Picture: REUTERS
Picture: REUTERS

According to a Dimension Data survey, 13% of companies self-rate the delivery of their customer experiences a healthy nine out of 10; the same survey, however, found that a third of companies aren’t even able to track customer journeys. This disconnect between perceived and actual capabilities has created an environment of mass-scale customer offers that at best distinguish customers by gender, age or geography. Larger tier one retailers - with their immense marketing budgets, large internal teams and expert service providers - have made strides towards more personalised customer engagement opportunities.

The concept of personalisation has been around for years, long touted as an effective way to reach consumers. Newly accessible technologies such as machine learning and advanced analytics are enabling marketers to base their understanding of individual customers’ unique preferences on their current interests and purchase history. With machine assistance, marketers can then introduce offers or information that is created in real time to build a deeper, more meaningful personal engagement with an individual customer.

Today’s confluence of technology innovations, shifting and varied consumer expectations, and broader economic pressures are forcing retailers to engage with customers at an individual level. A typical retailer today serves five distinct generations of consumers, each with their own preferred modes of engagement, maturity of technology use, consumption habits and motivators. Individualised customer engagement is simply impossible without the help of technology.

For the first time, next-generation technology tools such as artificial intelligence (AI)-enabled business intelligence allowing individualised customer engagement opportunities are within reach of midsize retailers. Thanks to their comparably smaller customer bases, midsize and small retailers have an opportunity to take a lead on their larger peers in the fight for better and more meaningful customer relationships.

And while technologies such as AI and machine learning are considered futuristic, in the retail environment what they’re really enabling is a return to the past.

Back to the (retail) future

A century ago, shopkeepers were at the heart of their communities, instantly recognising customers,  knowing their typical purchases and having insight into their lifestyles. Based on what a customer bought, how often, on which days and at what times, and how their purchases changed over time, the shopkeeper could build a well-rounded picture of who that customer was and how best to provide them with the goods they need.

Fast-forward 100 years and we are on the verge of taking a big step back to this older retail model of truly knowing every customer at a deep individual level. The enabling factor? Technology. By taking a data-driven approach to customer engagement, retailers can develop offers and rewards in real time based on actual individual customer purchasing behaviour. This opens the door to far more meaningful and personal connections with individual customers, with retailers better able to provide value to the customer’s purchasing decision and overall lifestyle.

Data is gold, but you must dig

Customer and purchasing data may be gold to your retail operation, but only if you do the hard work. By cleaning up data to ensure data integrity and analysing data to generate insights about individual customers and their purchasing habits, retailers can develop and implement strategies that improve customer engagement and support high-level key performance indicators  (KPIs) such as greater market share, profit or turnover.

The big take-out

Technology is allowing for a data-driven approach to customer engagement, allowing retailers to develop offers and rewards in real time based on actual individual customer purchasing behaviour.

This is impossible to do manually. Advanced business intelligence solutions featuring machine-learning capabilities equip retailers with detailed reports, dashboards and metrics utilising customer-engagement data to develop a single accurate view of individual customers. This intelligence can then be fed into marketing activities which take the insights into individual shopper behaviour and turn it into targeted promotions in real time, with the insights indicating not only which customers to target with certain promotions, but also what time of day they are most likely to visit the store to improve the timing of those offers.

Business-intelligence capabilities further support the retailer’s overall KPIs by producing actionable insights that go beyond just customer data: a retailer may find that the discounts it is providing through its loyalty schemes are eroding profits. For example, if a certain popular product is discounted, customers may only visit that store to purchase the discounted item while still buying the rest of their basket elsewhere, undermining the retailer’s efforts to boost profits while improving customer loyalty.

Three KPIs for individualised customer engagement

For business intelligence deployments to support customer engagement efforts in the delivery of bottom-line results, three requirements must be met. The first requirement is that retailers undergo a mindset shift in terms of the concept of individualised customer engagement. Loyalty programmes and other customer engagement activities are often implemented to improve profits and boost in-store spend. Many rewards are relevant only to the retailer, handing customers a percentage of their purchase back to spend (again) at that retailer. Any technology-enabled customer engagement should first and foremost focus on making individual customers feel valued and understood; from there, the retailer will be much placed to improve broader business KPIs such as increased revenue, greater profit and improved market share.

The second requirement is tech-enabled insights. The amount of data available with regard to people and their purchases is impossible to process into an individualised view of each person. While technology cannot yet always replace people, it has developed to the extent that it can now provide an individualised view of a person and their preferences. Business intelligence deployments should seek to uncover insights in the retailer’s data that build towards a single accurate view of individual customers and their specific habits, needs and purchasing behaviour. This enables merchants to convert insights into consistent actions that increase customer engagement.

The final requirement is real-time execution. Once data has been analysed and insights about customers have been developed, retailers need to rapidly use these insights by introducing individualised engagement opportunities with customers. With the correct IT systems in the background, accurate customer and sales data, and a business intelligence solution analysing data and producing actionable insights, retailers can hugely reduce the time needed to develop and launch customer promotions. This is where the real magic lies: in the ability to use accurate customer data to develop and launch individualised offers in real time. 

Ian Steyn is the product executive for customer engagement at Innervation PAN African Payment Solutions.

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