How to use data from the online store in a physical store in a simple way?
Nordic chains have great potential for improvement when it comes to utilizing the data from their online stores.
The online store has not only become a new and effective marketing and sales channel for the store chains. A lot of relevant data is also collected online that can make physical stores better, but in many cases it is unfortunately not used optimally. In this article, we will take a closer look at some opportunities to use e-commerce data in a physical store and argue that the store manager may have a great interest in working closely with the e-commerce department.
Competitive prices
Many retailers use advanced tools to monitor competitors' prices. Central to this is the scraping of competing websites and rapid adjustments of own prices using so-called "dynamic pricing".
For many years, it has been difficult to use this type of data in physical stores due to slow and outdated computer systems. The result has often been that physical stores and online stores have not had the same price, which has created internal conflicts and confused customers. In recent years, however, we have seen many technical improvements in the chains' technical infrastructure, which has made it easier to update prices continuously. Among other things, the electronic shelf edge labels have become better and cheaper. The result is that it is now possible to make faster price changes in store, where the prices harmonize with the own online store and the wider market at all times.
For now, the grocery trade is the furthest ahead in development here, largely thanks to the extreme price competition in the industry and the products' short shelf life, but with better systems in store and the use of online price monitoring tools, we believe that many industries will quickly change.
The customers' own experiences
Modern e-tailers are increasingly collecting the customers' own assessments of the products in the form of stars and more complementary free text. With the increasing use of shelf edge labels in store, it is also becoming easier to expose this type of product review in store.
E-tailers who have collected a significant number of online feedback experience a positive effect on both conversion rate, online traffic and customer trust. There is no reason why this information should not be made available in real time in the physical stores via digital systems such as shelf-edge labels and screens, but also in traditional channels such as flip-flops and campaign posters.
That being said, there is no reason not to collect product reviews only through the online store. Advanced solutions for product reviews allow you to collect data also from physical stores and make it available online. In the long term, this type of information should enter the PIM system, so that the whole organization can utilize this data (product reviews are not only useful for sales and marketing, but also purchasing and product development).
Identify best sellers using data
In a physical store, it can take time to identify new products that the market demands. In the online store, you often see the trends earlier (customers check in store before shopping in store) and it is easier to collect sufficient data (in stores, the insight you get from talking to customers often stays with the individual store employee). By looking at products that suddenly increase in the number of views and turnover, and preferably also the search history, there is a lot of exciting things to find that can have great commercial value.
Start with the simple
These are some examples of how you can use data from the online store in a physical world both via digital systems, but also through the selection of products for the store.
Why should stores use their limited and often expensive areas to expose products where the chain is "bad" in price, or products receive bad reviews from customers? The answer speaks for itself.
In addition, it is of course interesting to look more closely at the use of prediction and machine learning to predict future customer traffic and create solutions to give personal product recommendations, for example at the checkout, but our recommendation is to start with the simple, where you can make a difference already in day.
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