Data-driven decision making: Unravelling the substance amidst the speculation

Are you really making data-driven decisions?

The buzz phrase of the decade has to be “data-driven decision making”. Now don’t get me wrong, I buy into the promise of being a data-led go-to-market org – through pattern recognition it’s clear what customer preferences are, which allows you to make strategic adjustments towards your business goals. But unfortunately, it seems like the journey towards being a data-driven go-to-market org feels more like a hindrance than a growth multiplier. 

Headwinds to becoming data-driven

When data-driven decision making is executed poorly at the operational level, it can slow the speed of decision making down, which is a huge risk if you’re trying to learn fast as a rapidly growing tech company. Some of the problems I’ve noticed are;

 There’s too much noise in the data – it’s really hard to tell if the lever you pulled had an effect or if the metric moved due to natural variation or something in the macro completely out of your control. During any reporting period there are typically several internal and external shifts that could have an affect on acquisition. Change in performance channel mix, offline events, seasonality, interest rate changes, product releases etc. 

 Teams weaponise data. There’s no agreed upon metrics or centralised reports, this allows numbers to be cherry picked to paint a team (sales, product or marketing) in the most favourable light. This leads to an inordinate amount of time spent squabbling over which department created report is correct.

 A large amount of time is spent on past decisions, without looking objectively at how processes, go-to-market and product can be improved to better serve the customer. Too much time is spent on explaining outcomes (lagging indicators) rather than incrementally improving the inputs that drive the outcome (leading indicators). 

 Quantitative data needs to be overlaid with qualitative data, to get a full picture behind the mechanics of why there is movement in a metric. Time is not allocated to speaking with customers to get the true insight behind why a customer behaved the way they did. There is often a belief that purely looking at the data will paint a rich enough picture. This is rarely if ever the case.

“In the competitive landscape of SaaS, being a data-driven go-to-market organization isn’t just an advantage; it’s the essential compass for strategic decision-making. Data transforms ambiguity into clarity, uncertainty into opportunity, and empowers agile, informed actions. In the world of digital evolution, precision matters, and a data-driven approach ensures your SaaS company not only survives but thrives by understanding and meeting market needs effectively.”

With this quote as a guiding principle toward agile informed decisions, below are some of the ways to close the gap between the promise of data-driven decision making and the complex ambiguous environment that so many organisations get stuck in. 

 Firstly, metrics should be simplified. Looking at 62,000 different metrics will again lead to cherry picking a data point that confirms existing biases. Have L1 should be business outcome metrics which are generally revenue and pipeline generated. L2 can be a more granular deep dive (KPI healthcheck) into the components of pipeline (average deal value, MQL/SQL conversion rates, Cost per SQL) and revenue (average deal value, revenue cut by plan). L3 is a deep dive into channel performance – performance, outbound, partnerships. Don’t get too caught up in an individual channel, as they will all interact together as a growth multiplier if you have spent some time and effort on audience refinement.  

 Centralise reporting. So many times I’ve seen different teams using different reports that don’t match up.In the first instance just pick one location. It might have some degree of lag or be slightly off in some instances but that’s OK. What’s most important to be monitoring is the movement – are we doing better than the last reporting period. 

 Gather qualitative data. For me this is the most important step that is most often overlooked. It’s a mistake I’ve made far too many times. The true insight comes from marrying quantitative and qualitative data. From doing customer interviews, I was able to save a previous company approximately 60% of performance spend, as there was cannibalisation between paid and organic search.

 Less is more. Being focused on one growth lever at a time over a sustained period of time allows you to see with more clarity the effect it is having on acquisition. Doing too many things at once creates noise.