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Retail customers have ever more demanding expectations of value, choice, availability and accessibility of products and services. Finding ways to service these expectations is forcing the retail landscape to change very rapidly.
Many of these changes are clear and well documented. The dexterity of customers, with access to e-commerce, social media and mobility enabling them to seek out the best deals, is forcing traditional bricks and mortar retailers on line in an attempt to compete with e-tailers with different margin and operational structures. The power of social media to make or break a brand is forcing retailers into communication strategies that are interactive and immediate, a far cry from the traditional communication approach retailers have been used to.
The other reality of the retail landscape is the current economic climate, which is creating consumers who are happy to wait for special deep discount promotions to make their purchases. This is forcing retailers into long sale periods, which can impact the bottom line.
Given these challenges, it can be safely concluded that it is a difficult time to be a retailer. On the one hand increasing consumer demands are driving significant strategic decisions around new channels, new business models, new operations and new communication, on the other, the bottom line is not exactly glowing with the necessary good news to fund these new directions.
However, all is not lost. The trick is in servicing consumers economically. As in all walks of life, the chances of making good economic decisions is directly proportional to the quantity and quality of 'intelligence' or data insights about the factors involved that are available to the decision maker.
In retail, inevitably, business intelligence starts with understanding the customer. Therefore, the most significant strategic decision is to ramp up data collection, analysis and use. Just as exceptionally strong corporate commitments to data have fuelled the success of the biggest retailers such as Walmart and Tesco, these same principles apply to all retailers, whatever their size.
The really good news is that as consumer behaviour with retail brands is changing so is the availability of data that can drive meaningful insights and 'intelligence' that allow the brand to manage the customer engagement and experience. Retailers who can delight their customers across all contact points and occasions will build relationships that will deliver incremental revenues.
The consumer behavioural trends of personalisation, virtualisation, mobility and interactivity are delivering customers who both want to be involved with brands and to be seen to be involved. This involvement may be as simple as 'liking' a brand on Facebook. These trends are allowing for the collection of new data about customer engagement.
For some time retailers have been collecting demographic and transactional details about who the customer is and what, where, when and how much did they purchase, etc. Collection of demographic data is one of the major hurdles to entry for loyalty programmes due to lack of interest in filling out lengthy forms. One example of the way new customer habits are opening up improved data capture is just the simple ability to extract personal data information from a customer's Facebook page or quickly through a mobile app after receiving authorisation from the customer.
The increased desire for involvement by customers is opening up opportunities to collect data around a customer's attitudes and interactions. Attitude data is information about a customer's preferences. Interaction data reflect the customer's offers, responses, likes, dislikes. The other information capture their non-transactional interactions. These new data sources are easiest to understand in an online retail world where we are used to recommendations being presented to us based upon products we have looked at but not bought (that is our interaction preferences).
One way of creating value from this increased understanding is simply a function of timing. Traditional customer relationship management programmes have been limited by the fact that the customer in physical retail is more often than not identified only at the end of their store visit. While in such a scenario the customer's next visit can be influenced through the analysis of data and targeted interaction, the opportunity to really delight a customer and offer a winning engagement experience is a little limited as the customer is leaving the store.
In the online retail world, a customer is typically identified early on in the transaction, by logging in, which allows the understanding of the customer to be used to manage that engagement and drive greater sales. Pulling this capability into the physical world allows a seamless brand experience for the customer. For large format stores the technology can allow tracking of the customer's store footprint. This data can be used in conjunction with real-time communication of offers to a customer around the store presenting additional purchasing options.
The changes to more sophisticated data analysis and insights can be distinguished easily by thinking of a very simple standard lapsed customer case. Traditionally, we have identified a target list of customers who have stopped visiting and made them an offer to entice them back. However, if we further analyse the target group utilising a broad range of earlier data such as offer responses and likes and dislikes, it gives the chance to personalise the 'influence' to the specific customer. This increases the chance of success.