Data-Driven Decision Making: Avoiding a Dreaded Data Mirage
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 about incorporating third-party data like customer service inquiries, aggregated product utilization stats, or peer review site data into the marketing automation process for driving growth. I believe importing this type of data into Hubspot via Zapier would provide that context.
Granted, these are still early days for marketing automation and 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 better understand what works and what doesn’t. But knowing what good looks like still requires you to 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 to triangulate causality.
For example, if the same digital marketing case study gets 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 do not measure, like that rep’s interpersonal or digital marketing 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 interpreting data led John Bogle to create index funds, favoring 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-driven sales and marketing will increase in popularity as leaders trade efficiency for growth despite uncertainty.
The promise of artificial intelligence is steeped mainly in 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 yet, gut instinct and chance will continue to play an important role in business decision-making despite the data.