Improving retail customer experience through analytics
Retail has changed
The internet has had a massive effect on high-street retail. With customers able to order practically anything they desire online, often with next or even same day delivery, there is an increasing worry that physical retailers are starting to become a thing of the past. To counter this high-street retail needs to evolve. Gone are the days that retail can be seen as a simple exchange of money for goods or services. Customers expect a fuller “shopping experience”; one that offers something extra over simply using an online store. These experiences need to be developed in an effective and intelligent manner, which data analytics can provide. Customer experiences can now be improved, altered and measured through big data.
Big data has been a hugely successful tool for online retailers and services, such as Amazon and Uber, and there’s no reason it can’t be implemented for high-street retailers as well, to improve their services and better align their resources.
What can Big Data do for retailers?
Big data can serve a variety of roles in retail, both in developing new strategies or even updating some classic marketing tools. Coupons and sales have long been a staple of a retail marketer’s arsenal, and have been shown to have a measureable effect in getting people into a store. But it can be difficult to measure the actual effectiveness of these discounts in generating additional revenue once the customer is in store. By analysing historical data, analysts can create models of what could have happened if the discount scheme was never introduced and run these models side by side with real-time analytics. What about if a coupon had been released on a sunny day, in mid-June compared to what would happen if no coupon had been released on a week day in February when it was raining? Would a coupon deliver better results depending on the weather, football fixtures, location or recent news stories?
Retailers can now weigh up the gains of the new purchases against the loss of revenue produced by the discount. Businesses can then adjust their discount strategies in an effective manner, ensuring that they are used to their maximum benefit.
Analytics can also be used as a form of crowd sourcing. Rather than relying on focus groups or anecdotal reports, retailers can identify in real time the marketing language, displays, and pricing that is most attractive to both current and potential customers, and adapt their offerings accordingly. Furthermore it can be used to uncover the key issues that might cause a loss in customer loyalty, which is essential to any successful business. It is well documented that the cost of acquiring a new customer is up to six or seven times more expensive than retaining a current customer, so anything that can aid in retaining customers will be a massive advantage to any retailer.
The Big Picture
On a larger scale, data acquired at the Point of Sale (POS) can be used to develop national or regional strategies for retailers. Identifying top selling stores and the demographics that purchase from there can allow retailers to effectively target the right customers. It can even go beyond this: the physical positioning of these stores can be analysed to best identify where a store should be located for maximum gains i.e. Are the stores freestanding or outlets in shopping centres? How close are they to competitors? How are the stores laid out to create an easy shopping experience?
Historical data can be used to develop forecasts for order quantities at every level, from national forecasts right down to at an individual level. For example, in the past a clothing retailer might have seen an increase in swimsuit purchases in the summer months on a national level. Rather than simply increasing the quantity of swimsuits available in every store across the country, appropriately analysed data allows them to see which individual stores are reporting the highest sales increases and adjust their orders appropriately, allowing for more intelligent and efficient management of stock.
How to get the data
An issue facing physical retailers is simply acquiring the data needed in the first place. Perhaps the simplest way is just to follow online retail’s example. Most online retailers require creating an account to shop with them, which instantly provides them with customer data. Similarly, an in-store loyalty scheme (with incentives to attract customer engagement) can allow retailers to acquire data on an essential core of loyal customers. Several retailers have even began offering digital receipts, sent to a customer’s email at the physical checkout, allowing for easy acquisition of customer data.
Perhaps the most important strategy is offering an “omnichannel” retail experience. Customers in the 21st century like to do their research online now before purchase, even if they eventually end up going into a physical store. This can take a variety of forms, from providing instore Wi-Fi that requires customers to log-in with email and details, to a fully furnished app developed to enhance the in-store experience, but also acts as a way to acquire customer data for the retailer.
Finding the right tools
However this is all reliant on retailers being adaptable and intelligent over how they use data. Recent studies by eCommera found that 23% of UK retailers can’t make sense of data in order to make appropriate business decisions. 50% of retailers believe that they don’t have the correct intelligence and analytics tools to suit their needs, and only 16% have confidence in their data analytics to provide organizational insights. This just shows that retailers need to do their research to find the right tools for their needs.
Adapt and thrive
As customers become more and more tech savvy, retailers need to evolve too. With such a vast amount of competition out there, both from physical and online rivals, the high-street retailers that are going to not only survive, but also thrive, will be the ones that acquire, analyse, and utilize Big Data in intelligent and innovative ways to further the customer experience.
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