Three economists have looked at whether better data actually raises output, and they don’t agree. All three measured what data did to output at real firms, which is a different question from what a sales productivity software supplier claims its product will do for you.
UPS built its ORION routing system to compute driver delivery sequences from package, map, and vehicle data rather than leaving the order to manual planning, and by December 2015 the system had already saved the company more than $320 million against a projected full deployment cost of $250 million. At full deployment, INFORMS put the expected annual savings at $300 to $400 million, alongside a reduction of 10 million gallons of fuel and 100,000 metric tons of CO2 per year.
Data as a Productive Input For Data-Driven Sales Strategies
Erik Brynjolfsson at the Stanford Digital Economy Lab discovered something important about the impact of data. He argues that data functions as a productive input in its own right that should be separate from IT spending.
In a study of 179 large publicly traded firms with Lorin Hitt and Heekyung Kim, he discovered that companies emphasizing data-driven decision-making had consistently higher output and productivity by about 5 to 6 percent above what their other investments and IT usage would predict.
The practical implication here is that buying systems isn’t what pays off; it’s providing the best productive input possible. Data-driven sales strategies work the same way, which is why a lead generation pricing comparison tells you more about your cost base than the CRM you run it through.
The Case for a One-Off Gain in Sales Productivity
Robert Gordon is more sceptical here. He argues that the third industrial revolution’s productivity effect was a one-off in history, and that the collapse in the price of computer speed and memory, alongside a huge share of GDP going to IT investment, made for a unique period with uniquely high productivity.
When asked if future innovation could bring back this level of productivity, he suggests it’s unlikely. Read across to sales productivity, and the warning is clear enough, because a gain you can only capture once is not a gainyou can budget for annually.
Why The Gap Between Firms Matters For Increasing Sales Efficiency
What detailed production data reveals is the vast gulf between manufacturers. Chad Syverson found that within specific manufacturing industries, the plant in the 90th percentile of productivity distribution produces almost double the output from similar inputs as the plant in the 10th percentile.
His framing of data-driven productivity suggests that the important part is the constraint, and that the constraint is diffusion rather than availability. So how you use the data is more important than what data you have access to.
Increasing sales efficiency follows the same logic, because two teams with identical CRM records will not perform identically.
Improving Productivity Metrics With Expert Sales Insights
Take the three findings together, and the picture is consistent. Data pays, the payoff is real but bounded, and most of the variation comes from practice rather than from access.
The expert sales insights here all point at the same place, which is the gap between the team that acts on a report and the team that files it.
Sales data analysis is only ever as good as what the team does next.
This article was prepared by an independent contributor which helps us continue delivering quality content to our audiences.
