There is no doubt about the fact that performing a store clustering exercise is necessary. However, merely completing one is not enough. You also need to execute it correctly. Unfortunately, mistakes happen. That’s regardless of if you're a retail veteran or have minimal experience. What's critical, though, is that you admit them, understand why they happened and then correct them.
That’s what you’ll find below; a collection of the most common store clustering mistakes that we’ve come across. More importantly, we take a look at why they occurred in the first place, how much damage they can cause your retail brand if you fail to acknowledge them, and in some cases, what you can do instead.
1. You haven't analysed the data of your categories carefully
As a retailer, it’s easy to assume that you know everything about the categories you stock in your stores. That’s especially true if you’ve been in the business for many years. And in many cases, you do know your products groupings inside out.
But that doesn’t mean you can’t or won’t make a mistake.
It is this guesswork that plays a significant role in why this error even occurs. That’s because such an assumption means you might not analyse your data as carefully as you should. By not consulting your weekly, monthly or yearly data closely, you place yourself at a significant disadvantage.
Let’s consider the fundamental idea behind store clustering - to group your stores based on similarities to meet the needs and wants of shoppers. The keyword here is similarities, and they are usually if not always identified by analysing the data you have collected.
Of course, when it comes to analysing your data, you could go to the nth degree and evaluate all sorts of KPIs and syndicated data and overlay it with any collected from your POS system. But it’s suggested that you start simple, analysing your sales and units data from the category level down. On top of that, it’s worth adding your store format and size, location, LSM and any specific demographics.
And if you don’t? That’s simple: incorrect clustering, whether your choose a store-based or category-based route will hurt your sales and profit, affect customer satisfaction, and even damage your reputation.
That’s because you’ll carry the wrong categories, which will have inaccurate ranges and prices that don’t match customer expectations or needs. What’s more, these categories and ranges won’t match the appropriate store format, its size or its location.
2. Assuming you only need to do a store clustering exercise once
In a previous article on store clustering, we touched on the numerous benefits of grouping stores together. In brief, they include allowing you to meet the needs of your customers and foster a reputation as the go-to retailer.
It’s for these reasons, as well as others that efficient store clustering can drive sales for your business.
However, any talk about the topic invariably leads to the question of how often you should complete this exercise. As the sub-heading points out, the mistake is in believing that you only need to do a store clustering exercise once.
Before we get to the answer, it’s worth first looking at why this happens. And there are generally two reasons.
Firstly, many view it as too big a task. Therefore, the focus is on always tweaking the product assortment for one cluster. That is the definition of inefficiency because what ends up happening is the other store groupings are forgotten. How long are you going to tweak the cluster before you’re satisfied? And who says that you’ve managed to get it right?
Secondly, as we noted above, it’s often viewed as a set and forget exercise. The thought is once you’ve completed it, you’re done; there is no need to rerun it. That is, of course, not true. Given the ever-evolving nature of retail, doing this exercise only once is bad business.
So how often should you conduct a store clustering exercise?
Ideally, you should review your total business annually but not more than twice per year. Any shorter period and you would not have sufficient data for proper analysis. That’s because once a store has undergone a clustering exercise, you assign it a certain range. This product range needs time to sit in the store for you to see what works and what does not.
If you don’t go back and analyse your data, you won’t know if the range is correct or if you must tweak it.
3. Trying to conduct a ranging exercise before clustering
If you have any knowledge of category management, you’ll know that there is a process that you need to follow. While some companies use eight, DotActiv has distilled the process into six steps. Of course, the number of steps doesn’t matter so much as their order.
It’s the same if you use category management software. There are steps to follow, and you need to complete them chronologically to reap success. In both instances, a clustering exercise always happens before ranging.
First off, you need to integrate your data with the software. Then, it’s a case of classifying your data into a display structure. A display structure is an arrangement of how you’re going to display your products in-store. From there, you would complete your clustering exercise. Only once done should you conduct a ranging exercise.
Of course, that’s not to say that this mistake isn’t easy to make. You may assume that you know your shoppers and want to jump ahead without the necessary research of data analysis.
But consider the perfect merchandising mix. For it to work efficiently, it must allow you to supply a customer with the right product at the right time, price and location. However, you cannot offer the right range of products to your customers if you don’t first understand their needs.
As for what would happen if you did attempt to range before you cluster your stores, let’s assume that you have no cluster set up at all. In that case, what would you base your data on? If you want to base it on your total business, then you would need a generic range much like a generic planogram.
But that’s not going to help you. It’s instead a stop-gap and what will end up happening is you’ll lose customers and money. But that’s not going to help you. It’s instead a stop-gap and what will end up happening is you’ll lose customers and money since you’re not meeting their needs.
4. Attempting to do too much too soon [or everything at once]
If you haven’t conducted a clustering exercise before, it’s a mistake to believe that you can attempt to cluster your whole business in one go.
Besides the point that its the retail equivalent of running before you can walk, undertaking such a task will do more harm than good. That’s because you’re essentially taking a cookie-cutter approach to clustering. What works for one category might not work for another.
Let’s say that you want to follow a store-based clustering approach. In that case, you need to consider your customer’s LSM when choosing your range. Expensive TVs and the latest gadgets won’t sell as well in a region where the customer LSM is low. The same goes for a store located in a high LSM area where customers want the latest gadgets as soon as they reach the market.
One recommended approach to clustering is to follow the advice of less is more. In other words, start simple. For example, it’s best to set up no more than three clusters per category with each having no more than three size variants. Once your efforts yield positive results, you can add complexity.
Another route is to start with your smaller categories which have fewer SKUs and drop counts and work your work up to larger, heavy SKU categories. By beginning with your smaller product groupings, by the time you get to your larger categories, you’ll have gained enough experience to realise that the task isn’t too big.
A third suggestion is to determine if you have enough similar stores that have the same shopper profile. That means analysing your data from all of your different stores. Doing that will allow you to spot trends that will assist you to make the right clustering decision.
5. Ignoring clustering and going directly for ‘store-specific’ plans
It’s common to hear requests for store-specific plans instead of clustering. You might believe your stores are too different from each other with too many variations within the ranges. You may also think that you have too many different shopper profiles for clustering to be useful.
On the other hand, if you’ve never attempted clustering before, there is the fear factor with which to contend. After all, clustering means changing the way you work and range your products. Meanwhile, you may have had a bad experience with store-based clustering and don’t know that category-based clustering is an excellent middle ground between store-based and store-specific plans. We get that.
But that shouldn’t stop you from attempting clustering.
Incidentally, this isn’t strictly a store clustering mistake. But many do view it as an all or nothing approach. It doesn’t help that a store-specific plan will yield the best possible result for you because the cost of setup is far too high for everyone to attempt it.
The alternative is to take the same approach as mentioned when you begin clustering. Start with your basic category-based clustering and as time goes by and your clusters mature, add more groupings. Using the above example and set up three clusters per category, and then gradually push that number up so that within three years, you have seven or eight clusters per category.
By managing your store clusters in DotActiv Enterprise, you can localise your stores by neighbourhood, thereby providing each of your customers a personal shopping experience. You can visit our online store here or book a custom consultation with a DotActiv expert here.