Data is a hugely valuable resource in retail and it’s generated with every customer interaction, whether that’s online or in-store. But, what happens to that data afterwards? Especially after large-scale events, campaigns or promotions. If you have invested in capturing and collecting this data, then it makes sense to use it to your advantage, by using it to provide valuable customer insights that can improve the bottom line. However, in order to achieve this, retailers need to focus on more than just reactive analysis. The question is, how else should retailers be using this data and avoiding any missed opportunities?
For most retailers, customer data often ends up in a data warehouse where it may be analysed and used to assess performance and forecast future sales. More sophisticated models can even predict which customers are likely to lapse in the near future. This internal customer data can be enhanced further by merging it with externally sourced data, customer demographics, for example, to make more informed marketing decisions around campaigns, promotions and events. However, this customer data needs to be analysed first and this can sometimes be a slow process.
For bricks-and-mortar retailers, using data generated at the point of sale (POS) avoids this delay. Retailers can capture data from every transaction in real-time, enabling them to build a complete and accurate picture of their customers. This data can be used to generate personalised customer offers almost immediately without needing to extract the data from the data warehouse and spend time analysing it. This is a much more timely use of data, but retailers still need to track redemption figures on any offers if they are to measure success. Taking this one step further, when redemption activity can be analysed in real-time, offers can be changed or switched off almost instantly, ensuring that shoppers receive promotions that are both relevant and timely.
In addition, it is quite common for retailers to see an increase in average transaction value when they use better quality data to run promotions on products which sell well together. This data can also be used to inform store layout. For instance, certain products may be purchased together, not because they complement each other, but because they are located next to one another in a store. Typically, for this to work, there needs to be a sufficient volume of high-quality, historic data. However, POS data can be used to help test potential new store layouts in real-time. Product displays can then be adapted as sales dictate — allowing the retailer to adapt its store layout quickly to customer behaviour and preferences in order to maximise sales.
So, while most retailers hold a breadth of data about their customers, it’s not much use left in a data warehouse. It’s a valuable asset and needs to be put to work to help make informed marketing and sales decisions and assist in driving the bottom line. All retailers have access to data but, for bricks-and-mortar retailers, it’s those that use it effectively, providing personalised and engaging store experiences to shoppers, that will thrive.