Data-Driven Decision Making: Avoiding a Dreaded Data Mirage

Image by David Feldman

The data doesn’t lie, we’re often told. But the assumptions we make about their intersections are sometimes blurry out of context, like a mirage shimmering in the desert’s dust.

I didn’t hear much said at Dreamforce 2019 about incorporating third-party data like customer service inquiries, aggregated product utilization stats or peer review site data into the process of driving growth. In my view, this type of data would provide that context.

Granted, these are still early days for sales enablement, which is growing fast alongside sales engagement (the management of repeatable, multichannel sales sequences) and sales readiness (the process of helping reps prepare for customer interactions through quick, microlearning and video training).

The idea is to integrate your lead to revenue stack and optimize based on the data, because if you have data on which deals convert, you can get a better sense of what works and what doesn’t. But knowing what good looks like still requires you make certain assumptions.

And they could be wrong. Just because two metrics move in the same direction, doesn’t mean they’re causally related. You’d need to put the data in the broader context against third-party data sets.

For example, if the same case study keeps getting sent to prospects who buy, you might assume it’s your best case study. But what if it’s just the case study your top performing reps prefer?

You might assume the case study is responsible for driving close rates, but in fact, it might be due to some other factor you’re not measuring, like that rep’s interpersonal skills or perhaps a contact at the company in the case study who’s tight with the rep and willing to talk to prospects.

Difficulties in interpreting data led John Bogle to create index funds, which favor passive over active investment strategies.

Bogle believed there were just too many invisible factors out there to beat the performance of the broader market through actively managed investing and created index funds — which allow investors to buy shares of a broader index like the NASDAQ Composite, the Dow Jones Industrial Average, or the S&P 500 — as a way of achieving diversification and lowering costs.

It’s fair to say the jury is still out on whether or not data-driven decision makers can beat the odds. But with so many venture backed companies stumping for exponential returns, it’s safe to assume data-drive sales and marketing will increase in popularity as leaders trade efficiency for growth despite uncertainty.

The promise of artificial intelligence is largely steeped on the hope that machine learning will someday more accurately interpret a multitude of data points and identify patterns that lead to profitability than humans can.

But since AI can’t predict when patterns change, gut instinct and chance will continue to play and important role in business decision making, despite the data.

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