Andrew McMillan

1. Case Study: Taking data cleaning and suppression in-house Andrew McMillan

2. Overview • Who are Kwik-Fit Financial Services?  • Our data quality challenges • What we did about it? • Why QAS? • Where are we now?

3. Who are we?

4. A quick overview • Established in 1995 • Part of the Kwik-Fit Group of companies • Trade under the brand ‘Kwik-Fit Insurance’ • Act as an intermediary for mainly general insurance products – we use a panel of insurers for most products • Over 340,000 car insurance customers • Total product holding of 700,000 • Have been cheapest online car insurance provider for last 12 months

5. Routes to market • We have a three way distribution strategy of inbound, outbound and web. • Over 60% of our new business now originates from the web. • 30% of our business originates from data that we use in our marketing efforts. • We have over 2 million leads to manage. • Our Kwik-Fit leads generation process is a significant data challenge.

6. Kwik-Fit leads model • Acquisition campaign at renewal date. • Direct mail • E-mail • Outbound call Customer visits Kwik-Fit centre Personal data is entered into system Over 98% of customers are satisfied and we attempt to collect car insurance renewal date Data is de-duped, purged and added to our single customer view Within 72 hours, a courtesy call is made to the customer

7. Our data quality challenges

8. Data quality challenges • Data accuracy • Data objections • Data decay “It’s not just an IT or database issue – it is a business issue. Unless all areas of the business tackle data quality, the results will at best be limited.”

9. Data accuracy • These were our main issues surrounding data accuracy: • Spelling of names • Invalid address details • Invalid telephone numbers • De-dupe processes • Impacts of poor data accuracy: • De-duplication processes are less effective • Leads that are no longer usable • Customer dissatisfaction/complaints • Negative brand image • Increased costs

10. Data objections • These were our main issues surrounding data objections: • Mailing Preference Service • Telephone Preference Service • Internal marketing objections • Impacts of poor data objection management: • Potential legal issues • Customer dissatisfaction/complaints • Potentially reduced response rates • Negative brand image • Increased costs

11. Data decay • These were our main issues surrounding data decay: • Royal Mail postal file changes • Customers who have moved house (goneaways) • Customers who have died (deceased) • Impacts of data decay: • De-duplication processes are less effective • Leads that are no longer usable • Customer dissatisfaction/complaints • Negative brand image • Increased costs

12. What we did about it

13. Start at the beginning • These are some of the questions we asked: • Where does our data come from? • What controls are in place to ensure the data is accurate? • Where do we store the data? • What do we use the data for? • What else could we do with the data? • What impacts the data quality after 1 month, 6 months, 1 year etc? • What legal requirements do we have regarding the data? • What opportunities do we have of updating the data? • What could we do internally to improve the data quality? • What is available externally to improve the data quality? • Are our systems up to scratch? • How do we deal with data that we can’t fix?

14. Some of our findings • Many of the data accuracy issues – spelling of name, invalid address and telephone number is caused by incomplete processes or our people. • We had opportunities to correct data but weren’t doing it. • Our systems didn’t have full data validation which was allowing poor data through. • We were not doing anything with data that we knew was inaccurate. • There was no MI available on invalid data and awareness of data quality was low. • We were assuming that data we collected 1, 2 even 3 years ago hadn’t changed. • Although technically we did not need to use TPS, by not using it we were generating complaints. • We could ask for more data from customers ie. e-mail address. • Across all channels, data quality procedures need to be put in place including call centre and website.

15. Set goals – it’s always good to know where you are trying to get to After we had went through a full review of our data processes, we sat down to decide what we wanted to achieve. Look inwards first • We found that many of our data accuracy issues were either due to people, processes or systems. Prevention is better than a cure Invest your time and money into sorting data quality before you use the data – it’s more effective. When everything else fails Deal with customer’s details that you know are inaccurate – don’t ignore them. It will just happen again!

16. Why QAS?

17. Competitive tender • We didn’t hand QAS our business on a plate. • We tendered our business and put the providers through a rigorous assessment process. • Look beyond the obvious items and really challenge data providers like QAS. • We asked the competing providers to clean the same data, so that we could compare any differences in their methodology and results. It threw up some surprising differences. They are not all the same!

18. What we bought QuickAddress Batch Windows version including built in suppression files. To be used by our marketing department for direct mail, e-mail and outbound campaigns to suppress goneaways, deceased and check against MPS/TPS. QuickAddress Batch Unix version Address cleaning software to integrate with our back office systems which run on a Unix platform. We are already existing users of QuickAddress Pro and Pro Web products for our website and call centre.

19. Why QAS got our business • Confidence in the product • Confidence in the company – both sales and support • Competitive pricing • Demonstrated the accuracy of their data • Allowed us to trial the software • Able to provide an easy to use in-house system

20. In-house are you crazy? • We have had several years experience of using a bureau service. Our experience was mixed. • Turnaround was sometimes slow • Didn’t always understand our data requirements • Costs had a habit of sneaking up • Several occasions where the data was wrong • We wanted to take control of our own destiny. • QAS solution allowed us to do exactly that. • Our data is now turned around in 1 day versus 2 to 3 days with a bureau. • It is more cost effective and it only takes minutes to set-up the data cleaning process. • Our marketing teams are more aware of data accuracy as a result.

21. Where are we now?

22. Benefits that we have seen • Both QAS systems are implemented and we have seen improvements to: • Response – response rates have increased slightly as the quality of our mailing has improved. • Our de-duping processes are more efficient as poor address matching has been significantly reduced. • The level of returned mail has dropped from 5% to 2%. • The cost saving in not mailing/calling poor data is greater than the cost of suppressing the data. A net gain. • We have met our timescales for our regular mailing packs and experienced no issues. • Improved customer satisfaction – but we do not have a tangible measure of this.

23. What we still have to do • Awaiting a new internal suppressions and objections system which will take us to the next level in dealing with invalid details. • Fully integrate QuickAddress Batch Unix into our main CRM system. It is currently linked to the back end only.

24. Any questions?