We knew that it is important for the grocery industry to understand pricing trends, especially in such volatile times, and since we have the largest, most hyper-local, and most real-time data with history, we created the index to give this visibility to ourselves, our customers, and to the general public.
The biggest difference is that we created price indices not only at a national level, but at local levels as well. We know from our customers that price competition happens locally. Consumers shop for the best prices at stores that are nearby, which is a key reason why the demand for Datasembly's solutions have grown so rapidly. And we decided to create this index, which is actually a series of indices at state and major metro levels, to show pricing trends where it is most relevant to consumers.
We had a hypothesis that pricing trends will likely differ from very urban to very rural areas because of issues related to supply chain, different levels of demand, and because the competitive dynamics are different with generally fewer stores over a large area.
We also found out that the NCHS (National Center for Health Statistics) had defined a six-level urban-rural classification scheme for all U.S. counties and that we could align our store and pricing data to that scheme. This allowed us to see pricing trends across these different urban/rural segments and we found some very interesting variances that you can see within the index.
The following is detailed description of the six segments:
Counties in MSAs (metropolitan statistical area) of 1 million or more population that: 1. Contain the entire population of the largest principal city of the MSA, or 2. Have their entire population contained in the largest principal city of the MSA, or 3. Contain at least 250,000 inhabitants of any principal city of the MSA.
We used the United States Office of Management and Budget (OMB) definition of metropolitan statistical areas (MSA) and took the top 54 of the 392 within that list. The OMB defines a Metropolitan Statistical Area as one or more adjacent counties, or county equivalents, that have at least one urban core area of at least 50,000 population, plus adjacent territory that has a high degree of social and economic integration with the core as measured by the commuting tie.
We did an analysis of the categorization taxonomy of the top 15 grocery retailers in the country and found the most commonly used high level categories and rationalized the differences to come up with what we think is an easy-to-understand and comprehensive set of categories.
Two things to keep in mind. First, the scale of changes in most instances are smaller than they look on the graph as many of the spikes are just fractions of a percentage point. Also, we have observed that during COVID, many banners stopped putting many of their products on promotion so the week-to-week spikes are sometimes attributable to changes in promotion. You will probably notice significant changes during the holiday season in many instances and those often happen because of holiday promotions.
We can already use our customer's own set of hierarchical categories within our applications to allow them to do pricing, promotion, and assortment analysis using our data. We are currently working on a capability to create the same type of functionality you can see in our grocery pricing index, using our customer's own categories as well. We don't yet have a date for when this capability will be available.
One of the unique aspects of our algorithm is that we actually create an index for each individual product represented in the index. This means that we can aggregate those index in anyway we like (by geo, state, rural/urban segment) and we have actually already calculated indices by banner as well but don't show that in the public version.
First, we chose the products in each category that had the most coverage across stores in the United States. To get rid of the "noise" of hundreds of thousands of products we have in each category, we decided to use the top 1,000 products in terms of store coverage.
The index will be updated on a weekly basis.