Platformit — Part Ten — Platform’s Multi-Dimensional Measurement Framework

In “Measurminator,” my objective was to propose a new measuring framework: If business models are changing, distribution channels are changing, customers’ behaviors are changing, shareholders and investors’ appetites are changing, then, the common sense dictates that measurement ideology, too, must change.

Conventional metrics (ratios) are designed to fit into columns (Excel) or charts (PowerPoint) to conduct basic (unproductive) comparisons. Allow me to whisper to you so that your CFO don’t get offended: comparing current quarter financial results with the preceding quarter has nothing to do with the famous notion of ‘comparing apples to apples.’

Platform-based businesses need proprietary measurement ideology, supported by metrics that push us to think, act, challenge ourselves, and change. Platforms need a zooming-based measurement approach that intersects two distinct dimensions: a theory-based measurement and a horizons-based measurement.

When a platform uses simple (conventional) metrics, such as Monthly Active Users, or Daily Active Users, the platform is merely comparing different calendar periods. Good luck with that. Furthermore, such metrics limit your vision of improvement and will widen your blind spots. You will either try to grow your user base or to incrementally upgrade your product’s features (such as celebrating a dark mode feature).

Let us take Instagram as an example. Instead of measuring MAU, or DAU, Instagram should be focusing on the jobs their users are trying to get done (as theorized by Clayton Christensen). Then, Instagram should try to stretch such jobs within expanding horizons to unlock new values (as per John Hagel’s zoom-out zoom-in concept). Such a multi-dimensional measurement approach will enable Instagram to extract, orchestrate, and envision new underlying experiences, by leveraging the richness of several ecosystems over different time intervals.

Such a measurement approach can assist platforms in improving their value propositions based on the variations in the experiences rooted within the jobs to be done, by discounting future vision into present time reality.

Now let us place the Measurminator concept within our story-telling white paper (Investment Banking Industry-wide Platform).

The challenge, now, is manifested in laying down the different measurement metrics. I am saying challenge basically because every platform is unique in its architecture, structure, stacks, etc. As such, it is almost not possible to have a one-size metric that can serve different platforms.

For example, calculating liquidity differs from a platform to another: Facebook measures liquidity monthly, Airbnb on a daily/nightly, and Uber hourly.

As per Sangeet Paul Choudary, liquidity is “a state where there are a minimum number of producers and consumers on the marketplace, and there is a high expectation of transactions taking place.” So, you might think that liquidity has one meaning but different ways of calculation. What if I told you that liquidity could have different meanings, even within the same platform?

Sangeet’s elegant definition is underpinned by intentions (intention-backed liquidity), whereas a producer of value and consumer of value shows an intention to engage in a transaction. The intention layer resonates with the passive/static layer within the longevity growth ideology, and the jobs to be done layer on the platform value proposition canvas (two concepts discussed in previous parts Six and Seven).

There are two other types of liquidity, underpinned by elevated human desires (ability and willingness).

Ability-backed liquidity: Airbnb is among a few platforms that ventured from the intention-backed liquidity to the ability-backed liquidity (from merely renting empty spaces to engaging in complex experiences). The ability layer gets nurtured via the active-dynamic layer as reflected in longevity growth, which enables the value proposition to rotate over a new context to be shaped, based on a more in-depth understanding of the users’ needs.

Willingness-backed liquidity: Even Airbnb is not yet navigating toward this frontier. The willingness layer can only thrive via the interactive-explorative longevity growth mindset, which is underpinned by emerging collaboration. A platform, at this layer, empowers its users to create new values by integrating them within intersecting ecosystems. A platform at such a layer also evolves from being selfish to selfless.

Ability/willingness-backed liquidity is not the mandate of this post, yet I just wanted to draw attention to the embodied complicity when measuring liquidity: A platform might enjoy a healthy and vibrant intention-backed liquidity, yet very weak ability-backed liquidity. Such a case — moving up-scale from merely facilitating transactions, into actively engaging in complex experiences within multiple verticals — is a death sentence. As such, ability/willingness-backed liquidity, must be measured differently.

In fact, I believe that both the Ability/willingness-backed liquidity, only materially proliferate within the brand-new network effect: recently discovered by James Currier, called “Expertise Network Effect.”

Pundits might celebrate a simple conventional metric, such as MAU, and markets frequently responses aggressively (unjustifiably) to such metrics. But a platform’s board, executives, staff, and users must behave based on robust inputs derived from custom made (unique and proprietary) metrics.

So how can we fix such a dilemma? Have you ever heard of Basel’s Capital Adequacy Ratio (“CAR”)? If not, CAR is a comprehensive equation that measures the level of underlying risks in a bank’s assets, accordingly assigning the minimum capital charge to support such a level of risks. Mathematically, CAR looks like this:

Maybe, for once, the aging banking industry can be useful in inspiring the platform universe with a new way of measuring. What if the platform universe embraces such a holistic metric? Inspired by the Capital Adequacy Ratio, let us construct a Network Adequacy Ratio (“NAR”)?

As such, NAR can be viewed as a percentage of a platform’s network effects to the level of underlying risks in the platform’s network defect. Like the CAR, the NAR covers three broad types of risks: liquidity (replacing credit risk), market, and operational.

Liquidity risk: refers to the underlying risks in the platform’s network effect, which are due to social, legal, or regulatory escalations.

Market risk: refers to the deterioration in the platform’s network effects due to adverse economic conditions or the emergence of a disruptive competitor.

Operational risk: reflects the severity of the hit that the platform’s network effect can take due to a breakdown of the platform’s system, internal controls, employees’ conduct, or externalities.

In “Network Chain,” I tried to redefine the perception surrounding the network effects from merely being a byproduct (a phenomenon) of a platform, to a more structured architecture. The first step was to separate the negative characteristics under a broader terminology, “Network Defect,” from the positive characteristics that almost usually strengthens the network effect. In other words, the NAR reflects the overall health of the Network Chain.

Likewise, the CAR, what if we expand the denominator (network defect) over three broad types of risks? An attempt to understand the underlying sources of the deterioration in the network effect (i.e., is multi-homing is triggered by liquidity, market, or operational risk?)

For simplicity, let us consider the MAU as an acceptable proxy for the network effect, with a caveat. Since we are talking about investment banking, how about replacing the word “users” with “investors”?

The word “active” above refers to the act of investing. Unfortunately, investment banks for a very long time understood investing as a verb (merely putting money in financial schemes). It is time to understand investing as an act. Investors vary (institutional, HNWI, accredited, sophisticated, etc.), and each type act with different investment objectives:

• to learn,
• to be part of a new venture,
• to earn higher returns,
• to save for retirement,
• to diversify their portfolio,
• to accumulate wealth,
• to support others,
• to benefit from tax advantages,
• to vote and support important issues ‘climate change,’
• to be part of a company that you believe in, and
• to have a political leverage.

Once again, let me ask you, with such a diverse investors’ base and a broad underlying investment objective, is it logical to use a simple MAU metric to gauge the progress of such an industry-wide platform?

The measurement focus must shift to the underlying job the investor is trying to get done by hiring a specific financial scheme. As a result, let us modify the numerator, from merely focusing on quantity (number of active investors) to quality (job to be done).

By doing so, the industry-wide platform will enjoy a crystal vision regarding its network chain, by focusing on different jobs to be done, each, at a time. This will help in understanding the underlying variation in investors’ behaviors toward each investment objective.

Next, we must stretch the (numerator) job to be done, which is, in the above example, investing for retirement, into an expanding horizon. At the same time, stress test the denominator (the three broad risks categories), by anticipating changes in investors’ experiences and behaviors. In other words, strengthening the network effect and mitigating the network defect, via orchestrating new experiences and behaviors.

What about pushing the envelope a bit further? Let us piggyback on another practice from the banking industry. A concept called Credit Conversion Factor (“CCF”). The CCF is Basel requirement, by which a bank is required to convert some off-balance sheet items into on-balance sheet equivalent credit exposure. Sound fancy? Okay, then how about inventing a new platform’s practice, Network Conversion Factor (“NCF”), whereas a platform internalizes external behaviors/experiences from other network effects within multiple ecosystems into the platform’s network effect.

For example, it is crucially important to know how many investors are also investing in other platforms (multi-homing), but this should only be the starting point. The metric must push you to investigate the underlying reasons, leading investors to get their job done somewhere else. It forces the platform not only to acknowledge the fact of multi-homing, but the platform must fix its root cause. Otherwise, it will continue deteriorating the platform’s overall NAR.

Network defects such as multi-homing, multi-tenanting, switching cost, etc. must not be treated (cosmetically) by finetuning a few features on the platform’s UI. Such network defects can be cured by holistically understanding the underlying experiences of how investors are getting their job done on other platforms.

The metric must absorb the data yielding from the knowledge inflow from the extended time horizon. Then it must internalize such knowledge by improving the experiences to regain investors’ trust.

Regaining investors’ trust is not a public relation’s job; the platform must effectively measure investors’ engagement across multiple touchpoints. The platform must fix such multi-homing situation by reinvestigating the modular and interdependent touchpoints (refer to part nine for further elaboration) to leverage investors’ personalized experiences by importing and integrating knowledge from the extended horizon (i.e., envisioning your competitors’ next moves).

Concluding remark: I am not promoting the above NAR or NCF. As you noticed, we (you and I) just made-up these two new terminologies. As such, they are not tested yet. My objective was merely to build upon the Measurminator concept introduced in my previous post and to encourage you to be inspired by your relevant industry to construct proprietary metrics. I hope you enjoyed this part.

See you in part Eleven.

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