Client Skipton Building Society | Partner Paragon DCX | Industry Financial | Key objective Always put the customer first

The challenge

With 95 branches across the UK, customers are able to interact with Skipton Building Society (SBS) in branch, online and by telephone. SBS are relentlessly focused on providing excellent customer experiences. This objective runs throughout their business and has driven numerous strategic programmes at the building society. 

One of the key business challenges SBS needed to overcome was customer retention. The building society were seeing drop off, especially prevalent for customers who had defined term savings products and were at the point of account maturity and so could easily take their funds to another bank or building society.

Skipton challenge summary image

SBS worked with Paragon DCX on a ‘Next Best Savings Product’ project in order to attempt to overcome this key challenge by understanding what product might be a suitable next recommended product to customers who looked like they were about to churn. The project included the creation of a number of predictive models and then 21 PWE predictive scores for every customer on their individual likelihood to take up different types of savings products. The model scores update daily for use within FastStats and Adobe Campaign.

Following this understanding of the insight required, Paragon DCX and SBS planned to create multi-faceted customer models using Apteco's FastStats® software with the aim of providing actionable insight that could transform the communication journeys that savings customers go on. The ultimate aim was to align around the 4 Rs: Right Product, Right Time, Right Customer, and Right Message.

Skipton challenge reporting example
The Apteco software supports the team with various marketing initiatives and is used to generate the actionable insight required for the team’s customer-centric strategy.

The Apteco Solution

SBS chose Paragon DCX as their strategic partner to support them with marketing objectives around a framework of the right product, to the right customer, at the right time, with the right message. They used Apteco FastStats® and FastStats Modelling™ to understand customer behaviour types and what product should be offered next.

On top of the single customer view (SCV), Paragon DCX implemented leading analytics platform, Apteco FastStats®, and a campaign management and execution tool. This new marketing stack provides SBS with the capability to make informed decisions about their customers, action the insight gained and automate multi-channel, highly personalised cross channel marketing communications.

The predictive models are now being used to drive customer journey planning to ensure the right product is marketed, to the right customer, at the right time, with the right message.

The impact on the business

Explore some examples of how companies currently benefit from Apteco FastStats®.

Improved customer retention
Improved customer retention

Fight early attrition
Use insight to predict the customers who may close their account early, and engage them so they stay with SBS for longer. This insight and understanding can be used to drive the SBS communication strategy to promote new savings products to a customer at the right time.

Analysis informed decision making
Analysis informed decision making

Turn insight into action
FastStats Modelling™ was used to analyse the most indicative characteristics of customer behaviour. 28 variables were used across the nine savings product groups. The statistics and feedback from the SBS team were then used to make decisions about the best variables to action against.

Predict customer behaviour to market more effectively
Predict customer behaviour to market more effectively

Understand customer behaviour
Skipton Building Society used insight and research of savings behaviours to predict customer attitudes and market to them more effectively.

Next best savings product modelling
Next best savings product modelling

Next best product modelling
Skipton were able to understand and promote the right choice of savings product to a customer at the right time. Profile reports showed that all three savings behaviour models proved to be very strong predictors of identified customers.

The developed propensity models had model power ranging from 0.50 to 0.71, indicating that the models can significantly identify customers deemed as very likely to want the specified type of savings product, as well as predict customers who are significantly likely to look like them.