Is your CRM working for You? A Guide to Data Health

There is a common experience of people who have been working with CRMs for a long time: the problem of low-quality data. Dirty, inaccurate, incomplete, or inconsistent data, not properly organized and related between them are some of the most usual and obvious issues.

Why is Data Quality in Your CRM Crucial?

How efficient could a marketing campaign be if many of customers’ email addresses are missing? Isn’t it frustrating if trying to call international clients without knowing the country code of their phone number? Could a business rely on inaccurate sales figures for end year reports and next year’s planning? If not being able to define customer base, due to duplicate customer records? These are just a few practical examples of how poor data quality in a CRM can affect a business.

As all the above examples indicate, data quality is not a theoretical issue discovered by consultants: it has practical implications, affecting directly business’ performance. More specifically, when looking at a CRMs data quality, we need to evaluate two other parameters. First, CRMs focus on relationships between different sets of data: contact information, company information, purchase & communication history, trouble tickets, preferences, scheduled actions etc. This mean that data must be properly matched and related between them, to give a global, consistent, and accurate picture of sales and marketing activities as well as the customers characteristics and potential. Second, customer data are constantly evolving, with new data arriving from different paths. It is not only new customers or new products who enrich the existing data sets but also data regarding existing customer change: Contact details change, buying habits shift, and new interactions occur. Furthermore, the business is evolving and new needs for data appear, either to store different other types of data or redefine relations between different datasets.

Signs of possible data quality problems

Is it time to peek under the hood of your CRM? Here are some questions to ask yourself:

• Do you get accurate reports from your CRM? Is your customer base inflated from duplicates? Are averages inaccurate due to empty fields?
• Can your sales & marketing teams provide a meaningful analysis about customers’ behavior?
• How easy it is to design, run and monitor marketing campaigns? Do you need hours of preparation and analysis every time you are trying to reach your audience?
• Does your sales or customer care team struggle to find the right contact information? Mainly if you are into b2b, could you easily tell who is the contact person and the roles for each company, with each one’s contact details?
• Is there any consistency between the data of your CRM and those held on other systems?
• What does the team believe about your CRM? Is it a useful tool or a burden to them?
• How much of your customers’ information is written down to sticky notes, papers, notebooks, excel files, mobile phones contacts?
• How many times you may ask the same information from the same customer?

The answers to the above questions will make it clear for you if a data quality assessment is a wise investment!
Is there a solution?

It seems a that CRM data quality is critical and but also complicated to deal with: is there a way to sort this out? How can a business resolve its main quality issues and achieve processes efficiency, sales performance, and customer satisfaction? How easy it is to turn your CRM into a goldmine of customer insights?

Best practices by leading companies worldwide, define a path for success:

• Data quality assessment to discover & clearly define the issues and prescribe next steps.
• Corrective actions to immediately and practically resolve specific quality problems.
• Setting of a Data Quality Policy to make sure that the quality will remain high.

Tasks included in the above are sometimes complicated and difficult to execute, requiring specialized people and tools, but the results are rewarding. Their impact may be immediate and tangible, changing every day’s work for many people, upgrading possibilities across the business.

Its not rare to see spectacular results in such projects. In a recent one, management was impressed to see that 45% of sales channel information seemed to be missing because it was stored to the wrong field. It was even more impressive how all those lost filed were retrieved, matched, and copied to the right field, with an accuracy of 99,9%. In other cases, we have witnessed changes of configuration to the CRM together with a couple of clear instructions preventing specific problems from happening again.

First stop: Data Quality assessment

Depending on your resources and the data complexity, this can be either insourced or outsourced. The aim of this process is to identify all quality issues, to estimate the impact on the business processes, and to suggest corrective actions. It is crucial to do a thorough analysis and not start wright away making ad-hoc corrections. This is the only way to have a solid ground, for any following project regarding data correction (cleaning, deduplication, format homogenization), data organizing and matching, as well as for any system reconfiguration to enforce or control quality rules.

Sales & marketing people usually have a good idea of data quality issues in their CRM, even before such an analysis. There are some cases though where quality issues are hidden the outcome of this analysis may be surprising. Furthermore, is impressive to see the whole picture, with accurate numbers and comparisons and it is certainly more persuasive when you want to present to management or other stakeholders. A good analysis / assessment will give some accurate quantitative, analytical, verifiable, and consistent results, describing the nature and extend of all quality problems. Every conclusion must be clear, justified and well documented.

A data quality assessment is not a one-time job, it must be done from time to time, in order to control and maintain data quality. It is not always necessary to make a big project out of it, mainly in the case of a small business. It is though imperative to do a minimum effort, just what is necessary to maintain quality at a level. For example, an accurate report of empty fields and duplicates are some of the basics that every business must do as a minimum effort to control the quality. Any business holding a CRM must be able to perform such actions regularly whether this is insourced or outsourced.

Next steps

The quality assessment is the base of a serious approach, but what is it next?

Resolving existing data quality issues, is usually an urgent matter and must follow as soon as the assessment finishes. This is about actively improving data quality by correcting, enhancing, or deleting the data that does not meet the quality standards or rules. Data quality resolution can be done manually or automatically using various techniques and tools, such as data transformation, data enrichment, data matching, data deduplication, etc.

Most modern CRMs offer some functionalities to help this kind of corrections, in a way that those can be done directly in a system, in the production environment. This is usually possible for limited number of corrections that may be done manually. In some other cases mass / semi-automatic edits are possible inside a CRM. Nevertheless, in most cases data must be exported, processed with the help of algorithms and re-imported to the system.

After correcting what is to be corrected, the priority is to maintain the quality level. Monitor and control processes must be established to identify and prevent potential data quality problems, as well as to evaluate the effectiveness of data quality improvement actions. This can be a CRM configuration such an automatic validation, an automatic report that points out empty fields or specific errors, a more sophisticated report that points out a specific inconsistency or a specific process to evaluate specific dimensions of data quality.

Finally, all those tasks and actions must be brought together, under the umbrella of a holistic data quality approach, a Data Quality Policy. All the company starting from the management must commit to this policy. A long-term plan must be in place, including roles, targets and scheduled actions with the ultimate goal to maintain a CRM with high quality data.