Sales managers spend up to 10 percent of their work week drawing up forecasts, yet 59 percent of sales forecasts are wrong. Too often we are asked to make the data in our CRM look the way we think our management wants it to look - even when the odds aren't good that we'll reach those projections.
Algorithms by themselves can't make your sales predictions more accurate. Computers can automate scheduling, provide reports and charts, but unless you have some heavy-duty sales expertise to go along with it, they provide little value.
This is why your CRM system doesn't solve the problem, and the sales analytics approach doesn't usually do much better. According The TAS Group, CRM systems and sales analytics typically fall short in four ways:
1. It's Hard to Identify Sales KPIs in CRM or Sales Analytics
The first question is, "What data in the CRM really matters?" Because there is so much data, it's hard to identify the key performance indicators (KPIs) that can move the revenue needle. Sales managers don't have time to dig through it all, or often don't know what data they should be looking for. How can we expect sales managers to garner insights when they can't get the data to inform their decisions?
2. It's Hard to Interpret Key Data
There is no shortage of data, reports and cool visualizations out there, but we often don't have enough knowledge-based insight delivered in a consumable manner. It can be very difficult for sales managers and peers to interpret the key data elements, then prescribe effective actions for each sales person. Many times, managers don't have a structured, consistent model to use, and the analysis and interpretations will invariably be subjective and potentially mistaken. Easy-to-access descriptive insights are essential for guidance.
3. CRMs Are Limited in How they Capture and Display Certain Data
Many sales managers don't have an accurate measure of win rate. When they do, they usually only measure it by number of deals they've won rather than their total value. Sales managers also have trouble with measuring average deal size. This is because of inherent limitations in the way CRMs capture data.
If you ask a sales manager their typical deal size, they might answer, "$100k." However, follow up by asking them about their average transaction value and they might not know what you're talking about. This is the language you need to be speaking, though. If your "typical" deal size is $100k, then it's not unusual for it to be followed by an additional $20k for some complementary service or product. The CRM doesn't understand "typical", it understands "average." So when your sales data is infected with a few very small transactions or a very large deal, analysis based on averages is of questionable value.
Additionally, most companies only measure the "won" cycle, but it's equally important to measure how long it takes to lose deals. Most CRMs can't deliver this information. A long "lost cycle" points to wasted resources and potential problems in the sales and qualification processes. Comparing won cycles to lost cycles gives great insight to the effectiveness of sales process execution, but it requires thinking beyond the limitations of what CRMs measure.
4. KPIs and Guidance Are Not Always Available to the Sales Team
Sales performance happens in the world of the sellers and their management. But sometimes analysis is done by operations or data scientists, and is not made available in a format that helps sellers run their business. This is a missed opportunity for businesses to enable sellers to learn best practices in real-time.
More often than not, sellers are remote from their managers, living their lives on their smartphones, connected to the workplace via the cloud. If we don't help them self-improve at their own pace, we slow down how fast they internalize best practices, and place more of a burden on the manager.
CRMs are the backbone of any high performing sales organization, but they're not without their limitations. There are certain things they don't expressly tell sales professionals, and the manner in which they convey information doesn't always make key data apparent as it could be. Managers and their teams have to be vigilant about identifying KPIs, interpreting different meanings in the data, as well as thinking beyond typical CRM categories. No matter how valuable a CRM has become to the process, a little experience and expertise go a long way.