You’ve committed to a sales strategy that relies on data-driven analytics to get the best results from your sales team…but what if the data you’re analyzing isn’t accurate? It is possible for your sales reps to enter data incorrectly, forget to enter data at all, or even tell you they’re entering data when they’re not.
Besides pushing reps that hate using CRM to log data about their sales calls, you also need to ensure that the data is accurate and clean. If it’s not, the data analysis you’re running is completely useless. So how do you make sure that the data you’re basing your business decisions on is valid?
The first step to tracking the quality of your data is creating exception reports for missing data. This report is a quality assurance check that will alert you to empty fields in Salesforce or another CRM that you require your reps to fill out. A recently-hired rep, for example, may initially skip entire steps when entering data. Below, you can see a screenshot of an emailed automated exception report.
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Luckily, this report is empty, which means your data for the day has been entered cleanly, and is not missing any fields. However, another time a rep might forget to include when they scheduled a meeting, the prospect’s evaluation process, time frame, or budget. If they don’t fill in this data correctly, you will get a report every day that shows they didn’t fill it in. In that case, you can make sure the rep goes back and fills out the empty field.
Data Entry Errors
While it’s important to know if reps are missing data fields, it’s also important to keep an eye on the data reps do enter data into SFDC on a daily basis. Instead of a report that notifies you of an empty field, the InsightSquared activity feed will alert you when a sales rep enters incorrect or incomplete data. You will see the error right away in your feed, notifying you of a data mishap.
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In this feed, you can see that your rep Jon Snow had a high priority data error. He entered the wrong contract end date for a deal, which could compromise the accuracy of your sales forecast for the month. Because of this alert, you can immediately talk to him about the problem and have him fix the error, restoring your data integrity.
These last two techniques help you fix unintentional errors in data entry, but what about the more serious problem of fake or bogus data? Unfortunately, some sales reps will enter false numbers into a CRM to get around what they consider to be an unnecessary part of the job. While data errors will happen and you can train your team to improve, faking numbers can have a more serious detrimental effect and can be much harder to detect.
One of the most effective techniques for spotting bogus data is Benford’s Law, also known as the First-Digit Law. This law shows that the number 1 occurs as the first digit in data about 30% of the time and larger numbers occur much less frequently – 9 is the first digit less than 5% of the time. This technique is often used to discern accounting fraud, rigged elections and even scientific fraud. If you suspect fraud on your team, this law can help you to uncover the more serious problem of dishonest sales reps on your team.
It’s also important to hold your reps accountable for their data entry on a day-to-day basis. Though less serious than faking data, some reps will tell you that they’ve called a prospect or done the work you’ve asked, when they haven’t. In this case, you can see right away what they’ve been up to with a look at their activity dashboard. A red flag would be when you see a rep change the Lead Status in Salesforce (to something like “Attempt 1”) but hasn’t logged any activities.
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In this dashboard, you can check on what Joe Smith has been up to lately. You can see which deals are at risk – in red – and which deals are likely to close – in green. The size of the circle indicates the number of times the rep has contacted the opportunity as well. You can then drill down on each opportunity and see the last time Joe called or emailed the contact at that company. If Joe tells you that he called Opp #379 yesterday, but you see the deal is at risk and the last touch was a week ago – you can hold him accountable.
Overall Data Quality
You also need to keep an eye on the big picture. This data quality dashboard allows you to keep track of all of the data your reps enter into the CRM, showing you the exact percentage of errors in your SFDC database. You want the percentage of errors to stay at a low level – less than 2% ideally.
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In this report, you can see that 98% of the opportunities created by your reps have been error-free. You can see exactly which opportunities have errors, when those errors were made, and how serious the errors are.
With these tools in your data arsenal, you should be able to immediately see the red flags that indicate missing data, data mistakes or just plain bogus data. This will enable you to quickly and accurately correct data errors to keep your sales reps on track and ensure accurate sales analytics for your team.[image source_type=”attachment_id” source_value=”26890″ width=”632″ height=”250″ align=”center” quality=”100″ link=”https://offers.insightsquared.com/right-metrics.html?blog_source=organic&blog_medium=blog&blog_content=blog-body-link&blog_campaign=sales-metrics”]