Smart Applications: Putting Big Data to Work

August 25, 2015 Tim Girgenti

Data science and analytics are changing how businesses interact with customers by helping to determine which data is valuable and actionable and which is simply noise. In this quick Q&A, Tim Girgenti, Chief Strategy Officer at PROS, shares his insights on why analytics and data-driven smart applications are changing the way we do business.

Q: Is this an exciting time to be involved in analytics?

A: Absolutely. It’s great to see the widespread acceptance of data science and analytics as a way to help businesses improve both top- and bottom-line performance.

Big data has been a buzzword for several years now in the B2B space, but now we are seeing it move from something poured over by business analysts creating dense reports to the fuel that powers our interactions with customers in the marketplace. Big data is powering a new age of smart applications that combine automation, analytics and data science in one package. Automation alone is no longer enough. Smart applications that provide real-time, actionable recommendations are more relevant now than ever.

PROS has a customer in the food distribution business that sells food and equipment to restaurants. When their sales reps create a quote in their sales tool, the quote is populated with data science-driven price recommendations that help reps determine willingness to pay. Intelligent upsell and cross-sell recommendation are also provided to help boost deal value. With PROS, customers bought on average 400 more items per month, and the vendor’s margins increased more than 260 basis points. Customers are happier, and sales reps are selling more. That’s the power of leveraging data science and analytics on the front lines of the sales cycle.

Q: Why should companies invest resources into an analytics platform?

A: The business value gained from investing in data science and analytics is too great to ignore. Today, purely retrospective analytics don’t provide the results companies are looking to achieve. Companies that invest in data-driven predictive and prescriptive analytics are more likely to gain a competitive advantage.

In a study by PwC, companies that described themselves as proficient in using demand analytics estimated they outperformed their peers in sales, margin and profit growth by more than two times, and also showed an 8x better total shareholder return on capital. At the opposite end of the spectrum–93% of the companies that didn’t invest in analytics admitted to lagging their competition. This is why smart applications that put data into action are starting to generate a lot in the interest in the market right now.

Q: Big data is not a misnomer — the amount of data being produced in the business world is enormous and continuing to expand exponentially. Does having more data improve analytics or just create more noise?

A: This is the very reason data science is important. With so much data available, data science helps us separate the truly valuable data from the noise.

For example, a food company identified that customers who bought salmon were also more likely to buy cream, so they created bundled offers. Another example is a car rental company that was able to improve their demand forecasts once they identified weather as a key attribute in driving demand. In these cases, data science helped distinguish the factors that mattered from those that didn’t, resulting in a more predictable and profitable outcome for the business.

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