Part one in a two-part series dedicated to helping businesses grow using a data-driven approach to network effects-based business models.
We live in the age of networks.
While underpinning the way we connect with technology, networks also impact how we connect with each other: socially and commercially. Unsurprisingly, business models that depend on network effects are - and will continue to - proliferate.
Gone are the days of winning the network effects business model race simply by having a network. The major players in this space (Facebook, et al.) won this race by being first.
Competing in any similar way now requires you to be better.
How do you do this, and more importantly, how can data help you to do this?
In this, the first of two parts, we'll look at the role of data in the creation of network effects-based business models, and the two main strategies you can use to drive growth in your network effects-based business model. In part two, we'll dive into 13 specific metrics you should monitor, and benchmark these against current high performers in the space.
Not all data is created equal, and knowing how to use it is just as important as knowing what to look for.
As Roger Martin says, "The point of data is intimacy." Often, businesses that begin the shift toward being data-driven lose sight of this and end up awash in irrelevant data, losing time to arguments and discussions around methodological purity.
The solution? Understand the relationship between data and metrics. Not all data - in isolation at least - gives a sensible indication of what to do next. Instead, you typically need to bind data to strategy, perspective, or hypothesis to give it the context that will yield insight.
Doing so is what gives you a metric, an opinion on what you think matters, why, and how that relates your business to the outside world.
Borrowing from Lean Analytics, there are three broad principles you should seek to apply when you use metrics as a tool for growing your business:
Good metrics are comparative, such as a ratio or rate:
Data in isolation tends to provide little in the way of actionable insight. Rather, seek to compare any data you gather against other data points. Ratios and rates are excellent for this purpose, giving you a clear sense of causality between two factors, or at least revealing the nature of a tension between two different data points.
Good metrics are understandable:
All of the data in the world is useless if you cannot sensibly relate it back to the problem you are trying to solve. The principle of Occam's razor is in effect in this sense: always start with the simplest approach, and work upward from there.
Good metrics change the way you behave:
The point of measuring something is to change it. Be sure that whatever metrics you choose for your business give a clear sense of what you should change and why, but that you are also prepared to be data-led.
As Sangeet Choudary mentions in his recent Network Effects playbook, the age of business that succeeded because they were a network is over. The problem at the core of this is user engagement - how you go about getting it and keeping it.
Choudary draws two variables into this equation: content and connection. What we know about networks in the age of the Internet is that networks have a close relationship with both content and connection.
Typically, content that is created will begin to generate a community around it (for example, Medium, Pinterest, and Instagram). At the same time, if the vertical you are operating in has an urgent enough need associated with it, users will drive connection on their own, and tend to generate content as a consequence of that interaction (for example, Match.com).
Each approach is valid, and the effectiveness of each depends on the vertical you are operating in, and how you've structured your business.
Content-first approaches tend to work best for business that cater to niche audiences with a high degree of passion around a particular topic.
These users tend toward generating content around their passion, which in turn leads them toward connection with others who share their interest.
In this scenario, the general approach you should follow is to reduce the friction of content creation, and encourage the spread of this content as much as possible.
If you consider your network as a virus in this instance, then the vector of transmission is content, and you should do everything possible to increase the amount of content created, as well as the virality of it.
In contrast, connection-first approaches tend to work best when you're business is solving a problem with a high degree of urgency associated with it.
When catering to users with an urgent need, the best strategy is to get out of their way as much as possible and facilitate the connection they seek. For example, in this scenario you should focus on reducing the sign-up time and effort associated with users who wish to join your network. Growing and nurturing that community going forward is as much about monitoring the intensity of the interactions between participants as it is about the quality of those interactions also.
Follow us for part two of this series, where we will dive into 13 specific metrics that network effects-based business models can use to enhance their success and speed of growth.
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Kristin Low is founder of On-off Design & Technology, a strategic design and digital services company, based in Hong Kong. He has a background in user experience (UX) design, service design, and product management. Kristin founded the Design Thinking network in Hong Kong, which has over 500 members. He’s also an MC, mentor, and trainer, and hosts jams and hackathons. He’s passionate about blending human-centered insights with quantitative data to drive business growth and innovation.
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