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Using predictive analytics to improve customer service and business processes

Reading time: 5 minutes

Many organisations find it hard to create and use analytics to support the improvement of services and processes.

In our experience within the housing sector, the use of predictive analytics is enabling organisations to look forward and plan for predicted changes in demand. The problems are primarily caused by a lack of understanding and commitment across the organisation in the correct capture and use of information. This requires a people based approach rather than technology alone.

Aligning the teams to a customer service approach, where they are reliant on each other’s data, and experience both the positive and negative impacts of this, will help change the culture. Where teams are isolated from the actions of other teams and the customers themselves, then the importance and interdependence of what they do is diminished.

The nirvana of customer insight and predictive analytics to forward plan service activities, increase efficiency, improve quality, remove wasted time and duplicated effort, will not be achieved by technology alone.

A great deal can be achieved using relatively simple and low-cost analytics software if the data is reliable because it is core to the way the organisation delivers services. With the best will in the world, the best AI engines can’t put back what is missing or create insight if the data does not exist in the first place.

The approach that has worked across multiple organisations in different sectors is one of ‘lean whole systems thinking’. It has been developed over more than 50 years based on academic research and real-life organisational experiences.

This approaches the identification of problems and the agreement of solutions ‘in the round’. It engages the people who actually work in the services and use the processes to deliver services to customers as well as those who manage and organise the service teams.

The technology requirements are part of, and come from, this holistic approach. It’s often surprising what can be achieved by a relatively simple and low-tech approach at the outset.

Once progress is made on agreeing the problems and the solutions, and trying a few ‘quick wins’, then the requirements for a customer relationship management (CRM) system can be properly articulated.

This will give a joined-up view across the different services and customer interactions of what’s needed and the benefits it has to deliver to customers, team members and the organisation.

Deploying relatively simple (and often already owned) reporting tools, such as Microsoft Power BI or Einstein Analytics, will deliver a lot of value, before progressing to more complex and expensive tools such as AI.

Good CRM systems already have these embedded within them, although sometimes they need further development from more technically trained staff to really deliver value.

To find out more about how we can help you use Salesforce Sales Cloud to balance customer management with big data and analytics, get in touch!


 

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