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Click and Collect and the New MAPPS (or AMPPS) Challenges for Retail

The core Category Leadership toolkit, i.e., the major commercial levers for a retailer and its vendors, are usually covered in an acronym like MAPPS; Merchandising, Assortment, Pricing, Promotions, Shelving/Space.

Our industry has gotten really good at them too. We have huge dedicated teams, automation software, and outsourcing for the repetitive and straightforward work like shelf space planogram design and maintenance. We do eye tracking studies, we have lots of data from SKU switching studies and cross-sku interaction pricing and tradeoff effects, we work hard to integrate our Promotion Management systems into our Supply Chain forecasts, and more. We’ve even further specialized, for example most CPGs have broken out Revenue Growth Management from Trade Marketing, which has been further broken down into a huge planogram/shelf space team and a smaller analytical insights function, which is then complemented by a Shopper Marketing team and often by a supplemental Shopper Insights capability, often dedicated to an individual key account or channel. And the list goes on….

The only problem is that these systems and processes have been perfected for a pre-Internet world of brick and mortar commerce, where, e.g., Walmart and Kroger would take full truckload shipments of display-ready pallets. Yet most of the growth in our industry is in eCommerce.

So what does it mean for MAPPS in our new reality?

As I’ve mentioned before, eCommerce isn’t a channel (digitally connected commerce is now the norm). I’d like to focus on just one shopping dynamic that, in particular, sits directly at the convergence of digital and brick-and-mortar commerce: Click and Collect (e.g., Buy Online, Pickup In Store). I don’t have all the answers (I don’t think anybody does yet), but I’d like to share a few of the questions that have been bumping around my mind for a while:

Merchandising: Out of stocks happen all the time… with Click and Collect, often it’s up to the individual picking the order to decide what substitute product to add to the order and it’s highly likely that sale will end up with a loyal customer trialing a different product. How do you replace it smartly? As a retailer, you may still capture the sale and care less about the switch than the CPG brand replaced, but what if the customer has a bad experience with the substitution? What if they love Heinz but not Hunt’s or vice versa? Too many substitutions (at least bad ones) could drive that customer to another retailer.

What about the sales impact of incorrect product information or images on a retailer’s website? The CPG brand wants to know who monitors this and how do they keep the quality where they want it. Retailers should ask: what impact does sub-optimal online merchandising have on my sales or basket size? How do we quantify and optimize that?

Assortment: with Click and Collect offers, there are all sorts of opportunities to consider limited time assortments, trialing and testing new products, etc.

Can we afford to offer a much more granular assortment, picked out of back stock, if it doesn’t need to be merchandised in store? What are the logistics required to efficiently fulfill that way – essentially having 20 center store aisles plus a DC in the backroom?

Pricing: Digital channels often come with price transparency. For online purchases I personally use Camel Camel Camel (The Camelizer) and Wikibuy … does this change the relevance of Minimum Advertised Price (MAP) pricing or Authorized Reseller Policies for vendors/categories who haven’t specifically used such things in the past? Should it lead to more product variations to reduce direct price comparison (similar to Club packages or Walmart-specific assortments in decades past?)

Promotions: Digital also comes with promotional transparency. For online purchases I personally use Honey, and eBates (many others use Retailmenot too). Who’s coordinating these things? How do we avoid a Krazy Coupon Lady-sized Moneymaker from happening?

We know that, for a CPG brand, getting on the shopping list is critically important because once products are on the list, shoppers are likely to reorder the same product over and over again with less re-evaluation (and, likely, with less participation in markdown dollars/promotions investments). What’s the value of getting on the list? And once something is on the list, and it’s time to reorder, what should the brand and/or retailer try to nudge? For CPG, should it be the same product? A smaller pack size to increase margin? A larger pack size to lock in loyalty? For a retailer, should it be a more premium variant to try for trade up? A complimentary item as well to increase basket size? Or a competitive product (and get a vendor to fund more trade dollars to buy it away)?

Space: Unlike the pure play brick and mortar environment, here there really are “rubber shelves” (i.e., add as many SKUs as you want). That said, where you show up in search matters, as do the prompts (what to highlight to whom). How much choice should be offered in search? Would a search that offers a smaller range (potentially of higher margin products) yield a better result than a full and unstructured assortment?

By the same logic, it’s not really fair to call it an infinite shelf…a lot of shopping is happening on mobile phones. One could argue it’s actually the smallest shelf that has ever existed, and so optimizing it matters more here than ever. With limited digital real estate “above the fold,” what are the implications of retailers selling or auctioning digital shelf space (prioritized placement on a tiny screen) similar to linear feet or facings in the store?

In addition, all of these functions will require a range of new skill sets that are table stakes in the eCommerce world, but outside of the normal category management toolkit for most Retail and CPG teams. Skills such as:

Experimentation: everything, literally everything, should be a/b tested. Many of the old tools for learning in brick and mortar, like conjoint, surveys, trade promotion optimization, marketing mix modelling, are being obsoleted in various ways by simple digital experimentation.

Personalization: Everything today can be customized to subgroups, to various levels of granularity, and in the case of Click and Collect, it can be done all the way down to the individual. Decisions now have to happen on a sliding scale from 1:all to 1:1, and tools are required to optimize just how granular you can and should get.

Tracking: Relationships aren’t fleeting anymore—digital breadcrumbs are left by consumers all over the place, and the actions that someone takes can prompt reactions down the line. This goes beyond simple ad retargeting, it gets into prompts for reorder cycles, identification of high probability churning shoppers, correlation analysis to understand what complementary products might be required (and when they might be needed) based on earlier behaviors that are highly predictive, and more.

Machine Learning: This stuff is going to be too complex, frankly, unless we can have all of the capabilities above automated so that a human being doesn’t need to manage it all, but instead can oversee it and make incremental decisions based on the insights generated. This is going to shift power back from brands to retailers because in the old way CPGs used to staff up huge Catman teams to compensate for a retailer’s limited staffing capabilities to get this analysis done. With machine learning, all the data is going to sit with retailers, and the CPGs are going to be on the outside of it. There will be very interesting implications for power dynamics.

Who’s doing this well? There are individual efforts in all of the largest retailers to get better at these, but I’d be kidding you if I said that we were any further than the bottom of the first inning in terms of where this could go in the industry. If you talk to the digital native leadership of Boxed.com, StitchFix, Rent the Runway, Jet.com, etc. you’ll hear much more emphasis on the technical capabilities in these areas than most traditional retailers.

And I likewise can’t predict what the future is going to look like—at this point I have more questions than answers about what really matters, and what the new organization structures are going to look like to answer these questions. Here’s what I believe will be true no matter what:

  • Digital shouldn’t be siloed—it should be a part of every function’s responsibility and it’s impossible to divorce from brick and mortar commerce—we live in a digitally connected shopping world
  • Machine Learning and AI will be required, in the form of software as a coach, if we’re ever going to get the existing teams to be able to pull off what’s needed to thrive in this world. (Here’s a previous post I did on this topic if you’re interested)
  • The companies who win here are going to focus on practical problems and solve them one-by-one. There’s an infinite number of buzzwords one could apply (personalization, big data, etc.) but the reality is, like most things there will be an 80/20 impact of a few smart actions, and I’d focus more on the practical things that you can do right now to influence the business over the overwhelming amount of things that you could do…fewer, bigger, more focused initiatives here are the way to go.