Retail Analytics. How to monetize the data generated by your stores - HMY Group

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Retail Analytics. How to monetize the data generated by your stores

There are three reasons that explain the exponential growth of eCommerce in recent years.

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Design and functionality come together in the new Comas Beauty Store

Data dominates all our decisions: which products we display, which ones we promote, or when we do so.

For brands and distributors, stores are a source of highly qualified data and a complement to their digital platforms.

In this post, we’ll give you the keys to put them at the service of your decision-making process.

Information is power, particularly in Retail

There are three reasons that explain the exponential growth of eCommerce in recent years. Convenience and cost are the two most obvious. The third one may not be quite so obvious, but it’s just as important, if not more: data.

Digital media provide us with 100% reliable and real-time information about everything that happens inside and outside our store. This knowledge can easily be put to use when deciding absolutely everything that has to do with our business, and it can even adapt the content of the point of sale in real-time for the user:

What products do I replace and which ones do I get rid of.

Which ones should I promote.

Adapt these promotions to the type of user who walks by a digital medium.

What type of consumer is attracted to each one.

Where are those consumers: in digital and physical terms.

Which campaigns would work best with each of my buyer personas (types of consumer).

And, if we have created suitable data architecture, why does everything happen.

Can Retail emulate this ability to capture data?

One of the most viralized cases of analysis of Retail data was the Target one, a multi-brand chain in the US, where it was speculated how Target, through a follow-up of the products bought by women in its stores, was able to analyse their consumer trends and predict when they were pregnant, anticipating their next acquisitions with a more effective, targeted advertising strategy.

It was never clear, even to this day, if the original article analysing the case was, in fact, covert advertising from agencies devoted to exploiting data, but it does illustrate perfectly the potential that data has for Retail in our business.

And best of all, the cost of these systems gets lower every day.

What data can be collected in a physical store

To explain this relatively new and complex world in the most orderly way possible, in this article we will establish two categories that help us understand the types of data that it’s possible to collect in a store.

The first, according to its origin:

Direct data

This is the data obtained from the direct interaction of the consumer with the store. When people touch a screen, go near a piece of furniture, play a game, fill out a form, buy an item…

The main use we make of this data is to measure the effectiveness of the design, experience and products that make up the store, by answering questions such as:

Does the route we have established in our store work?

Are there any dead areas or products that are not looked at?

Is the consumer willing to interact with our brand beyond the purchase of products?

What is the profile of the typical consumer going into the stores (gender, age, products that they are most interested in etc.)?

Why is cross-selling of certain products not working?

What makes customers decide not to finally buy a product?

Indirect data

They are those that we collect thanks to the fact that the consumer, inside our stores, makes use of their mobile phone and bridge platforms. This is passive data, collected without the consumer actually having any specific interaction.

They mainly help us to quantify and qualify the attendance in the store: type of consumers, at what time does each of them visit the store, average duration of the visit…

With that understanding, the next logical categorization is the technology used to collect them.

How to monetize the data obtained

The first step is to set a specific objective behind each piece of information that we want to collect. Ask the right questions.

Especially because a single piece of information won’t usually be of much use. It’s by cross-referencing the concrete data that we really begin to extract the Retail Consumer Insights. That’s when we learn about our consumers.

We will see this compiled in our dashboards, which automate this cross-referencing. But let’s get to the real point about the main benefits that we can obtain from Retail Analytics.

If you want to know more about content and digital signalling in this post we tell you all its secrets.

This is what we discovered

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