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GUEST COMMENT Unlocking customer lifetime value in an AI age

Almost a year on from the implementation of the GDPR, retailers are still figuring out the best approach to collecting, analysing, and utilising privacy-compliant customer data. They have multiple information sources to manage from websites to loyalty programmes, and even e-receipts, which could breach the regulation if they include marketing materials, according to a Which? study.



But at the same time, retailers are more dependent on customer data than ever before as it delivers the vital insight required to provide the personalised experiences today’s shoppers demand. A global survey reveals almost three-quarters of consumers expect retailers to ‘treat them as an individual’ and a third expect brands to anticipate their needs before they arise, all of which depends on accurate and accessible customer information.



Tailoring marketing messaging is essential for retailers to attract and keep shopper’s attention in an ever-more competitive market, and drive customer lifetime value. Ensuring existing customers feel appreciated and understood will encourage them to buy time and time again, amassing a higher ROI. In fact, increasing consumer retention rates by 5% has been found to result in a more than 25% increase in profits, demonstrating the value of building relationships with loyal customers.



Clearly, achieving the perfect balance of compliance and profitability is a significant challenge for retailers. But developments in artificial intelligence (AI) based marketing technology are helping them leverage customer data to tailor the shopping experience, while still conforming to regulations such as the GDPR.



By stitching together customer journeys across a diverse range of touchpoints to deliver a unified view of how individuals shop, and then intelligently segmenting customers based on highly specific interests and behaviours, the technology empowers relevant, personalised marketing, and fills the gaps in available data caused by consumers withdrawing consent under GDPR.  



Connected data and actionable measurement is the foundation for effective decision-making in marketing, to communicate with the customer in a coherent, joined up (and tailored) way. So how can retailers ensure they have the right strategy in place to make the most of AI-powered personalisation and gain that all-important lifetime value?



Use holistic, unified data

The benefits that can be achieved through the use of AI and ML are only ever as good as the information that feeds them, so retailers need to ensure their approach to data connects all touchpoints to gain a clear view of the customer before they attempt personalisation. First, they need to break down the data siloes that prevent effective measurement and attribution, consolidating information in a Customer Data Platform where advanced matching logic can be used to stitch together multiple identifiers that belong to one individual, such as email addresses, cookie IDs, and phone numbers. By unifying data from online sources such as web systems, and offline sources such as call centres, retailers gain a granular understanding of the customer.



Second, retailers should stop relying on outdated performance metrics provided by first-touch or last-click. Such measurements only provide a partial view of the path to purchase, while multi-touch attribution (MTA) measures the precise impact of every touchpoint along the customer’s unique journey. MTA enables retailers to understand which channels are engaging customers and driving sales, empowering the implementation of decisive operational change.  



By unifying data and employing MTA, retailers can achieve a single end-to-end view of the customer journey and gain a robust data foundation to feed AI-driven personalisation.     


Understand the goal

Some retailers see personalisation as a nice-to-have, a way to encourage friendlier, more human-like interactions with customers in an attempt to improve brand awareness or perception. But the goal of personalisation is far more tangible than that. True personalisation uses data collection, analysis, and automation technology to move beyond customer journey optimisation to entirely customer-centric campaigns, tailored to the individual’s unique needs, desires, and preferences.



Retailers can use everything they know about a customer, including behavioural, demographic, and psychographic data, to change the way they communicate and drive real commercial value. By adapting the experience to the individual, retailers can increase customer engagement, drive loyalty, improve conversion rates, grow revenue, boost average order value, and reduce bounce rates. They can optimise the cost of acquisition and retention, and of course increase customer lifetime value.



The goal of personalisation isn’t just to make a retail brand appear more friendly or approachable, it’s to use data insight to deliver individualised experiences that drive measurable commercial value.  


Start small and expand

While retailers might be tempted to make big disruptive changes to their organisation to maximise the rewards of personalisation, this approach can require a disproportionate amount of resource and may be unsuccessful. Instead retailers can start small, addressing specific challenges or pain points and tackling them one at a time to achieve overarching goals.



They can put in place an agile data infrastructure that adapts to new ways of working, trends, opportunities, and threats, and deals incrementally with business problems. Building a solution around a data-related challenge will result in better attribution, enhanced measurement, and a unified understanding of the customer journey to be leveraged for personalisation.   



Using AI capabilities alongside accurate and holistic data allows retailers to understand customers, anticipate behaviour, and deliver the creative, tailored experiences shoppers now demand. By understanding the true goal of personalisation and starting small with specific business challenges, retailers can make use of their data to drive commercial advantage while remaining compliant with data regulations, and unlock customer lifetime value in the age of AI.

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