APRI L 2 0 1 9
TECH SOLUTIONS GUIDE:
Artificial Intelligence/ Machine Learning THOUGHT LEADERSHIP:
Network of Executive Women on Inclusion
RETAIL AND CONSUMER GOODS
COMPANIES ALIGN ON DATA, INSIDE & OUT
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CONTENTS APRIL 2019
VOLUME 27 NUMBER 2
CGT ADVISORY BOARDS
5
Retail and Consumer Goods Analytics Study 2019
Data and analytics are the fuel that powers the consumer goods industry, yet many companies are still in their infancy when it comes to this increasingly vital capability. Collaboration is critical to future success for both CGs and retailers, yet many partners still struggle to establish mutually beneficial data sharing practices. CGT’s 10th annual analytics study again surveyed both consumer goods and retailer executives to gain a 360-degree view of analytics maturity and data sharing across the industry landscape.
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Departments 03
31
EDITOR’S NOTE
More than ever, consumer goods companies need to align internally, collaborate externally, and implement analytics-driven, consumer-centric business strategies. To help them get there, CGT will be undertaking its own transformation. NEW HORIZONS
Legal and societal pressure is on the rise for companies to increase gender diversity in their top ranks.
24
EXECUTIVE COUNCIL
Julia Anderson Smithfield Foods
EJ Kenney SAP
Denny Belcastro Kimberly-Clark
Werner Graf Mindtree
Tony Bender EBITDA Consulting Partners
John Phillips PepsiCo
Tony Costa Bumble Bee Foods Kerry Farrell Eversight Michael Forhez Oracle Mike Gorshe Accenture Jon Harding Conair Corp. Justin Honaman Georgia-Pacific
Kevin Puppe Johnson & Johnson Rich Scuteri L’Oreal Doug Rammel BAI Suavecito John Rossi Steve Sigrist Newell Brands Cheryl Williams Wakefern Food Corp.
EDITORIAL
Kevin Barnes Ferguson Enterprises Tony Bender Fmr. Edgewell Personal Care Rick Brindle Mondelez International Ann Dozier Southern Glazer’s Wine & Spirits Michael Ferrara HairUWear Jon Harding Conair Corp. Peter Hatch Reynolds American Inc. Service Co.
Chris Hobson VF Corp. Constance Howlett Estée Lauder Betsey Nohe Morton Salt John Phillips PepsiCo Kevin Puppe Johnson & Johnson Doug Rammel BAI Suavecito Steve Sigrist Newell Brands Filiz Yavuz Perry Ellis International
TECHNOLOGY SOLUTIONS GUIDE
Artificial Intelligence/Machine Learning CGT presents a comparison chart of solution providers on the forefront of artificial intelligence/machine learning tools that consumer goods companies can implement to enhance a variety of business-critical enterprise functions. Plus, a roundtable of industry experts provides thought leadership on key issues involving AI.
RESEARCH
Werner Graf, Chair Mindtree Gene Alvarez Gartner Michael Forhez Oracle Nona Cusick Capgemini Simon Ellis IDC
Don Lanham Hitachi Consulting Meena Surti Patel Cognizant Cheryl Perkins Innovationedge LLC Steve Rosenstock Clarkston Consulting
Consumer Goods Technology (USPS 0011-255, ISSN 1530-8421) is published 6 times per year: February, April, June, August, October and December, by EnsembleIQ, 8550 W. Bryn Mawr Ave., Ste. 200, Chicago, IL 60631. Subscription rates: $89 for U.S. addresses; $99 for Canadian addresses; $109 for all other addresses. Single copies are $20; add $2 for postage to Canada, or $5 to other countries. For Air Mail, add $65. Copyright 2019 by Ensemble IQ. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording or information storage and retrieval system without permission in writing from the publisher. Periodicals postage paid at Chicago, IL 60631 and additional mailing offices. Reprints, permissions and licensing, please contact Wright’s Media at ensembleiq@ wrightsmedia.com or (877) 652-5295. POSTMASTER: send address changes to: Consumer Goods Technology, PO Box 1842, Lowell, MA 01853-1842.
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CGT is on the RISE Technically speaking, you’re reading the final issue of Consumer Goods Technology. But we’re not going anywhere. We’ve just decided to practice what we’ve been preaching for the last few years. For the history buffs, CGT originally launched in September 1992 as Consumer Goods Manufacturer with a heavy focus on “technology partnering for supply chain management,” as its tagline proclaimed. As technology became increasingly important throughout the enterprise, the Consumer Goods Technology name was adopted about 10 years later to more broadly cover “the intersection of business and technology.” Today, of course, we’ve entered an era in which discussing consumer goods technology as a distinct topic is a bit naive, since technology now provides the foundation for every aspect of the business. More importantly, the discussions about technology that CGT has covered in recent years have evolved from tactical examinations of function-level “management” and “optimization” to strategic, full-scale “transformation” across the enterprise. At the core of this transformation is one critical need: Companies that were built through the methodical, efficient development of systems and processes that successfully created (and then fulfilled) consumer demand at scale must now reinvent themselves to become agile organizations that can nimbly respond to rapidly evolving, consumer-driven demand. Put more simply, consumer goods companies must strategically shift from product-centric to consumer-centric. And to do that, they need to better align internally, collaborate externally, and implement analytics-driven, consumer-focused business strategies. While CGT has been chronicling this need for the entire enterprise to better understand consumers, our sister publication, Shopper Marketing, has been chronicling how the marketers, insights professionals and brand managers within its community need to better understand the rest of the enterprise — because the consumer demand they now encounter is just as likely an issue of product sourcing, or manufacturing, or order fulfillment as it is about one of the classic “Four Ps” within their traditional job descriptions. With both communities looking for many of the same answers, it seemed only logical to bring them together, for CGT and Shopper Marketing to undertake their own transformation and align themselves to better address the needs of this industry. Beginning next month, we will do just that through a combined publication called RISE: Retail Intelligence for the Strategic Enterprise. Our goal with RISE is to guide the industry toward sustainable growth and operational excellence by identifying unique solutions to the challenges of a consumer-centric marketplace. We will cover every aspect of the business that impacts consumer purchase decisions, from product ideation and development through the manufacturing and fulfillment processes and right on to the various sales and marketing activities that now drive engagement. Of course, we’ll also continue to examine the existing and emerging technologies that companies are leveraging to make these transformations more efficient and more effective. “Technology” won’t be in the name anymore, but the topic will be present on every single page. To that end, many of the well-known event and content initiatives you’ve enjoyed over the years will continue. We’ll host the Retail & Consumer Goods Analytics Summit in Chicago later this month, and we’ll again stage the Consumer Goods Sales & Marketing Summit — although at a new time and place this year: October, in Boston. Correspondingly, we’ll continue publishing an annual Retail and Consumer Goods Analytics Study (as you’ll see, starting on page 5), and a Sales & Marketing Report — along with solutions guides for trade promotion, retail execution, supply chain management, digital marketing and other areas with key technology needs. The consumer goods industry is facing more challenges than it ever has before. At the risk of sounding too corny, CGT wants to help you “RISE” to those challenges. We’ll start proving that to you next month. Peter Breen, Editor-in-Chief
PRESIDENT, PATH TO PURCHASE INSTITUTE Terese Herbig therbig@ensembleiq.com EDITORIAL Editor-in-Chief: Peter Breen pbreen@ensembleiq.com Contributing Editors: Tim Binder, Jacqueline Barba, Patrycja Malinowska, Charlie Menchaca, Cyndi Loza SALES Associate Brand Director: Bill Little blittle@ensembleiq.com EVENTS Vice President, Events: Ed Several eseveral@ensembleiq.com Director, Event Planning: Patricia Benkner pbenkner@ensembleiq.com Director, Event Content: John Hall jhall@ensembleiq.com AUDIENCE ENGAGEMENT Director of Audience Engagement: Gail Reboletti greboletti@ensembleiq.com Audience Development Manager: Shelley Patton spatton@ensembleiq.com ONLINE MEDIA Director Product Development: Jason Ward jward@ensembleiq.com Online Project Manager: Whitney Gregson wryerson@ensembleiq.com PROJECT MANAGEMENT/ PRODUCTION/ART Vice President, Production: Derek Estey destey@ensembleiq.com Creative Director: Colette Magliaro cmagliaro@ensembleiq.com Production Manager: Patricia Wisser pwisser@ensembleiq.com Art Director: Lauren DiMeo ldimeo@ensembleiq.com Subscriptions: 978-671-0449
CORPORATE OFFICERS Alan Glass David Shanker Dan McCarthy Joel Hughes Tanner Van Dusen Ann Jadown Ed Several
Executive Chairman Chief Executive Officer Chief Financial Officer Chief Operating Officer Chief Innovation Officer Chief Human Resources Officer Executive Vice President, Events & Conferences
CORPORATE OFFICE 8550 W. Bryn Mawr Ave. Ste. 200 Chicago, IL 60631 Phone: +1 773-992-4450 Fax: +1 773-992-4455 www.consumergoods.com
CONSUMERGOODS.COM | APRIL 2019 | CGT
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Turn questions into actions. Fast.
www.answerrocket.com/cgt
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APRIL 2019 PRODUCED BY
RETAIL AND CONSUMER GOODS
ANALYTICS STUDY 2019
COMPANIES
ALIGN ON DATA, INSIDE & OUT TITLE SPONSOR
SPONSORED BY
EDITOR’S NOTE RETAIL AND CONSUMER GOODS
ANALYTICS STUDY 2019
PRESIDENT, PATH TO PURCHASE INSTITUTE Terese Herbig therbig@ensembleiq.com
Catching Up with Catchphrases
EDITORIAL Editor-in-Chief: Peter Breen pbreen@ensembleiq.com
Business catchprases related to the topic of data are plentiful: • “Data is a business asset” • “Data is a competitive advantage” •“Data is the differentiator” • “Analyze or die” OK, no one has ever used that last one (at least as far as Google or I know). But they might as well be, considering the critical importance that companies are now placing on business intelligence. It’s not too much of a stretch to say that analytics has become as vital to future success for consumer goods manufacturers and retailers as, well, products and stores. For me, the most illustrative quote on the subject still comes from former Hershey chief executive officer John Bilbrey, who in 2017 told The CEO Forum Group, “Even though we are a confectionary company, we now like to think of ourselves as a knowledge company” that uses consumer insights to drive the business. Bilbrey’s comment gained even more relevance early last year, when confectionary rival Nestle divested its namesake U.S. candy business. When an iconic candy maker decides that it shouldn’t be in the candy business anymore, you know the consumer goods industry is undergoing a massive transformation. In this kind of environment, pivoting toward “data as strategy driver” as your catchphrase isn’t such a bad idea, because if you don’t stay ahead of consumer demand these days you won’t even have any flagship brands worth divesting. Broadly speaking, there are two critical factors for becoming a successful “knowledge company”: internal analytics capabilities that can inform and direct the entire enterprise, and mutually beneficial methods of data sharing with retailer partners. They’re not mutually exclusive, of course, but they most definitely are mutually beneficial. That’s why we decided for this year’s Retail and Consumer Goods Analytics Study to combine the two themes we’ve used in the past, looking at the state of analytics maturity within the organization as we have since 2016, but also checking in on the status of data sharing as we did from 2010 to 2015. In general, we find that both CGs and retailers are making steady progress toward attaining the level of analytics expertise they’ll need to stay relevant with today’s evolving consumers. The one wrinkle impeding their efforts — aside from the traditional obstacles of corporate reticence and limited resources — is the continued emergence of new data sources to interpret and new shopping behaviors to understand. On the other hand, after a three-year break, we were hoping to find more progress in regard to data sharing between retailers and manufacturers. Although there has certainly been an increase in the types of data being shared, an improvement in the methods of sharing being used, and even an uptick in the frequency of delivery, it seems as if not everyone has fully embraced the idea of another catchphrase: “data as collaborative catalyst” (perhaps because some retailers have adopted another one: “data as alternative revenue stream”). Overall, however, the industry does seem to be putting its money where its mouth is (figuratively if not literally, although spending on analytics initiatives is also on the rise). Consumer goods manufacturers and retailers alike aren’t just talking about the need to become “data-driven” or “data-informed,” they’re taking the steps necessary to get there.
Contributing Editors: Tim Binder, Jacqueline Barba, Patrycja Malinowska, Charlie Menchaca, Cyndi Loza SALES Associate Brand Director: Bill Little blittle@ensembleiq.com EVENTS Vice President, Events: Ed Several eseveral@ensembleiq.com Director, Event Planning: Patricia Benkner pbenkner@ensembleiq.com Director, Event Content: John Hall jhall@ensembleiq.com AUDIENCE ENGAGEMENT Director of Audience Engagement: Gail Reboletti greboletti@ensembleiq.com Audience Development Manager: Shelley Patton spatton@ensembleiq.com ONLINE MEDIA Director Product Development: Jason Ward jward@ensembleiq.com Online Project Manager: Whitney Gregson wryerson@ensembleiq.com PROJECT MANAGEMENT/ PRODUCTION/ART Vice President, Production: Derek Estey destey@ensembleiq.com Creative Director: Colette Magliaro cmagliaro@ensembleiq.com Production Manager: Patricia Wisser pwisser@ensembleiq.com Art Director: Lauren DiMeo ldimeo@ensembleiq.com Subscriptions: 978-671-0449
CORPORATE OFFICERS Alan Glass David Shanker Dan McCarthy Joel Hughes Tanner Van Dusen Ann Jadown Ed Several
Executive Chairman Chief Executive Officer Chief Financial Officer Chief Operating Officer Chief Innovation Officer Chief Human Resources Officer Executive Vice President, Events & Conferences
CORPORATE OFFICE 8550 W. Bryn Mawr Ave. Ste. 200 Chicago, IL 60631 Phone: +1 773-992-4450 Fax: +1 773-992-4455 www.consumergoods.com
Peter Breen, Editor-in-Chief, CGT
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PA R T 1 RETAIL AND CONSUMER GOODS
ANALYTICS STUDY 2019
Data Sharing in the Age of Analytics BY LISA TERRY
eadlines everywhere proclaim this to be the “Age of Analytics,” observing that the consumer goods and retail industries, like many others, are becoming more and more data-driven. Analytics technologies are advancing at a rapid pace, while a broader range of data sources offer the potential for unprecedented new insights into every aspect of the business — most especially the all-important consumer. All that new analytics firepower is exciting. Yet for many retailers, and particularly for consumer goods manufacturers, some longstanding data sources are still sharply limited: each other. Data sharing is a long-touchy topic in the retail demand chain, with issues such as scope and frequency, as well as the “data as revenue stream” question, continuing to plague supplier-retailer relationships. In this age of analytics, no one doubts that data is the key to unlocking critical new insights. So the question becomes, is it a strategic advantage to share, or not to share, precious data with trade partners? The impact of data leaders like Walmart and Amazon, along with the changing nature of retailer-manufacturer relations — thanks to thriving private label businesses among retailers and direct-to-consumer selling on the part of CGs — are only making the question more complex. Retailers have been the most reticent, due in part to fears of disintermediation. “Retailers that don’t share their data with suppliers are protecting their role in the retail channel,” says Ken Morris, principal at BRP Consulting. “By sharing too much data with suppliers, they may eliminate the need for their own existence.”
DAILY SHARING
But there are clear benefits to some level of data sharing at least, particularly in areas where collaboration produces mutual benefits: think
FIGURE 1
Retailers Providing Daily Data 5% 14%
20%
14%
10%
10%
46%
46% ONE 2 TO 5 MORE THAN 10
35%
NONE 10 TO 50
6 TO 10 NONE
Q: From how many retailers do you receive daily POS data?
LESS THAN 10 ALL
Q: With how many suppliers are you sharing daily POS data?
FIGURE 2
Frequency of Data Sharing POS transaction
2% 10%6% 18%
Loyalty or CRM
22% 16%
Online sales Online customer behavior
32% 22%
26%
Pricing
16%
Promotions performance
16%
DON’T SHARE MONTHLY
44%
16% 14% 16%
12% 18% 36%
28% 34%
18% 2%
16% 12% 4% 18% 10% 6% 4%
12% 8%
24%
10% 14% 20%
10% 2% 6%
NO SET CADENCE/ON AD-HOC BASIS QUARTERLY OR LESS OFTEN WEEKLY DAILY MULTIPLE TIMES PER DAY/NEAR REAL-TIME
Q: On average, what best describes how often your retailer partners share the following data with you?
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PA R T 1 RETAIL AND CONSUMER GOODS
ANALYTICS STUDY 2019
maintaining correct inventory levels or gauging promotion success. Daily sharing of POS data should be considered foundational to achieving those and other goals. It takes infrastructure and resources to share that data, of course, but advances in integration and data management mean it’s not as hard as it used to be. That means if two trading partners want to share data more often, it’s more easily achieved. But looking back historically at the levels of daily POS data sharing (the most-commonly shared type) through the years offers a concerning contrast: Fewer CG companies report receiving daily POS data from large numbers of retail partners in 2019 than did back in 2014. This anomaly could be the result of having a larger number of small, newer manufacturers responding to this year’s survey than in 2014. However, the number of retailers who claim to send daily POS data today is remarkably consistent with that of five years ago. So, little progress is reflected here. “It’s a daily struggle to get the information needed,” was the un-ironic comment of one consumer goods survey respondent. The frequency of data sharing also lags expectations. No more than 20% of consumer goods companies report receiving any data type from retailers daily or multiple times a day — including POS transaction data (see Figure 2). CGs are least likely to receive online customer behavior and customer loyalty/CRM data. Retailers tend to hold these sources close to the vest as every participant in the demand chain competes to win the attention and loyalty of increasingly demanding consumers.
THE MONEY GRAB
CG companies have long pointed to the revenuegenerating appetites of retailers as one major obstacle to collaborative data sharing. Whether they view it as compensation for the costs of sharing, a way to bring in extra dollars or a financial incentive to ensure CGs will really make use of the data, many retailers are charging. But while the majority of CGs (76%) say some portion of the retailers they
work with charge a fee for data, just 33% of retailer respondents say they do (see Figure 3). Bear in mind that 76% of CGs responding to the survey are consumer packaged goods manufactur-
ers, whereas just over half of retail respondents (51%) sell them. Companies producing fast-moving consumer goods historically have been more data driven than makers of apparel, electronics or other goods,
FIGURE 3
Who is Charging for Data? CGs
23%
Retailers
37%
33%
16% 23%
SOME ALL
67%
MOST NONE
YES
NO
Q: How many of these retailers are charging you for the data (as a distinct cost)?
FIGURE 4
Types of Data Shared with Retailers Consumer/shopper demographics
70%
Category-specific consumer/shopper insights
70%
Retailer-specific consumer/shopper insights
70% 66%
Brand-specific consumer/shopper insights
54%
Universal consumer/shopper insights Causal data
36%
Social media data
36%
Competitive information
34%
None
6%
Q: What types of data are you sharing with retailers?
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particularly when it comes to studying consumers. So, these companies have long coveted retailer data for its ability to help them drive brand loyalty. But even with this caveat, charging for data remains alive and well. “Greater frequency is better because it drives closer connections, and hence enables better integration across the supply chain that results in better order fill rates, less out of stocks and overall more efficiency in inventory management,” says Jon Harding, global chief information officer at Conair Corp. Does it make a difference whether retailers charge? It’s worth noting that, of the two names that stood head and shoulders above the rest of the industry when consumer goods companies were asked to name the best data sharers, one – Walmart – provides data for free while the other – Kroger – charges rates that several CG companies even labeled expensive. Walmart’s program is praised for its simple access, granularity, breadth and extensive reporting tools and, perhaps most of all, its price. “Due to the granularity and breadth of the data provided, managing the daily demands of such an important retailer is made much easier from data sharing,” one CG respondent noted. Kroger gained similar praise, but also earned kudos for allowing manufacturers to mask and share the data with other retailers, according to one respondent. And despite the high cost, partners are generally happy. “Good partner with mutual interest in growing revenue and profitability,” one CG respondent commented. Incidentally, Walmart and Kroger also topped the ranks of best-sharing retailers five year ago. Retailers were consistent in their rankings as well, once again naming Coca-Cola Co. as the top data sharer among CGs. Those results are testimony to the early recognition at these companies of the benefits of data sharing, which has enabled them to continue extracting value and maintaining their market leadership today — even when fees are a part of the deal.
THE DATA THEY SHARE
Sharing is a two-way street, of course. Retailers benefit from the 50,000-foot view that consumer
10
FIGURE 5
Types of Data Retailers Want from CGs Competitive information
70%
Brand-specific consumer/shopper insights
68% 59%
Category-specific consumer/shopper insights
57%
Consumer/shopper demographics
51%
Retailer-specific consumer/shopper insights Universal consumer/shopper insights
35%
Social media data
35%
Causal data
19%
Q: What types of data do you want from suppliers?
FIGURE 6a
Benefits of Data Sharing: CGs Improved on-shelf availability
4.24
Sensing product acceptance in new product launch execution
4.22
Improved promotion design, forecasting and execution
4.2
Improved shopper/customer experience
4.1
More accurate demand forecasting Better sales force targeting and campaign execution
4.08 4.0
Demand insights to drive new product development
3.84
Better management reporting
3.78
Reduction of demand latency
3.68
Lower inventory levels
3.64
Q: What types of data do you want from suppliers?
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PA R T 1 RETAIL AND CONSUMER GOODS
ANALYTICS STUDY 2019
goods companies can provide about performance of their brands, the overall category and consumers. CG companies gain more granular insight into what’s happening inside each chain and store. A shared set of facts also helps both sides better understand the consumer and develop more successful programs. But the fact remains that CG companies are more likely to share data toward these goals than retailers are. Figure 4 shows that the majority of CG company respondents share five different types of data, while retailers only top the 50% threshold for POS and inventory data. “An additional benefit of sharing data with suppliers and getting reports on the generic movement of product is that it provides visibility on sales trends on an aggregate level, which can help identify if specific products are being sold more effectively by your competitors,” says BRP’s Morris.
THE DATA THEY WANT
CG companies have been developing direct-toconsumer channels to better understand consumers and build stronger connections with them. Yes, part of the plan is to establish direct sales vehicles, either to drive immediate growth or to prepare for what could be a more-direct future. But the need to directly cultivate shopping insights — due in no small part to the fact that it’s so hard to get shopper data from retailers — is often the primary goal. Hershey, for one, considers its various DTC plays more as “data acquisition vehicles” than sales opportunities. “That’s where the real value comes out of DTC,” said Doug Straton, the candy maker’s chief digital commerce officer, while speaking earlier this year at ShopTalk. For one, “You get a more expansive view of category management” when digital shopping data is added to the understanding, he said. Not surprisingly, now that their partners have even deeper insights, retailers want access to that broad treasure trove of consumer data (see Figure 5). Moving well beyond general demographic information, retailers want specifics to bolster their new, stepped-up focus on customers. They’re most interested in competitive information (70%), followed by brand-specific consumer/shopper insights (68%), category-specific consumer/shopper insights (59%), as well as more general consumer/
12
FIGURE 6b
Benefits of Data Sharing: Retailers Improved shopper/customer experience More accurate demand forecasting
3.73 3.7
Improved promotion design, forecasting and execution
3.62
Improved on-shelf availability
3.62
Improved joint business planning
3.51
Better category management
3.43
Better store execution
3.41
More successful product introductions
3.35
Lower inventory levels
3.35
Improved joint replenishment programs (VMI, DSD)
3.3
Q: On a scale of 1 to 5 (with 1 being “no benefit” and 5 being “significant benefit”), please rate the benefits of retailer-manufacturer data sharing.
shopper demographics (57%). All of that would help retailers put their own data in context to better understand how to compete and win customer attention in an increasingly high-pressure marketplace. “The key component to selling is conveying to the consumer what makes it special, and that boils down to experience and ease of shopping,” says Amanda Astrologo, associate partner at retail strategy shop Parker Avery. “The brick-and-mortar experience is about see, feel and touch. But that must translate across all shopping channels, so the more detailed data suppliers can share with retailers to drive the overall experience with the brand, the more successful they will be.” “The Holy Grail is shopper-specific information and the ability to target efficiently based on that data,” says CGT Executive Advisory Council member Kerry Farrell, senior vice president of sales and customer success at Eversight. “Ultimately, the ability to send the right offer to the right person at the right time — if the infrastructure and systems can ever catch up to the business desires — would have a transformational impact.”
WHAT DATA SHARING DOES
Exchanging key data is critical for the mutual goal of driving sales among consumer goods companies and retailers. Therefore, the top benefits of data sharing for both sides naturally support block-and-tackle operations:
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HAIRCARE
SKINCARE
BABY FOOD & NUTRITION
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PET FOOD
BEAUTY
CHOCOLATE & CONFECTIONARY ONCOLOGY
AUTOIMMUNE
See it live.
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maintaining stock levels, ensuring successful corporate-wide tool. Still, 42% of from attempts to adopt a single tool promotions, launching new products, correctly manufacturers still leave it up to because every retailer’s sharing is forecasting demand, and improving joint business each team to decide how to manage different,” says Conair’s Harding. planning (see figures 6a and 6b). their retailer-direct data. “In some cases, there is a custom All of those are also key to satisfying shoppers Moving to a single tool isn’t solution for a retailer, which always from both the retailer and consumer goods necessarily the right approach for has ‘first mover’ advantage on that company perspectives. But some things have everyone. “We have moved away retailer’s data format changes. changed. Five years ago, lowering inventory and safety stock levels was a top benefit: it was rated FIGURE 7a third among CGs and first among retailers. Today, that goal has fallen well down both lists. Primary Owner of Data Sharing Process Another area retailers can increasingly benefit CGs Retailers from manufacturer data is sustainability, according to BRP’s Morris. “As more consumers are making 4% 4% more socially conscious buying decisions, it’s 4% 35% important to know where clothing was made and 11% the source of its raw materials,” he explains. It 30% 46% 14% also provides visibility through the supply chain, 18% increasing a retailer’s understanding of where the product is through traditional EDI, shipping 22% 24% 24% notifications and inventory availability information, he adds.
SHIFTS IN RESPONSIBILITY
SALES CATEGORY MANAGEMENT IT MARKETING SUPPLY CHAIN OTHER
IT CATEGORY MANAGEMENT SUPPLY CHAIN MARKETING PROCUREMENT
Retailer data is highly important to CGs, so it’s natural that the largest group (46%) of respondents place responsibility for the sharing in the hands of their sales departments, the folks who typically manage those relationships (see Figure 7). That number Q: Which function primarily owns the data sharing process? is up from about one-third (32%) of respondents five years ago. Since then, it would appear that CG FIGURE 7b companies have shifted data sharing duties away 35% from the supply chain and marketing functions. Internal Data Alignment For retailers, IT is most likely to own data sharing — CGs Retailers although less likely than it was five years ago (41% vs. 30%). “Retailers look at data sharing as an obstacle 12% 10% 16% and a technical hurdle,” says Joe Skorupa, editorial 24% director at RIS News. “They are most likely to have licensed on-premise software, with proprietary code 30% and database structure. They have to clean, massage 48% 38% 22% and reformat it, and that requires IT.” Those proprietary retailer systems impact the mechanics of how consumer goods companies receive data as well. These days, CGs are much THE ENTIRE ORGANIZATION IS SHARING THE SAME DATA SOURCE more likely to use the same tool across all customer MAKING PROGRESS TOWARD A SHARED DATA MODEL teams: 34% do so, up from just 9% five years ago. STILL HAVE A LONG WAY TO GO And another 24% are currently implementing a STILL WORKING IN SILOS Q: Which statement about internal data alignment is most true at your organization?
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Changing the Face of Analytics in Retail. VSBLTY is changing the face of measurement with machine learning and artificial intelligence.
30 Female Face Id: abe92236
can
99% Coke
Happy
age gender smiling no glasses
30.0 female
anger contempt disgust fear happiness neutral sadness surprise
0.00178 0.00030 0.00110 0.00024 0.70541 0.00321 0.00045 0.99675
VSBLTY ‘s software suite leverages machine learning through computer vision with camera and sensor technology to deliver powerful real-time, qualitative measurement and analysis at every point of the customer journey. By engaging customers with VSBLTY’s Edge and/or Cloud-enabled Digital Display solutions, retailers can provide proximity-aware, interactive brand messaging triggered by demographic, identity or even sentiment, while simultaneously gaining groundbreaking levels of measurement and attribution. This matches and surpasses what is capable in the online world and delivers where it counts... at the moment of decision. VSBLTY’s object recognition software will soon assist retailers and brands with critical inventory and replenishment analytics by using computer vison to identify objects at time of conversion. All data registered by VSBLTY’s DataCaptor™ software module is fully anonymized, and conforms with federal and state privacy laws.
Give your brand VSBLTY. Visit us at VSBLTY.net to set up a demonstration.
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PA R T 1 RETAIL AND CONSUMER GOODS
ANALYTICS STUDY 2019
FIGURE 8
Instead, we’re adopting the most appropriate tool for each situation but with minimal customization and as much automation as possible.” The means to move that data has also modernized. Five years ago, the most-used method was manual sharing, followed by EDI. While those methods persist today, 65% of retailers are now using web-based portals to share their data.
WHERE THE DATA GOES
While useful across numerous functions within consumer goods organizations, retailer data plays a bigger role in powering some processes than others. Those levels of usage have remained relatively consistent over the years — with two notable exceptions. Five years ago, category management was one of the lowest ranked areas for retail data usage; in 2019, it’s the area that CG respondents say is most powered by retail data. Promotion management also has moved up significantly. Retailer data does not act in isolation, either. Consumer goods companies are combining it with an increasingly diverse array of outside data sources to generate richer, deeper insights. But much of that integration is still done on an adhoc basis; that’s the most dominant integration approach (50% to 60%) for most categories of data (see Figure 9). Just under one-fourth of CG respondents have fully integrated their field sales/merchandiser store check data, syndicated data, internal data and their own promotional data with retailer data. The newest data stream, social media, is the least likely to be integrated with retailer data.
PUTTING THE DATA IN ‘DATA-DRIVEN’
Data has been called the new currency. Nowhere in the retail demand chain is that clearer than in the data sharing programs between retailers and consumer goods companies. As competition for the attention of a distracted and empowered consumer becomes more acute, the choice on what data to share and when, and how to make the best use of that data, will further complicate a process that has long been fraught with tension. CGT
Processes Most Powered by Retail Data 3.82
Category management Forecasting/replenishment
3.64
Planogram management
3.56
Inventory management
3.5
Promotion management
3.4
Out of stocks
3.4
Pricing management
3.16
New product introductions
3.16 3.1
Field sales enablement
2.8
End of life/package transitions
Q: How much are you using retailer data (not syndicated data) to power each process below (with 1 representing ‘not at all’ and 5 representing ‘systematically’)?
FIGURE 9
Integrating Retailer Data with Other Sources 54%
Social media
36%
46%
Behavioral
52%
28%
Shopper/Loyalty
32%
Competitive information
26%
Field sales/merchandiser store check
2%
58%
14%
52%
16%
50%
28%
Demographics
10%
62%
24% 10%
Syndicated
16%
60%
24%
Internal
16%
60%
24%
Own promotion calendar
16%
60%
24%
DO NOT USE WITH RETAILER DATA
INTEGRATED ON AD-HOC BASIS
FULLY INTEGRATED WITH RETAILER DATA
Q: Which of the following data sources are you integrating with the data you receive from retailers?
16
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PA R T 2 RETAIL AND CONSUMER GOODS
ANALYTICS STUDY 2019
The Big Challenge: Balancing Maturity with Scope FIGURE 10
n a fast-evolving, increasingly data-driven marketplace, consumer goods companies and retailers must progress along the maturity curve in traditional analytics practices even as the number of business areas that require analytics expertise continues to explode. That’s a tough challenge, particularly when there are limited resources to spread around. Companies must carefully consider their strategies to determine how to leverage their resources for maximum benefit. And the priorities continue to shift in the wake of technology advancements, shifting competitive forces and changing customer priorities. “Disruptive technology is widely available at this point, and accessible to all almost instantly via the cloud. How to fit this into a sustainable, repeatable business process to scale, however, is another question,” says CGT Executive Advisory Council member Kerry Farrell, senior vice president of sales and customer success at Eversight. “This often gets short-handed as ‘culture,’ but candidly I think it’s more than that. It’s about the human processes and flows, and about reimagining decision-making in a world with access to transformational technology.” Traditionally one step removed from intimacy with the consumer, consumer goods companies have long relied on data and analytics to run their businesses. They invested early and have adopted newer analytics technologies as they have emerged. Retailers were often the recipients of that research, but their own analytics investments began much more recently. That pattern continues to play out in terms of budgeting across the industry. The percentage of IT budgets that CG companies allocate to business intelligence and
18
Percentage of IT Budget Allocated to Business Intelligence/Analytics 2019
2020
Retailers
19%
23%
CG Companies
26%
30%
Q: What percentage of your IT budget is/will be devoted to Business Intelligence/Analytics in...
analytics already outstrips retailer budgets by seven percentage points: 26% vs 19% for retailers. In 2020, retailers and CG companies will both boost their spending, but the ratio will stay the same, 30% to 23%. CGs might still be spending more, but “in terms of rate of knowledge gain, I think we’re seeing the tide turn toward retailers being more in the driver’s seat,” says Farrell. “With the rise of the cloud, advanced analytics, increasing experimentation capabilities and shopper-specific data driven by digitally connected commerce, we’re seeing retailers have access to far more information about their customers.”
INVESTING IN ANALYTICS MATURITY
Investments in BI are fanning out across an ever-widening range of application areas. The two areas where both retailers and CGs are most advanced are demand forecasting, where 40% of the former and 24% of the latter are using predictive or prescriptive analytics, and replenishment, where the percentages are 35% and 24% (see Figure 11). Only category management (at 26%) has reached the predictive/prescriptive analytics level at CGs (see Figure 12). On the other end of the spectrum, relatively recent tech-driven capabilities are the least mature when it comes to applying analytics: personalization, social media influence, social media presence and omnichannel/digital communications. CG companies lag, understandably, for in-store analytics, while most retailers continue to do only basic reporting and analytics in marketing. Basic reporting and analytics capabilities are still the reality for large segments of both sides. Moving up the maturity scale is not just a matter
CGT | APRIL 2019 | CONSUMERGOODS.COM
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o b
q G g c i S o
e o
FIGURE 11
Level of Analytics Maturity, Consumer Goods & Retailers Demand forecasting
Assortment planning
Space planning
Pricing & promotions
f t f , y s
n : d , c
e r
24% 26%
5%
38% 41%
22% 8%
24%
11%
35%
28%
14%
19%
8%
34%
22%
30%
16%
22% 32%
30%
11%
19%
14%
19%
16%
34%
34%
22%
46%
22%
42%
14%
16%
30%
22%
24% 22%
3% 24%
BASIC REPORTING BASIC ANALYTICS INVESTIGATIVE ANALYTICS PREDICTIVE ANALYTICS PRESCRIPTIVE ANALYTICS
4%
5%
20% 8%
2%
8%
18%
16%
6%
8%
28%
24%
27%
8% 16%
30%
28%
6% 4%
40%
26% 32%
3%
28%
27%
2%
11%
42%
35%
22%
Transportation/Logistics
35%
12%
20%
Inventory management
Replenishment
16%
30%
Promotional effectiveness
: h
e e , n r
19%
10%
2%
RETAILERS (TOP ROWS) CONSUMER GOODS (BOTTOM ROWS)
Q: Which best describes the most prevalent level of maturity of analytics for each of the listed areas in your organization?
of increasing investment; data pools also must be made ready for analytics. “The analytics are way ahead of the data quality and cleanliness for most retailers,” says Greg Buzek, president of IHL Group. “The biggest gaps are in two areas specifically. One, when it comes to anything inventory related, retailer data is woefully inaccurate — it’s off as much as 25%. So the analytics, no matter how good, is running on bad data for most retailers in this area.” Artificial intelligence can help, both here and elsewhere. “On the analytics side, the packages often fall short of what is truly needed, which
is the connection with AI,” Buzek says. The output of that should ask the user to choose what the next step should be, offering a button to approve or not approve the suggested action, Buzek explains. “This requires deep integration with the solutions.” Even companies that have advanced past the basic level must consider whether the organization has the analytics talent in place to make optimal use of investments in more sophisticated applications. “The biggest obstacle is the availability of experienced analytical people within the sales and marketing teams,” says Jon Harding, global chief information officer at Conair Corp. “IT teams can deliver the most advanced analytics technologies, but the value is not realized if there are not enough people within sales and marketing who can understand, use and act on the analytics delivered.”
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PA R T 2 RETAIL AND CONSUMER GOODS
ANALYTICS STUDY 2019
FIGURE 12
Level of Analytics Maturity, Consumer Goods & Retailers Marketing spend
38%
32%
22%
30%
28%
35%
Omnichannel/Digital commerce
22%
22% 27%
28%
8%
3%
8%
30% BASIC ANALYTICS
12% 30%
46% PREDICTIVE ANALYTICS
22%
18% 46%
BASIC REPORTING
8%
32% 62%
14%
6% 4%
34%
28%
46%
In-store analytics
16%
32%
18%
Personalization
5% 5%
16%
24%
INVESTIGATIVE ANALYTICS
PRESCRIPTIVE ANALYTICS
5%
2% 8%
30%
32%
24% Category management
11%
19%
52%
20%
8%
16%
30%
24%
11%
16% 28%
41%
Customer analysis
11% 20%
27%
46%
Social media influence
2%
30%
41% Social media presence
18%
22%
42%
5% 3%
22%
3% 6%
11% 4% 11% 4% 4% 5% 5%
12%
10% 2%
RETAILERS (TOP ROWS) CONSUMER GOODS (BOTTOM ROWS)
Q: Which best describes the most prevalent level of maturity of analytics for each of the listed areas in your organization?
WHERE THE DOLLARS WILL GO
Both CG companies and retailers know well where maturity levels lag their needs. Moving forward, those increased BI/analytics budgets will be funneled toward a broad array of systems updates (see Figure 13). The top areas of focus for application upgrades among retailers are security (65%), data
20
visualization/dashboards (57%) and web/online analytics (57%). All three areas led the list for planned changes in our 2018 survey. This year, in keeping with an industrywide zeal to enhance the consumer experience, personalization (54%) joins the list of planned upgrades, while an additional 19% of retailers are investing in personalization for the first time. Mobile BI capabilities are also seeing a surge in new software investment (22%). For consumer goods companies, upgrades are more about the data
CGT | APRIL 2019 | CONSUMERGOODS.COM
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%
%
e n , l e
a
FIGURE 13
Planned Software Changes, Consumer Goods & Retailers 11%
Data Visualization/Dashboard
18%
Big data analytics tool
Data warehouse/Storage
40%
16%
43%
16%
40%
8% 2%
Master data management
Mobile business intelligence
40% 14%
Software-as-a-service BI tool
40% 8% 48%
46%
8% 22%
32%
8%
35% 52% 8%
57% 44%
12%
41% 6%
42%
43% 22%
8%
8%
32%
68%
19%
54% 22%
5%
66% 43%
14%
46%
11% 10%
22%
6%
10%
38%
6%
46% 30%
22% 34%
49% 46%
6%
32%
12% 35% 8%
34%
22%
8%
48%
2%
Enterprise BI and reporting tool
38%
8%
16%
Customer personalization
34%
65%
6%
In-store analytics
10%
12%
11%
Social media analytics
35%
8%
14% 10%
Web/Online analytics
36%
5%
46%
6%
24%
6%
46%
5% 4%
Data security
8%
57%
38%
8% 6%
35% 54%
ADDING SOFTWARE FOR THE FIRST TIME MAKING UPGRADES TO EXISTING SYSTEM CHANGING TO A NEW SUPPLIER NO CHANGES PLANNED
RETAILERS (TOP ROWS) CONSUMER GOODS (BOTTOM ROWS)
Q: Which of the following best describes your planned changes for each of the following software within the coming 12 months?
platform. Nearly half (48%) will upgrade their master data management (MDM) tools, and 46% will upgrade data warehousing and storage and enterprise BI and reporting tools. CGs are
also most likely to be adding software for the first time to address data visualization/dashboards and analytics tools. Such investments are critical for overcoming obstacles to advancement. “Data normalization, or the elimination of data redundancy across multiple
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PA R T 2 RETAIL AND CONSUMER GOODS
ANALYTICS STUDY 2019
FIGURE 14
Analytics Investments at Work CG Tools Next 12 Months
Retailer Tools Next 12 Months
CG Greatest Impact on Data Sharing
Retailer Greatest Impact on Data Sharing
CG Helping Internal Data Alignment
Retailer Helping Alignment
Big Data analysis
56%
38%
30%
35%
50%
54%
Cloud infrastructure
50%
43%
28%
16%
46%
32%
AI/Machine learning
38%
24%
24%
8%
16%
22%
Internet of Things
16%
22%
2%
19%
16%
16%
Open source analysis
16%
14%
0%
14%
16%
11%
Blockchain
8%
3%
8%
3%
6%
11%
Other
6%
14%
8%
5%
12%
8%
Q: Which of the following analytics tools initiatives are high on your organization’s priority list for the next 12 months? Q: Which of the following analytics tools will have the greatest impact on data sharing? Q: Which of the following technology initiatives are helping you achieve internal data alignment?
systems, requires MDM to bring together the disparate islands of customer, product, sales and inventory information within the organization,” says Ken Morris, principal at BRP Consulting. For retailers, the the best-of-breed approach many have taken has created multiple data elements and redundancy that must be overcome, he says. “Retailers need to bring all the information in their enterprise together in a real-time environment. They need to replace antiquated store and forward retail technology with systems interconnected in real-time to reduce latency.” However, companies shouldn’t let a lack of complete data alignment impede their adoption of tools that can drive specific business wins. “We’ll still be talking about master data accuracy in 50 years. To the extent that specific business needs require systems to better share data, by all means pursue shared data sources,” says Farrell. “[But] it’s not a prerequisite to being able to make a large impact immediately. One single shared data source [doesn’t facilitate] a customer getting an order faster, a price being more competitive, a new product innovation being awesome, a marketing campaign being better targeted, or food being safer.”
22
TOOLS ON THE PRIORITY LIST
Big data analysis and cloud infrastructure for uses such as data storage and management are getting the lion’s share of analytics investment by both CGs and retailers over the next 12 months, and at even higher planned numbers than in 2018 (see Figure 14). CG companies are also moving more aggressively into AI and machine learning than retailers with investment plans that have accelerated compared to last year’s survey. Retailers, on the other hand, are shifting a bit away from AI and machine learning in favor of Internet of Things-related tools. These upgrades promise multiple benefits. One is supporting improvements to data sharing capabilities. Both retailers (35%) and CGs call big data analytics the most impactful on data sharing (Figure 14). CGs also consider cloud infrastructure and AI/machine learning as important. After big data analytics, retailers are most likely to cite IoT as having the greatest impact. “In terms of data sharing between retailers and CGs, the availability of ‘best-of-breed’ analytical solutions delivered in a ‘software-as-a-service’ (cloud) model is a critical success factor,” says Conair’s Harding. “The use of SaaS solutions makes the initial investment easier and switching solutions within a few years (as the original ones age out) so much easier, both in terms of cost and ease of changeover.”
ALIGNING INTERNALLY
There also are internal benefits, of course. Retailers (at 54%) and CG companies (50%) both cite big data analysis as delivering the most assistance
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e y d
e d m
g s s . e
f ’ f s n
G e
FIGURE 15
Use Cases for AI/Machine for internal data alignment (see Figure 14). Retailers increasingly see AI/machine learning as helping with internal alignment as well: 22%, up from 12.5% last year. Retailers in particular need tools that assist with internal alignment. Nearly two in five (38%) say they “still have a long way to go” when it comes to migrating to a single, shared database across the organization. But the good news is that 24% have achieved that milestone and 22% are making progress toward it (see Figure 7b). “The need for a single, shared view of data is crucial,” says Amanda Astrologo, associate partner for Parker Avery. “The consumer does not know boundaries, they only know that they’re interacting with a brand. So a retailer needs to adeptly leverage analytics to understand the demand source, as well as consumer shopping behavior and drivers, and be able to react quickly. The time and resources spent piecing together multiple sources and trying to make sense of them represents missed opportunities.” Consumer goods companies are a little further along. While only 10% of CG respondents are sharing one database across the entire organization, a significant 48% are making progress toward a shared model (Figure 7b).
THE RISE OF AI
Data sharing is not the only place companies see the potential for AI. CG companies continue to consider the greatest promise in demand forecasting/planning (34%) and supply chain planning/execution (32%), and despite a long list of application areas to choose from, also suggested there are other application areas where they see promise. Pricing and promotion also hold promise (see Figure 15). For retailers, AI is most commonly being explored in marketing/promotion planning/ execution (46%), followed by inventory planning (41%) and personalized marketing (38%). Last year, as a comparison, retailers were more likely to be exploring or using AI in merchandise planning/execution and customer relationship management (39%).
Learning Retailers Trade promotions
11%
28%
New product development
14%
20%
Assortment planning/category management
16%
20%
Logistics optimization
16%
14%
Consumer-facing service/interaction
19%
Supply chain planning and execution
22%
Allocation
24%
Merchandise planning and execution
27%
Demand planning and forecasting
30%
Consumer relationship management
30%
16% 32% 16% 12% 34% 20% 30%
32%
Pricing Personalized marketing
38%
Inventory planning
41%
Marketing/promotion campaign planning and execution
16% 26%
46% 14%
None of the above
18%
34%
CONSUMER GOODS RETAILERS
Q: Which of the following uses of artificial intelligence/machine learning are either being explored or used in your organization?
THE DATA-DRIVEN FOCUS
Consumers face a dizzying array of choices for when, where and from whom to buy a product — and then the best way to receive it. Consumer goods companies and retailers face a similarly overwhelming array of choices when it comes to the best way to tease apart the choices consumers are making. Even as analytics budgets increase, there are more and more things that demand analysis, as well as an ever-increasing amount of data waiting to be analyzed. And that is only intensifying the need for more sophisticated methods of predicting and prescribing consumer behavior. CGT
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Tech Solutions Guide
2019 AI/MACHINE LEARNING SOLUTIONS
GUIDE
CGT presents a comparison chart of solution providers on the forefront of artificial intelligence and machine learning for the consumer goods industry. Plus, industry experts provide thought leadership on challenges, opportunities, and implementation issues for companies navigating this new playing field.
DAVID MORAN Chairman & Co-founder Eversight
RICH WAGNER President & CEO Prevedere
SPONSORED BY
24
Q
Are there any obstacles remaining to the widespread implementation of artificial intelligence at consumer goods organizations? If so, how do companies get past them? MORAN: There is no barrier to widespread implementation per se. Rather, the opportunity is to create the user applications to solve specific business challenges. Otherwise, pushing AI is a bit like a hammer running around looking for nails. I’d rather start with what you want to solve. Machine learning — the branch of AI that has generated the most buzz — is really good at looking at lots of data to pick out patterns. But without a context and an application, it’s a bit like saying “big data.” All data has been big for a while, that’s why [the field of] statistics was invented. I’m far more interested in starting with an
opportunity or a problem, and then working backwards to see how a range of modern analytics solutions might be applied to address it. WAGNER: While there is certainly enough quality data available to build models that provide meaningful learning and results, consumer packaged goods manufacturers are struggling to find relevant use cases to model after. When we establish a small proof of concept that delivers evidence of value and demonstrates the overall worth of artificial intelligence across the organization, it goes a long way toward greater implementation of AI within the industry. In addition, support from the c-suite early in the initiation process will gain their buy-in on implementation throughout the entire organization. Beginning simply with a pilot program to build upon is the first step in the direction of more pervasive deployment. When we do these pilots, we focus on high-level numbers and inquiries that impact the entire company as well as the sales and performance of a large region or category.
Q
In which business function have companies enjoyed the most measurable success so far? In which areas has implementation been lagging? WAGNER: We’ve seen the most calculable success within the finance areas of CPG and retail companies, undoubtedly due to the fact that our solution utilizes AI to focus specifically
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Eversight_April Tech Solutions Ad.pdf 1 3/14/2019 2:37:20 PM
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Tech Solutions Guide
on predicting future headwinds and tailwinds to demand. In our experience, even small use cases yield significant value to the bottom line in these areas. On the other hand, we have observed that the biggest challenge comes when clearly defined business objectives for using AI are not established. Identifying objectives with executive visibility is paramount to quantifiable success and, ultimately, to more widespread implementation. Executives who are willing to maintain an unbiased attitude and embrace the latest progressive solutions will enjoy the most success as AI becomes more prevalent within the industry. MORAN: Certainly, programmatic advertising has been transformed by AI. Machines do a great job of looking at lots and lots of inventory for ads and determining the best balance of cost, quality, and reach to maximize marketing objectives. Programmatic selling isn’t there yet, but I think it will get there in the next five years. In a lot of ways, selling should be easier to have machines do than advertising. Prices just go up or down, and perhaps promotions are much more complex, but at the same time they’re still quite measurable: either the sale happened or it didn’t. So, compared to the nuances of brand building and activities at the top of the brand funnel, the selling function could dramatically retool. There’s a lot of friction to this being feasible today, from siloed data and access to manual processes to POS limitations, but I’m hopeful.
26
Q
What are the next steps in technology development? How do we improve the tools that we already have? MORAN: I was once told that software ages like fish. There’s a reason that technology as a whole has broadly turned over to software-as-a-service applications. Improving old, on-premise software is often a waste of time and a magnitude more expensive than just swapping to a modern stack. A lot of retailers especially have been slow to do this, and as a result the big innovations in POS software, for example, have (ironically) happened in the small to midsize business market instead. WAGNER: Unlike 10 to 20 years ago, companies can now leverage cloud-based AI solutions to augment or enhance current systems without the tremendous expense of having to replace them. Tools across the analytics workflow have required a significant amount of manual analysis and human interpretation, an approach that isn’t scalable in today’s environment. Undoubtedly, the next steps in AI technology development will involve embracing and implementing cloud technology as well as augmented techniques to modernize solutions. New development will complement — not replace — existing data and analytics initiatives. As the technology progresses, solution providers will help CPG and retail executives develop a strategy to evolve roles, skills
and responsibilities as well as support increased investment in data literacy.
Q
Do you have any examples of how AI has dramatically changed a client’s go-to-market strategy? WAGNER: We worked closely with a large beverage producer to implement the use of AI to help the company understand how external data — specifically, economic changes — impact consumer demand for its products. We discovered that factors such as the number of hours that architects build is a strong influencer in driving product demand. As a result of this improved insight, the client was able to develop predictive models that tightened its demand variable and, thus, dramatically reduce safety stock and improved distribution without impact to service levels. MORAN: Our retailer clients are using shelf-edge experimentation tools to better compete with e-commerce in dynamic pricing while optimizing the total store experience. Merchants spend less of their time on pricing administration and more on the highest-value judgment tasks, and as a result their categories are performing better. In a recent steering committee meeting, a retailer saw it was driving 1.5% higher sales, higher transaction volumes, and approximately 50% higher EBITDA margins vs. control stores by taking this AI-led “software-as-a-coach” approach for merchants. CGT
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ACCURATELY PREDICT CATEGORY DEMAND WITH AI Every category has a unique set of external factors that influence performance. Integrating this “domino effect” in business analytics can accurately predict shopper behavior.
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Prevedere’s Demand Planning for CPG/Retail solution combines human intelligence with aritificial intelligence from Microsoft Azure to provide future-focused insights delivered at the speed of business. The ERIN Engine (External Real-time INsights) constantly analyzes millions of external economic, consumer behavior, online and social data sets to provide a holistic view of future demand by category, product and region.
Prevedere is an industry insights and predictive analytics company helping business leaders make better decisions by providing a real-time view of their company’s future.
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Phone: (888) 686-7746 Email: inquiries@prevedere.com Web: www.prevedere.com
4/3/19 12:43 PM
Tech Solutions Guide
AnswerRocket www.answerrocket.com
E2open www.e2open.com
Eversight www.eversightlabs.com SEE AD ON PG 25
Fractal Analytics * www.fractalanalytics. com
AnswerRocket
Offer Innovation, Retail Pricing
CoE Concordia & Cuddle
Contract Assistant
IBM www.ibm.com/ customerengagement
IBM Watson Supply Chain Insights
Stand-alone
X
Stand-alone
Eversight solutions automatically sense when prices and promotions have gone stale, and AI engines test thousands of new permutations with real shoppers. Winners are surfaced and automatically deployed in-store and/or users coached on how to best take high-impact action.
Stand-alone
The tools let users tackle data sets at scale to improve brand management, innovation, customer service, retailer relationships, and anomaly detection; and to understand consumer sentiment using deep learning and natural language processing for personalized BI and cognitive search across functions.
Stand-alone
Did not provide
Contract Assistant is an AI-driven solution that automates contract creation and deduction reconciliation processes. It works with existing TPM and ordering applications to drive productivity and eliminate revenue leakage.
Add-on
X
Did not provide
The platform leverages IBM Watson cognitive AI machine learning technology to provide visibility across the entire supply chain, enabling users to predict and mitigate disruptions to the business.
Add-on
X
Did not provide
X
X
X
New Product Development
X
X
X
X
X
X
X
X
X
X
C OM
IRI www
Ivy M www
X
Orac www /indu X
X
X
Prev www SEE A X
X
X
X
X
X
r4 Te www
X
X
X
X
X
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*I
Logil www
The suites help to reduce data cleansing, improve near-term demand prediction, automate multi-tier inventory optimization recommendations, identify out-of-stocks, improve demand planning, respond to collaborative manufacturing events, and provide supply planning recommendations.
•Coca-Cola Co. •General Mills Mars Inc.
X
Trade Promotion Management
X
Pricing
Stand-alone
Marketing/Promotions
Unilever
The platform is designed for the business user, who is able to type/speak natural language questions and receive answers in seconds. The AI-generated insights answer complex, open-ended questions by pinpointing the root cause.
E2open Intelligent Did not provide Applications
Genpact www.genpact.com
28
Did not provide
The NAVIK AI Platform uses AI and machine learning to create scalable business impact across the enterprise. Solutions that run on the AI Platform include NAVIK MarketingAI, SalesAI, ResearchAI, TradeAI and PriceAI.
STA ND A LONE V S . A D D -ON
Demand Planning/Forecasting
NAVIK AI Platform for the Enterprise
UNIQUE FEATURES / BENEFITS
Retail Account Management/ Planning
Absolutdata www.absolutdata.com
KE Y CG CUST OME RS
BUSINES S FUNC TIONS
CRM
P RODUCT
*Information compiled by CGT
Direct Consumer Engagement
C O M PA N Y / W E B S I TE
SOLUTIONS CHART
Supply Chain/Logistics
2019
AI/MACHINE LEARNING
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Revio www
SAP www
Tech Solutions Guide
Ivy Insights
Did not provide
Logility, Inc. www.logility.com
Logility Voyager Solutions
• Citizen Watch America • Moen • Vista Outdoor
Logility Voyager Solutions helps to quickly drive actionable insights for better decision making, planning and optimization of the global supply chain.
Stand-alone
X
Oracle Retail www.oracle.com /industries/retail
Oracle Retail Science Platform Did not provide Cloud Service
Oracle's Retail Science Platform provides a suite of applications that accelerate business performance, including customer segmentation, advanced clustering, affinity analysts, decision trees and others.
Stand-alone
X
Prevedere www.prevedere.com SEE AD ON PG 27
ERIN Predictive Analytics Cloud
Did not provide
The AI engine monitors macroeconomic trends, identifying future changes in demand.
Stand-alone
X
r4 Technologies www.r4.ai
XEM r4 CrossEnterprise AI
Did not provide
The plaform works with existing systems and learns continuously, providing the c-suite with a current, holistic view of the market and business to enable better decisions and faster actions.
Stand-alone
X
Revionics, Inc. www.revionics.com
Revionics Life Cycle Price Optimization Solutions
Did not provide
The solution is an AI toolbox with real-world pricing. The models offer a decade of real-world customer production data and are continuously learning to provide more accurate pricing decisions.
Stand-alone
Did not provide
SAP Leonardo is a combination of intelligent technologies, services and industry expertise that can help optimize processes and resources, as well as ignite innovation in a variety of business areas.
Stand-alone
SAP www.sap.com
SAP Leonardo
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
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New Product Development
Ivy Mobility www.ivymobility.com
Ivy Insights uses AI models to anticipate out-of-stocks, predict business outcomes, reduce order times, and provide faster decision-making capabilities in the field. It also provides mobile functionality to provide sales reps with SKU predictions and recommendations.
Trade Promotion Management
Add-on
X
Pricing
Stand-alone
Marketing/Promotions
The suite leverages IRI Liquid Data, IRI Unify visualization, alerts and automated prescriptive analytic capabilities, which are augmented with newer AI and machine learning algorithms, allowing for quicker, smarter, real-time decisions.
CRM
IRI Strategic Analytics Suite
•Conagra Brands •Hormel •Starbucks
Demand Planning/Forecasting
IRI www.iriworldwide.com
P RODUCT
UNIQUE FEATUR ES / B ENEFITS
Direct Consumer Engagement
C OM PA N Y / W E B S I TE
S TA ND A LONE V S. A D D -ON
KE Y CG CUST OME RS
Retail Account Management/ Planning
X
*Information compiled by CGT
BUSINES S FUNC TIONS
Supply Chain/Logistics
New Product Development
*Information compiled by CGT
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
APRIL 2019 |
X
X
CGT
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Tech Solutions Guide
*Information compiled by CGT
X
X
X
X
X
New Product Development
X
Trade Promotion Management
X
Pricing
X
Marketing/Promotions
X
Demand Planning/Forecasting
Add-on
Retail Account Management/ Planning
UNIQUE FEATURES / BENEFITS
CRM
STA ND A LONE V S . A D D -ON
KE Y CG CUST OME RS
B USINES S FUNC TIONS
Direct Consumer Engagement
SOLUTIONS CHART
Supply Chain/Logistics
2019
AI/MACHINE LEARNING
C O M PA N Y / W E B S IT E
P RODUCT
SAS www.sas.com/en_us
SAS Viya Analytics Platform
Did not provide
The platform provides fast, accurate forecasts for more confident supply and demand decisions, including new product forecasting, demand signal analytics, and inventory planning and optimization.
Satisfi Labs, Inc. * www.satis.fi
Satisfi Labs Platform
•Mall of America •Miracle Mile Shops •The Irvine Company
The AI network is completely scalable across complex tasks. The speed and accuracy of its online automation, with the personality of a live person, increases customer engagement, transactions and loyalty.
Add-on
Shortest Track www.shortesttrack.com
Marketplace and Intelligence Services Management Platform
Did not provide
Sourced from a blockchain-protected marketplace for securing IP, the platform leverages analytic solutions optimized on margin and revenue to solve business problems, and also monitors intelligence-as-a-service to ensure business impact and ROI.
Stand-alone
Signals Analytics www.signalsanalytics.com
Signals Playbook Platform
Did not provide
Signals Playbook is a cloud-based data platform that combines data sets, machine learning and analytic engines to provide decision support.
Stand-alone
Did not provide
The system tracks shelf placement, assortment, promotion, facings, and share of shelf by using image recognition. It also helps prioritize stores with the highest impact and help better prepare for buyer meetings with execution data.
Stand-alone
Stand-alone
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Snap2Insight Inc. www.snap2insight.com
Shelf Image Recognition
Symphony CPG|AI www.cpgai.com
Strategic Revenue Management
Did not provide
Strategic Revenue Management incrementally increases revenues and margins by sensing, predicting and shaping consumer behavior to optimize product, price, promotion, space, assortment, and inventory strategies.
Visualfabriq www.visualfabriq.com
Trade Promotion Master
• L'Oréal •Ocean Spray • Unilever
This AI-enhanced TPM tool provides an end-to-end predictive solution combining zero-touch planning optimization and evaluation from a single point of entry. It offers integration of internal and external data with inbound and outbound interfaces.
Stand-alone
X
Wipro Ltd. www.wipro.com
Auto ML-based Outcome Shaping Tool
Did not provide
The solution provides prediction and inferences to shape revenue, as well as causality analysis on factors that impact the outcome and revenue.
Add-on
X
30
X
X
X
X
X
X
X
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NEW Horizons
Break the Glass Ceiling (or the Law) Pressure to increase gender diversity at the top is on the rise
Last fall, California became the first state to pass a law requiring public corporations to add women to their boards. Hmm, “All male? Go to jail.” Not quite. The law requires publicly traded companies headquartered in California to appoint at least one woman to their boards by the end of this year. By the end of 2021, a minimum of two women must sit on boards with five members. There must be at least three women on boards with six or more members. Companies that fail to comply face fines between $100,000 and $300,000. “People would prefer that you wouldn’t have to mandate,” Tierney Remick, vice chairman and co-leader of Korn Ferry’s Board and CEO Services practice, said at the time the law passed. “But in reality, [progress is] not moving fast.” What does it say about deeply entrenched bias in corporate America that the threat of a $300,000 penalty will do more to move the needle on gender equality than the myriad proven competitive and bottom-line benefits associated with women’s leadership? Scores of studies have shown the business benefits of greater representation of women at the most senior levels. Gender diversity and inclusion bring better decision-making, higher returns on investment, improved efficiency and lower turnover. One report, by Lehigh University’s Corinne Post and Georgia State University’s Kris Byron, found that women tend to think more broadly and holistically and companies with women board members are more socially responsible. When that type of thinking is brought to the boardroom, decision-making implications for employees and the communities where companies do business are more likely to be given a voice. Women’s preparedness — fueled by feelings that their qualifications may be questioned — impacts male board members, Post told Forbes.com. “When women participate on boards, the attendance of male directors goes up, too,” she said. “There might be some type of contagion effect where, if women come better prepared, then everybody starts preparing better. That can help in making better decisions overall.”
PRESSURE FOR PROGRESS
At the current rate of progress, though, true equality at the senior level is decades away. The glacial movement is caused, in part, by the many men and women who are satisfied with so
BY SARAH ALTER
little progress. Nearly half the men and a third of the women surveyed for McKinsey’s “Women in the Workplace 2018” study believe women are well represented at the senior level when they fill just one in 10 roles. Even so, the California law comes at a time when public, shareholder and institutional investor pressure to increase gender diversity at the top is on the rise. Other states (including Illinois, Massachusetts, Pennsylvania, Ohio and Colorado) have issued resolutions encouraging diversity on corporate boards. More than 80% of institutional investors surveyed by the EY Center for Board Matters reported board composition, with a focus on diversity, would be a top priority last year. “This may include gender, race and ethnicity, age, nationality and geography, socio-economic backgrounds or other forms of diversity, but gender was most commonly cited, partly due to the lack of consistent disclosure on any other characteristic,” EY reported. In February 2018, BlackRock Inc., the world’s largest asset manager, announced it wanted its portfolio companies to have diverse boards, noting, “We would normally expect to see at least two women directors on every board.” The firm also asked some 300 companies in the Russell 1000 that have fewer than two women on their boards to disclose their approach to boardroom and employee diversity. Still, California’s law has been opposed by nearly three dozen business groups and will most certainly be challenged in court, likely by the California Chamber of Commerce. But if it takes a state mandate and fine to break down barriers and move toward gender equality, I say, “One down, 49 to go.” CGT
Sarah Alter is president and CEO of the Network of Executive Women, a learning and leadership community representing more than 12,000 members in 22 regional groups in the United States and Canada. Learn more at newonline.org.
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The launch that will empower the entire consumer goods industry is
ALMOST HERE.
Next month Shopper Marketing magazine and Consumer Goods Technology will unite to bring a stronger voice to the industry.
Learn more at p2pi.org/Rise
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