A robust category management dashboard can go a long way to helping you to understand how your retail business is performing. In fact, it can be argued that it’s only through such a dashboard that you can really know if you’re on the right track or not. That’s because you can measure so much about your product categories.
What is a Category Management Dashboard?
In order to understand the category management dashboard, it’s important to first take two steps back and look at the overall goal of category management. The goal is thus: “to obtain long term improvements in the efficiencies of the retailer, which leads to increased sales, an improved shopping environment, and customer loyalty”.
For the above to happen, there is the six-step category management process to go through. We’ve written about it previously. Part of this process includes a step where you develop what is known as your category management scorecard.
Simply put, your category management scorecard is the document that sets out what you’re trying to achieve at a strategic level. For example, you may want to reduce your inventory holding or you’re interested in increasing your stock turns. Whatever your overall strategy or goal is, it needs to be written down.
Once that’s done, it’s time to create your dashboard.
Why only now, you may ask. Good question. The answer is simple though: your dashboard is a live tool that allows you to continuously track and monitor the success (or failure) of your category management actions. The composition of your dashboard is thus completely reliant on your scorecard.
Design Your Dashboard to Support Your Strategy / Scorecard Goals
Every business has a different strategy that they want to pursue. Since your category management dashboard is reliant on your strategy, it goes without saying that each dashboard will also differ accordingly to your specific strategy.
So what kind of measures do you need to include in your category management dashboard? It all depends on what you want to achieve. You need to design a dashboard that will support your strategy and scorecard goals.
Your scorecard goals can include, among others, the following: increasing your revenue, increasing your margins, reducing inventory holding and increasing your stock turn. It can also be a combination of any of these.
If your overall strategy is to increase your revenue, for example, you should consider including a number of different sales measurements in your dashboard that will speak to this. In doing that, you could take a macro or micro view of your sales revenue. You could do both too. It all depends on what you want to measure.
You Need a Relevant Toolset (Hint - regular BI won’t work)
If you want a robust and powerful category management dashboard, you’re going to need software. Not just any software, of course. You need the type of software that will enable you to create your own custom data visualisations while simultaneously allowing you to take action whenever you need to.
Why? If you can see that a particular product needs to be deranged or needs more space on shelf, you need to be able to act as quickly as possible. It’s no use knowing that something needs to be done and then having to wait until you can actually do something about it. That’s never good for business.
That’s why the right software makes all the difference.
It also helps if on top of that, you have a custom reporting tool that is so easy to master, you don’t need any technical skills. Also, you can approach it yourself and don’t have to wait on your IT department to help you out. Your regular business intelligence software won’t work here.
Have access to the necessary skillset
If you want to create and get maximum value from a category management dashboard, you need to have access to the right skillset. There is a lot that goes into the retail data behind a category management dashboard and you will need a team that is proficient in SQL to put the foundation in place.
What is the skillset of your staff who are involved in the category management process? Are they comfortable enough to work with the different datasets and analytics? What is their current level of knowledge?