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3 Ways Data Science and Data Exhaust Can Optimize Revenue
Get a clear window into sales pipelines.
Posted Nov 7, 2014
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For decades, sales managers have forecasted revenue with two tools, CRM systems and spreadsheets. This is 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 know 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? 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. That data includes calendar events, emails they send, interactions with leads that affect the probability of closing a deal—information that, in an ideal world, should be entered into a CRM system. Often this data requires significant processing to be converted from raw information into insights that matter. Advanced algorithms and probabilistic modeling, infused with signals from data exhaust, make the data inherently complete and unbiased. Even better, the machine learns as time goes on, improving 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. By identifying where and when 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 they would normally be identified. Think of it as a real-time risk barometer for each deal in the pipeline.
  2. By pinpointing the right next step to save a deal when it's on the fence. Once a deal is flagged as at risk, 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. By scoring the 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 foreclosing deals and using 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 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 best, managing sales teams to generate the most revenue possible out of the pipeline each quarter.


Venkat Rangan is the chief technical officer at Clari, to which he brings more than 30 years of technology innovation and leadership experience. He is the cofounder and former chief technical officer of e-discovery company Clearwell Systems.


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