Were does your data come from?

Retail data comes over looking at 104 weeks, or two years worth of data to be able to make multimillion dollar decisions for CPG brands.

All of the purchases at point of sale goes through a syndication of data providers like Neilson and IRI who collect and sell the data to manufactures. This is layered on with other contextual information like products being sold, and promotions going on in the store at the time.

They clean and structure the data so it’s ready to use.

What makes data messy?

Brands who start DTC or in ecommerce are used to having great first party data sets. When using data from different sources, it doesn’t always mesh well, skewing the results you’re looking to achieve or uncover.

For example, your on-site data, discount wholesaler, Amazon, premium grocery etc.

The goal of these data sets is to uncover what’s driving sales, if a website promotion is going on, did it impact offline sales as well?

How does 3rd party data help?

1st party and owned data is great, but it can lack context. Having access to all the data, for example, offline sales, can tell you when you are increasing sales but compared to the market as well. It can unlock insights like increasing prices at one store, but decreasing prices at another bring the greatest lift in sales. 

The data will surprise you!

Rarely will there be findings on a national scale. There may be some generic trends, but it will differ from store to store. For example, Publix, a Florida grocery store is great at running BOGOs. The same promotion at Publix won’t work as a nationwide go to market strategy.

Chocolate always sells.

New flavors are tricky, chocolate and vanilla always sell, and people love new unique flavors. When you walk through a grocery store, you’re hit with survivorship bias. Those products and flavors made it, but what happened to the ones that didn’t.