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.
I have a different take on this question. AI in this context is about analytics to monitor and improve the efficiency of multi-cloud environments. I think the challenge has as much to do with balancing the advantages of best-of-breed clouds with cloud customer experience platforms that account for many business processes (and have therefore integration efficiencies built in). AI then becomes secret sauce to inform those business processes in differentiated ways -- which retailers absolutely require if they hope to realize the reported benefits of digital transformation. I called it the coming wave of efficient effectiveness back in October of last year.
I'll add a different point of view to this development. Other brick-and-mortar retailers have watched Amazon Go for some time, and I know it's caused some discomfort in terms of how to respond or understand the impact to their businesses. Most are nowhere near capable of testing this degree of analytics and sensor solution. What's needed is a clear and actionable roadmap of how to use your customer data to improve overall CX for your customers and ensure this is actionable across all customer-facing business processes. Without this, too many retail marketing and technology managers are going to be chasing a shiny new object they have no capacity to match. For Amazon, it's a great way to create anxiety among the larger retail competition and distract them from attacking their most immediate opportunities to improve.
I agree with other commenters that this is definitely the future, but what's likely to happen over the next 12 to 36 months is the emergence of companies that package similar capabilities for retailers to deploy cost effectively. At that point, once again, it's going to be the data about your customers that will make the difference, not just having the cameras and sensors in your stores.
One way is to view personalization as an analytics use case that could feature spend/earn rewards as a factor in driving a consumer to buy. Most progressive retail and brand execs would like NOT to give away value in return for sales volume because it hurts margins and creates the perception of price as the main decision factor. If growth were the trade-off, that would be fine, but growth is challenged. The way around this is to gear your marketing and consumer relationships more around value and content -- like the form of life-stage marketing you see from P&G. Exclusivity is an important concept but to make it about monetary rewards only is a mistake. It’s got to include other dimensions your consumer may care about, like events, new product trials, special content and more. And don’t worry necessarily about the cost of these programs -- because what they do is help insulate you from price pressure and reinforce the value of your brand experience. To know this with confidence and to scale it to 1:1 interactions requires both analytics and operational applications to work in unison. That unfortunately is very hard for most companies to attain.
This is a great idea but the retailer would probably not require the entire real estate footprint. What seems clear is that maybe 50 percent of sales opportunities can be won by serving a customer online, but having the customer themselves fulfill the order by picking up merchandise in the store. That’s a big figure that should help retailers struggling with profitability optimize stores and e-commerce.
Taking this a step further, the retailer can use the physical footprint to not just fulfill orders, but cross and upsell based on insights showing those kinds of selling opportunities.
Overall, this is going to be an analytics problem at its core -- knowing what that particular assortment looks like how to serve that shopping mission is a “must know” to avoid stocking irrelevant products. I tend to disagree with this statement though, it’s not about transitioning customers to online sales, but better serving them how they wish to be served: “With this smaller, yet valuable product assortment, a brand can maintain a toehold in a market area and better transition a customer base to online sales.”
Another good example of experimentation. But what’s potentially really interesting here is that the Walmart Labs data science team may be involved to see to what extent they can apply AI to this process, to scale the effort to the masses without the need for human personal shoppers. I’d expect to hear more about this.
If you start with the premise that the more you understand about your customer, the better you can serve them -- then it makes total sense to fulfill customer needs at as many touchpoints and via as many business models as can be justified. Subscription services I think are offered for similar reasons -- it’s as much a data collecting and analytics means as anything. The data you can collect in a physical store is today very similar to what’s possible on a website -- using IoT technology like sensors, cameras, interactive displays and beacons allows retailers to instrumentalize their stores just like a website. So you can monitor inventory, target promotions and optimize store layout. It gets even better when you put that data to work with tech like interactive chatbots, clienteling by associates and mobile shopping assistants for your customers.
Coming back full circle, you must understand how the in-store shopping experience fits within your customers’ overall journey -- so having that vaunted 360-view of the customer becomes critical to realizing the full value.
I don’t think it’s important that consumers understand AI, what it is, how it relates to data or any of the details. What AI promises is to create more satisfying shopping experiences by leveraging a variety of data sources and machine learning. Retailers and brands should focus more on use cases and business outcomes first, then seek the options to test and rollout the right supporting technology solution.
It’s important to keep in mind that consumers must realize more value from technology driven interactions than their concerns about data privacy or ease of use. The use of virtual assistants for more than simple commands will happen, but to scale these interactions they must allow consumers to easily confirm their inputs, make changes and understand how payments work. For this reason, I think virtual assistants that include visual interfaces are probably going to be necessary to see more complex use cases work well at scale.
There's a lot of evidence to suggest that loyalty is a function of customer experience, not a reward system. Rewards can play a role in overall marketing to high value customers and to spur infrequent customers to spend more. Rewards themselves can be some combination of monetary value (discounts) and more qualitative benefits. The outreach opportunity varies by customer -- the key is to use analytics to identify key moments of truth to engage your customer.
Research proposes that purchase decisions, even for current customers, happen in the consideration phase where a customer can evaluate many options very easily. The one they choose is probably not going to be grounded in a transactional-based loyalty program given the intense price competitiveness in many retail segments. Whoever wins with this customer will offer the optimal blend of price and experience.
Marketers looking to best leverage the data at their disposal should collaborate across agency, internal IT and data science around their customer journeys or customer experiences. The label “Big Data” doesn’t really matter -- it’s all about the analytics maturity of a retailer and how they are organized.
Too many retailers have bolted on data science, marketers don’t speak their language, and the data that agencies manage remains fenced off from transactional and customer interaction data created by internal systems. Speed, speed, speed and test, test, test, while quickly deploying to production or scaling the highest value use cases has got to be a priority.
Advancements in AI are like an octane booster to get retailers moving faster and taking advantage of methods they haven’t yet productionized like relatively straightforward personalization. The leaders are extending the gap here and most retailers have got to catch up quickly.