Why stability matters to marketing

In this week's episode of the SaaStr podcast, Shan Sinha explains how iterating on their business model placed a huge tax on the organization, especially the growth function.

Why stability matters to marketing

If you work in SaaS you're likely familiar with the SaaStr podcast. Produced once per week by Harry Stebbings, it provides a lot of practical and actionable tips.

In this week's episode with the CEO of video conferencing company Highfive, Shan Sinha explains how iterating on their business model placed a huge tax on the organization.

Iterating your business model is an incredible tax to your organization. Whenever you change your pricing model, your sales and marketing teams aren't able to take advantage of any consistency or rhythm they were able to build. They feel like the clock just got reset again.

The importance of stability to the sales and marketing function cannot be overstated. The absence of clearly defined, long-term corporate goals (vision, mission, etc) leads to erratic short-term decision making (objectives), which in turn leads to marketing perpetually resetting it's activity. Marketing becomes inefficient, expensive and in extreme cases ineffective.

Whenever you go to forecast from one quarter to the next to build your plan, it's an apples to oranges comparison because the variables from the last quarter are different to the quarter you're about to go though.

For marketing analytics to be meaningful, I believe it's not until we create ratios that we obtain actionable insights. E.g. MQLs are mostly meaningless (aside from their trendline). But Traffic:MQL, CAC:MQL, etc, allow us to create hypothesis and in turn experiment. Too frequent business model iterations, or changing corporate objectives, prevent marketing from establishing a baseline that will generate meaningful insights.

Finally, there's a latency associated with it because you have this quarterly boundary whenever you happen to have a sales effort. So whenever you make a change, you have to wait at least a quarter to see what the results will be.

We can and should run many experiments concurrently, but we do need patience to obtain statistical significance. It's incredibly important for managers to understand that frequent change places a massive tax on the growth function.

Enjoy this week's episode, it's another good one.