How to determine KPIs for a Retail BI Platform
When talking about KPIs we must understand their applicability and what they actually mean. In any data science and data engineering project, the determination of KPIs, or rather metrics and insights, is an important step. How would you know what to measure and what can you actually do with all that data if you don’t define which values you need and want to show? When working on our retail business intelligence platform, we first needed to understand the industry and data itself. Which results are important for retail? How do they measure success? Which data is actually available to us? In the end, each value represented something, and we needed to analyze and understand what that something is. How to approach data It was easy seeing data for what it is. A series of values over time. And when you look at certain values, they represent an action. In our case, we had sales quantity, price per SKU, location, date, product ID, department, and product category. The data set told a story of retail traffic and consumer spending. If observed as individual values, they can be easily interpreted. But, we wanted to create a solution that will draw extra benefits and explanations derived from the original data. Our aim was to use this data set to bring value to more than one department or cost and profit center. We didn’t want to see data through a singular explanation. Certain numbers can speak to a larger variety of people and departments. One metric can influence decisions and actions in more business operations and procedures. Since the dataset came originally from a data challenge, our task was to estimate unit sales. So, if we have retail data and SKU analysis, we needed to start from that. The question we asked ourselves was what is important in a simple SKU analysis? What does our data tell about SKU performance? And from our data set, the obvious answer was sales. What is the level of revenue per SKU is a pretty straightforward metric. We can now observe which SKUs are better performing and what level of revenue they can bring to a retail store or location. KPIs through multiple variables and dimensions But, this one metric won’t suffice. We needed to observe values across more than one attribute or variable. And we didn’t want to focus just on static data. Our goal was to be able to predict future SKU movements. So, it was time to develop a matrix where we could distribute our data to outline basic metrics our solutions should show and continuously calculate. Table 1: KPIs through multiple variables and dimensions Individual SKUs, if observed across time and locations, tell a variety of stories. For example, one product can have high sales in one location and low in another. This tells us that there are possibly different segments of consumers. They can differentiate based on income, culture, age, consumer preferences, upbringing, etc. But it can also be an indication of different marketing and sales effort in this certain store or area. Or even perhaps, this SKU has varied in stock in different stores. There could’ve been backlog or overstocking. Maybe there was a new trend in that location that stimulated the attractivity of that particular product. So, as we can see, one metric serves multiple explanations and applications. And that’s why this matrix was developed. These SKU KPIs can be compared across multiple variables or dimensions. This is also a basis for the streaming component of this software solution. These metrics needed to be available to users in real-time, so they can access them anytime for their reporting and instant reactions to unexpected changes. Our objective was to allow users to use data in accordance with their needs, so they wouldn’t need to wait until month-end reporting to see their retail performance. Also, based on those simple metrics and SKU analysis the solution can send real-time notifications and present them to users. Of course, it’s not only values but through visualizations, users can more effectively interpret results and share them with other stakeholders. What we can take from this approach is that even the simplest metric analyzed through more than one dimension can provide insights and explanations for numerous events. What we must pay attention to are restrictions derived from the chosen technology. We can imagine and define KPIs and metrics, but sometimes data doesn’t support them. And the chosen technology can offer limitations in executing calculations. Not every value can be turned into an impactful KPI. And not every value should be measured. The business requirements have to be aligned with the data science and data engineering side of the process. KPIs that tell a story After the matrix and simple KPIs validation, we turned our focus to more complex KPIs. We wanted to see which KPIs are possible to calculate if we have only certain values. For example, if we have units sold and a price of SKU, what can be calculated from that? And of course, the KPI had to make sense to the end user. This is why we decided to create another table that outlined our major KPIs across periods of time and location since that was our main filtering determinant. Table 2: Major KPIs groups across different locations and time Revenue indicators Our first KPI is sales or revenue a certain SKU can bring. We observed it across 5 different levels (from product level to cumulative stores level) and 6 time dimensions (from one day to all time or the whole period for which we have data). Sales or revenue KPI is calculated as sold quantity x price per SKU. This KPI answers the question of which SKUs are outperforming or underperforming others. It’s an indication of which ones play a bigger role in sales performance. When also looking at revenue per unit, we can get so many insights about products sold in the store. We can answer questions like: Market share The next one