As you consider investing in retail scanner data, the first question to ask is: what problem am I trying to solve? In other words, what am I trying to accomplish with this data? The answer to that question will determine whether investing in scanner data is truly worthwhile for your company.
In this article we cover what scanner data can and can’t tell you, resources needed to interpret it, and other methods of gaining certain key insights so that you can decide whether the data serves your goals and justifies the investment.
Insights on market size and competition
What insights can retail scanner data offer? To begin with, the data is a means by which to approximate the size of your market. Although you may be able to gauge general market size numbers for certain products by mining data nuggets on the internet (ex. plant-based burgers are a $280M market), such data is often too general to be of use for even slightly niche products.
If your brand makes plant-based cheese spreads, for example, the odds of finding any free and accurate data on the market size of your products is slim to none. Even readily available data (like stats on plant-based condiments) may still be too broad for your category, or may be misleading due to the challenge of categorizing such a product. Are plant-based cheese spreads considered condiments, dairy, deli?
Taking a top-down approach as you try to size such a market may therefore prove essentially impossible. In this case, scanner data can help shed light on the market size in question, so that you’re not flying blind. It should be noted, however, that even scanner data has its limitations as no data provider covers all retail outlets.
Another area in which scanner data can be useful is in understanding your competition. Of course, you can gain insight by tracking perceptions of your brand versus your competitors or by conducting qualitative, observational research on competitors (both online and in-store). But the truly telling research comes from the behind-the-scenes data: sales numbers and, more importantly, growth percentage. Scanner data enables brands to understand their competition not just on the whole, but also product by product.
With such visibility to competition, brands can understand pricing dynamics and how they may compete on price relative to other, similar products. The data can also help reveal what product attributes are currently resonating with consumers based on the growth rate of products with those attributes. These various insights can be helpful in showing you the hot spots and white spots (or, white space) in the market.
Making sense of the data (spoiler: it’s NOT easy!)
Retail scanner data is notoriously expensive – easily costing $10k for historical data (as opposed to ongoing data) for a couple of niche categories, in our experience – so ensuring that the data can actually solve your problem before you invest in it is important. To that end, keep in mind that the data is only as useful as one’s ability to clean, sort, and categorize it, and then to apply the learnings. And that is no small feat; the raw data is often extremely messy and counterintuitively categorized, with products grouped in unexpected ways and columns contradicting each other.
As a brief example, suppose you receive the data and want to sort by and group all gluten-free products. This seems simple enough, but when you get into the data you may end up with an attribute list full of hundreds (or thousands) of non-uniform descriptors like this:
GLUTEN FREE PROBIOTIC
FREE OF GLUTEN FREE OF SOY
COCONUT MILK IS A GLUTEN FREE FOOD
GLTN FR PEA TPC PTT SOY FR
FR CSN FR EGS GLTN FR SOY FR
CNTNS LV ACTV CLTRS GLTN FR
And that’s just one of many attributes you may want to sort by. So it is crucial to have someone on your team – whether in-house or as an outside hire – who understands the nuances and issues within the data. This person will need to create a taxonomy by which to identify underlying trends in the data, while parsing out the useless and/or erroneous information.
Because most small teams don’t have a data expert in-house, you must be prepared for the cost of bringing in outside resources to clean up and make real sense of the data.
Limitations with the data and the value of deeper insights
There are many things that scanner data simply cannot tell you, with or without an expert interpreting the data. While the data may be useful in understanding the external market and competitive landscape in more depth, it is not the best tool for understanding one’s target audience. For example, meaningful consumer insights – like plant-based shoppers’ motivations, hesitations, burning desires, etc. – will not be apparent with scanner data. For these insights, you’ll need to turn to consumer research.
While scanner data tells us “this is what happened,” (i.e. sales of products), consumer research tells us, “this is why what happened happened” (i.e. the sales were higher because these attributes are resonating with consumers). At Moonshot Collaborative, we specialize in working with clients to understand this vital “why” and how they can harness it to avoid mistakes and skyrocket sales.
Be strategic with purchasing scanner data
If you’ve determined that scanner data is the right investment for your company based on your end goal(s), knowing when, where, and how to buy data can save you significant time and money. See our tips below for best practices to ensure that you get the most out of your investment when purchasing data.
TIP: Be selective in choosing your data provider – three of the biggest data providers are Nielsen, IRI, and SPINS, and which of those you choose will depend on what insights you need. For example, if you need insights on Sprouts, you will need SPINS data because they are the only company with access to Sprouts’ data.
TIP: Data firms will often sell multiple categories much more cheaply than just one, because they bundle them at a discount. Carefully consider all categories that would be useful to you and see if you can get them all at once for only an incremental increase over the cost of purchasing the first category.
TIP: Clearly convey your end goals to your data rep so you ensure alignment on the depth and breadth of what they’re delivering to you. Along that same vein, make sure that the category you’re looking for aligns with the data providers’ definition of those categories, or else you may be looking for data on specific items that won’t actually be included in the categories provided. Similarly, prior to purchasing data, make sure the data provider can offer the attributes you’re seeking (plant-based, gluten-free, etc.) and the markets you need (both retail accounts and physical geography).