For any B2B Sales and Marketing Organization the data of customers and prospects is the most valuable asset. The value of this asset grows higher when the accuracy of data is very high and is maintained so over time.
However, sourcing accurate data of customers and prospects is a challenge for B2B organizations. Even more challenging is to maintain this data accurately over time. Data, especially customer data, tends to age over time. As quoted by Kissmetrics in their blog:
- 60% people change their job titles in the organization every year
- 40% of email users change their email address every two years
- 25% of the email addresses become outdated every year
- 20% of the postal addresses change every year
With data churn like the one indicated above, Sales and Marketing Organizations have to constantly struggle to keep their data current.
The primary reasons for data quality degrading over time are:
Inaccurate Data Collection: Data that was collected by the sales team or through other means was not accurate. In a large scale data collection exercise, it is very likely that a good number of data points are inaccurate.
Incomplete Data: Large data collection exercises often lead to incomplete data because of non-availability of relevant data points.
Data Ageing: As given in the example above data collected at a point in time is subject to change. If the changes and updates to this data are not put in effect then it would age and start losing relevance.
What is the impact of Bad Data on Marketing?
Bad customer and prospect data can impact a B2B Marketing Organization in the following ways:
Create High Churn Rates: If we take the example given above, a quarter of the email addresses in the mailing list are likely to be outdated within a year. If these are not identified and corrected then the email campaigns that are run by Marketing team would not reach the intended audience.
Email Gaffe: Again from the example given above, 60% of the people in the list are likely to change their job title in a year. If a person is addressed in an email with an old title or an incorrect name, you create a barrier between your brand and that person.
Missing the target audience: The impact of a marketing message is maximum if the right product is pitched to the right person at the right time. This would require building the right buyer persona using the collected customer information. Inaccurate and incomplete customer information would lead to an incorrect buyer persona being created and targeted.
What is the impact of Bad Data on Sales?
A B2B Sales Organization is affected in the following ways by bad data:
Faulty Assumptions: The assumptions made using bad data often times are faulty. For example when sales teams base their process of approaching a client based on these faulty assumptions they are misled only to discover that the premise on which they began a process itself was incorrect.
Incorrect Conclusions: This could be a consequence of the Faulty Assumptions made at the beginning. For example Faulty Assumptions derived from bad data can mislead the sales team to make Incorrect Conclusions.
Loss of Time & Money: From Faulty Assumptions and Incorrect Conclusions it derives that a sales team not only loses time in its pursuit of a customer deal it is also in danger of losing the deal itself thereby preventing it from achieving its sales targets.
Inefficient Processes: Bad data can lead to an Enterprise designing its internal and external processes inefficiently. This in turn will affect the ability of the Enterprise to service its customers as well as its employees.
In this blog we listed some of the main challenges that bad data can cause to B2B Sales and Marketing Organizations.
Therefore, it would be prudent for them to ensure that their customer data is:
- collected accurately at the source
- complete in all respects (without any key data points missing)
- constantly updated.
Organizations that strive to achieve this would be effective in the execution of their sales and marketing strategy.