Indexing is a classic analysis technique which can show us where a particular beer style, wine varietal, package type, etc., may be lagging or overpopulated in a specific retailer or market vs the wider market.
One value of this data is being able to see how a particular retailer, e.g., Publix Corp RMA, has invested in a particular alcoholic beverage attribute, such as 4ct 16oz cans, vs the wider market (Publix Corp CRMA). Or, we could compare a market geography such as Columbus, Ohio- Food, vs the wider market of Ohio-Food or even Total US-Food. We could also compare a market's or state's Food geography with its corresponding Multi Outlet Geography, e.g., Ohio-Food vs Ohio-Multi Outlet, to see how the share of that 4pk of 16oz cans in Ohio's pure grocery channel compares to this package size's share in Ohio's wider market (Multi Outlet would include Walmart and Target and Drug retailers in addition to Grocery chains).
The first order of business on these dashboards is to choose a Primary Geography (such as Publix Corp RMA) and a Competitive Geography (such as Publix Corp CRMA). Again, another approach would be to choose Primary Geography: Ohio-Food and Competitive Geography: Ohio Multi-Outlet
For whichever Segment Level you’re investigating, a segment, style, or package size group will be “underdeveloped” or “overdeveloped” in the Primary Geography, based upon its share percentage vs other attributes in the same attribute bucket. For instance, what is the share % of 4ct 16oz cans vs all other package types which have sold in the Primary Geography? Do 4ct 16oz cans have a greater share of the sales than other package types in the Competitive Geography than they do in the Primary Geography? If so, we can say that 4ct 16oz cans are an underdeveloped package type in the Primary Geography. If something is underdeveloped, we see “opportunity for improvement”.
Risk is what the retailer stands to lose if they aren’t able to keep/protect something’s dollar share %, but it can also represent dollars which could be gained if something is indexed under 100. If its index is raised to 100, the dollars gained would be comparable to the risk number.
An index of less than 100 means that something is underdeveloped in comparison with the rest of the market.
The idea is to help the retailer win – “here’s what I’ve discovered about your business”. Then, you can spin the story in your favor: “we have an item which is performing well/showing high velocity and fits into one of your underdeveloped categories” and “the market is supporting this upward trend – no reason you can’t jump onto that”.
From a technical point of view, how does this "indexing" work?
For instance, how do we arrive at the “Index” number?
- Divide a dimension’s Primary Geography Share % by its Comparison Geography Share %
- Multiply the result by 100
How is $ Gap (risk) calculated?
- divide the total beer category dollars for the primary geography by 100 to get the value of a “SINGLE SHARE POINT”
- subtract the Primary Geography’s share % from the Comparison Geography’s share % to get the “SHARE POINT GAP”
- multiply the value of a SINGLE SHARE POINT by the SHARE POINT GAP to get the “$ Gap” or “Risk”