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...
Continue Reading This Article
Enjoy this article as well as all of our content, including E-Guides, news, tips and more.
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.
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.
Why over-engineering of mobile CRM is a nonstarter
CRM data integration big obstacle to mobile CRM success