Sales professionals want good wins. Translation – they want to spend their time on opportunities that will close. If given the choice, they will work on the opportunities most likely to close.
Every minute of a sales professional day counts and must make a difference. Stiff market competition and limited resources so dictate. Sales teams should be spending their time on priority opportunities, and sending the least promising opportunities to marketing for their nurturing. But how should sales executives prioritize the time and effort of their teams?
The number one issue I hear from sales executives is that they have no confidence regarding the data in their CRM solutions, and their ability to make good decisions with it. It’s a valid concern. Very frequently CRM sales data is captured either incompletely or inconsistently.
Every minute of a sales professional day counts and must make a difference.
Most sales teams aren’t thinking data quality or analytics as they record information into your CRM solution. In spite of that, every sales person and sales manager wants the following:
– Prioritize their sales pipeline based on outcome (win/loss)
– For deals at risk, tell me why! (So I can correct it)
– For deals that are likely to win, also, tell me why!
Knowing the reasons for win and loss are critical to predict sales, for corrective action, to increase win ratio.
Here is a simple roadmap to predictive analytics with reasons so that you can have insight into your sales pipeline, forecast with confidence and increase your win ratio.
Enrich your CRM data to improve quality. Instead of making decisions based on incomplete data, you can enrich your CRM environment with higher accuracy data from multiple sources. A good example is D&B data or Data.com. These two sources have detailed information on every account. E.g. Revenue size, location, SIC codes, NAIC codes, trends, etc.
Enriching your sales pipeline data with this information can provide a predictive analytics tools the ability to detect patterns in your sales wins and losses. The historic patterns are important for high accuracy forecasting that can provide reasons for the predicted outcomes.
Predict Outcomes based on historic patterns One of the top peeves of sales reps is meeting with their sales manager to review their sales pipeline and forecast. The deals are typically ranked by some scoring system that does not give any insight into why a deal looks good or bad. It’s just a number!
Emcien’s CRM Sales Win/Loss prediction solution leverages historic patterns to predict most likely outcome for the deals in your sales pipeline, and provides the reasons for the prediction. For example, “This deal is at risk because it looks like these deals from the historic pipeline and the common patterns of risk are x, y and z.”
The screen grab shows a typical sales pipeline sorted on opportunities most likely to lose. Deals that are risk share patterns with historic deals that were lost, and that can deliver tremendous insight. In the example shown, the NeoPower opportunity shares risk factors with deals in the past that did not close. An example reason is “No POC” was conducted for this opportunity. This insight can provide the sales rep with a corrective course of action to improve the odds of wining this opportunity.
Emcien’s predictive analytics deliver the ability to rank your opportunities along with the reasons “why” “for the sales prediction. This empowers a sales rep and a sales manager to correct course and convert some of the deals at risk into wins. Knowing why the prediction was made enables the team to tailor their sales strategy to each individual opportunity.
Similarly – not all deal that are marked as wins, are in the clear. Most deals have risks! The worst-case scenario is when a sales rep thinks that the deal is in the bag, and that turns out to not be so. Emcien’s predictive analytics provides risks for all the deals based on historic win/loss patterns. This increases the odds of wining and improves your sales forecast.
Improving CRM data quality, and giving the sales professionals justified recommendations on which opportunities to pursue, why and how, ultimately results in better use of their time, and more wins.
What are your thoughts on how predictive analytics can help you improve your sales wins?