Retailers are increasingly focusing on data and analytics to help make better business decisions and guide marketing initiatives. But when it comes to decision-making, putting plans into action and focusing on the customer, is this data being used effectively?
We know the value of data within the retail environment. Data can inform marketing campaigns, personalisation efforts and enable retailers to be more agile when it comes to capitalising on seasonal events or spur of the moment occurrences. But in order to derive true value from the data, it must be good quality, and it must be current.
The key question is what ‘current’ means to a given retailer. The definition can vary depending on the context in which it is used. In the retail sector, does current mean data from today, from yesterday, or even from last month?
In a typical retail marketing scenario, data is used to segment customers into different groups. Unfortunately, there’s often a delay between the segmentation or model and the execution within a campaign. This lag can sometimes be up to two months between the data and the use of the model/segment. The danger is that what was relevant for a specific customer last month, may not be the case today.
The same logic can be applied to seasonal trends. If a retailer plans to run a campaign around a local sporting event (for example, a targeted promotion if the local sports team makes the final), it can’t rely on customer data generated a week or two ago. The data needs to be near real-time.
Retailers can obviously plan campaigns around the event and offer promotions on products that will appeal to that specific audience segment. But with the right use of data, that can go even further; getting customers in-store is just one part of the journey.
The rest of it is about keeping them there and enticing them back by offering them the most relevant promotions. These can be delivered at the point of sale and will either relate to what is in the customer’s basket that day, or their historical purchases, making use of near real-time data.
The point of sale plays a key role in this approach, as it does with a retailer’s personalisation efforts.
Consider the following example: a shopper visits their local supermarket and buys the ingredients to bake a cake. Based on this profile, this makes the customer an ideal candidate for inclusion within a “Great British Bake Off” TV show themed promotion. Instead of adding the customer to the campaign, which might take a week, or potentially longer to deliver, the retailer can use software at the point of sale to capture details of basket contents, crunch that data and then return a relevant, personalised offer to the customer either through paper or digital channels, during the same transaction. This could be a stretch spend offer, a discount on baking-related items, or even a recipe for one of the showstopper cakes featured on the programme.
This kind of personalised offer demonstrates that the retailer understands its customers’ behaviour and preferences. Our own research supports the fact that consumers want personalisation, and can help to enhance the customer experience, build loyalty and incentivise them to return to the same store.
Data plays a vital role in sales and marketing efforts within the retail sector. To maximise the ROI from their marketing campaigns, retailers need to ensure that the data they’re using is as up-to-date as possible. For many, this means using the point of sale to capture that data and generate offers in real-time to complement wider marketing promotions and boost long-term customer retention.