From the art of selling to the science of growth

By Barbara K. Mednick

Growing top-line revenue is becoming increasingly difficult for many technology companies. Even for traditionally fast growing software-as-a-service (SaaS) companies, the new revenue generated for every dollar spent on sales and marketing has fallen 34 percent since 2011. This creates a tremendous challenge for many companies, which can neither afford to spend additional dollars to sustain the same rate of growth at deteriorating margins, or accept declining growth rates that put their valuation at risk.

“The answer is simple in theory – just focus every dollar on the most promising marketing campaigns, sales leads and customers,” said Jorgen Ericsson, lead corporate strategy partner for Data and Analytics in the Technology, Media and Telecom (TMT) industry sector. “However, this assumes you have a crystal ball that can predict the future with better accuracy than your intuition.”

To overcome this challenge, leading companies are now turning to data science to transform how they market, sell and retain customers. “It is essentially about moving from the ‘art of selling’ to the ‘science of growth’,” said Ericsson. At least two global trends are critical in enabling this:

• First is the ability to digitally capture, store, structure, and combine customer interaction data from multiple sources. Most companies may have CRM, ERP and customer support applications already in place, but the volume and value of their combined data is now growing exponentially.
  
  
• The second is a rapidly maturing field of Artificial Intelligence (AI) called “machine learning”. This allows a computer to examine massive amounts of historical customer interactions, find patterns that are highly correlated with desired outcomes, and then predict what is most likely to happen in the future (and even learn automatically if it makes mistakes).

That’s why KPMG published a new white paper on Data Driven Growth, which outlines how to leverage data and analytics and “machine learning” to accelerate growth by adopting five leading practices:

1)      Establish a virtual collaboration loop between field teams and data scientists

2)      Apply an agile approach to quickly move up the learning curve

3)      Build an appreciation for predictive insights in field teams

4)      Work with currently captured data, while moving in parallel to fill gaps

5)      Think big but start small – transition to broader transformation when opportunities arise

“We have seen companies build machine learning engines that can increase the conversion rate of sales leads by six-fold in a matter of weeks. In other cases, we have seen machine learning help drive 2 to 4 percent growth in topline revenue by shifting 20 percent of the marketing budget away from marketing activities that we predict will underperform,” explained Ericsson.

This paper includes contributions from five KPMG partners and managing directors in the Deal Advisory and Corporate Strategy team: Ajit Dansingani, Per Edin, Jorgen Ericsson, Kevin Jackson, and Joel O’Hair.

To learn more or to arrange an interview with Jorgen Ericsson, please contact Barbara K. Mednick.

Biographies