Customer Analytics

Customer understanding is at the heart of your business, and customer analytics - measuring and combining customer behaviours, like engagement and spend - is at the heart of customer understanding.

There are three levels of customer analytics. The most basic is simply reporting the data. Beyond that is descriptive analytics - exploring the data, to uncover trends and patterns.

The third and most powerful level is predictive analytics which uses the latest machine learning tools to predict likely outcomes from combined datasets - whether at an individual level (eg how likely someone is to buy) or a macro level (eg the outcome on sales of investment across channels).

Predictive models let you identify triggers of likely behaviour, and nudges to encourage desired outcomes. Want to find out who’s most likely to switch away from you, or to turn service subscribers into engaged users? Customer analytics provide the answer.

They’re your most powerful tool for improving customer acquisition, customer activation, and wider customer adoption of desired products or services. In other words, customer analytics help you get, grow and keep your customers.

Churn Risk

Acquiring a new customer is anywhere from 5 to 25 times more expensive than retaining an existing one. So, for any business, keeping hold of the right customers is critical. Our Churn Risk models identify customers most at risk of leaving, allowing pre-emptive, targeted intervention.

We use the latest machine learning algorithms and Natural Language Processing to pinpoint at risk customers. Our models are strengthened by blending a variety of historical data, including customer behaviours, customer touchpoints, customer logs, Net Promoter Score® surveys and more.

We can produce a snapshot of churn risk by customer, or embed advanced models in your systems so you can score your customers over time.

Propensity Models

Successful marketing reaches the right customers with the right offer through the right channel at the right moment.

Propensity models predict consumer behaviour, enabling marketers to steer resource allocation and highlight which customers warrant specific attention. For example, you could reduce direct mail spend by targeting only customers who are most likely to respond. Or identify customers who are more inclined to complain.

We build propensity models for your current customers today, and your prospective customers of tomorrow.

Recommendation & Personalisation

Recommendation engines are ubiquitous across the eCommerce landscape, but a small number of transactions is often not enough to understand what an individual may do next.

Our learning algorithms help you stay one step ahead of the consumer at all times, and increase your cross-selling conversion.

We achieve this by combining the transactional data with as much non-transactional data as possible, such as website engagement.

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