Behind every profitable web shop is a team boasting an impressive amount of knowledge that can help to increase sales in brick-and-mortar shops. Just like we can measure number of visitors, conversion rates, net profit and shopping paths for our online customers, we can do the same for our shops.
Among the strategies that we have explored are:
- Counting the number of visitors to our stores by using camera technology that recognizes entries and exits to the shop.
- Capturing the information on customer entries and exits and sending it to Google Analytics using their API.
- Having our POS system send information about all purchases to Google, also using their API.
We now have a situation where we can use Google Analytics for the shops, just as we can use it for our online business. We can see the number of visitors, conversion rates and basket values. We can also see peak and low times. Having access to this information in a real-time environment allows us to react based upon certain parameters — such as low conversion rates, low amounts of customers, and combinations of both.
We then set up different action plans for each of the above scenarios. For instance, low conversion rates equals super offers with high price reductions, rearranging store displays, etc. We track the customer flow using analysis of the recordings from the cameras to find out an array of information, such as the customer walk-through flow in the shops, the amount of time customers spend in each section of the shop and the patterns of where to approach a visitor to convert them into a sale.
In our case, since each store has been allowed to run its own campaigns, discounts etc., we had problems checking that these campaigns or discounts were also correct. Thus, we implemented Power Business Intelligence that looks through all orders, and by predefined parameters finds unacceptable orders. Based upon that information, the correct action is taken, such as stopping the campaign or changing prices. We can drill down each order to find out exactly what the reason was for an order not being completed.
Using Power BI, we combine orders and adwords data. Here we can see the actual net profit on each campaign. This tool helped us increase our ROI in adwords by 90 percent. Using a real-time tool to check adwords’ profitability also gave us the possibility to test the impact of changing sales prices — in certain areas, during different times of day, on any given weekday. We have the capability of segmenting the sales prices and appearance of the product, thus giving us an opportunity to test different scenarios in order to get the best sales and profit.
Furthermore, our use of Google Analytics as a strategic tool enables us to follow the sales for both our web shop and brick-and-mortar shops. These real-time tools are accessible by mobile phones, thus making it possible to monitor even from a distance and to react in a proactive way to ensure maximum sales and profit.
Collecting data also gives us the ability to forecast expected sales for a given day, using old sales data for the same period. This allows us to make decisions based upon the previous sales period. We can forecast the number of employees needed for the shops any given day or time. We can predict bottlenecks and make plans to avoid them, thus giving the customer a better experience.
Also, our purchase predictions can be based upon sales from previous days. Having a collection of all orders, we can forecast when to change sales or purchase prices by using previous orders to calculate new net profit based upon changed sales or purchase prices.
Henrik Castenlund is the e-commerce manager of Sweden-based retailer M Shop.
