One downside to virtual events: a barrage of content. When it comes to the retail industry, that certainly holds true. NRF's last Retail Converge session was a tsunami of content. Now, NRF Retail Converge, Chapter 2, is on deck for June.
We've seen a slew of online retail events this spring, but: how should retailers distill the burning lessons from hundreds of sessions?
We know one Vaccine Economy lesson: consumer behavior is still unpredictable. Retailers that enable consumers to move fluidly across *all* their channels - without losing track of their intent - have a big edge. Those that earn storefront trust, and adapt to consumer risk tolerance? A bigger edge.
That's easier said than done, of course, as this requires a fluid data platform on the back end, well-designed bots and apps, and highly-trained/motivated/fairly compensated humans who can act on that data. But, while this is not an easy thing to achieve, it's what I'd call an obvious lesson. Also, the more flexible the omni-channel, the better the hedge if consumers pull back on certain behaviors (e.g. in-store visits).
So what about the less obvious lessons? No one better to ask than Srini Rajamani, Vice President & Sector Head – Consumer and Life Sciences, Wipro Limited. With NRF Converge Chapter 2 on the way, I boiled down Rajamani's top lessons for retailers - via Wipro's own retail projects.
1. You can't separate supply chain disruptions from retail CX.
I thought our interview would start with the consumer, but Rajamani started with suppliers. If your supply chain is inefficient, your consumers are disrupted, and not in the good sense. As Rajamani told me:
Companies are undertaking a number of initiatives. One that we've observed is: a complete end-to-end supply chain risk assessment. Some of these include dynamic source determination, product substitutions, review, consolidation opportunities, and so on. In other words, 'Who are my vendors?' And: 'How do I leverage AI models to compute risk scores for the suppliers?'
De-risk the supply chain? Yes. End-to-end visibility? That's required now. But as Rajamani says, it's really about "driving sustainable supply chain resilience."
In other words, setting up supply chain cockpits to monitor and alert key supply chain events. Examples include monitoring specific news events that can impact the supply chain positively or negatively, adjust recovery plans, recommendations based on lockdown relaxations, logistics restarts, demand variations, and custom alerts for specific suppliers, regions, or parts.
2. It's time for SKU rationalization.
The pandemic brought harsh SKU proliferation lessons. It's a fragile balance between SKU variety for demographic targets, versus coming up short on inventory at store locations. Does on-site product personalization help here? Rajamani says SKU rationalization is high on his customers' lists:
80% of the creation of demand comes from fast-moving goods. Some common examples tend to be toilet paper and napkins, and stuff like that. But the fact is: there are a lot of consumables that have actually picked up in velocity.
How do you make sure you go in that direction, and focus on it to help simplify processes, optimize labor, raw materials, making it easy to work with channel partners, and so on?
For some select categories, such as soda, we've seen companies try to automate the process of creating specific flavors for the customer touch point through kiosks, where the basic ingredients of the same, but then you calibrate the other ingredients to adjust to the taste - moving things faster like that.
3. We need market intelligence models - for demand sensing.
If there's one thing we've learned in pandemic times, it's that our ability to predict consumer demand is subject to upheaval. Wipro's retail team is heavily involved in market intelligence projects:
We have developed market intelligence models for demand sensing. Some of the work we did was for a confectioner. We were looking at the impact of the pandemic on their sales, at the granular level. So can we blend in data from different sources, such as health, social, economic, mobility data points - to predict local consumption demand, and thereby improve sales?
And can you predict such demand?
It's a good question. To a large extent you can, except when some of these things are happening at warp speed. Sometimes your ability to adjust does constrain you. But to a large extent you can - especially with mobility data. There's a fair amount of specific data that's available.
4. AI for retail is now.
We can debate the proper use of "AI" as a label for certain projects, but it's clear from these examples that machine learning is now for retailers - though it's always important to bear down on the use cases, skills needed, and privacy implications. Rajamani added another project example:
With one manufacturing leader, we're looking at procurement data pattern recognition. We're looking to tailor algorithms to categorize unstructured procurement spend data, and making them into a logical cluster. The resulting categorization and detailed spend visibility can help you with activities - and subsequent savings.
5. B2B versus B2C is an artificial - and unhelpful - distinction.
Some distinctions between B2B and B2C still hold up. Example: complex software sales, which has unique B2B purchasing dynamics. However: I agree that creating a B2B and B2C divide isn't helpful for retailers. Rajamani sees this firsthand:
We are helping companies move their business models from B2B to B2C. How can we help from a supply chain perspective to support this new B2C model? Because many of their current distribution centers are geared up more towards B2B, supplying a retail store, rather than supplying to your house. So how do you make those changes in your software?
We also assisted a large food distributor with their IT strategy for expanding product assortment, during the drop in some of the vendors' sales (and supply) as well.
6. Store re-openings are not a return to normal - the consumer has changed.
Some consumers have no problem heading back into stores. Others expect digital speed with BOPIS and more (Buy online, pick up in-store). Others will gravitate towards stores that are more effective (and transparent) about consumer safety. I asked Rajamani: can retail tech help with all this? Short answer: yes - including store automation, contactless checkout, and video analytics.
This has several implications, starting from back to work - to back to living your life the way you used to. But there are some fundamental differences. One is store automation. If you were to look at it from a consumer's standpoint, they're saying: 'How much of the store is getting automated?'
In other words, contactless checkout is here to stay. Many consumers will go towards a contactless checkout, because they know that it's constantly cleaned. Therefore, they try to leverage that, rather than actually having to interact with someone, for example.
Rajamani cited other technologies that can aid in-store transformations, such as video analytics and RFIDs - "Increasingly used in the store to track inventory accuracy, but also to track people movement, and things like that."
Curbside pickup is a clear Vaccine Economy winner, and a focus of Wipro's retail consulting. Rajamani says that especially in congested areas of Europe and Asia, we're seeing more "dark stores," where people buy online, but save on delivery by picking up at the store: "The store is just a supply point," he says. He expects similar changes in the US:
Stateside, we're going to start seeing some of these things. People will want to walk into a store and buy the product that they have either customized or bought online. And they're going to want to do the curbside checkout.
Bottom line: the new retail normal won't be the old one.
These are some of the things we see as tectonic changes - people won't go back to where they were pre-pandemic.
At retail events, Wipro demos its retail tech, including the "Retail Store of the Future" and "Connected Worker." They are also working on blockchain scenarios. With the pressing need for omni-fluidity and contactless shopping, I'm not going to knock a look into the future. Consumer behavior has changed; the tech needs to speed up.
The big challenge remains the retail employee experience. Digitize all you want; employees still dictate customer experiences, especially when you get near a store. Brands that don't invest (smartly) in employees will find their dazzling AI tech falls short. Granted, putting superior retail workplace apps in the hands of employees can help. These topics were beyond the scope of my talk with Rajamani, but I'll return to them next time we speak.