Poor quality of CRM data costs enterprises revenue

This remarkable disconnect is revealed among the headline findings of the 202O State of CRM Data Management report (produced by Validity). What’s more, only 8% of participants in this year’s study acknowledged that they had adopted what the report identified as the three key characteristics of organisations with the highest quality CRM data. These are: 1. that leadership prioritises CRM data quality; 2. that an effective data governance process is in place and 3. that CRM data management is the full-time responsibility of a cross-functional team.

The findings are concerning, given that none of the above appears to be that difficult to aspire to and implement and that elsewhere, the survey reveals that high-performing companies do rely on and employ effective data quality management as one of the keys to their success.

Although the figures for companies in that top 8% are much higher, those from the majority of the enterprises surveyed reveal a surprisingly negative view of what organisations think of their own data quality management. Over a third of participants in the study claim to have either no CRM data management process, or one that is “ineffective”. And yet, more than a quarter (27%) report that bad data costs them 10% or more in lost revenue annually. Almost half of participants actually believe their systems are so poor that they cannot even estimate bad data’s effect on revenue.

Only 55% rate the overall quality, accuracy and usefulness of their CRM data as good or very good, which means a remarkable 45% do not. Even worse, only 42% rate the trust that users have in their data as high or very high and only 54% believe that sales forecasts are accurate or very accurate. Just 35% of the overall sample claim to be satisfied or very satisfied with their lead-to-customer conversion rates.

Over 75% of study participants agreed that inaccurate CRM data negatively impacts the performance of multiple departments. On the ground this means inaccurate sales forecasts causing failures that include incorrectly budgeting for operations, insufficient working capital, higher turnover rates and even potentially a loss of shareholder confidence.

So, what exactly do respondents believe to be the data issues that impact on CRM quality? Top of the list (69%) is missing or incomplete data, followed by duplicate data, incorrect data and expired data. Other data issues include siloed data or disparate systems. It means only 27% of respondents believe that they have a 360-degree view of their customers.

The report does at least attempt to lift the gloom surrounding the findings by suggesting ways in which enterprises can clean their data, align their teams and improve ROI. Chief among these appears to be requiring the interest and buy-in of senior leadership beyond data functions and a team approach to data management that focuses on it being a full-time responsibility of a cross functional team. Most notably, when leadership prioritises CRM data quality, then 90% of respondents report good to very good CRM data quality.

While a reassuring 90% of those surveyed reported taking steps to improve their CRM data quality the most frequent step reported is to manually identify and correct data quality issues. The problem with this approach is that it is very difficult to scale a manual process. Automating the process is therefore one of the four key steps identified in pursuit of making better decisions – the number one preferred outcome of implementing changes, which may actually seem relatively obvious.

Getting leadership on board to make managing data a priority; putting the right team in place and ensuring it is cross-functional; and making CRM data management a job not a task are the other three chief recommendations from those who are ‘walking the walk” in CRM. The surprise finding that so few believe they are getting it right is only matched by the opportunity for improvement this represents.

This State of CRM Data Management 2020 survey was administered online during the period of February 6 through February 25, 2020. During this period, 294 responses were collected, 276 of which were qualified and complete enough for inclusion in the analysis.