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This article was published on July 6, 2015

3 data science trends that can supercharge your sales

3 data science trends that can supercharge your sales
Ritika Puri
Story by

Ritika Puri

Ritika Puri is a San Francisco based blogger who writes about trends in business, internet culture, and marketing. She's inspired by the int Ritika Puri is a San Francisco based blogger who writes about trends in business, internet culture, and marketing. She's inspired by the intersection between technology, entrepreneurship, and sociology.

Data science is one of the biggest trends in business. For many teams, however, the concept remains a black box. Positioned at the intersection of engineering and statistics, we often think of data science as closely tied to IT, marketing, analytics, and product. We imagine complex algorithms, messy datasets, and endless lines of statistical code.

What we often overlook is the direct link between data science and sales. At the end of the day, predictive models, datasets, and trend forecasts are about people and processes—not numbers. Data science programs can help sales leaders run their operations more efficiently, focus efforts on the ‘right’ sales prospects, and uncover missed opportunities.  Here are three trends that every sales leader should know.

1 – The ability to generate accurate sales forecasts, faster

For large, complex organizations, sales forecasts are as critical as they are slow and tedious. It’s common for companies to spend months—and hundreds of thousands of dollars—analyzing data without giving sales teams enough lead time to put findings into action.

Recognizing the gaps between information, insight, and enablement, industry leaders like Cisco are finding new ways to analyze data faster. Like many billion-dollar-giants, Cisco uses forecasts to optimize its resources. For the past several years, the company has maintained a collection of 60,000 propensity to buy (P2B) models that it uses to forecast demand for products ranging from routers to IP phones, blade servers, and cable TV boxes.


Image Credit: Ben Taylor

Cisco uses these models to help sales teams focus on the highest-yield initiatives. The problem, however, was that these models took months to deploy—leaving customer-facing teams with very little response and reaction time.

In 2014, Cisco began using a new piece of technology called H2O, which allows data scientists to build, validate, test, and run models faster. Cisco can now run its 60,000 propensity-to-buy models in a matter of hours—giving sales teams more time to learn, create enablement materials, and engage with prospective customers.

2 – The forethought to transform ‘analysis’ into ‘what-if’ optimizations

A recent Forrester report points out that the field of predictive analytics is becoming widespread and that tools are more accessible to businesses than ever before. Companies with the ability to create accurate forecasts will have clear competitive advantages from their counterparts. As Alex Woodie from Datanami puts it:

“The better a company is at predicting what will happen in the future, the better positioned they are to do something about it.”

Recognizing this trend, some technology vendors are creating tools that streamline the gap between ‘analysis’ and scenario based prediction. Rather than modeling trends, companies can use software to test scenarios and optimize channel investments.


One example platform is MarketShare Decision Cloud, a tool that helps marketing teams determine where to invest their efforts and resources. CMOs, campaign managers, and analysts can use this software to evaluate multiple scenarios before making a final decision.

Imagine these same capabilities, applied to complex sales operations.

3 – The opportunity to tell a stronger customer story using licensed data

Companies are often limited to their own perspectives when evaluating their prospect and customer bases. That’s where licensed data comes in—third party datasets can help create more comprehensive, consolidated, and accurate analyses.

NetProspex, for instance, is a vendor that maintains records of good and bad customer contact data. Sales, marketing, and enablement teams work with NetProspex to cross-check their own records for accuracy. This process allows teams to identify ‘bad data’ in their CRMs sooner, thereby increasing sender scores and performance rates.

When companies use NetProspex, they can clearly see what data is outdated or incomplete, thereby supplementing internal information with additional insight.

Of course, organizations will need to vet data quality, compliance, and accuracy before deciding to work with a third-party data vendor. Regardless, there is a clear area of opportunity for sales leaders who are looking to deliver more tailored experiences for their prospects and existing customers.

data flow

Licensed data can supplement the information that you don’t have and help you flesh out your customer story.

Final thoughts

Data science seems like a ‘black box’ because it’s a brand new field. As the space matures, it’s critical for sales teams to position themselves as leaders and stakeholders. At the end of the day, data science isn’t about numbers or even insights. It’s about action, revenue, and people. Sales needs to be at the helm.

Read Next: How data scientists are changing the face of business intelligence

Image credit: Shutterstock

This post first appeared on HireVue.