In the latest in a series of articles, designed to provide advice on data-driven marketing strategies in these turbulent times and beyond, we look at the utilities sector.
The Decision Marketing Data Clinic, in association with REaD Group, is open to all companies and organisations big and small. If you have a burning issue which you would like advice on please email us on email@example.com.
Utilities companies operate in a highly competitive market environment. How can data help improve acquisition, prevent churn and increase insight?
We have an ever-increasing debt ratio and change of tenancy (COT) is increasing this. Can data help?
We can supply data on a monthly basis at a property level, which identifies all properties in the previous month which have been listed for sale/rent or have been sold subject to contract (SSTC). This can be cross-referenced to your existing customer base/supply points. Matching records can be utilised for communications etc., while all unmatched records can be provided back and can be appended with a mailable consumer name.
Not only does this help to identify COT, providing an opportunity to communicate with this household and incentivise them to take your services with them – but you can also identify those tenancies you weren’t going to be told about, thus helping to reduce the debt ratio.
Debt reduction can also be improved through further remediation of data to:
– Identify customers who have passed away
– Provide new addresses for customers who have moved
– Identify individuals who have moved (where new address is currently not available)
– These can be rescreened monthly to see if a new address is available
– Append dates of birth to improve customer tracing
– Validate or append phone numbers
Having the right name, address and contact details allows utilities suppliers to collect debt efficiently through various processes. As an example, new address provision on up to 5% of supplied client files could lead to a considerable reduction in debt ratio.
How can we identify customers who are likely to switch – and is there anything we can do to keep hold of them?
Additional data can be provided to support insight and acquisition, such as prospect names, addresses, and telephone numbers. Applying a ‘switchers’ model identifies and removes customers likely to switch again and therefore not best suited to be acquired, as well as those highly unlikely to switch. This enables the targeting of those who are a potential to switch and who won’t immediately switch back to another supplier at the earliest possible moment.
This reduces the resources spent on customers who will switch while concentrating resources on customers unlikely to switch, ultimately improving customer acquisition and retention.
Is it possible to upsell to customers?
ROI per customer can be improved through data profiling and modelling to identify properties in the UK for ‘beyond supply’ projects, such as where the roof is sufficient to take a PV panel, or where there is a garage in close proximity to the house that could be connected for an EV. A secondary data model can also be supplied to predict the likelihood of a household to adopt the ‘beyond supply’.
Data segmentation by our data scientists unlocks the value of data and maximises ROI. Past behaviour and advanced modelling can also be used to predict what people are most likely to do in the future.
How do we improve our targeting and communications?
Personalised, relevant and timely communications are made possible by appending data where previously this wasn’t known. This can include appending date of birth, and property information, such as number of bedrooms, year property built, adult occupants and number of children.
Efficiently and effectively managing customer data improves targeting and communications, as well as maximising its value.
We lose a lot of customers when they move home. Can data help reduce customer churn and maximise retention?
By applying data-sets that rack indicators of the intent to move to identify existing customers who are about to move home, such as Moment in Time data (MinT), utilities companies can make informed decisions of what type of communication to send to encourage retention.
The same data can be utilised as part of your acquisition strategy by identifying consumers who are not currently customers but may be open to switch when they move.
We have a large volume of data – how can we utilise it more effectively and strategically?
The best way to use your customer data more effectively is to create a single customer view (SCV). A complete and holistic view of your customer data, an effective SCV can transform how you can use and capitalise the data assets within your business.
It combines data from multiple sources and channels across your business – from product and purchase history, call centres and customer service logs to online and social activity – providing a complete picture of your customers and their interaction with your business.
When cleansed, brought up-to-date and enriched with external data, your SCV will reduce incorrect customer data, improve customer comms and reduce wasted resources.