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How Big Data Analytics Transforms Impulse Buyers Into Loyal Customers

  • Jed Mole

    Jed Mole

Created at October 28th, 2013

How Big Data Analytics Transforms Impulse Buyers Into Loyal Customers

In the world of retail and fast moving consumer goods, getting customers through the door to grab that first great deal and product is the easy part. Today’s modern challenge is in transforming those one-off or impulse-buy consumers into loyal consumers. Especially as loyal customers may form just 20%-35% of a customer base, but can be responsible for 60%- 80% of the sales.

As consumers gain access to vast amounts of information (the choice of the internet is a click away with more consumers favouring eCommerce – and mCommerce in particular as smartphone and tablet ownership soars!) brand loyalty is becoming more endangered. But, vast accessible and varied information – in the form of big data – can be a solution as well as a problem, as data analytics build on flighty impulse activity by sending consumers enticing, personalised brand messages after that first purchase.

Information sourced from ‘Big Data’s Impact on Retail Customer Loyalty’ on

How big data helps retailers keep choosy consumers

Once a first-time customer presents him or herself to a brand, that brand needs to be able to recognise them to assess their potential worth as a consumer and their likeliness for loyalty. Not all impulse consumers are worth converting. Assessing this is simpler if consumers have purchased online, though collecting consumer data in-store is also possible. Then, assembling and using big data, the retailer can intelligently assess and manage the appropriate journey and messages for each value segment and if advanced, at the individual level.

Appropriate big data analytics and analysis of structured and unstructured information in real time allows retailers to:

  • Increase understanding of unique consumer needs. Simply speaking, bigger data, appropriately harnessed, results in bigger insight. Retailers can respond intelligently, and act and engage with consumer needs at the right moment. Big data can also help retailers adjust stock to suit – the better suited product is for consumers, the more likely it is that they’ll return.
  • Increase responsiveness. Combining real-time insight with historical purchase data/ consumer information means retailers can act quickly to offer appropriate messages in the right places. For example, listing ‘recommendations’ at the checkout/POS/ during the purchasing process.
  • Make decisions easy. Consumers are more likely to respond to simple choices. If retailers present relevantly tailored offers, targeted individuals are more likely to accept them. And if the purchasing process is made simple (linking to checkout quickly through mobile ads for example), they can minimise consumer showrooming (using phones to make price comparisons elsewhere while in store) and keep hold of wandering shoppers.

As a result of the above, customer satisfaction increases, as does customer service, and consumers are more likely to respond positively and repeatedly.

It’s big data that enables this precise personalisation of communications (in targeted promotions; adverts, email communication and offline collateral) letting retailers see exactly what consumers want, and why and when they’re purchasing through analysis of unstructured information. Generated as a result of online searches, mCommerce, app use, social data etc this is pretty clever; effectively you’re using the information consumers create while shopping online to better tailor their complete browsing and shopping experience uniquely – online and in store – which as a result enhances the lifetime value of customer relationships, boosting retail profit and loyalty.

Big data analysis provides the actionable insight to achieve this quickly, cohesively and successfully, and analysts are continually looking for new ways to use data from varying sources, unpicking the complex patterns of consumer purchasing behaviour for best retail results.

This balanced personalisation approach is integral to loyalty – whether used as part of an explicit loyalty scheme or not, as it shows consumers as first-time customers or not – that retailers are customer-centric and committed to them as individuals. The key to securing and nurturing customer loyalty is not just about more highly targeted promotion of product, but of a cohesive understanding and importantly, relevant, relationships between retailers and consumers – old and new.