Brand loyalty amongst Millenials (those born in the nineties onwards) is not easy to keep. They are surrounded by digital channels all day and bombarded with marketing from your competitors.

Marketers face a dilemma on customer attrition. By the time a customer decides to leave for another brand it’s way too late. Functions outside the marketers’ control like customer service, telesales, delivery, floor staff have either failed to pick up the signals or, even if they have picked up a signal (e.g. A complaint, a comment that another product seems better, lack of any recent interactions), they simply haven’t been able to capture it, or can’t be bothered to enter the information into slow unwieldy software.

Imagine devising an algorithm that highlighted to you when customers were likely to leave – exactly what data would it require, what would it calculate and more importantly how would you implement such an algorithm into your front-line systems and analytics engines?

How should marketing departments respond?

In acquisition marketing, there has been a shift from the one-to-many approach to a one-to-one model, through personalisation and usage of social channels, empowered by third parties such as Google and Facebook. In loyalty marketing, the same one-to-one model has been harder to implement because of the customer data and capabilities required internally to execute.

However, one-to-one marketing has greater value in loyalty than acquisition marketing. Whereas acquisition marketers are content to wait for customers to enter the Information-gathering phase of the shopping journey before starting targeted marketing, loyalty marketers need to move more quickly to identify and convert the customer even before they reach that phase and signal their purchase intent to competitors.

For the Millenial generation, this is more crucial than ever, as the plethora of channels they use mean that competitors have ever more ways of stealing these customers through savvy acquisition marketing. The loyalty algorithm for Millenials is about leveraging the power of data, and of technology, to engage customers appropriately post-purchase, and identify when they are ready to make their next purchase. It is about building relationships with customers on a personal level, engaging with them through content that matches their interests, and being in the right place at the right time. Much like Mr Carson on Downton Abbey, an old fashioned butler, who anticipates the needs of the household even before they know it themselves.

How do we create a digital version of Mr Carson for our customers?

The answer is a loyalty algorithm that is driven by a Single View of the Customer – through rich customer profiles that are up-to-date and with holistic behavioural data.

Executing this is tricky. Loyalty marketing teams need to move from a campaign-driven approach (where customers are bombarded with generic content and promotions in the hope of catching them at the right stage) to one where the loyalty algorithm is able to work out what stage the customer is in to deliver only the right messaging and promotions with surgical precision.

Customer data will be key. Start with a data capture audit through all your customer touchpoints. Are you collecting the data you need and can obtain at each touchpoint? Audit your data refresh policy – customer data loses value over time as it becomes less relevant, and could even negatively impact your efforts, by turning off the customer. What is the useful lifespan of the data you currently have? How often will you need to refresh that data?

As data is the driver for the personalised loyalty algorithm, a dedicated team with the right analytical and data management capabilities is required. This customer data team will be responsible for the centralised management of all customer data and for developing data collection and usage policies.

A rational approach to data collection and storage is still required, even as data storage costs become cheaper over time. More data doesn’t mean additional insight – you get the same insight, only with higher confidence. A small data approach results in the same insights but sourced more efficiently, permitting spare resources to gather other types of data, to get different insights.

More data but of the same type gives better resolution, but gives you limited additional insight


Different types of data though (i.e. colour) can provide you with much better insight

The outcome of this loyalty algorithm is an effective strategy to retain your customers, leading to increased customer lifetime value and higher margins, and less of a reliance on (more expensive) acquisition marketing.

Through personalised marketing and content, not only will there be increased brand retention and loyalty, but also brand affinity, leading to more spontaneous customer advocacy and word-of-mouth marketing. A good loyalty algorithm will not only increase existing customer lifetime value, but also bring in new customers.