In-Store Customer Decision Making

In-Store Customer Decision Making
March 2, 2022 Omnibus

Every succesful retail store plays an interactive game with customer decision-making process. The offer must meet the shopper expectations and solve the pain points.

But that’s not all. Surprise the shopper with a valuable offer of something she didn’t plan to buy, and she will award you with many happy returns.

So the good retailers game also demands diverting the expectations. When retailers use proper strategies, and this is done succesfully, unobtrusively, we as customers are satisfied. Even delighted. And, the store thrives.

Article describes customer decision tree – a hierarchical model of a shoppers’ decision making process.

Properly done, customer decision tree is a powerful tactical tool which lies not only behind planograms (visual merchandising) but every succesful store layout.

Thus, it plays one of the central roles in our store space management framework called #levers.

Although omnichannel retail environment heavily impacts the decision-making process, the principles of properly used customer decision tree stay just as relevant as before.

— A SIMPLE BUYING TASK, A LABYRINTH OF CHOICE

Let’s start and stroll down the street to visit the nearest grocery store. Own bag, please. With a simple goal → to buy a little milk. No bread needed, just milk. The easiest thing, right?

Also one of the most regular shopping tasks. Our recent shopping basket analysis shows that 10% of all tickets in a Central European city market include milk (and another research proves that shoppers buy milk from the nearest convenience store 9.4 times per month).

Approaching the shelves, we just need to make a little decision. Long-last or fresh, right? Ah, and another one. Will it be whole, reduced-fat, low-fat, or fat-free type? How about lactose? Organic or not? Is it cow’s milk, rice, plant, or nut-based? And size? And origin? And brand? And price? And then the expiry date?

Simple buying task causes an unexpectedly huge cognitive load. Especially in digital times when shoppers’ demand gets increasingly fragmented. While the older generation would still embrace the regular cow milk, the younger might lean in a completely different direction – towards the craft variations of coconut milk. Subchoices galore. We might say shopper in digital times is thrown into a labyrinth of choice.

— THE OTHER SIDE OF THE SHELF

On the other side of the shelf there’s someone pulling out the hair? The retailers.

Usually heavily armed with computers, sales data, specialized software, and research they try to decipher shoppers’ decision-making process. They tend to organize products and shelves in such a way that shoppers can find their beloved milk product (finding means selling). And maybe – just maybe – even to divert them a little away from the intended purchase (direction: higher margin or bigger size, maybe).

To make sense out of a huge assortment mess, the main tool in a clever retailers’ hands is a customer decision tree. It breaks down product attributes like brand, price, package, size, origin, etc, and combines them in the tree hierarchy. The tree thus tries to follow the shoppers’ decision-making process and provides criteria for positioning products on shelves.

In advanced retail environments, these customer decision trees serve as a key input for creating planograms – a visual representation of store and category layouts.

 

Sample Customer Decision Tree (c) Omnibus – Ljubljana

Let’s get back to our milk shopper. One possible captured customer decision path from the top-down goes like this:

“Something for breakfast”  → MilkLow → Dairy Free → Low Fat → Brand → Flavour: Hazelnut → Package: transportable by foot → Promotion → Final Product X 950g.

A surprise here. Milk searched is technically not milk but a dairy-free product! Even at this stage, we see the chasm: some retailers recognize this substitutability and put non-dairy products close to dairy products while other retailers still say: “No! Milk is (cow) milk and almond drink is something completely different.”

Whatever. Here enter the quantifiable sales data that indicate the importance of each node. It might be a volume or value share or some other performance indicator, possibly a combination.

One step further and the retailers add a calculated share of shelf space – this finally brings sales and space share together and pushes the decision tree further towards possible implementation.

So, the customer decision tree brings all sides of the shopping process together, right?
For the shopper, it eases up the choice. It helps her navigate from top to bottom until she finally finds the right supplier’s milk product. In particular case non-dairy milk products.
The suppliers also get their share of shelf space according to the clear criteria.
Not to mention: the decision trees create order out of the mess for the retailer as well. Thus decision tree also serves as a guiding line for preparing a planogram = pictorial representation of the products on the shelf which also significantly decreases the amount of time store staff needs for shelf replenishment.

A perfect tool!

— BUT!

As it often happens, the idyllic view hides a trap.

The simple look of a tree often entices corporate practitioners (not to mention sneakers-wearing startups creating all-purpose magical algorithms!) into believing that the decision-making problem could be solved by a winning mathematical equation.

How? We choose the decision criteria, gather the past data, and set the genius rules. More sales more space, fewer sales less space. We turn machine learning algorithms with fancy charts on and voila! the space management problem of the store is automatically solved. Once and for all. Time to move over to other, more serious problems!

But that’s a corporate illusion.

Transforming past data and growth opportunities into the store layout IS the ultimate challenge for a physical retailer. It combines all of the retail variables at once. Assortment, price, presentation, place – all come together here. No magical formula will solve this once and for all. Sorry.

Sure. Good mathematics helps. Might be even a necessary condition for success.

Bad news: good mathematics is not enough! It should be complemented by other inputs. Even by some gut feelings. By understanding how customers buy, how they make decisions, feeling their pulse. Also future customers. By relentlessly assessing our market position and the value we bring to customers. And more: decisions have to be regularly checked, refreshed, and improved.

Machine learning can come only after we find the working solution.

There is also good news: once a valid CDT is set up and implemented the following iterations will take considerably less time. Plus: with a feedback loop and further improvements CDT will very likely accelerate positive results.

— KEY CHALLENGES FOR BUILDERS OF CUSTOMER DECISION TREES

Here are four challenges for builders of customer decision trees.

1) The decision-making is neither hierarchical nor purely logical & rational. The consequence: the above decision path doesn’t fit the process of shopping. In fact, some neuro research shows that up to 90% of our purchasing decisions are irrational.

Meaning: That even a regularly bought product such as milk is bought with a blend of your senses. You might get attracted by the colours or maybe the memory of the mountain valley once visited vividly overwhelms your mind when approaching the shelf.

2) As decision trees are prepared on a category level they fail to recognize our real shopping goals. Namely, our shopping basket is usually a pattern of products from different categories – only together do they represent a fulfilled shopping mission.

Meaning: Organic milk shoppers will possibly like to add some other organic products in their shopping basket: eg. organic cheese, organic butter, and even organic snack. It probably isn’t the high-turnover products, but if you as a retailer don’t provide them – the disappointment might permanently push the shopper towards a competitor.

3) Shopper decision-making process is not a system with one solution. On contrary, it is heavily influenced by a context – place, time and the goal of the particular mission.

See how shoppers’ attention varies regarding his / her goals:

  • Say you just want a quick breakfast solution on the way to the office: you probably wouldn’t be at least interested in heavy multi-packs of milk. The situation would completely change a few hours later when you return home and need to fill the gaps in your home wardrobe. The decision tree is different from mission to mission.
  • If you go to a specialty dairy shop, you’d be less sensitive to prices, but you’d also expect more diversity in terms of local variations. The decision tree is different from retailer to retailer.
  • Another important factor is familiarity with the store. In familiar places, people tend to skip the unnecessary options and buy habitually (auto-pilot buying), while in unknown places the decision-making process is more time-consuming.
  • One more context that heavily influences our behaviour: is it a category you buy frequently (like milk 9.3 times per month in your nearest local store) or a category you buy every 5 years (like refrigerator).

4) The case of buying milk showed us how fragmented the needs of the customer in the digital age are. By logically splitting the branching in a hierarchical way, we can easily get to the tiny segments that the retailer can no longer viably fulfill.

A fresh article published by a consultant and shopper marketing expert Mike Anthony can further substantiate the problems of the CDT as a merchandising tool. In his well-thought-out practitioner’s view, he points out some of the key issues.

The key takeaways from his article could be:

  • There is no single ‘right way’ to layout products on the shelf
  • Much of shopper’s decisions are made subconsciously
  • Algorithm or Artificial Intelligence that exactly knows the right solution for a layout is highly improbable
  • “Any Shopper Decision Tree is going to be ‘wrong’ for some shoppers”
  • There is a difference between need and find
  • Shopper Decision Trees map the past, not the future
  • Shopper Decision Trees is not a guaranteed recipe for success

— SUCCESSFUL CUSTOMER DECISION TREES

Does this mean we shall completely abandon CDT?

Our experience answers: No!

CDT remains the best framework we know to clarify a very complex assortment and space management picture. It helps manage the complex system and make decisions that connect retailers with shopper expectations. It brings so many tangible results, we can claim it is a central tool for (almighty in retail universe) succesful store layouts. 

No, don’t abandon CDT. Rather execute properly!

So how do we then properly grasp the shopper decision-making process? How do we fill CDTs with the right inputs to success in retail stores?

We’ve been studying the shopping process for years. Also learned from first-hand experiences. Many decision trees later, here is a solution that works for us – and our partners. A framework called “levers for successful store spaces from tip to toe”.

The methodology consists of the three big stages:

1. RETAIL STRATEGY

First a disclaimer: the word “strategy” itself is often something you approach with contempt. In our parts of the world (Slovenia, “Adriatic region” …), even for seasoned corporate people strategy means a set of bland words on paper no one cares about. Let’s avoid the pitfall – the described is bad strategy: long on goals, short on policy and action. But good strategy is the opposite – it leads organizations across the business challenges. It fundamentally provides the difference that matters for customers.

Here are some examples:

“Organic products at mainstream prices.” (one of the discounters strategies like Aldi / Hofer)

“Buy fast, eat slow” (Eataly)

“Furniture is not an investment, but a life style” (Ikea)

Store and category layouts should mirror and grasp your store strategy.  In this perspective, each category layout should be a piece of a store layout necklace.

Even lower levels of product management should be aligned with strategy. Therefore, setting up a particular category must start from

  • well-defined store / retail strategy
  • key competitive advantages
  • identified growth opportunities

2. ANALYSIS

This stage links retail sales data and customer behaviour data (segmentation).

We analyze the data, choose the criteria, define the shoppers’ decision path and create data insights that will lead to actionable findings.

Mathematically sound customer decision tree (CDT) is an essential result of this stage.

3. SYNTHESIS

Synthesis follows the analysis.

Here we add the tactical part to the customer decision tree.

Here is a glimpse into “how” of tactical improvement:

  • The relative importance of particular nodes should reflect the strategy from stage 1. If you’re trying to position as number one organic provider, emphasize the organic products etc.
  • Balance past data and trendlines  with informed hypotheses about future developments
  • Link inputs to objectives by informed space hypotheses. Those are ranked according to the expected performance and feasibility. After prioritization, we get to the actionable solution.

Mathematically and logically correct customer decision trees from previous step should now also be made more shoppable. How? By combining navigation logic, sales data, AND emotional value for core users. One very succesful practice from the past: we created blocks of confectionery snacks by blending brands & colors (the Swiss cow has its recognizable color! and Ferrero and …). Essential is to feel the pulse of the customer – which is more than just logically correct process of step 2.

As a result we get tactically sound CDT that reflects shopper expectations and clearly emphasizes key competitive advantage. With addition of shoppability it is finally ripe for execution.  

— KEY LEARNINGS AND HOW TO USE THEM

The key learning from our long-time pursuit of the field: proper management of shopper options in the retail space is instrumental for sales success.

A customer decision tree is a central analytical and tactical tool for the creation of efficient & succesful shelf layouts.

A mathematical model that links sales data with spatial data is a very important intermediate step. But for better “shoppability” (stimulation of buying) and efficiency, additional tactical decisions should be added.

Store space organizing criteria should also clearly link to the key competitive advantage of a retailer.

All of the above is collected in our shelf & store layout methodology called #leverages.

Our workshop serves as a primary facility to get through the stages of strategy → analysis → synthesis → succesful layout. Combined with analytical projects, it’s a proven methodology with results supported by different success cases.

CDT, properly done, is a powerful leverage that can provide your retail store thrive also during the digital times.

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