"The data quality in my CRM is bad, I don't trust the performance metrics" This is a line we hear often from sales management. This leads to the vicious cycle where efforts on improving data quality are not sustained because short term insights will always be flawed, thus eliminating any hope for a long term resolution.
There are obvious implications on how this impacts the productivity of the sales organization:
Creates parallel work streams, work duplication and increases time spent on overhead forecast meetings (and less time with customers).
Makes difficult other core sales processes like Account Territory Management and Forecast Planning (Quota Setting).
Limits adoption of modern analytics tools that could increase effectiveness of sales and marketing.
This can lead to stress and frustration, in particular to sales managers who are stuck in the middle between their teams and upper management. It's imperative to have a strategy in place that can break the vicious cycle, with a long term vision.
Who is to blame?
This is certainly a challenge not exclusive to Customer Relationship Management platforms and Sales organizations. Many other enterprise software applications that rely on heavy user data entry go through this. Take for example Accounting, and proper classification of expenses and income or Online Lead prospecting and correct profiling of customer data. In each case motivators and degrees of success vary (Legal Compliance, Customer Value, etc.).
In the case of a CRM there are specific factors that drive poor data quality:
CRM value to seller: If the main responsible for data entry sees no value of doing this in the first place it becomes an uphill battle. Sellers often perceive the CRM as a pure control mechanism, and choose to keep some of the cards under their sleeve. In addition, raising the quota on those who overachieved, without proper account planning can result in an impossible goal, impacting seller's compensation and morale and his/her desire to be transparent.
Poor CRM implementation: Going overboard on what the company requires as fields in the CRM is also a barrier. Many assume the more detailed data the better, but more fields also translates to user fatigue and more "garbage in, garbage out". Often, it's not clear to the seller how some fields are being used to improve his/her job which makes it even less likely that it will be entered correctly.
Lack of data driven culture: The organization failed establish clear expectations and accountabilities from the top of the management chain. Think about a scenario where the CFO doesn't use the official ERP for reporting rather relying on a parallel process and data. Aside from the clear compliance issues, you can see how this would quickly undermine data quality in the system of record.
Lack of proper, end to end process to improve data quality: Even if upper management is committed to the CRM data quality, there needs to be a process that cascades this down to the seller level. This is connected to proper Sales Enablement & Readiness, Sales Process adoption and of course the Pipeline Management discipline as the first moment of truth.
How to fix it (and sustain it)
There are several ways companies choose to address the CRM data quality issue:
Drive broad data cleansing effort
We've seen organizations, usually big ones, drive a one-time push to clean existing data using typically a 3rd party vendor resource to not take away time from the sellers. While this might help in the short term, if the other core issues are not addressed, then it quickly returns to stale data. Also the process itself can be expensive, and the actual accuracy of the data might not be the best. This is best used for Account and Contact profile completeness.
Tie data quality to seller compensation
As they say "Compensation drives behaviors". Some companies choose to go this route and while the carrot/stick approach seems as something that can help, it also has its challenges. There could be legal compliance issues (check with HR) depending on how the role is defined. It can also be difficult to carry out (in particular with a poor CRM implementation), and may lead to data completeness vs actual data quality if not supported through a process. Finally, it can be challenging to find the right balance between the compensation impact of quota attainment vs missing on data quality, i.e. at what point it becomes effective and does not drive the wrong behavior.
Better yet, drive a comprehensive approach
A multi-pronged approach is needed to address the root causes above. It's important to internalize that it's a journey. This means having the patience to understand short term benefit might be limited and also that it's a constant effort vs. a one and done.
Rationalize CRM data entry: Do a comprehensive review of the core fields you need, i.e. what's the minimal profile for Accounts, Contacts, Opportunities and other core data. Both Sales and Marketing have to be able to articulate the value of additional fields added in terms of benefit to the seller ideally or to the sales organization, otherwise it should be scrapped. Even if it's valuable, ask if it's something that can be obtained elsewhere with services like LinkedIn or Dun and Bradstreet. Often the fields are not the issue but the number of domain values (e.g. asking for a 6 digit NAICS industry classification). Establishing a simple governance of process and taxonomy, avoids proliferation over time of unnecessary fields.
Have a consistent Pipeline Management process: Data quality is core to the pipeline management discussion. The Pipeline Management Review is the fundamental process where CRM data will be consumed and data quality will be improved. Data quality needs to be discussed during the pipeline reviews between sales managers and sellers, and the later need to be held accountable as part of the follow-up process. In addition, a well-run pipeline review discussion with proper coaching based on insights gathered from the data creates a positive value exchange for the seller that helps lands the value of data quality overtime (i.e. I can't help you if I don't know what the problem is).
Secure Executive Sponsorship: Sales leadership needs to be clear on establishing the CRM as the single version of the truth. Successful, data driven organizations, do not tolerate secondary systems, offline spreadsheets and post-it notes to drive sales performance discussions. There has to be a clear message from the top on accountability on CRM transparency and accuracy and walk the talk even during high pressure situations.
Have a Sales Process: A well-defined sales process, where each sales stage has activities and verifiable outcomes, will help with data quality as well as it provides structure to the data entry and CRM usage.
Drive Readiness: Once there is clarity on priorities and accountabilities, there needs to be a comprehensive readiness effort to make sure the sellers understand what is required and more important why.
Land value of CRM to the seller: Clearly there is value for the sales organization to use a CRM, and definitively the need for transparency and predictability for the main growth driver for the company is a huge argument. But there is more than just the "necessary evil" as value to the seller:
Keep organized: As a productivity tool, especially with integration to email, it helps the seller organize and structure. Keeping track of conversations and next steps.
Facilitate collaboration: In sales processes where a team is involved it helps keep everyone up to speed on activities and next steps. It can also avoid the awkward situation where another seller creates a new opportunity in the account, putting at risk the existing one.
Proper coaching: As mentioned above, only good data can help a sales manager provide proper coaching to accelerate deals and help the seller be successful.
Proof of Execution: Great data quality in the CRM can help the seller keep a record and tell their sales manager the story of the work that has been done in the account.
Help Marketing work for you: Having good data allows marketing teams to design better campaigns that can help create new leads or assist in moving an opportunity through the pipeline.
Although keeping data quality in the CRM is not easy, the cost to the sales organization of not doing it is greater. Companies challenged in that area, need to cut the vicious cycle and start progressively improving, with a long term mindset. With the right combination of organizational support, process and tools, data quality will improve and both sales and marketing organizations will reap the benefits by creating a virtuous cycle.