Why Startups Fail by Tom Eisenmann

Author:Tom Eisenmann [Eisenmann, Tom]
Language: eng
Format: epub
Publisher: Crown
Published: 2021-03-30T00:00:00+00:00

Cumulative Free-to-Paid Conversion Rate for Cohorts Acquired through Google AdWords

One problem with using cohort analysis to assess saturation risk is that by the time a trend is evident, saturation has already occurred. My HBS colleague Mark Roberge points out that most measures of cohort performance—for example, subscriber retention rates and free-to-paid conversion rates—are lagging indicators of customer satisfaction and engagement. If you focus solely on retention rates, you don’t know you have a problem until an unhappy customer disappears. One solution is to track Net Promoter Scores. An NPS survey asks, on a scale of 0 to 10, how likely a customer is to refer the product to a friend or colleague. The score is calculated as the percentage of all customers who are “promoters” (scoring 9 or 10), minus the percentage who are “detractors” (scoring 0–6). NPS scores over 50 are considered excellent. A declining NPS can serve as an early warning sign of problems and can allow managers to take corrective actions before severe damage is done.

Roberge recommends that scaling startups go a step further and focus their cohort analysis on early indicators that are both 1) highly predictive of long-term customer satisfaction and 2) observable soon after customers are acquired. As an example, Roberge’s former employer HubSpot, a marketing services startup, tracks the percentage of new customers that use at least five of the HubSpot platform’s twenty-five features within sixty days of signing up. This measure is strongly correlated with long-term customer retention and spending. When the result exceeds 80 percent, HubSpot management believes a cohort is staying “on the rails.”

Beyond giving startups an early warning, indicators linked to product use can provide more focused solutions than a broad measure of satisfaction like NPS. Every function within a startup affects NPS in some way, so any downward trend requires further analysis to figure out which function to address. By contrast, there are far fewer ways to activate feature use by new customers, so managers can decide on corrective action more quickly.

Conducting cohort analysis will help entrepreneurs avoid the temptation to inflate their LTV calculations—say, with overoptimistic estimates of retention rates or average order size. They should also use cohort analysis to track CAC—customer acquisition cost—over time, by customer segment and marketing method. That way, they’re sure to keep their LTV/CAC ratios in sharp focus. To continue with the earlier “freemium” example, the table below shows how the cost of acquiring a free user using Google AdWords is rising. CAC for the three most recent cohorts has roughly doubled, compared to CAC for earlier cohorts.

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