Three ways big data is helping companies make more money

SAN FRANCISCO — One of the ways big data can help companies is by providing more information on who the best sales leads are — and helping salespeople connect with those leads.

At VentureBeat’s DataBeat conference today, several companies talked about how exactly they are optimizing their sales efforts through the use of data.

Demandforce and InsideSales.com are both focus on business-to-business sales, while Bonobos and Intuit are consumer companies. But in all four cases, the companies found that analyzing and using data intelligently allowed them to increase sales contacts and conversions. Here’s how.

Increasing sales lead contacts

Demandforce and InsideSales.com use data to help their sales representatives be more efficient — and less annoying to the folks they reach out to. In short, these companies collect and analyze data about their sales teams’ interactions with prospective leads to optimize their salespeople’s efforts. They look at factors such as time of day, weather, stock market conditions, current events, and so on.

For example, Demandforce saw a 10 percent increases in contact rate and a 15 percent increase in decision-maker contact rate through tracking and analyzing its lead development representatives’ phone calls.

“Demandforce is not a huge company, but we’ve been able to leverage data to drive sales,” said head of business applications Andre Pimentel.

“Science… can be applied to improve human performance. To get results from big data, you [have] got to know from interactions with your data sets, what are the outcomes?” said InsideSales vice president of R&D and software architecture Rob Christensen.

Insidesales.com invests heavily in a particular kind of science: continuous learning, which posits the idea that continuous iterations on those models and machine learning refines these models over time.

Christensen also pointed out that while the company puts in a lot of resources into creating these models, its sales people “don’t even see the data” and are simply provided with tools they can immediately and easily get to work with without worrying about overwhelming amounts of data. –KK

Keeping data efficiency high

Internet giants like Netflix and Groupon aren’t that old, but in this fast-moving tech world, they’ve already latched on to some legacy data technologies. After working as a business intelligence manager at those companies, David Glueck saw his move to online menswear retailer Bonobos as a chance to start fresh, to question some of the assumptions his former employers made.

At Bonobos, Glueck embraced shared services and open-source software, which require less cash up-front then on-premises services and proprietary software, he argued. As the senior director of data science and engineering team at Bonobos, Glueck selected Amazon Redshift for data processing, SnapLogic for data integration, and GoodData for data accessibility.

Cloud-based services like these are cheaper, because you don’t frontload all the costs and you don’t need a huge IT team to manage them. By starting with a green field (Glueck was the only member of the data science team for six months), Bonobos has been able to keep its costs down and its efficiency high.

Now, let’s wait another five years until the tech ecosystem changes again and Bonobos seems behind the curve. –EB

Oh, yes, your tax data

Intuit wants to get people to follow through the process of filing for tax returns online. So it simplified the process of entering one string of letters and numbers from the paper version of a W-2 form — because usage was falling off at that step.

Engineers built a “lookup service” to enhance its Intuit’s TurboTax product. The service accepts a company name and automatically spits out the proper code for the nebulous Box B of the Internal Revenue Service’s Form W-2.

The feature resulted in “a 12 percent improvement in conversion,” said Chris Chapo, Intuit’s vice president of data science and analytics. –JN