According to the Sales & Marketing Institute and D&B data, up to 96% of email addresses and contact data within customer files are at least partially inaccurate. Although 99% of businesses have a data quality strategy, about 38% of companies still perform manual cleaning processes on Excel sheets. The workers surely get bored to death, while spending 50% to 90% of their time manually preparing the data.

Data failures and misinterpretation can cost the company a lot. On average, companies spend about $8.2 million annually on poor data quality, according to The Gartner Group. It’s better to prevent the inaccuracies instead. It would be about 100 times cheaper than fixing the consequences. While data cleansing companies are earning millions of dollars, lets get to the bottom of the problem.

The cost of outdated information

Every year about 6 million people in UK change their home addresses. 600 000 people pass away and 5.7 million company and individual details change.

It’s scary to see how many people actually die every year. It is also scary to see how many data changes need to be handled.

The cruel truth about it is that it doesn’t influence the data geeks only. The entire marketing and customer experience sectors can feel it, too. They claim to have more than 30% incorrect records. The 84% of cases find the inaccurate contacts being the largest barriers in multichannel marketing. 67% of emails sent in email campaigns bounce back and 70% of organizations claim that their loyalty programmes suffer from inaccurate customer information. Looking specifically at email addresses, 26% of interviewees mentioned inability to connect with customers due to inaccurate information.

data quality

Not only company’s marketing depends on quality data but also decision making and planning in general. Given that fact, the data should be prioritised and automatically processed to the extent that at least it doesn’t impair ongoing decision processes. Due to the size of the data, it is often impossible to manage it manually. It’s a seriously time-wasting issue for professionals from non-data-cleansing fields. About 76% of data scientists view data preparation as the least enjoyable part of their work. The dissatisfaction of the workers, however, can’t make up for the costs of poor data companies suffer from. Shockingly, on average a company loses yearly about $8 million, according to Gartner.

Data cleansing efforts a waste?

83% of companies claimed to prioritise data quality initiatives last year. They spend on it about $1 billion a year, according to Gartner Inc. Surprisingly, according to Forrester, only about 12% of them actually use the data-driven approach for business decisions.

That means that the vast majority of the efforts and money actually sinking into the data cleansing industry is without major outcome. What is worse, although majority of companies apply data quality strategies, 86% still estimates their data to be inaccurate.

It’s bad news. We can safely assume that clean data is the lead driver of business efficiency, customer satisfaction and saves costs. Integrated data is the base for Single Customer View being the most desirable especially among marketers. Omnichannel marketing and maintaining customer relationships would be nothing without appropriate identity management. It is worthwhile since 73% of consumers prefer brands that use their personal information to make the shopping experiences more relevant, according to Digital Trends. The era of consumers hiding their identity online comes to an end. Hence, we’d better create a more consistent and contextual customer view in order to boost retention.

For many the inaccurate and missing data turns out to be a common issue. 41% of interviewed professionals can see outdated details as their biggest problem. The process of data cleaning need to be conducted regularly in order to maintain the good quality data. Despite the efforts the data mounts and more information gets out of date. The regular data cleansing processes can’t be a long term solution. Unless the data management issues get fixed organically, the money spent on data cleansing solutions will raise, while the data consistency problem will probably keep growing.

Future of personal data

The updating of contact details became painful along with the increase in popularity of online and offline services. There are two ways to fight with the problem: spending money on repairing the effects or preventing the consequences by restructuring the inefficient system. The data management would benefit a lot from centralisation and regularisation, in order to manage the overwhelming flood of data.

Poor data quality in the US costs up to $600 billion a year and the cost is only going to grow across different countries unless one will introduce better data collection and management solutions. When it comes to managing consumers identity, you need to adhere to multiple privacy policies. Choosing a solution with the ability to automatically manage these privacy updates in real-time is the key to staying compliant and maintaining customer trust.

Static data is particularly hard to keep updated since it’s one-off nature of collection. Contact details entered at the sign-up process most probably will become outdated within a year. Next big challenge is better definition of what should be modelled using dynamic data to better profile customers’ activity.

static and dynamic data


In order to fully model and profile customers, both static and dynamic data need to be reconciled and moved into centralised model.  In case the data is not gathered and managed correctly, instead of helping to build stronger customer relationships, it will work the opposite way. After receiving irrelevant information or products from a brand, 43% ignore future communications from the company, while 20% stop buying from it, according to Gigya. Fixing data-quality issues you can improve customer cross-selling and retention, by delivering more-consistent and more-valued customer experience.

Check at how we can help you prevent contact data from becoming outdated.