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.
Without knowing for sure, I expect Accenture helped them take a step back and think about business (or marketing) problems that lend themselves well to new forms of analytics, like AI. Then plotted out a use case path to test, rollout and scale.
When companies drive right to the technology “thing” as the solution to their problems, they typically fail and AI analytics is no different. Longer term, companies of all sizes and maturities have opportunities to apply AI to many elements of their business to improve results. It’s what really large companies like Walmart, Starbucks, P&G and others have landed on after learning while they go over several years.
Companies that are less progressive can learn from their experiences to accelerate the value and scale they achieve with AI -- in marketing, supply chain and many other elements of the business.
I think this is a complex question with a few dimensions. I do think it’s more analogous to complaints about advertising. The fact is many retailers use email as a revenue channel that works in spite of what sometimes appears as too much volume. I think it’s basically an attempt to be present when the consumer is ready to buy.
The challenge happens when it comes to engagement and gaining the consumer’s attention and mindshare – over time, I believe most consumers like me stop looking closely at these communications due to fatigue. At the same time, when that consumer is ready to buy, having an email there handy from the past few days with a discount certainly helps drive a sale. That then places the value of this channel as a “discount and offer” communication mechanism at the expense of developing a closer relationship with the customer. Most retailers want to move away from discounts and toward value-based customer relationships -- which does depend on greater personalization and relevance.
Back out the lens further to the simple question of understanding your customer’s consideration and buying process. Marc Pritchard at P&G recently said something like they now know 3 touches are needed to drive a sale so anything more is waste -- and they probably know what those 3 touches should look like. Maybe answering questions like this should be prioritized above whether or not too many emails are being sent?
I would reframe the question around how analytics can support better personalization, and machine learning is one method to support the use case. I would also say that the drawbacks such as staffing and learning should be mitigated by having a strategy that includes how analytics supports both marketing and the overall retail or consumer goods company in pursuit of better CX. Questions like this I think are too tactical in nature. Companies should step back and evaluate their maturity with regards to analytics, the competencies of their people, and their business objectives. The drive to improve marketing and create more personalized email doesn’t have to slow down, but it should be considered within a broader construct in my opinion. Too many companies have failed or struggle as a result.
The retail industry seems to be in the thick of a correction or disruption that’s been building up over the past several years or more -- namely the bifurcation of consumer demand into luxury goods and discount goods. Retailers not aligned with these markets have been struggling, but working hard for the most part to transform around better and more appealing shopping experiences. That’s an economic impact, but Amazon’s ongoing growth has exposed the excess retail square footage in the U.S., leading to store closures and the abandonment of malls. If nothing else, the “stack em high and let em fly” mantra so long associated with retail has got to be shelved -- in its place, there needs to be a focus on the end-consumer, their household, lifestage, interests and connections. Retailers need to design an experience they can defend.
One final note about the discount market -- just because the consumer has less to spend does not mean you can take their business for granted when it comes to a pleasant shopping experience. Companies like Walmart have foreseen these market shifts and have the supply chains to go even more down-market, while their size makes it possible to invest also in new segments more luxury in nature. In the U.S. at least, the retail industry is becoming more and more reflective of its population’s economic situation.
At the very least, all retailers should emulate Amazon’s passion for using data to drive all dimensions of their business. The latest advancements make excuses such as a lack of data science skills and expensive technology much less legitimate than in the past. The challenge now is instead prioritizing use cases and focusing on what you can do best and most uniquely for your customers in a manner Amazon can’t match. Too much of retail middle management is in the dark about their company’s analytic strategy, if one even exists. This would make a great followup RetailWire discussion.
This post asks the question from the POV of research and analytics teams – people I have worked with and presented to many times in various roles. From what I have seen, these groups need to align better with LOB leaders to develop, activate and measure analytics that impact the company’s CX. There is too much separation from each group’s charter – thus you see LOB leaders examining analytics on their own, which is great from a time-to-value standpoint, but lacks connection to the broader analytic strategy the retailer should be pursuing.
There’s actually a third group, IT/data management, that has to be engaged since ultimately the best insights will come from sources across and outside the company – all of which must be managed, governed and secured.
In the end, retailers big and small should step back – even just briefly – and examine what they are doing with analytics, and why and how it relates to serving customers throughout their journey. Think agile and fast, pursue tests, and don’t get bogged down into multi-year planning horizons.