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Improving data quality with better CRM system design

If your CRM system isn't designed for ease of data entry, it can undermine data analysis and decision making. Here's how to promote cleaner data.

Sales analytics is the cornerstone of good customer relationship management (CRM). It's key to educating salespeople...

and their managers about core metrics, such as revenue quotas, the number of customers that have bought a product, or how many calls a sales person makes daily. Better forecasting, hiring and territory alignment can be improved by enhancing sales reporting and analysis within a CRM.

But you need the right system to get this data. So think long and hard about how to design your database and its fields to get the most meaningful numbers. Here are a few steps to consider in designing a CRM system.

Designing your CRM to improve data analysis

Consider the data you really need. Every admin should scrutinize the CRM system to determine what can encourage better-quality data entry. This does not mean every field should be required, but make every required field matter.

My favorite technique is formulas. If there is a way to auto fill fields within a report, it makes sense to do so. You can often pull amounts, territories or product descriptions from other sources within the same databases or from a formula. Auto filling fields helps promote consistency of data and prevents data entry errors , while also requiring less input from the user. As you run reporting, this consistency of data becomes paramount.

Once data entry is set up to work for you, it opens additional CRM options to your business.

Additionally, use record types to auto fill certain fields for certain products and assignments. You can also eliminate unnecessary fields on a page layout that isn't relevant based on the record type of the opportunity or case. By using these techniques, you can reduce error and the need for data clean-up projects down the road.

Define fields for accurate reporting. Good system design promotes better decision making because the decisions are based on more accurate data. IT and the sales team need to define which data goes into which fields. If a company is still developing CRM and an expanding sales force, any gaps in the system's logic can make forecasting and running analytics difficult. If you haven't resolved problem areas in CRM fields, it can skew information and analysis.

For example, I was once responsible for creating weekly sales team reports. My team devised a scoring formula to determine the health of teams and individuals in the sales department. One of the main aspects of the formula was how long the product had been in trial without being closed, won or contracted. The system spit out low scores on certain verticals because everything that lingered in the system after 30 days was given a lower score. Scores for sales reps with sales cycles longer than 30 days were very low, even though many deals ended in a closed-won opportunity after 90 to 120 days, according to past reports. By changing the formula to account for longer sales cycles, we got a more consistent scoring view that allowed us to see real problems rather than skewed results.

Sell the system. Salespeople need to understand how good data entry can make their jobs easier. In selling sales analysis , you must consistently cultivate the relationship, make improvements or lower costs to ensure adoption. Keep your salespeople well-informed by sending them weekly updates with graphs and other charts on last week's closed opportunities, sales in progress and other data. Salespeople will understand the benefit when they see how it saves time they once wasted searching through multiple spreadsheets. You can make this information accessible from anywhere through customized CRM mobile applications, making it easier to institute tactics like gamification  to drive sales.

Once data entry is set up to work for you, it opens additional CRM options to your business. You can locate new vertical potentials and redirect territories to better meet the needs of your customers; forecasting is more accurate and sales reps can be better-trained, thanks to more accurate reporting.

Next Steps

Why over-engineering of mobile CRM is a nonstarter

CRM data integration big obstacle to mobile CRM success

This was last published in February 2015

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Adoption and ownership are very important when it comes to the CRM. I like how you outlined ways to improve data quality as well as what the benefits. As you mentioned testing and monitoring can be very important when it comes to reporting as well as data input and it should be emphasized that everyone benefits... not just the managers.

Marika @ RadiumCRM
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I don’t think that too much emphasis can be placed on selling the system to the people that are going to use it. Sadly, I’ve seen too many instances where a system, CRM or otherwise, was pushed on the users with no attempt to sell it to them at all. One of the better ways that I’ve seen to help sell a new system is to actively seek out their input and incorporate it into the design process when you consider the data you really need and define the fields for accurate reporting. That inclusion not only gives them a sense of ownership in the new system (“I helped design this”), but it also helps sell it because they know why each piece of data is being recorded, and the benefit that it provides.
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