Many independent agencies are being rolled up into holding May 2018

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Applied AI


just getting comfortable with mobility and modern design applied to the web5 (Brutalism anyone?), new channels, including chatbots and augmented reality (AR) are picking up speed. Marketers must begin to think—and act—based on new channels such as in-vehicle content6 and the trillion-dollar AR consumer marketing opportunity7. If that sounds like a Black Mirror fiction, IKEA and Sephora are already bringing these experiences to market (and consider, four years ago, Alexa didn’t even exist…); and Getting the “marketing + AI” equation wrong is having unintended consequences. Some companies are facing scrutiny, ill-will and potential liability, while others (e.g. Cambridge Analytica8) are even bankrupt.

This puts marketers in a tough spot. The debate is over. We know we must embrace AI to win in the digital economy. But AI can also scorch the earth—and your brand, and even your job—if not properly deployed.



companies or acquired by consultancies and technology service providers2; In the digital ad space, Google and Facebook already own more than 60% of the US digital ad market and all the growth3. Check out Scott Galloway at DLD 2017 where he noted on this YouTube video, “If you are not Facebook or Google, you are officially in structural decline.”4; Data moves faster than companies. While some are

May 2018

… And AI is a solution But, hang on. It’s not just risk and bad news. In fact, the future of AI and marketing will be fantastic; there’s just actual work to be done. Though we are really just getting started, we can apply lessons already learned as we connect AI to our own marketing practices. Here are five ways how: 1. Don’t be creepy. Instead take action to avoid evil. Privacy and ethics must surround every moment of connection with consumers; they can no longer be an academic debate or required training courses. As we noted in our book Code Halos, “Just as the Hippocratic Oath emerged millennia before there was any such thing as the American or British Medical Association, people and organizations of good faith will have to step forward to do the right thing because it is the right thing—not just because a politician or a lawyer or a journalist is watching.” This won’t happen on its own. Marketing leaders must partner with technologists and act now

to hard code self-control with modernized organizational structures and investments to protect companies and customers. 2. Recognize that to be more digital, we need to be more human. In a world of infinite content and finite attention spans, insights derived from social science and ethnography—beyond focus groups and surveys—are more important than ever before. Anthropologists and sociologists are not doing studies just so marketers can feel reassured about their decisions. Instead they are gaining deep insights into how people want to engage with products, services and technology. Marketing without this insight is mostly guessing, and in a winner-takes-all market, no brand can afford to guess. 3. Demand more. Over the years, marketers have been promised that personalization and analytics will drive response, but many of those promises remain unfulfilled. Marketing will never be easy, because although human nature doesn’t change much, our wants, needs, wishes (and machines) evolve constantly. It’s time to demand more from your partners, their technology and their services. Insist that they link insight, creative, technology and marketing services like ad ops and content management to create more compelling consumer-grade experiences with AI at the centre. 4. Use AI to bring the content economy to life. The digital economy runs on content delivered to the right person, at the right time, in the right place and via any device or channel every time. The good news is that marketers today have almost unimaginable access to new AI tools. Adobe Sensei injects machine learning into multiple platforms, reports TechCrunch9. Salesforce says its Einstein AI platform, “delivers predictions and recommendations based on your unique business processes and customer data”. With each passing day, marketers have more AI tools they can use to lower cost while helping grow

the top line. It’s simply time to get moving. 5. AI is your next generation marketing productivity improvement tool. Most companies do not have a bag of cash sitting in the box labeled: “In case of marketing, break glass.” To explore and deploy AI, marketers need productivity improvement to free up the cash needed to move into the digital future. Whether it is campaign management, research, or content distribution, AI systems are available today that can automate some work away and improve productivity of other workers in the marketing value chain. AI is a challenge for marketers, but it’s also a force multiplier that can help achieve lower cost, mass personalization and growth at a scale that was unforeseeable on the day the first iPhone shipped. Taking the right steps can set marketing on a growth trajectory and avoid a down quarter, a painful headline or even an extinction event. It’s up to marketing leaders to stay optimistic—“never short human imagination”10 —and get moving to flip the AI “on-switch” for their companies. Paul Roehrig is a co-founder and head of

strategy for Cognizant Digital Business. He is the founder and former global managing director of the Center for The Future of Work at Cognizant. He is also—along with Malcolm Frank and Ben Pring—a co-author of What To Do When Machines Do Everything: How to Get Ahead in a World of AI, Algorithms, Bots, and Big Data and Code Halos: How the Digital Lives of People, Things, and Organizations are Changing the Rules of Business. 1 Alison Weissbrot, “2017: The Year The Holding Companies Fell To Earth”,, December 29, 2017. 2 David Gianatasio, “Global Consultancies Are Buying Up Agencies and Reshaping the Brand Marketing World,” Adweek, March 12, 2017. 3 “Google and Facebook Tighten Grip on US Digital Ad Market Duopoly to grab more than 60% of 2017 digital ad spend”, eMarketer, September 21, 2017. 4 Scott Galloway, “Winners and Losers, 2017”, YouTube, video, January 16, 2017. 5 John Moore Williams, “19 web design trends for 2018”, Webflow, blog, December 8, 2017. 6 Matt Posky, “GM Revamps OnStar: Take a Long Look In the Mirror”,, blog, April 30, 2018. 7 Jay Samit, “Augmented Reality: Marketing’s Trillion Dollar Opportunity”, AdAge (with Deloitte Digital), July 18, 2017. 8 Nicholas Confessore and Matthew Rosenberg, “Cambridge Analytica to File for Bankruptcy After Misuse of Facebook Data”, New York Times, May 2, 2018. 9 Ron Miller, “Adobe CTO leads company’s broad AI bet”, TechCrunch, May 12, 2018. 10 Donna Thach, “Artificial Intelligence is the Future of Marketing”, ITSMA, blog, April 18, 2018. ❰

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Applied AI

Enabling productive B2B marketing with human-assisted AI This method can also spot revenue leaks By Caroline Japic


f a unified view of the customer is important in consumer businesses, it’s even more crucial in businessto-business (B2B), where the relationship is everything. It’s harder to achieve, too, in a world where negotiation dominates sales interactions and relationships are embodied in the complex legalese of contracts, amendments and addenda. In B2B, “knowing your customer” means knowing the terms and conditions they expect, what discounts are active and when their contracts come up for renewal. Providing an exceptional customer experience requires placing critical information at the fingertips of your customer-facing teams so they can shape fluid interactions that build customer loyalty and expand the business. But most B2B companies struggle with this. And understandably so, given the complexity of customer relationship data, that is scattered across many systems, including contract repositories, CRM platforms and billing tools.

The human AI-enabled breakthrough There’s exciting news, though. New artificial intelligence (AI)based commercial relationship intelligence solutions are finally enabling B2B organizations to construct complete views of their commercial relationships, including customer lifetime value. The breakthrough comes from combining machine learning technologies with a managed services platform that supplies crucial human expertise, i.e. human-assisted AI. The technology stack includes document capture systems and AI algorithms that extract relationship data from unstructured text in complex and highly-negotiated contracts with up to 99% accuracy and combines it with data from other systems. The managed services team of data scientists, commercial experts and lawyers then configure and tune the technology stacks to address any data quality gaps. The result is a single system of record and source of truth for all of a B2B company’s commercial relationship data. It provides a centralized location for critical details on pricing, commitments and key dates and associated documentation.

New AI-based solutions are enabling B2B organizations to construct complete views of their commercial relationships. ❱

Hunting revenue leaks for fun and profit Not only does humanassisted AI provide a unified approach to the customer, but it will earn its keep in additional revenue that might May 2018

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Applied AI otherwise be “leaked” when front line employees don’t have the latest or most accurate data available. This “revenue leakage” is a major concern for many B2B organizations. It occurs when they do not have good grips on their customer relationship data. For example, if customers commit to purchasing certain volumes of products in a year, but they are below that level towards the end of the year, sales reps that don’t have either the contract or usage data available won’t know how much to bill at the accurate rate. Another commonly missed event is the expiration of a customer’s time-limited rebate, resulting in continued billing at the discounted rate. That’s revenue leakage.




opportunities. Result: missed revenue growth; Sales process productivity. Relationship information is scattered, inaccurate and incomplete, so sales must spend time assembling and correcting it. Result: fewer deals processed; Renewal management. Understanding which customers need special encouragement to renew their contracts requires sorting through years of documentation. You miss opportunities to expand the customer relationship. Result: excess churn; Service obligations. Finding and figuring out complex service obligations is

Revenue leakage is a major concern for many B2B organizations. It occurs when they do not have good grips on their customer relationship data. Filling gaps like these can have a significant impact on your company’s bottom line, which can be demonstrated even before you invest in any solution. Here are three best practices and practical steps to help companies like yours eliminate revenue leakage: 1. Identify and understand the leakage zones. To track down revenue leaks, it’s helpful to know the environments in which they thrive. We’ve identified several: ❯❯ Entitlement and billing reconciliation. It’s hard to compare actual usage to the contract, leaving you unsure what the customer owns versus what they’re paying for. Result: overcharged or undercharged customers; ❯❯ Contracted pricing variables. Sales teams lack programmatic guidance and make pricing decisions on the fly. This leads to missed contractual increase May 2018



challenging, so you have no real idea of your performance. Result: unnecessary service penalties; Expansion opportunities. You’re not sure what customers own versus what you could sell them; precious time is lost in tracking down the data. Result: suboptimal expansion offers; and Deferred revenue. Unbeknownst to you, the payments terms are too long for a customer, so you defer way too much revenue. Result: delayed revenue.

2. Identify the areas of your customer base where you can have the biggest impact. This is where the marketer’s expertise and deep knowledge of the customer comes in. Choose the customer segments that will likely give you the biggest impact in the shortest time. It may be your top dozen or so accounts, unless they

The human component ensures that the solution is good for the long haul by keeping all of the data accurate and up-to-date. are already getting a lot of support and attention. If they are well serviced, you may want to consider a wider band of mid-level accounts. Look for customers with contracts coming up for renewal in the near future. Do you have any relationships that involve product sets or pricing structures that are particularly knotty and hard to manage? Is there a strategic area of your business, where you’re focused on increasing growth or profitability? Those are good places to look and start segmentation. 3. Look for quick wins. Now you’re ready to assemble the data needed for each of these relationships. Most of the data will reside in documents such as contracts, amendments, order forms and statements of work. You may also need information from order systems and billing tools. Comb through the materials looking for opportunities based on the revenue leakage zones you selected. Prioritize those that can deliver fast time-to-value. If you’re focusing on pricing variables, for instance, you may be able to identify opportunities to take action immediately without modifying the existing relationship. As examples companies often overlook already-negotiated price increases based on the consumer price index or cost pass-throughs. If you’re focusing on contract renewals, you might want to look at those coming up in, say, the next quarter. Renewals are a great opportunity to renegotiate unfavorable terms and conditions and broaden the offer to grow the revenue stream.

Once you’ve road-tested a revenue leakage project based on this approach, you’ve not only demonstrated the bottomline value of better customer relationship intelligence, you’ve done much of the legwork needed to implement a human-assisted AI solution like the ones I described above. With the right combination of machine learning and human expertise, you can automate the entire process, extend it to new revenue leakage areas and ensure that it’s sustainable for any new relationships you develop in the future, as well as for customers already on the books. The human component ensures that the solution is good for the long haul by keeping all of the data accurate and up-to-date (something machines alone find hard to do). Look for cloud-based platforms that can integrate with the systems your customer-facing teams use every day, so that they always have access to the most up-to-date customer information. Whether you go the humanassisted AI route or not, look for ways to make the elimination of revenue leakage a habit in your business: one that flows naturally from a deeper understanding of the customer. Caroline Japic is chief marketing officer of Pramata. Formerly with Hewlett Packard Enterprise and joining in 2016 she is responsible for determining the strategic direction for Pramata’s marketing initiatives. Caroline has a long history of leading high-impact marketing teams and helping companies establish their brands. Previously, she led marketing at Tidemark and Bunchball and held senior marketing roles at Taleo, Polycom, Hyperion and Tibco. Caroline graduated magna cum laude from San Jose State University with a B.A. in public relations and an MBA in marketing management from Santa Clara University. ❰

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Applied AI

Reinventing customer service with AI chatbots

By Michael J. Martin


ot long ago, chatbots were fairly basic. They were programmed to perform specific tasks, responding to straightforward command scenarios where information revolved around single turns. Many chatbots were used as online or search pop ups and could send only simple messages like “How can I help you?” to online users. Today, chatbots have evolved to be able to discern the meaning of queries by analyzing and comparing different elements. Innovative technologies like artificial intelligence (AI) are making this transformation possible, thereby transforming how businesses and marketers use chatbots to interact with consumers. Science has made leaps and bounds in AI and natural language processing (NLP) to allow individuals to interact and converse with computers much more naturally. ❱

As a result, chatbots have become more than greeting tools. They now play significant roles within marketing and customer service for organizations across every industry, including retail, e-commerce, banking and healthcare. Chatbots are helping customers with almost any function, from ordering pizza to online shopping. Chatbot benefits For marketers, some of the key benefits of AI-powered chatbots include: ❯❯ Instant communication with customers that avoid long wait times and complicated explanations; ❯❯ Driving more personalized customer experiences by understanding specific customer needs, wants and concerns to ultimately boost brand advocacy and increases business revenue; and ❯❯ Better engagement and open rates when compared to methods of old, like direct email

or mail-ins, which audiences typically (and easily) ignore. In industries like retail companies can use chatbots for personalized marketing systems. For instance, a floral retailer can use the system to help detect user tone. With an AI-powered gift concierge to interact with online customers, the chatbot can be designed to use NLP and ask users questions about the specific occasions for their purchases. With this information, the chatbot can then suggest personalized gift options. More companies are realizing the competitive advantage of chatbots. According to an IBM report, chatbots could bring cost savings of USD $8 billion annually by 2022, up from USD $20 million in 20171. A recent Gartner study2 states that by 2018, 30% of our interactions with new technologies will be through conversations with smart machines. IBM believes organizations can use AI platforms to build chatbots that assist customers at a

higher level of understanding and intelligence and with a wide range of tasks than ever before. Here are several ways companies can use chatbots to boost the customer experience: Man and machine working together. Many companies are tapping virtual assistants to help human customer service agents provide more personalized guidance. Incorporating a chatbot into a customer service program is an opportunity for human and machine to work together to give the best outcome for a customer. For example, an e-commerce site could use virtual customer service to reach out to customers (subject to the Canada Anti-Spam Law), and help marketers better and more quickly understand customer queries. After a few months of training, the chatbot can respond to questions with 80% more accuracy, leading to more satisfied customers. AI makes chatbots customizable. AI-enabled May 2018

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Applied AI chatbots are built on many different technologies, such as cloud, that equip them to accomplish various tasks like analyzing huge amounts of data and NLP to understand customers’ tones and sentiment. Chatbots can be built to suit the specific needs of their organizations rather than fit into standard models. As a result, businesses and marketers can provide personalized experiences, advertising and

promotion items that are tailored to specific end-customers. Building emotional intelligence into chatbots. Advances in AI will progress and allow businesses to add emotional intelligence to their chatbots. Cognitive computing capabilities can gather data about users’ preferences and intentions through the words they use. The primary purpose of every

Qoints enables micro influencer campaigns with IBM Watson AI Qoints is a Cobourg, Ontario-based company that provides access to live digital marketing data from the campaigns of many leading brands. Its customers use this data to create benchmarks that tell them how their marketing campaigns are performing against their own internal targets and against industry benchmarks. Qoints has been focusing on micro influencer and microtargeting programmes for its customers. The company says influencer marketing is shifting away from celebrity influencers to micro influencers as the latter are more relatable, genuine, trustworthy and authentic. Micro influencers have been especially popular as they generate an average engagement rate that is almost five times higher than what macro and celebrity influencers get on their sponsored posts, according to Qoints. But a major issue that marketers face is actually finding the right micro influencers, which today is a largely manual process. To help its customers find micro influencers quickly and affordably Qoints offers AI Social Discovery, powered by IBM Watson. It is an app that taps Watson AI and psychographics (personality profiling) to identify highly engaging micro influencers for influencer marketing campaigns based on their social activity. Qoints AI Social Discovery dramatically reduces the cost of finding micro influencers for brands that want to run their influencer marketing campaigns internally, as well as for brands that use marketing agencies to execute their influencer marketing initiatives. Qoints AI Social Discovery creates social language profiles by pulling the last six months of tweets from each prospect that meets the customer’s parameters. These profiles are processed through IBM Watson to determine popular themes from each potential influencer and to create the psychographic profiles that inform the results that are reported. These profiles, along with available social engagement metrics, are analyzed by a machine learning algorithm that is trained by Qoints’ growing database of past influencer campaigns. “IBM supports Qoints with commercialization, so we can scale as fast as we need without worrying about infrastructure,” said Cory Rosenfield, Qoints co-founded and CEO. “We know the AI tools we use will be of high performance quality and so do our customers. It helps us bolster our company profile and even lends credibility to our recruitment process.” May 2018

conversation with a chatbot should be to certify that the customer feels understood. The future of customer service The progression of AI means that chatbots will continue to become more skilled in natural communication, emotional intelligence and data analysis. To better serve customers, automated conversational agent systems have to be trained. Chatbots cannot just be programmed to perform specific tasks. They have to learn. They have to fundamentally interact with us like humans and know that we have emotions that can vary throughout the course of a conversation. That said, as more AI conversation agents have a deeper level of understanding of people and become more skilled at mimicking human behaviour, it will be critical that businesses become transparent about whether the system interacting with the end user is human or AI. To maintain customer loyalty, users should never be misled into thinking that

the agent they are interacting with is a real person if it is not. Companies must also take responsibility for protecting customer data that is presented in a conversation and maintain strict privacy of the individual to ensure that they are not unnecessarily revealing information that is irrelevant to specific issues and in compliance with applicable regulations. The future of the customer service experience will ultimately be fueled by AI through learning, reasoning and understanding customers’ intentions. AI will help businesses and marketers make smarter, responsible decisions that will benefit customers by providing them with respectful loyaltybuilding service. Michael J. Martin is senior executive, of Internet of Things lead, broadband networks and network services, IBM Canada. 1 IBM, “Chatbots worth talking to”, study, October 2017. 2 Gartner, “Gartner Says Smart Machines Will Enter Mainstream Adoption By 2021”, news release, December, 15 2016.

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Disruptive Technology

Revolutionizing marketing by merging Big Data, AI and blockchain Disruptive transparencydriven “democratized data” methodologies promise more accurate, accessible and economical direct marketing

By Adam Mittelberg


ne of the biggest challenges marketers face is customer acquisition and retention. The key to both strategies is possessing the critical data that can help communicate effectively with the highest qualified contact possible and to further identify the needs of current customers to foster longterm loyalty. Unfortunately, today’s data industry is both far too complicated and highly fragmented, offering a confusing glut of choices that are overwhelming marketers who are in desperate need of this missioncritical information. This situation couldn’t be truer in the Canadian markets. The existing data marketing ecosystem of data and direct marketing list owners, managers and brokers in Canada is wildly inefficient and often ineffective. It costs Canadian businesses untold millions in unnecessary time and money and untold more in opportunity loss. ❱

Even so, given the fundamental truth that data is the backbone of digital advertising, marketing and traditional direct marketing, marketers have just struggled along with what the market has been able to provide, for better or for worse. A conundrum as effective data sources are becoming even rarer as the need for—and actual dependency upon—data becomes more essential. The escalating demand for Big Data sources that provide quality and complete data has skyrocketed in today’s digital age. Big Data sources crux of problem Unfortunately, it’s the fundamental Big Data sources that have been the very crux of the problem for marketers. Today, a professional or business looking to acquire specific data sets will have to spend extensive time and resources locating sources that meet their target audiences, negotiate costs and establish privacy standards for the transferring of the data. Attempting to generate revenue

from existing datasets brings its own unique set of challenges. The first is the time and money it takes to create data cards and collateral for data owners to monetize. At the same time, they need to identify the right organizations or marketplaces with the widest reach: one that represents the highest demand for their data. The second major challenge is integrity and accountability. Data owners do not trust outside organizations to properly store, manage and monetize their data. The third and last major concern is storage environment security. Data abuse and lack of transparency in the revenue share business model are underlying fears that will ultimately prevent list owners from making their unique data set available for purchase. These three challenges make it extremely cost prohibitive to identify and acquire the various parameters required to compile the exact dataset that is needed. That creates barriers for small and medium sized businesses to entering the data marketplace.

Why merge? Merging Big Data, artificial intelligence (AI) and blockchain technology will revolutionize data-driven marketing worldwide, across all industries. Here are five reasons why: 1. Empowerment. A blockchainbased system empowers data source providers to monetize their data and better capitalize demand, allowing them to access the large global marketplace. In the same way that eBay provides a marketplace for vendors of physical products, a blockchainbased digital marketplace can create growth potential for data source providers of all sizes, while also reducing barriers to entry. 2. Transparency. A blockchain approach provides data providers with full transparency, traceability and auditability, overcoming many of the hurdles data providers currently face in the existing marketplace. Anyone who has operated in the Big Data space knows that duplicate data, false data and questionable sourcing are May 2018

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Disruptive Technology unfortunate industry truths. However, a blockchain-based approach provides complete transparency, allowing buyers to see where the data has been and where it came from prior to purchasing. 3. Confidence. A more transparent vetting and grading system for data will improve confidence building between end users and data sources. Currently, most data purchases are practically blind transactions, whereby buyers won’t really know what kind of data they’re receiving until they actually buy it, because no vendor would ever reveal the data prior to money changing hands. Once you have the data, it’s then up to you to determine its quality, but by then, the money has been spent. Rather than this archaic process leaving much to be desired, having a third party scoring system improves quality and increases trust in the marketplace, while facilitating more transactions and leading

to overall higher levels of confidence in the industry. Giving data acquirers quality and verified data that’s been vetted and scored externally reduces (if not eliminates) false or outdated data, which is a significant problem plaguing the industry. 4. Simplification. By simplifying and aggregating world data transactions into a single point of sale, the result will be an “Amazon” like marketplace, where economies of scale and data aggregation will facilitate a smoother, cleaner and better checkout process, thereby creating more data trade worldwide. Giving end users a simplified, easy-to-use and robust interface with a quick and secure payment system between the business or individual and data sources is a requisite means toward this end. 5. AI. Smart indexing engines are now utilizing predictive analytics (a type of AI using data analysis and machine learning)

for confidence scoring to provide continual real-time accurate data. Based on immediate business conditions, this will allow for record sets that can be a single individual that matches all parameters or millions of records that match desired parameters. Canadian business benefits In Canada, the opportunity for these merged applications couldn’t be greater. Companies seeking to scale internationally will have access to data from various international markets in one convenient platform to better run their respective marketing and advertising campaigns. Localized businesses will be able to enjoy better targeted marketing campaigns, thanks to the data provided in a way that is both democratized and cost-efficient. Localized data vendors realize a great benefit by having their data reach international markets, while also enjoying the benefit of having the purchase transaction

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information fully secured and transparent on the blockchain. Ultimately, democratizing big data levels the data playing field by providing the most comprehensive marketing data solution to businesses and individuals. It will provide a robust interface between the business or individual and the data sources. The back-end systems will ensure full confidence in data quality for the end user, as well as transactional finality for the data providers. Adam Mittelberg is chief marketing officer of, a Media Direct, Inc.-partner company at the forefront of democratizing big data and leveling the data playing field. He oversees the most comprehensive marketing data solution available to all businesses and individuals featuring a robust interface between users and data sources and transparent backend system ensuring data quality, confidence and transactional finality. He may be reached at

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Disruptive Technology

Transforming visual communications with responsive design advances By Paul Vincent


he release of the first iPhone in 2008 and iPad in 2010 sparked massive growth in smartphone and tablet usage. This left web designers with a major challenge. Websites were designed for desktop, often with a one-sizefits-all approach. How would web design adapt to these new smaller screens with both portrait and landscape orientations? Responsive design, originally defined as an approach to website design to enable web pages to render on screens of different sizes, at the time was not considered a technology disruptor. Instead it was a response to digital disruption. This article summarizes how responsive design has evolved and will transform digital communication in the years to come. Before responsive design (2007-2010) With the launch of mobile browsers and smartphones, some organizations decided to keep their older desktop sites, creating separate designs and codebases for mobile versions of their sites: sometimes using an “m.” suffix in front of their domain (m.sitename. com). However this often meant limited functionality and content for mobile devices. Another major issue was that if someone received an link on a desktop device, the mobile version would be displayed with that limited content and functionality, thereby creating a negative user experience. Mobile apps were also being developed by many organizations with a strong media or e-commerce presence. These mobile apps not only used a different coding language from both the desktop and mobile websites but were developed by different teams. This resulted in a third set of unique user experiences, creating ❱

more complexity, increased maintenance costs and greater potential customer confusion. Responsive design phase 1 (2011-2012) As mobile device traffic grew, deeper customer experiences were required. Consequently, the need to reuse as much of the codebase across devices became a necessity. Wired magazine published stories about major U.S. publishers like the adopting responsive design to create a more seamless customer journey and experience across all screens. The early concept of responsive web design started to gain traction in 2012 and the standard approach had three versions: smartphone, tablet and desktop. Sites would detect the width of the viewport of the users’ browsers and display one of these three layouts based on “breakpoints”. Breakpoints are the point at which a design’s width and height will change to a different layout, thus providing an improved user experience. Responsive design phase 2 (2013-2014) By 2014, an increasing number of variations in screen sizes and resolutions across all devices meant that a more fluid layout was needed. Using a variety of breakpoints and percentage-based widths covered a wider range of devices and provided more consistent user experiences. This was a step forward for organizations but efficiencies were still lacking. For example, content such as images were not optimized effectively, often resulting in slow load times especially on mobile devices. While responsive design adoption was growing rapidly, according to an article in Marketing Land there were significant discrepancies between what

Google was reporting and how many of the Fortune 500 brands had actually made the transition to responsive design1. Responsive design phase 3 (2015-2016) The HTML5 revolution was in full effect. Flash was obsolete. HTML5 and related technologies allowed both animation and touch capabilities across all devices. Touchscreens weren’t just on mobile devices anymore; they were now common on new laptops and hybrids like the Microsoft Surface. The richer experiences that HTML5 brought to browsers meant that it was also becoming more popular for publisher and e-commerce responsive websites to reuse their website codebase within native apps, rather than the old approach of separate codebases and experiences. Responsive design phase 4 (2017-2018) More advanced browser-based web applications were made possible with responsive design software like Flexitive Design Cloud and messaging software like Slack. Created as a single codebase, they allow access within browsers and through native apps for consistent cross-device user experiences across devices. The advances in responsive design also saw the transformation of digital advertising creative. Brands and agencies can now utilize technology to not only create efficiencies but scale their advertising across all devices including out-of-home digital display and social advertising. Responsive design phase 5 (2019+) While the first phase of the responsive design revolution focused on the browser, the next phase will focus on a consistent

cross-device user experience optimized across all channels. Content management systems that provide basic scrollable paginated experiences with simple blocks of text, images and videos will be replaced by full page, animated and interactive experiences. Responsive design tools like Flexitive Design Cloud enable businesses to build more advanced responsive components at scale, with simple and easy to use technology that will transform digital advertising and design. Responsive design will expand to all future content channels including wearables, augmented reality and virtual reality (AR and VR), vehicles and even robotics. Connecting live content or data feeds will help streamline internal and external operations especially for global companies supporting many languages, product variations and audience demographics. Additionally, operational alignment of creative and production, combined with responsive design tools and technology will create massive efficiencies and allow businesses to scale and engage customers in new and dynamic ways. As we enter this new era, organizations that understand how to evolve their design communications workflow and operational processes around these new capabilities will thrive. In a world where real-time is no longer a buzzword but the norm, consumers drive engagement and content consumption, digital design is more important than ever. Paul Vincent is CEO of Flexitive Design Cloud.

Flexitive helps designers and developers to create responsive, animated design components that adapt to all device and screen resolutions quickly—with no coding required. 1 Bryson Meunier, “82% Of Sites Use Responsive Web Design In 2015? Try 11.8%”, Marketing Land, January 15, 2015.

May 2018

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Disruptive Technology

Profiting from sponsored content platforms By Jerrid Grimm


he key to advertising success has always been to tell great stories. And in recent years, many companies have translated this into developing sponsored content. In fact, 30% of global ad spending is set to be invested in native content by 2020, according to a research paper, Global Ad Spend Growth published by Adyoulike, based on data from BI Intelligence, IAB and eMarketer. But with content having seemingly stolen the show, how can marketers skilled in other areas work to stay current? They jump on the bandwagon. With the introduction of selfserve sponsored content platforms like Pressboard, direct marketers can now manage sponsored content on their own. By eliminating the labor-intensive processes typically needed to execute content campaigns, they can enable brands to find homes for their content in minutes. Self-serve platforms not only help direct marketers round out their skills but can also help the brands they work for seriously expand their reach. Here’s how: Access to a new audience On Facebook, more than 2.3 million people follow Vox. More than 3.5 million people follow The May 2018

Verge. And an incredible 8.4 million people follow The Economist. Now picture what these publications’ email lists look like. In general, consumers have shown a preference for email; 60% of them favor getting regular updates from brands by email, while just 20% prefer to get updates via social media, according to the MarketingSherpa Consumer Purchase Preference Survey1. So, you can imagine their email lists are pretty extensive. Publishers often promote top articles in their targeted email newsletters. And by having a piece of sponsored content included in one of these emails, marketers have the opportunity to piggyback off publishers’ direct marketing efforts to expand their own reach. While many marketers couldn’t dream of having access to millions of email subscribers, working with top publishers can make it possible. Increasing campaign quality According to MarketingSherpa 26% of consumers unsubscribe from emails because they receive too many of them. It also found that about 21% unsubscribe because they feel the emails aren’t relevant to them, 19% unsubscribe because they feel the emails are always trying to sell them something and 17% do so because the content is boring, repetitive or uninteresting2.

Email campaigns need to provide consumers with real value, and what better way to do this than through sharing sponsored content? Direct marketers have the opportunity to take content they’ve published on major and reputable news sites and send it through to their brand’s email subscribers. This way instead of pushing out highly promotional emails, consumers are provided with valuable reads or watches. Better yet, the brand stands out for its expertise in the industry. The better the reach, the more successful the campaign too. Sponsored content in itself does well and brand who’ve leveraged Pressboard’s platform have witness high engagement and conversion rates. Toyota BC Dealers in Canada, for example, saw readers convert back to its website at a rate of over 1.3% after publishing content on 12 different media publications. Tourism Kelowna published 13 stories, which together got more than 11,000 social engagements on social media. Making marketers more valuable Simply put, having access to a selfserve sponsored content platform can help marketers up their ante. Sponsored content expertise previously lay solely with media buyers and planners at advertising agencies. But direct marketers can

now learn how to do it themselves. This not only makes them more wellrounded, but helps direct marketers play an even more valuable role within their organizations. And since self-serve platforms are made for any marketer to use, they’re also designed to protect the brands they work for. Brand safety on native programmatic platforms has been a hot topic as of late (in early 2017, Adweek reported that Procter & Gamble pulled up to $140 million worth in digital ad spending3. But with self-serve platforms marketers can choose exactly where their articles end up, with just a few simple clicks. Evidently direct marketing and sponsored content do go hand in hand, giving marketers the opportunity to expand their reach and engage their audiences in a whole new way. But instead of going back to night school to learn how it’s done, self-serve platforms help direct marketers develop sponsored content campaigns in minutes. Jerrid Grimm is the co-founder of Pressboard, the platform brands use to buy and measure sponsored content. 1 MarketingSherpa, “Marketing Research Chart: How consumers prefer to receive updates and promotions from brands”, chart, February 23, 2016. 2 MarketingSherpa, “Email Marketing Chart: Why consumers unsubscribe from brands’ email”, chart, April 4, 2017. 3 Lauren Johnson, “Procter & Gamble Cut Up to $140 Million in Digital Ad Spending Because of Brand Safety Concerns”, Adweek, July 28, 2017. ❰

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The power of purpose-driven marketing By Braden Hoeppner


t’s no secret that there are incredible dollars available in the marketing industry. Digital marketing spend worldwide reached USD $209 billion last year while TV advertising reached USD $178 billion1. Celebrity sponsorships can be astronomical: exceeding tens of millions of dollars. It is staggering to see investments made to convince people to buy our products. But what impact could we have if we used a fragment of these dollars towards causes that positively impact the greater good? What if brands had a responsibility to use our cultural influence to drive and inspire positive action? Corporate social responsibility (CSR) is often a business model strategy, where there is an exchange of product one-for-one based on a certain cause. Or a percentage of sales is donated back to it. But even if your company isn’t structured that way, the ability to impact a meaningful cause beyond a traditional CSR campaign is possible. Positive impact can still be achieved thanks to the idea ❱

(and power) of purpose-driven marketing. Purpose-driven marketing enables consumers to use their purchasing power to drive social and environmental change. It is changing the way companies approach influencer campaigns, CSR and marketing in general and for good reason. Brands could make a huge difference. We could create an impact that helps foster community, expands our consumer base and gives our consumers something to be passionate about alongside our products. And the good news is that more consumers are looking for that kind of influence from businesses. According to a 2017 study by Cone2 consumers factor companies’ core beliefs into their shopping decisions. 87% of those surveyed said they’d purchase a product because that company advocated for an issue they cared about. Eight-in-10 (79%) expect businesses to continue improving their CSR efforts and nearly twothirds (63%) believe businesses will take the lead to propel social and environmental change moving forward.

Brands’ social influence Brands large and small have a truly unique opportunity to build and foster a community that is far beyond just a product transaction. The brands we choose to buy and use often reveals our own values. The reach and influence of a brand can even be compared to that of celebrities. Brands exist because of a passion, a drive to create something that impacts the lives of our fellow humans through a product or a service. That same passion is often propelled by those who become advocates of our brands. People who become part of our community and who interact with us on a regular basis through many transactions, purchases, social media, blogs or in-store experiences. Your most passionate fans can be more powerful than any marketing campaign as they share the story of your brand through their circles of influence. But are you feeding their passion and engaging with them? Launching a purpose-driven campaign Making a shift to purpose-driven marketing doesn’t have to be difficult. For example, it could be as small as altering your influencer

marketing program by choosing influencers who share your values and support causes that resonate with your consumers. At SAXX Underwear, we recently launched our “No Status Quo” campaign, which was built with the goal of supporting positive change through partnerships that promote innovation and meaningful impact. We define “No Status Quo” as the refusal to accept the existing state of affairs or live in tolerable mediocrity. That’s what we’re doing with this purpose-driven shift in our marketing strategy by connecting the innovation in our products with change-makers around the world who are creating solutions to very big challenges. SAXX exists to support guys (literally through the product and also through our content marketing) in order to empower their relentless ingenuity in creating positive change in the world. To make a better world: one that is more human, connected and resilient. SAXX’s founder challenged the status quo ten years ago by asking the question: why can’t men’s underwear be better? He then reinvented underwear by creating May 2018

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Feature the patented BallPark Pouch. SAXX now provides men with the world’s most comfortable underwear. But we wanted to do more than that. Our goal is to be a collective for men driving positive impact in the world. Our planet and society face many challenges today, and we feel it’s up to each of us to step up and create solutions for a better world. We’re inspired by the community of guys who wear SAXX and who are pushing the world forward. We want to bring their stories to a wider audience. Our purpose-driven campaign shifts our marketing dollars and resources to invest in and empower the guys who are making an impact. These initiatives can be as global as the men we selected, but also on a micro scale as well. The dads that are stepping up to improve their children’s worlds, the men helping out at the local shelter and the guy creating social change in his neighborhood, all of whom are trying every day to make a positive impact. Those are the inspirational men that we want to help promote in order to inspire others to get started on creating the change in the world they want to see. Here are the first three changemakers we are supporting: 1. Topher White, Rainforest Connection Topher’s mission is to stop illegal deforestation, which is the number one contributor to climate change. He has developed a way of turning old cell phones into listening devices, and is combining that with artificial intelligence and machines to detect sounds of loggers, poachers and animals. Using this bioacoustic monitoring, his team is able to alert the authorities of illegal logging and will be able to contribute statistics to the scientific community about animal populations. 2. Cesar Jung-Harada, Protei & Maker Bay Coral reefs are dying before our eyes and the systems we use to understand them need an update. Cesar’s latest opensourced innovation is ScoutBot: an ocean-mapping robot—made from inexpensive recycled products, like plastic bottles— that is paired with drone May 2018

propellers, laser projectors and a GoPro to measure the ocean floor far better than a human ever could. 3. Dylan Jones, Coast Protein The supply-chain of protein is ridden with pollution. Using crickets to source protein takes 13-times less land, 2,000times less water and produces 100-times less emissions than the same amount of protein from cows. Dylan is developing protein powders, bars and chocolates to help promote a cleaner form of protein in the world.

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With this new initiative, we hope to inspire men to actively create positive change in the world and to not only support men with comfortable underwear, but also to encourage them in their daily lives. Though this purpose-driven model just launched for us in late April 2018 feedback on our efforts has already been incredibly well received. But no matter the shortterm results, we are committed to this platform as a long-term strategy for the life of the brand. With companies spending an average of 11% of their total company revenue on marketing3, it’s easy to get consumed in the shiny stuff: the ads, the influencers, events, digital and social media. There is so much we can be doing in the marketing toolbox to achieve our goals, that we often lose sight of the impact a brand can truly make when we build human connections. I challenge you to think about your strategy and how it can be used even in the smallest way to drive positive impact for our world. Braden Hoeppner is the chief marketing officer at SAXX, one of the fastest growing men’s underwear companies in North America. His passion in marketing is at the fusion of human psychology, data, communications and creative. He was formerly head of brand and online at Kit & Ace, and CMO of Coastal Contacts. In his spare time, Hoeppner enjoys adding to his PEZ dispenser collection. 1 Peter Kafka and Rani Molla, “2017 was the year digital ad spending finally beat TV”, Recode, December 4, 2017. 2 Cone Communications, “2017 Cone Communications CSR Study”, study, May 17, 2017. 3 Fuqua School of Business at Duke University, Deloitte LLP and the American Marketing Association, “The CMO Survey, Highlights and Insights Report”, February 2018.

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Why and when to use AI-based marketing modelling By Richard Boire


he concept of artificial intelligence (AI) is the sort of thing that sets imaginations ablaze. To the general public, AI evokes images of everything from automated contact centres to advanced robots intent on global domination. It sounds futuristic, but to experienced analytics practitioners in marketing—and direct marketing in particular—AI is not necessarily new but simply another predictive modelling methodology.

Richard Boire is senior vice president of the Innovation Hub at Environics Analytics, where he focuses on transforming data into insights to drive more effective CRM results. He is a recognized authority on predictive analytics and data science, and is the author of Data Mining for Managers: How to Use Data (Big and Small) to Solve Business Problems.

AI drives productivity Direct marketers have been using predictive models for almost 30 years. Back in the mid-1990s, for example, a major Canadian bank developed the modelling methodology and process to target customers who were most likely to purchase something more expensive than what they already have. That early work yielded net savings of $120,000 from a single campaign. The only drawback: it required considerable staffing resources and the output wasn’t in any sort of presentable format. In the early days of my career, analytics work that once took a dedicated team a week to complete can now be executed by a single person in a couple of days. Some of these gains can be attributed to advancements in computing power. But over the past five to seven years we’ve also seen a significant improvement in AI’s ability to produce accurate models, particularly in the area of image and language recognition. If we were able to achieve a given lift in marketing response of 20% with traditional predictive models, AI can potentially improve that lift to 30% and more, and therein lies its appeal. How analysts use AI The increasing ability to quickly process very large volumes of data and the emergence of more advanced software and tools are helping to automate the some of the manual-driven analytical tasks of analytics practitioners. But it’s important to understand how AI fits in. In essence, AI adds another arrow to the analysts’ quivers; it doesn’t replace the need to have companies or analysts help prepare and interpret the data. In customer analytics, analysts are still required to process the data before AI systems can work with it. Someone also has to decide if it makes sense to use AI as part of the solution, since it doesn’t work for every model. Here is how it might apply to direct marketing today. Let’s say a bank wants to get a certain segment of its existing credit card customers to charge more to their cards. The first thing the organization needs to consider is what type of modelling approach to use to


get the desired results. Traditional modelling techniques such as regression and decision trees are still the go-to approach for most applications. But for more complex analyses they may not be sufficient to yield the desired results on their own. Traditional methods are easier to work with when there is a clearer relationship between variables, but these approaches consider fewer than a dozen variables at a time. While that’s more than sufficient to create a reliable predictive model, some organizations may require a little more analysis. For instance, if you have extremely large volumes of data and are considering dozens of variables then an analyst might turn to AI for assistance. Sticking with our credit card example, in the past the bank might have taken a sample of their customer list to build predictive models, but with the new processing power available today it’s becoming just as easy to run the model on the full customer list. This offers another advantage of AI since more accurate predictions require larger volumes of data. While traditional models may look for specific statistical relationships with the data, such as linear or log-linear, AI isn’t bound by such constraints. By using AI or neural nets the computer can run a limitless number of variables to come up with the optimal model, or combination of models, to find the best prospects to contact. This allows organizations to significantly lower their costs without compromising their overall engagement. Avoiding AI overreliance But a word of caution. AI isn’t a magic bullet. One of the severe limitations of using an AI predictive model is its interpretability. With traditional models, it’s easier to understand the relationships between variables and understand their importance in the model. AI, on the other hand, exists in a “black box” and it can require considerable research to fully interpret the findings. While AI can provide data-driven solutions, but in our experience telling clients the answer based on AI isn’t enough. Our customers want to understand how certain variables are impacting that answer before they act on it. Relying too much on AI can cause problems over time these models no longer work, since there is no way to know which variable inputs are affecting the model. For now and for the foreseeable future, the ability to figure out the right analytical technique can now be augmented through the use of AI. At the end of the day, AI is another tool in toolbox. Organizations just need to know when to use it. May 2018

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take the guesswork out of your marketing initiatives Direct marketing is more effective when you can target people who may already be looking for your product or service, or looking to donate to your campaign. Our custom-built predictive models can help you find and target the right prospects.

Direct Marketing Magazine May 2018  
Direct Marketing Magazine May 2018