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DQ Blog Delete Duplicates How to Improve Customer Winback With Quality Data

How to Improve Customer Winback With Quality Data

Martin Doyle November 12th, 2013 Data Quality, Master Data Management
Data V Information

In business, it’s cheaper and easier to encourage existing customers to make another purchase, rather than chasing new customers and leads. The advertising industry is built on the idea of brand loyalty, and massive retail chains don’t market loyalty cards without good reason.

Often, customers purchase once and disappear, never to buy again. But it doesn’t have to be that way. If you can maximise the value of your current customer database, your return on investment will be much higher, and you’ll be able to act quickly if a customer is ‘lost’.

This practice of regaining lost custom is called winback, and with quality data, it’s surprisingly simple to achieve a good result.

How Customer Data is Leveraged

In order to tempt those customers back for a repeat purchase, you’ll need to ensure the data you hold about them is accurate. You might need to pool data about customers from a variety of sources. Your social media accounts, purchase history, accounting records, mailing lists, CRM, order forms – these are all great sources of data.

The secret is to combine this data so that it’s in one central database. That’s the single customer view. This benefits the customer, since their data can be easily maintained. It clearly benefits the business, too.

But pooling data presents problems. Duplication, errors, out of date records, conflicting information… this kind of data inaccuracy can throw your winback project wildly off track. Some data cleansing is needed to ensure that doesn’t happen.

Winback and Data Quality

According to data quality experts, a customer database that isn’t maintained well will be useless within three years. Ensuring the ongoing accuracy of your data set means using data quality tools and data matching software.

Without data quality tools, the potential impact on winback can be quite severe. Imagine:

  • Spelling a customer’s name wrong
  • Using the wrong name after marriage or divorce
  • Writing to customers who are deceased
  • Targeting mailouts at the wrong people

Cleansing data stops this from happening.

Apart from anything else, poor quality data costs you money. You market to the wrong people, send out wasteful direct mail and can even fall foul of the Data Protection Act.

Acting On Your Data

Once your database has been created, you’ll know an awful lot about your customer. Rather than marketing to cold prospects, you’re marketing to a group of people you know a lot about. That makes all the difference.

Using a quality dataset, you can:

  • Reward existing customers with the best offers
  • Market a new product to the customers most likely to buy it
  • Offer an upgrade to a newer model
  • Promote accessories for their purchase
  • Send special offer codes or vouchers
  • Remind your customers about the quality of your goods or services
  • Invite customers to product launches
  • Give your valued customers chance to feed back in a survey
  • Ask customers to refer new leads for a discount

Every communication – be it via email, direct mail or phone – should be coupled with a clear call to action. That might be an invitation to contact you for more details, or you might want to ask that your customer updates their contact information if the data you hold is not accurate.

The Benefits of Existing Customer Conversion

Hopefully this article has shown you how valuable your mailing list can be. Assuming you hold quality data about your clients, you can build a one to one relationship with them over time. Cleansing data isn’t a cost; it’s an investment, and one that could make or break your customer winback project.


Written by Martin Doyle

Martin is CEO and founder of DQ Global, a Data Quality Software company based in the UK. With an engineering background, Martin previously ran a CRM Software business. He has gained a wealth of knowledge and experience over the years and has established himself as a Data Quality Improvement Evangelist and an industry expert.