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August 07, 2015
Foundations of Data-driven Selling: Analyze
In our last post, we talked about how collecting rep and customer activity data along with CRM is the first step to running a true data-driven sales organization. Synthesizing CRM, mail & calendar data can give you new levels of visibility into deals and the forecast like never before. When we think ‘analyze’ — we think about it along 3 dimensions — historical, real-time & predictive. A data driven sales organization needs to employ technology that enables meaningful insights across all 3 dimensions of your deals. Let’s dive in to see how these insights can help drive better forecasting.
From talking to 100s of companies, we’ve heard that reps would love a ‘crystal ball’ for every time they are asked by their manager to commit to a sales forecast number. To say that forecasting is the bane of existence of most sales managers and leaders is a bit of an understatement. How valuable would it be for a sales manager, to know what the team is going to sell every month and quarter and which sales reps are accurately forecasting their number and which reps aren’t?
Accurate and consistent forecasting provides a strong grasp on what is happening in the sales cycle, how the teams are going to bring in a deal and when Here are 3 ways to analyze data to create an accurate data-driven sales forecast:
Embrace your history: There is simply no way of being able to effectively forecast and manage predictable revenue without having full visibility into what happened in the past. Examine your historical deals and learn —
- What is the customer buyer journey and how long is my sales cycle?
- Where do deals historically get stuck?
- What kind of customer and rep activity occur in different stages in the sales cycles?
Know your trends: Look for trends to understand patterns —
- How long does it take for reps to get to negotiation phase?
- How many days in each stage is typical for a successful close?
- How is this quarter compared to your last quarter?
- What is happening in certain regions, product lines or reps and how are each of them trending?
Apply for the future: Comb through the current pipeline deals and apply data trends —
- Which deals are statistically expected to close at which stage, and which are at risk?
- Which deals in the pipeline have patterns that look like they’ll close as a win / loss?
- Which deals in the pipeline have patterns that look like they’ll be pushed out?
The key to ensuring you’re getting value from your data is by understanding the “what” behind it. Gaurav Kataria, guest lecturer at Stanford on Data-driven decision making, states in his Data Science lessons from the field that data science isn’t about numbers — its about constantly iterating and adapting to the trends, predicting and changing the future. Sales needs to be at the helm.Check back here for our next post on making insights actionable for the entire sales team.