Are you making these mistakes with your SaaS metrics?

Anyone who tries to convince you that the numbers aren’t important is trying to hide something.

I want to focus on some points raised by Drew Oetting, partner at 8VC in his article entitled – Metrics: The Writing on the Wall – as I think Drew raises a number of key nuances concerning metrics in technology companies from an extremely educated viewpoint.

Think about the following and health check the way your firm delegates, monitors and achieves improvement around key business metrics.

Don’t Operationialize Reporting Metrics

Operational vs. reporting metrics – if you don’t already, you need to know the difference.

In my own words – reporting metrics refer to figures you need to watch and are commonly requested by your board like gross margin, customer churn and monthly recurring revenue.

Operational metrics are more day to day numbers that affect these headline metrics – such as unique visitors, average time on site and number of new signups.

I agree with Drew in that many companies commonly don’t understand the difference between operational and reporting metrics and often let concerns over particular reporting metrics impact key decision making in an adverse sense.

Taking some inspiration from the types of data above – a mandate to increase monthly recurring revenue might lead to a decision to increase price levels by 5% on the next billing cycle. This is an example of what Oetting calls – attempting to “operationalize around reporting metrics.”  Instead a firm may find more success in focusing on improving operational metrics at the bottom level – expansion MMR,  net new leads e.t.c which if increased should result in more revenue over time.

I tend to remember the difference as focusing on the base/foundations of the house rather than the view from the roof.

Overcome complexity with more resources

Even if you breakdown reporting metrics into their operational parts Oetting raises two key concerns. The first is that attempts to improve particular metrics may grossly affect other metrics at the same time – a problem we call interdependence.

An example of this would be that if based on customer feedback, discounting was recommended to improve satisfaction. Although this might boost retention and increase NPS scores – account managers may start to miss renewal targets and staff churn may increase.

Secondly – the way metrics influence certain outcomes may be non-linear or non-continuous. In other words, adjusting a particular metric may not always result in a predictable level of change. An example of a non-continous metrics is: if currently on site conversion enquiry to sale conversion is 2%,  simply increasing enquiries by X% may not cause conversion to hold at 2%, as factors like the type of traffic and the increased demand on the sales team may actually cause this rate to fall.

Similarly – an example of a non-linear metric is that an increase in consumer happiness from 8.0 to 9.0 may result in higher retention levels than a jump from 7.0 to 8.0- I call this the relativity phenomena.

Please get in touch with me in the comments if these notions require more explanation but put simply, Oetting and I agree that the way to overcome interdependence and non-linearity/continuity is to invest in in-house data science teams who can map, monitor and predict the changes that different decisions will have on the business as a whole.

Gauge your Business Health by Ease of Reporting

A particularly good example is raised by Oetting, who tells the story of an unnamed PE fund who would ask a company during the diligence process to produce a salesperson-level commission data report across the organization. He states that if the company isn’t able to produce such a document within 24 hours – then this PE firm will never proceed with investment.

At your company – think about the ease in which you can derive answers to data-related problems. If it is complex or simply an unknown – it is highly likely you don’t have the right structure in place conducive for growth and success.

Can you find owners for all your metrics?

If you don’t know who owns a particular metric within your firm, or your CEO has failed to assign individuals to drive particular areas – the likely fact is that no one is probably owning these tasks and as an organization you are bound to suffer. We talked earlier about how these metrics are often intertwined – if one KPI starts to outshine the rest, not only is it potentially indicative of organizational inefficiencies but could create bottlenecks and issues for other departments.

Conduct regular meetings and make sure you know who is managing what and touch base regularly to make sure everyone is achieving the desired goals.

Summary –

Understand that data-orientated business improvement comes from a bottom level commitment to improving organizational metrics. These need to be owned by particular individuals and/or teams to make sure targets are being met and their needs to be communication company-wide to ensure that these targets are met in unison to avoid distortions in the model. Furthermore – invest in a data science team to avoid potential concerns over decision making having ripple effects throughout the entire organization. Take the points above and give yourself a health check – you just might need it.

About The Author

Dailius Wilson

Dailius Wilson is the 24 year old founder of – helping the world's top SaaS companies to optimise their sales and marketing efforts. Dailius is currently a Director at TrustRadius and a digital blogger at Dailius was named as one of the Top 30 Entrepreneurs in Australia for 2015 by Anthill Online and was ranked in the Top 100 SEO Experts in the World. Dailius has also been a guest on the Ellen Degeneres Show and has over 10,000,000 views on Youtube