Photo: Stephanie Gonot

How Indexing Adds Depth to Your Analysis

In order to make the most of our data analysis, our data needs context and our analysis needs depth. As good as we can get at filling in the contextual blanks to tell a story with data, the truth is, metrics carry very little weight without some kind of comparison or reference point. What does it actually mean to capture 500 leads from a sponsorship activation or sell a million tickets during a baseball season? Without reference, those numbers represent a single pixel of a bigger picture.

Brand and Category Development Indexes are two different tools we can use to add depth to our data analysis by adding reference points as backgrounds to our data points.

Defining BDI & CDI

Brand Development Indexes (BDI) and Category Development Indexes (CDI) both allow us to use benchmarks to index various performance metrics per capita at two different levels: our company’s performance and our industry’s performance.

With BDI, we can compare one of our company’s individual segment’s performance, say a particular city or age group, versus our company’s performance in the entire market. This allows us to gauge our company’s relative performance in each segment. During this current pandemic period, we could use BDI to determine which markets over or under index on the sale of say, flour, as more and more consumers turn to baking as a means to stay active during the quarantine.

For a company like King Arthur Flour, this could help them identify markets where their marketing strategies are working or even begin to identify holes in the supply chain. For example, a city that under indexes on King Arthur sales at a time where flour sales are at unprecedented heights could signify that the company isn’t getting enough flour to satisfy demand.

CDI, on the other hand, allows us to determine our category’s performance in a given segment versus the entire market. While on the surface this metric only gives us category or industry level information, we can use this information to determine how the company’s performance outside of our own bubble. Even if our per capita performance in a given segment scores a high BDI, indicating strong performance compared to the rest of the company’s segment, if it scores a low CDI, this indicates that our given segment is actually underperforming compared to our competitors.

In the case of King Arthur Flour, if our BDI tells us Miami is over-indexing on sales of five-pound bags of all-purpose flour, we might be inclined to believe our strategies are succeeding. However, if our CDI tell us Miami over indexes at a significantly higher rate for sales of all five-pound bags of all-purpose flour, we would need to revisit why our strategies aren’t performing even better.

Turning Flour into Bread

Ultimately as data analysts, we must always seek to add more context and depth to both our data and analysis in order to arrive at the most accurate and valuable insights. Using BDI and CDI is one way you can begin pulling the most out of your data.

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