For decades, sales managers have forecasted revenue with two things: CRM systems and spreadsheets. It's usually a painful, labor-intensive process riddled with assumptions, hunches, and bias. Let's face it: It's tough for anyone to run a business like that. Without an accurate forecast, how can executives decide where to invest, focus resources, or what number to give the Street?
It doesn't have to be that way anymore. Recent advances in data science and advanced analytics have created new ways for sales managers to get a clear window into their pipeline.
How? Manual data input into CRM systems by reps can now be paired with behavioral data, or what is typically known as "data exhaust," real-time, automatic data collection from sales reps as they work. As I described in my last post, data exhaust includes calendar events, emails sent, and interactions with leads that affect the probability of closing a deal—information that should be entered into a CRM in an ideal world.
Often, this data requires significant processing to convert raw information into insights that matter. Advanced algorithms and probabilistic modeling, infused with signals from data exhaust makes the data inherently complete and unbiased. Even better, the machine learns as time goes on, which improves accuracy of insights and quantification of risk.
That all sounds great, but what does it really mean in terms of quantifiable business value? Data science can optimize revenue in three ways:
1. Identify when and where deals are exposed
We all know that even minor mistakes or missteps can turn a hot lead into a dead lead. Rather than relying on sales reps to enter updates into the CRM system so that sales managers can identify a red flag and step in, data science has learned the pattern of successful deal closure and closely monitors each lead to ensure that it's progressing. Data science can flag the deals at risk much earlier than would normally be identified. You can think of it as a real-time risk barometer for each deal in the pipeline.
2. Pinpoint the right next step to save a deal on the fence
Once a deal is flagged as "at risk," then data exhaust is analyzed in the context of best practices and historical data of successful deal patterns to determine the best next action to get the deal back on track.
3. Score an opportunity and likelihood of close based on history
Machine learning is capable of retroactively studying behaviors and steps taken in successfully closed deals. Rather than relying on biased memories or haphazard CRM entries, data science is capable of distilling the most effective methods used to close deals and use that information to develop a data-based estimate on how likely—and when—a deal will close.
Data exhaust inserts a layer of truth into deal flow data that helps sales execs, managers, and reps understand what's going on in the business at any point in time and to optimize deal flow. By leveraging data science in sales, reps in the field can focus their time and efforts on what they do best—selling—and sales managers can focus on what they do bes—managing sales teams to generate the most revenue possible out of the pipeline each quarter.
Request a demo today to see how our data science can help you close business.