How to boost your sales staff’s customer data culture through Data Quality
The more mature the customer data culture is in a company, the better data quality is deployed at all levels. However, in the field, we often see bad practices that jeopardize data quality! When these practices have been the norm for a long time, not everyone appreciates the decisive value of working with quality customer data.
The good news is that when a company engages in a data quality approach, this fosters efforts to correct practices and prove the added value of quality customer data.
1 - Getting rid of bad practices that undermine the customer data culture
As long as the company has not started a data quality process, the customer data culture is not always very well-developed, and this lack of sufficient development extends to the right reflexes for collecting and recording data correctly. In particular, data entry can be compromised by practices that are contrary to data quality:
- Bypassing the collection of customer data
Let’s take an example from the retail industry, where a brand requires salespeople to at least capture the email addresses of customers. Salespeople naturally give priority to advice and sales. Entering and manipulating customer data on the screen is not automatically part of their priorities, especially if they don’t have a digitalized approach to the business. Before the conversion of teams to data quality, when this collection seemed to interfere too much with the customer relationship, or when the customer didn’t know their email address or wouldn’t divulge it, the salespeople applied a workaround strategy: entering a fake email address to be able to validate the form.
Strategies used to bypass controls can also be observed when the creation of a new customer file is incentivized. It is tempting for sales associates to systematically create a record for each customer, regardless of whether the customer already exists in the database. And if the CRM system blocks the creation of a record to the entry of an email address that is already in the database, the workaround strategy is to modify the email address a tiny bit (adding a dot, switching the place of two letters, etc.). This practice results in erroneous customer data and also creates duplicates.
- Incomplete entry of customer data
When the data entry is not intentionally incorrect, it can be incomplete, or even completely omitted. This is the case in points-of-sale, where close contact with customers is enough to ‘know everyone’. In the absence of a reinforced customer data culture, store teams do not always see the point of doing more.
Another observation from the field, this time from car dealerships, is that customer data was primarily collected manually, at the purchase stage. In addition to the inevitable data entry errors, this type of data collection misses an essential audience for developing the business: prospects! Consequently, it was impossible for the company to conduct prospecting campaigns.
If these bad practices can come from within the company, customers who fill out forms on the company’s website are just as guilty! As a result, the customer data that enters the database has an obvious problem in terms of quality and completeness. The consequences on operational efficiency are quick to show up in the business lines that work with customer data – customer service, call centers, points-of-sale, marketing, etc. Without data quality and unified customer data, it is impossible to have a 360° view of the customer journey, to reach customers, to send them messages or mailings, or to answer their questions with the right information. The entire customer relationship is affected.
2 - Make data quality an element that fosters acculturation to customer data
The deployment of a data quality solution helps you to cut short bad practices, in particular by blocking the validation of forms if the data entered is inaccurate, invalid, or already in the database.
Data quality also contributes to the acculturation of teams to customer data, in parallel to the training and awareness that the company can implement. Its unwavering argument: the results it brings for the users of customer data. Thus, using the operational benefits resulting from data quality allows for demonstrating the interest of well-used customer data. For example:
- With qualified customer data, contact information is reliable. Merging different data from the same customer allows for more complete profiles, for example with the confirmation of a first name, a key element for personalizing communications.
- In customer service and points-of-sale, qualified and reliable data being available makes it possible to answer customers’ requests quickly and well. No more approximations or hesitations on customer files. All of this enhances the associate’s mission, they can take more initiative to serve their customers – which in the end increases satisfaction. These results are enough to motivate teams to adopt the data quality movement!
- When the marketing department optimizes its results through better delivered campaigns and a higher open rate, sales departments gain better qualified leads. This efficiency linked to data quality proves the relevance of working qualitatively on customer data. It also helps to put an end to practices such as manual imports of more or less qualified Excel files, or inappropriate modifications in customer data.
When supported by evidence, data quality contributes directly to enriching the customer data culture!
About DQE
Because data quality is essential to customer knowledge and the construction of a lasting relationship, since 2008, DQE has provided its clients with innovative and comprehensive solutions that facilitate the collection of reliable data.
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April 19, 2024
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