The importance of clean data

Data is considered a valuable aspect by companies. Businesses that have never considered using data in their marketing have now realized the importance of data in increasing the effectiveness of their marketing and in making better business decisions. However, bad data costs companies money. Each year Experian Data Quality commissions research on data quality in companies in the UK, Europe and the US. A summary of the results is below.

Why does data quality matter?

Inaccurate data has a direct impact on the bottom line of 88% of companies, with the average company losing 12% of its revenue. 99% of companies have a strategy in place to manage the quality of their contact data. What are the reasons behind ensuring data quality? The most popular reasons where increased efficiency (over 60%), customer service (54%), better decision making (47%), cost savings (44%), and increasing sales (43%). 

Why is clean data important


What data is most important?

Not all data is of equal importance. 54% of organizations consider customer contact data as the most important data for marketing success, followed by sales data (44%) and demographic data (38%). Information on customer behaviour (26%) and geolocation (21%) were lower down the list. This makes sense to me because without accurate contact information marketers can’t get their message to their customers and prospects. Sales and demographic data help to determine the message and to know who to contact for specific promotions.

Although contact data is considered the most important 90% of organizations enrich their contact data to target more effectively. Among the most popular forms of information added to contact records are geolocation data (48%), demographics (47%) and enhanced address data (42%). Almost 70% of organizations add two or more categories and 47% add three or more.

What marketing channels are most important?

Below is the breakdown to what organizations consider their most important marketing channel – email is by far the most important channel with 36% of respondents naming it as most important, followed by 22% naming social media as the most important.

marketing channels


Data and omni-channel marketing

Gaps in contact data are seen as the biggest barriers to success in omni-channel marketing. The increasing number of channels leaves businesses with the difficult task of communicating seamlessly with customers across a wide mix of media. 84% of companies report obstacles in the path to effective omni-channel marketing. The biggest of these is not having accurate information about consumers (42%) and not having enough information (41%). Technical and creative issues such as linking the channel technologies together (37%) and targeting the message (35%) are also important. Ironically, companies with a large number of contact databases (11+) are more likely to cite lack of information as a problem. Unsurprisingly these companies predominantly stated that a siloed departmental approach to data quality was a clear problem to successful cross-channel marketing.

How do organizations collect data?

Websites, call centres and face to face sales are the main sources of contact data. On average, respondents use 3.4 sources to collect contact data for their customers and prospects. The chart below shows the sources companies use to collect data.

How do companies gather data


Managing Data

Having a strategy to manage data is important, but that’s only the first step, what type of strategy is even more important. Below are some interesting stats on how companies manage their data. 

– Only 30% of those with a data quality strategy manage it centrally through a single director.

– just over 50% say that data management is partly centralized but that internal departments continue to operate their own strategy.

– 96% have procedures to make sure their contact data is accurate, relatively few use automated methods.

– Only 38% use specialized software to check data at the point of capture, while 34% use software to clean it after it has been collected. Automation is slightly higher among organizations that manage their data quality centrally with 45% of these using point of capture software

– 38% continue to carry out regular manual checks on Excel spreadsheets, while 26% say they use one-off manual checks for seasonal campaigns.

What are the biggest data errors

Knowing where your data errors are is the first step to being able to fix them. Below is a chart outlining the most common errors in data.

Data errors


Other interesting stats about inaccurate data are:

– businesses estimate that 22% of all their contact data is inaccurate in some way

– almost 60% of companies say human error is by far the biggest contributor to inaccurate contact data

– 24% said their data strategy was at fault, while 20% blamed lack of budget for their accuracy issues, 34% of these respondents blaming poor data strategy and 27% insufficient budgets

– More than half of organizations (52%) say call centres are the biggest source of problem data.

– contact information collected by mobile websites is also problematic for 42%, while 43% had issues with data from mobile apps. This contrasts with 37% for catalogues and mail order and 39% for physical stores and branches.

The costs of bad data

The average company loses 12% of its income because of bad contact data, through wasted marketing spend and resources, as well as lost productivity.

Bounced emails

But the hidden costs can be even greater. Of the 67% who reported problems delivering email, 28% said that customer service has been impacted as a result, while 26% had been unable to communicate with customers and 21% had suffered reputation problems.


A high proportion of loyalty and customer engagement programs have suffered because of bad contact data. More than 70% of businesses running these programmes reported problems, with inaccurate customer information (34%) and not having enough information (24%) among the top causes.

Poor business and customer intelligence

More than 80% of companies who use their data for business intelligence had problems generating meaningful analytics from it, with 40% blaming inaccurate information, 29% insufficient data and 16% too much information.

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