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.
This is a form of lifestage marketing. Doing this well requires a plan to engage with your consumer and their household on a routine basis. You need to plan to collect and update their data and provide incentives and compelling reasons for your consumer to volunteer this information. You can of course infer or predict circumstances, but the real question here isn’t about data mining or analytics -- it’s about “what’s your consumer engagement and insight strategy?”
The AI coming to market today can help midsize and smaller online retailers adopt methods long used by Amazon and other large retailers far along the analytics maturity curve. So in some ways it’s like achieving parity. I think it’s important to think about AI, and more broadly analytics, as powering a retailer’s relationship with its consumer throughout the journey -- not just in the active shopping phase. A more holistic approach is today realistic given the number of options to accelerate use cases that fuel a satisfying journey across the pre-shop, shop and post-shopping phases. That wasn’t the case in the past where a broader view required a longer adoption time horizon that few people had time for.
I think she's a bit correct. You expect leaders to have a vision that is bigger than current trends. Retailers are at different stages of maturity and execution for sure and that matters a lot. But I see her point.This quote from her article is what spurred my thoughts here: "When executives proudly discussed achievements like knowing their shoppers’ hobbies or whether they use an iPhone or an Android device, all I could think was: Isn’t this simply the standard now that we’re many years into the era of Big Data? What retailer isn’t doing something like this, or at least trying to?"
Seems like investment strategy should be dictated by how tech and digital supports the retailer's delivery of a compelling customer experience. For traditional retailers with stores, that footprint woven into digital is probably going to underpin this. How it's architected and delivered though is based on insights reflecting the customer experience. It probably needs to be an ongoing effort, not a fixed end point, that gets tuned over time based on insights. Leading with a technology conversation is probably not the right approach.
I would say so. I have a lot of sympathy for what retailers are going through. In the past with less competition your experience could be very broad, almost indistinguishable from others, to attract a large audience. Today the mantra is all about knowing YOUR customer really well and crafting an experience for them. The challenge with that is targeting a particular customer while ensuring that a base of customers is sufficiently large enough to drive your business forward. And so you get trapped between trying to craft a compelling experience for a targeted customer and simply bolting on new service concepts to try and better serve that larger, but shrinking, diverse audience. Finding the middle ground to own and build upon is really challenging.
As an analytics use case, personalized marketing exists on a maturity curve. Most retailers are doing very simple personalization using facts about individual customers. When it comes to predicting the best content, offer, call to action, treatment strategy, etc., retailers should test new methods aimed at improving response and sales. It's not an either/or question, it's about how you go about exploring the capability and determining its business value before deploying it more broadly.
Unfortunately I think this is not such a simple question. Price and value, plus convenience, selection and overall experience must all be blended optimally to achieve positive outcomes with consumers. Low-price leaders are doing well but you might be able to group them as 1.) companies that embrace other elements of the mix besides price (like Walmart I’d argue) and 2.) those that are simply discounting in an attempt to keep customers.The retail success equation for high growth retailers of the future is complex but that’s precisely what leaders are using analytics to address.
As it relates to analytics and the use of new insights based on data, I think the key challenge is the operationalization of insight and the adoption by business stakeholders who are aligned with the different stages of the shopping journey.In my experience data science and analytics teams need better ways to activate their work. Ensuring consumers experience the value of analytics at the physical store level first requires that employees who have those face-to-face interactions leverage new insights as part of their day-to-day work. Making the use of analytics as transparent as possible -- to either people or systems -- is where I think brick-and-mortar retailers should focus their attention. In many cases this is simply too hard, costly and impractical for many retailers struggling with slow to no growth and slim margins who at the same time are pressured to test, experiment and transform their businesses while forces like Amazon continue to disrupt the retail industry.There's no better argument in my view for adopting cloud solutions enabled with the kind of embedded AI that helps businesspeople realize the value of analytics.