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How can the physical stores use the data from their online stores in a simple way?

A person on the computer

Nordic store chains have great potential for improvement when it comes to using the data collected from their corresponding online store.

This is written by Martin Granberg from Priceindx and Ole Martin N. Evensmo from Springboard Martech.

The online stores have not only become a new and effective marketing and sales channel for the store chains. In these, a lot of relevant data is also collected that can make the physical stores better, but in many respects the data is not used optimally. In this article, we want to take a closer look at some possibilities where you can use e-commerce data in a physical store and argue that store managers can have a great interest in working closely with the e-commerce department.

Competitive prices

Many companies use advanced tools to monitor competitors' prices. The most important function is usually scraping the competitors' webshops, where then the own prices are quickly adjusted using dynamic pricing.

It has long been unsafe and not always possible to use this data in physical stores due to slow and often outdated computer systems. This has often resulted in the physical stores and online stores not having synchronized prices, which has created internal conflicts and confused customers. In recent years, however, we have seen many technical improvements in the technical infrastructure of various chains, something that has made it easier to update prices continuously.

Among other things, electronic shelf edge labels have become both better and cheaper, which has resulted in the fact that it is now possible to make both more and faster price changes in store. This in turn leads to store prices being harmonized with the own online store and the rest of the market.

The customer's own experiences

With increased use of shelf edge labels in store, it will also be easier to expose product reviews in store. Modern e-tailers increasingly collect customer reviews in the form of e.g. stars but also more detailed free text field.

Merchants who have collected a greater number of online reviews experience a positive effect on conversion rate but also traffic and customer trust. There is really no reason why this information should not be made visible in real time also in the physical stores via various digital systems such as shelf edge labels and screens, or in the more traditional channels such as shelf tilters or campaign signs.

With all that said, there really is no reason not to collect product reviews through your online stores. Advanced systems can also collect data from physical stores and expose it online. In the long run, this type of information should be collected in e.g. a PIM system, so that entire organizations can use and benefit from this data. Product review is e.g. not only interesting for sales and marketing, but also for purchasing and product development.

Identify best sellers using data

In the physical store, it can take time to identify new products that the market wants. In the online stores, you often see the trends much earlier because the customer often searches online stores for the products they are looking for and it is easier to gather enough information online.

In a physical store, the insights lie in what the customers share with the individual employees who often do not have a way or system to collect and measure this data. By seeing in your online store which products are trending and have increased sales or by looking at the search log, you can gain insights that have great commercial value.

Start with the simple

These are just a few examples of how you can use the data from online stores in the physical trade, both via digital systems but also in how you actually choose products for the shelf.

The obvious question therefore becomes, why should the stores use their often expensive square meters to expose products where they have a "bad" price compared to the competitors, or for that matter products that have received bad reviews from customers.

As an addition, it is of course interesting to investigate more closely how, with the help of the data, machine learning and purchasing behavior can be used to e.g. predicting customer influx in physical stores and solutions to provide personalized product suggestions when the customer stands at the checkout.

But our recommendation is to start with the simple, and where you can already make a difference today.

Read article at it-retail.se

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