1. Foreword

Matching strategy with reality

The issue of data quality is rapidly moving up the corporate agenda as an investment priority and it’s easy to understand the motives behind this change in perception. The link between data quality and business process optimisation, strategic decision making, cost control and regulatory risk reduction has been reinforced by most of the leading industry analysts in the technology market today. The cold hard fact is inaccurate data costs businesses and fundamentally impacts on their performance. Our research supports this conclusion with 88% of respondents suggesting that inaccurate data has a direct impact on their bottom line alongside a 12% impact on income generated.

The headline contradiction that our 2014 research report flags up is that while 99% of organisations claim to have a data quality strategy in place, 91% of that same sample still struggle with basic issues surrounding contact data quality. This drives interesting debate around data quality strategy best practices. The key is to ensure that data improvement initiatives are effectively scoped and have the appropriate metrics in place to diagnose success and failure. Data quality technology can enable this, but it is essential to consider the entire organisation when selecting these tools – not just the IT department.

Success is driven by effective prioritisation. It is evident in the research that customer data remains the priority area for improvement initiatives. This is primarily down to the impact it can have on most KPIs commonly found on business scorecards such as revenue and customer satisfaction. However, there has been notable growth in projects around alternative data domains such as product and financial data which is also an important move in the market.

2. The results: At a glance

[Download PDF to see Table]

Data Definitions

Data quality: refers to the accuracy, reliability and usefulness of data

Data strategy: internal policies and processes to maintain data quality over time

Big data: unstructured data that is not easily organised or interpreted by traditional databases

Single Customer View (SCV): an organisation’s single, coherent view of all of a customer’s touch-points across all channels and business areas

3. Key findings

Our 2014 data quality survey interviewed more than 1,200 organisations in the UK, US and Europe. They came from across a wide range of industries and business sizes from large multi-nationals to SMEs.

We asked them how they collect their contact data, how they manage it, the issues they face maintaining accurate information and what impact bad data has on their business.

3.1 Does data quality matter?

Almost all organisations (99%) have a strategy to manage the quality of their contact data.

The need for such a strategy is now universally recognised. But it’s the factors motivating strategy that demonstrate how important data quality has become. More than 60% of organisations say the main driver is to increase the efficiency of their business, while 54% say it’s to improve customer satisfaction and 47% to enhance decision making. This is coupled with more explicit but not mutually exclusive business requirements such as cost savings (44%) and increasing sales (43%). Industries such as financial services, telecoms and utilities are, understandably, more motivated by regulatory compliance and the risk of fraud than other sectors.

[Download PDF to see Graph]

Bad data is costing businesses

The 2014 survey shows that the average organisation loses 12% of its income because of bad contact data, through wasted marketing spend and resources as well as lost productivity.

But the hidden costs may be even greater – 28% of those who have had problems delivering email because of bad data say that customer service has suffered as a result, while 21% experienced reputational damage.

3.2 Which data is most valuable?

Consumer contact data is considered the most essential data for marketing success.

The majority of organisations (54%) named contact records among the top three kinds of data driving marketing success. This was well ahead of sales data (44%) and demographic data (38%), while information on consumer behaviour (26%) and geolocation (21%) figured lower down the list. This is consistent across the survey groups, with contact data topping the lists in all six countries and all industry sectors.

3.3 Enriching contact data

More than 90% of organisations enrich their contact data to help target communications 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 organisations add two or more categories and 47% add three or more.

But some are still missing out on the opportunity to enrich their contact data. For example, less than half (48%) of those organisations that named contact data as essential to marketing success add enhanced address data to their records and a third of those who named demographics, geolocation, or sales data as essential, fail to add those categories of data.

It is apparent that organisations see the correlation between accurate data and driving improved top line performance. Getting a clear view of the customer is a large portion of the battle but increased gains can be made by adding additional insight to customer records, driving value from effective segmentation.

3.4 Which channels are most important?

Email is the top marketing channel in 2014, while social media advances more slowly than expected.

More than a third (36%) of organisations said that email will be their most important channel for communicating with customers and prospects in 2014. This is clearly ahead of social media on 22%. It appears that the buzz around social media is running ahead of its actual use by marketers, with the number of organisations who consider it their top channel growing steadily if unspectacularly, from 17% over the last two years.

Similarly, while mobile telephones will be the top communication channel for 12% of organisations, this is only slightly ahead of landline phones on 10%. And even postal marketing, which has been the biggest casualty of channel shift in recent years, is still named by 10% of organisations as their top channel in 2014.

The tide of change is moving most slowly in France and Germany, where social media does not even make it into second place. In those countries, physical mail and landline telephone respectively remain more important.

Its clear that the volume and variety of data in the market is ever increasing and companies need to take into account this diversity of channels when building a data quality strategy.

[Download PDF to see Graph]

3.5 Cross-channel marketing

Gaps in contact data are seen as the biggest barriers to success in crosschannel marketing.

This increasing volume of channels leaves businesses with the difficult task of communicating seamlessly with customers across a wide mix of media. The survey shows that gaps in contact data could be problematic. A startling 84% of companies report obstacles in the path to effective cross-channel marketing. The biggest of these are 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 important but not as critical as the quality of consumer data. 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 prohibiter to progress.

3.6 How is data collected?

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 continuing popularity of traditional methods is striking. After the website, used by 73%, the next most popular sources of information are face-to-face sales teams (60%) and call centres (54%). More than 40% collect contact data from stores and branches, while 24% continue to use catalogues and mail order coupons. However, almost half (47%) of organisations now collect contact data via mobile websites or apps and this is expected to increase in future years. This exacerbates the requirement for organisations to consider all channels of collection when planning their data quality strategy.

[Download PDF to see Graph]

3.7 How is data managed?

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

Although most organisations aim to improve their contact data quality, they have not yet built into a corporate wide agenda. The result is a tactical and ad hoc approach. For example, just over 50% say that data management is partly centralised but that internal departments continue to operate their own strategy.

A corporate wide approach is required here with time taken to understand the broader data quality processes that exist within the business. Quick wins and tactical solutions are fine but need to be rolled up as part of a broad overall strategy that focuses on the primary objectives of the company.

3.8 Managing accuracy

Many organisations still use manual checking to ensure their contact data is correct.

Although 96% have procedures to make sure their contact data is accurate, relatively few use automated methods. Only 38% use specialised 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 organisations that manage their data quality centrally, with 45% of these using point of capture software.

Meanwhile, 38% continue to carry out regular manual checks on Excel spreadsheets, while 26% say they use one-off manual checks for seasonal campaigns. Perhaps the most worrying result is that the number who rely solely on manual checks to check their contact records (23%) considering the statistic that 60% of respondents cited human error as the reason for bad data quality this seems unwise.

3.9 What are the biggest data errors?

More than 90% of respondents report at least one type of common error in their contact data.

This figure is high but perhaps not surprising given the multiple sources from which data is collected, the ad hoc way it is managed and the extent of manual record checking. The most common problems are missing information, cited by 44%, outdated records (41%) and inaccurate data (39%). Further down the list, almost a third of respondents (30%) name duplicated information as an issue, with typos (23%) and spelling mistakes (22%) the next most common errors.

[Download PDF to see Graph]

3.10 Inaccurate data

Organisations estimate that 22% of all their contact data is inaccurate in some way.

This figure is up from 17% just a year ago and suggests increasing waste as incorrect addresses, emails and phone numbers prevent communications reaching their target. Worryingly, estimates of inaccuracy are even higher among marketing and sales professionals, who think more than 30% of their records are wrong.

3.11 Causes of inaccuracy

Human error is by far the biggest contributor to inaccurate contact data.

Almost 60% of respondents named this cause as a reason for their lack of accurate contact data, way ahead of the next most common problem – poor communication between departments.

However, a significant number (24%) said their data strategy was at fault, while 20% blamed lack of budget for their accuracy issues. These factors are seen to be more critical among those in hands-on data management roles, with 34% of these respondents blaming poor data strategy and 27% insufficient budgets.

3.12 Which data sources are dirtiest?

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

However, whether the data comes from a traditional source such as call centers or one of the newer channels it is not an accurate guide to the quality of the data that you will be collecting from it. For instance contact information collected via 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.

A better guide to quality is the volume of data collected, as two of the busiest sources, call centres and websites (a problem for 49%), are also the dirtiest. Only a few years ago, in 2009, less than half of the organisations surveyed had a strategy to maintain the quality of their contact data records. At that time, the number one imperative was to collect as much contact data from as many people as possible. Little attention was given to whether that information might be wrong, or how it would degrade over time, or the business risks if communications misfired as a result.

This year’s survey demonstrates the era of quantity over quality data is well and truly over. Businesses have become increasingly aware of the value of consumer data as both customers and regulators become more protective of it – and the cost of bad data hits the top and bottom line. This understanding is reflected in the fact that almost all organisations now have a data quality strategy in place.

Aspiration vs action

However, organisations have not yet moved past the awareness stage to take the kind of action that’s needed to eliminate errors and turn contact data into a valuable asset that can drive business success.

In fact, the 2014 survey shows a small step backwards. Our findings show that fewer organisations than in 2013 are now using automated methods to check contact data at the point of capture, or clean it after submission. Conversely, the number that rely on manual checking has increased.

As you might expect, this has led to an increase in errors and inaccuracy, with more than 9 in 10 respondents saying their records contain data errors and more than a fifth of all contact data thought to be inaccurate in some way.

Why progress has stalled

The disconnect between aspiration and action seems to be getting bigger. But why? Some of the gap can be explained by simple economics. With budgets still constrained, and staff and resources in relatively short supply, companies are adopting a make do and mend approach to their databases.

Surprisingly there has been a decrease this year in people driving towards a single customer view with only 34% supporting that goal with their data strategy. We would suggest this downturn is due to companies failing to execute on relatively expensive customer data transformation projects as a result of inadequate scoping and understanding of broader business objectives.

However, although the landscape is changing, it isn’t necessarily happening as quickly or in exactly the ways that may have been predicted. One of the most striking findings this year is that email remains firmly in place as the most important channel for marketing and customer communications in 2014, well ahead of social media.

likely this year that some channels will be eclipsed by others and more probable that the range of channels in use will become even wider. It seems that future success will be defined by the ability to use a range of different channels seamlessly and effectively.

This means good quality contact data will become more important, not less. A fact that is already apparent when we look at the problems organisations identify as the main barriers to effective cross-channel marketing. The biggest obstacle is inaccurate information about customers, followed closely by insufficient information.

Towards better quality data

Errors and inaccuracies will only increase – along with the fallout from misfiring communications (see panel) – until organisations get beyond the awareness stage and start to turn strategy into action. It is important to note that strategy will be the key to delivering a successful data transformation program. Scoping out priority areas for improvement and expected business outcomes is critical. Then comes the need to understand and get clarity on where data quality processes can be improved and further evaluate supporting data quality technology.

Understanding these different drivers and uses will make it easier to decide what people, processes and technology you need to deploy to drive measured success. Failure to grasp this issue could mean increasing costs, not just in terms of wasted budget or lost revenue, but also to your reputation, your ability to engage with customers, the empowerment of your work force and ultimately your business efficiency.