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Platform metrics: Some ideas

I was chatting with Lauri [1] about platform research. I claimed that the research has not that many implications for real-world companies apart from the basic constructs of network effects, two-sidedness, tipping, marquee users, strategies such as envelopment, and of course many challenges, including chicken-and-egg problems, monetization dilemma, and remora’s curse (see my dissertation on startup dilemmas for more insights on those…).

But then I got to think that metrics are kind of overlooked. Most of the platform literature comes from economics and is very theoretical and math-oriented. Yet, it’s somehow apart from practical Web analytics and digital metrics. Those kind of metrics, however, are very important for platform entrepreneurs and startup founders.

On the face of it, it seems that the only difference that platforms have compared to “normal” businesses is their two-sidedness. If we have supply side (Side A) and demand side (Side B), then the metrics could be just identical for each and the main thing is to keep track of the metrics for both sides.

However, there are some dynamics at play. The company has one goal, one budget, one strategy, at least typically. That means those metrics, even though can be computed separately, are interconnected.

Here are some examples of platform metrics:

  • Number of Users/Customers (Side A, Side B)
  • Revenue (Side A, Side B)
  • Growth of Revenue (Side A, Side B)
  • Cross-correlation of Number of Users and Revenue (e.g., Side A users => Side B revenue)
  • Cost per User/Customer Acquisition (Side A, Side B)
  • Support cost (Side A, Side B)
  • Average User/Customer Lifetime (Side A, Side B)
  • Average Transaction Value (Side A, Side B)
  • Engagement Volume (Side A, Side B)
  • Profitability distribution (Side A, Side B)

Note the cross-correlation example. Basically, all the metrics can be cross-correlated to analyze how different metrics of each side affect each other. Moreover, this can be done in different time periods to increase robustness of the findings. Such correlations can reveal important information about the dynamics of network effects and tell, for example, whether to focus on adding Side A or Side B at a given point in time. A typical example is solving the cold start problem by hitting critical mass, i.e., the minimum number of users required for network effects to take place (essentially, the number of users needed for the platform to be useful). Before this point is reached, all other metrics can look grim; however, after reaching that point, the line charts of other metrics should turn from flat line to linear or exponential growth, and the platform should ideally become self-sustainable.

Basic metrics can also be used to calculate profitability, e.g.

Average Transaction Value x Average Customer Lifetime > Cost per Customer Acquisition

Business models of many startup platforms are geared towards “nickel economics,” meaning that the average transaction values are very low. In such situations, the customer acquisition cost has to be low as well, or the frequency of transactions extremely high. When these rules are violated, the whole business model does not make sense. This is partly because of competitive nature of the market, requiring sizable budgets for actual user/customer acquisition. For platforms, the situation is even more serious than for other businesses because network effects require the existence of critical mass that costs money to achieve.

In real world, customer acquisition cost (CPA) cannot usually be ignored, apart from few outliers. The CPA structure might also differ between the platform sides, and it is not self-evident what type of customer acquisition strategies yield the lowest CPAs. In fact, it is an empirical question. A highly skilled sales force can bring in new suppliers at a lower CPA than a digital marketer that lacks the skills or organic demand. Then again, under lucrative conditions, the CPA of digital advertising can be minuscule compared to sales people due to its property of scaling.

However, as apparent from the previous list, relationship durations also matter. For example, many consumers can be fickle but supplier relationships can last for years. This means that suppliers can generate revenue over a longer period of time than consumers, possibly turning a higher acquisition cost into more a more profitable investment. Therefore, churn of each side should be considered. Moreover, there are costs associated with providing customer support for each side. As Lauri noted based on his first-hand experience working for a platform company, the frequency and cost per customer encounter differ vastly by side, and require different kind of expertise from the company.

In cases where the platform has an indirect business model, also called subvention because one side is subventing the cost of the other, the set of metrics should be different. For example, if only Side B (paid users) is paying the platform but is doing so because there is Side A (free users), Side B could be monitored with financial metrics and Side A with engagement metrics.

Finally, profitability distribution refers to uneven distribution of profitable players in each market side. This structure is important to be aware of. For example, in e-commerce it is typical that there are a few “killer products” that account for a relatively large share of sales value, but the majority of the total sales value is generated by hundreds or thousands of products with small individual sales. Understanding this dynamics of adding both killer products and average products (or complements, in using platform terms) is crucial for managing the platform growth.


[1] Lauri Pitkänen, my best friend.

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