AI or advanced analytics of any form for demand forecasting or any use case continues to be a confusing topic for retailers, consumer goods companies and really any industry. Viewed as a one and done point solution or one time effort, it’s certainly possible that the hype exceeds the reality. The challenge companies have is developing analytics as a competency, and having the ability to test, learn and improve. The reality is the analytics leaders are using AI to improve forecasting because they make the investment in the right people, processes and technologies. For any other company they need a plan beyond looking at a single use case with a fixed outcome. The reason this is so challenging for retail and CPG is because of silos, decades-old processes and challenges to meet earnings quarterly. It’s a hard problem, but IMO it’s one that retailers thriving in five to 10 years will have overcome.
I liked Bob Amster's comment. He's talking about the elephant in the room here which is peronsalization of the CX, which consists of numerous elements including price. It's the optimization of the personalized experience that is the challenge - which isn't easy, but obviously worth exploring. You can look at price in a silo and all the related factors (competitors, demand), but I think future retail winners are going to balance price with other CX elements like as Bob suggests assortment, store size/format, online/offline fulfillment, loyalty, offers and content.
Personalization needs to be driven by the CX a retailer is trying to execute. My sense is that it’s becoming table stakes to personalize on transactional and behavioral data where the outcome is a more tailored sales offer. The contrast with travel, hospitality and insurance sectors that rely less on transactional data is interesting. I think about how these other service industries use personalization throughout the customer relationship (your life stage, family situation, interests, etc. – relying on a lot more than just transactional and browsing behaviors). The challenge retailers have is determining the role of personalization beyond offers to really delivering a differentiated CX – I think too many fixate on marketing offers. That's an analytics problem requiring a good data foundation and strategy.
This is a terrific AI use case that all grocery retailers either should be looking at or are doing already. It affects not just the waste factor, but also improves outcomes with customers. Note the pilot to production mention - generally regarded as best practice for AI and productionized analytics work. Given the apparent success, I wonder what other AI use cases the company will approach next? The one mistake they can make is stopping here.
This is interesting given the very small relative size of Dick's versus Home Depot. Diverting a lot of resources in this direction as opposed to sourcing best in class tech may be risky. Absent in the news is anything about Dick's investments in analytics to better inform how it runs its business through technology. If anything, retailers need to invest more here and have a strategy.
One way to think about BOPIS differently is in terms of the store as a point of customer interaction and experience, inclusive of many elements that include the buy online/pickup in store ability. The problem I think most retailers implementing this model will have is bolting it onto a legacy environment – legacy process, legacy tech, legacy mindset, etc. – and the outcomes will not be as strong as hoped for as consumer adoption starts to scale (“most BOPIS solutions are not ready to scale to 40 percent of transactions as predicted for coming years”).
It’s hard to step back and view the store as a multi-use interaction point versus a fulfillment center but there is so much more to winning retail today that needs consideration IMO.
Remember when a Target analyst was criticized for predicting a young woman's pregnancy before her father knew, and how the company's marketers personalized their Sunday Circular with new baby products? This pricing issue is another similar example of not being transparent with your consumer.
The tactics of analytics are hard to get right, but so are the softer issues about how it unfolds with your customers -- needing a real executive commitment to trust.
Last point: dynamic pricing is a good analytics use case (think Kroger), but better applied based on supply/demand and done at a store level than individually, IMO.
Having the capacity to test analytic use cases like this is important for retailers in many segments to possess – fast fashion, big box retail, discount, and grocery. The hypothesis that consumer sentiment expressed in social channels – their words and language, phrases, and images can serve to gauge and predict product preferences and demand is a solid one. However it’s just one signal that must be combined and analyzed with others as pointed out (sales, PII, weather, economic). It requires testing relative to how merchandising and assortment is accomplished today – what’s the goal, how much can you improve? More ideally, it’s part of a plan across the company to deliver a seamless customer experience using available data and analytics methods. There’s also an element of organizational success required here since much of these decisions are today done more qualitatively. So it needs executive support.
Out-of-stocks remains a big problem for many retailers and it is a problem worth looking at with AI. For Walmart in particular, I think they once calculated the cost of this problem at about $3 billion a few years ago. The challenge is in the activation of the AI -- the operationalization of the analytics to automate processes. This has proven the biggest barrier to many use cases. For the use case underway at Walmart there are a lot of moving parts, human and technical. Companies taking a serious approach to AI but lacking the resources and will of Walmart might think smaller and more focused on improved human decision making with AI. Later with greater resources and expertise, it will make sense to automate related business processes.
The pros are that both off-the-shelf services and custom analytic methods based on a lot of data sources can help inform CLV scores to enable this approach. The con is that once high value potential customers are identified, what’s the optimal approach to engagement and treatment? I think that is a much harder question for retailers to answer. It speaks to the way the unique customer experience is articulated, and challenges related to content marketing, and life stage or lifestyle marketing. Otherwise, simply targeting unique discounts to these customers continues the drain on margin affecting almost all retailers.
Evidence so far suggests having a corporate plan for analytics and AI first is the winning path that might land on any number of supply chain use cases. Few retailers have done this. Chasing supply chain AI use cases discretely apart from an overall plan isn’t likely to result in success. At the same time, planning and strategy do not have to be a laborious and long term activity. It does take C-level commitment and a focus on prioritizing use cases based on potential business value. I just posted about this subject.
I appreciate what the other commenters say so far. What I would add is that the overall state of insights driven retail/supplier collaboration has been poor for many years (overall, with some exceptions), and so any new idea to further move the needle is a good one. Although this center has been around since 2006, I had lunch at NRF a few years ago with a Hershey's insights lead who said they continued to work toward becoming whole store advisers to their retail partners. I think this is a good example of that.
Great idea. Unprofitable e-commerce shipping costs was an analytics use case discussed at Toys "R" Us near the end. The creative application of not just ads on boxes, but promos and calls to action that link to a mobile or online interaction, help make these efforts more measurable for the advertising brands. Think too about the potential to build this into a broader retailer ad marketplace across the website, mobile application, digital ads and in-store displays, and you can create an entire retail service to supplier partners. Great data monetization use case.
For consumer packaged goods, this is a complicated question. For certain categories like paper goods, it’s possible that the most appealing aspect of the product is how it’s made and whether it’s environmentally friendly. Making sure your consumer understands this, and how your company more broadly supports that idea, can fuel a lot of different interactions -- both digital and mass. For other categories, like sports drinks, it may be all about ensuing hydration and superior performance. To me it seems to come down to a content marketing strategy based on rich consumer insights. Those insights ideally should reflect the consumer’s lifestage to provide the most personal and contextually relevant interactions. Otherwise, it’s all about price and coupons.
Whether this is a long term thing or not, it’s great to see simply from an experimentation standpoint. Dollar General will learn a lot more about its customers – from the extent to which they shop using mobile to how it helps customers make the most of their dollar while shopping. The key use case here appears to be tracking your shopping list relative to your budget, and being able to apply maybe in real time savings via coupons and other offers. That in turn can help build a larger basket ring and sell into new categories.