One of the biggest challenges vexing sales teams today is the amount of information and time it takes to conduct research about prospects. In fact, over 70% of sales leaders believe better data would help them win more deals and over 85% have admitted to missing sales opportunities due to information overload.

Rather than getting bogged down by information, some salespeople are getting nerdy and turning to advanced data science to do the dirty work for them. Data science uses predictive algorithms and machine learning techniques to find hidden gems within massive amounts of data.

Here are five things salespeople can avoid wasting their time on with the help of data science:

1. Researching Prospects

The typical sales rep searches up to 15 different sources before making a phone call to a prospect, according to CSO Insights. That eats up a lot of time. In fact, Aberdeen reports that up to 24% of a sales rep’s workweek is dedicated to research and other administrative tasks, as opposed to selling. Data science can free up sales reps by identifying the attributes of a contact and account that actually indicate buying intent for them.

2. Prepping for Sales Pitches

The best sales reps are the ones who take the time to find out what really resonates with their prospects. Data science replicates the habits of the best sales reps by offering immediate insight into the latest news and happenings within an account.

3. Manually Evaluating Leads

Many sales and marketing pros rely on their instinct to identify the best leads. Sophisticated organizations focus on ranking and routing the marketing qualified leads (MQLs) to sales based on a combination of attributes related to fit (or firmographic information) and activity (or response to marketing program) data.

But SiriusDecisions estimates that about 94% of MQLs will never close, indicating that we aren’t making the right level of progress. Data science improves the lead scoring process by identifying the right fingerprint or combination of predictive buying signals so that marketing and sales can get on the same page when it comes to defining an MQL.

4. Building Your Own Pipeline

Once the definition of an MQL is determined, marketers can then focus on passing the hottest leads to sales, even if the number of MQLs dips to a lower amount. While this sounds counterintuitive, this allows sales reps to focus on the opportunities that are most likely to close within a given quarter and more accurately forecast pipeline.

5. Identifying Selling Opportunities

For most companies, at least 50% of revenue comes from existing customers, yet most demand generation activities focus on new customer acquisition. Data science helps salespeople identify the most likely opportunities for cross-sell and retention when it comes to the existing customer base.

The benefits of data science used to just be reserved for PhDs but now are accessible to everyone to do their jobs more intelligently. Now everyone can embrace his or her inner nerd.

About the Author

Shashi Upadhyay is CEO and founder of Lattice Engines, a fast-growing provider of predictive applications that is helping companies such as DocuSign, Mindjet, Pure Storage, RingCentral, Bank of America and Staples to increase their top line revenue. Lattice is based in San Mateo, CA and is backed by NEA and Sequoia Capital.

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