Leveraging loyalty

In these companies - or companies running loyalty programmes - there is a constant drive to improve service, better understand profitability and decrease churn, leading to increased loyalty. All of these can be achieved through a deeper understanding of the member base and the associated service and delivery costs.

Therefore, the challenge for management in these businesses is to understand their members so that their needs can be better serviced, as well as to identify candidates for increased uptake of services, and identify and curtail any churn taking place.

At the same time, companies need to draw on in-house and external information to identify opportunities for growing their member base.

Businesses with large membership bases are often in the enviable position of having a substantial, captive and relatively loyal member base with strong opportunities for cross- and up-selling. Their challenges don`t lie in brand awareness, but rather in what members know of the often diverse range of services offered by the company.

For these businesses, the key driver is to sustain and enhance customer loyalty through targeted and customised communication, which keeps members updated on their portfolio of services.

The key to carrying this out lies in segmenting the member base. Optimising the returns from a member base through segmentation and customised communication depends primarily on the quality of the information that lies within the database itself. However, it is common for much of the data to be inaccurate, with inconsistencies in the underlying member information between the various divisions.

CAMPAIGN FOR INTEGRITY

These businesses need to implement a data integrity campaign across the organisation so that the ultimate goal of deriving a `lifetime value` for its members is made easier.

The success of business intelligence hinges not only on the integrity and comprehensiveness of the underlying information, but also the ability to utilise it both timeously and efficiently. Nevertheless, many member-based businesses don`t have a clear idea of methods that can be used to achieve desired levels of data integrity; supplement data for enhanced value; or to extract key insights from the vast amounts of underlying data.

When embarking on the road of business intelligence with clients we prioritise the importance of the challenges to be met or the opportunities to be had by the business upfront.

This ensures that a clear business intelligence roadmap is established at the outset and that all resources are invested efficiently and effectively.

PREDICTING CHURN

Through using revelations from a member focus group together with relevant demographic variables, such as the number of products being utilised by the members and the length of time individuals have been a member in the active and inactive membership user base, the company is able to build churn prediction models.

These models give an indication of which members are likely to churn before they actually do so, by scoring every member in the base on a periodic basis and comparing the resultant score to predefined benchmarks.

On the back of these results, retention campaigns, including bespoke product offerings and communication, can then be made to the `vulnerable` members to reduce churn. These businesses can then go on to use predictive analytics in the form of propensity modelling to identify members who are not using all the services, with the aim of up- and cross-selling on a prioritised basis, thereby also increasing the perception of value for members.

Additionally, analytics adds value by integrating the view of member performance between the sales, marketing and finance departments so that they can collaborate better to evaluate, monitor and maximise return on investment.

About the author: Kate Elphick is a `catalyst` at marketing insight firm, Knowledge Factory