How to Make Money from Data Networks?

Data networks can use five of the six possible monetization models, depending on how they receive data from users and how data is consumed.

Data networks are unique in the world of network effects. Most types of networks create value by allowing participants to interact with each other somehow. However, data networks do not directly connect participants. Instead, they collect data from participants to improve the product for all participants. First, it automatically overrides one of the monetization models (interaction taxes (or commissions)) used by other networks. Because there is no direct interaction between participants, they cannot be taxed.

This is because monetization (or value capture) must be aligned with the product’s primary value proposition (value creation). The data collection method (data collection) and the nature of product use (data consumption).

First, let’s take a look at data retrieval. As I explained earlier, there are two broad ways data networks receive data from users:

• Active crowdsourcing, where users must actively and directly interact with a product to collect data, such as on. In other words, the quality of the product now depends on user participation.
• Passive crowdsourcing where data is automatically extracted from all product adopters regardless of direct interaction, such as. It depends entirely on user acquisition, not user engagement.

Then, data consumption can occur in two broad ways:

Some data networks, such as Tripadvisor and Waze, need to be actively and deliberately used by end-users to derive value from them.

Others, like Mapbox, are used passively and tend to be embedded in third–party products or workflows. While they do add power to some capabilities, they are not necessarily used intentionally by end–users to derive value from them.

Premium Network Layer

As I explained earlier, premium networking tiers are built into users with a free product and get paid with an optional, paid deck. Instead, interactions are limited to feeding and consuming data into the product.

As a result, it isn’t easy to use this model with data networks (for example, Waze) that must be actively used to realize value propositions. Actively used data networks encourage participation through data availability–restricting this availability will also hinder value propositions. For example, if Waze limits data access based on the type of event reported. As a result, premium network layers are a better suite for passively used data networks.

In both free and paid plans, all users can contribute to the data network.

Data Network with Paywall

As I explained earlier, active crowdsourcing requires many participants to initiate a data network effect. Putting the entire network behind a paywall would interfere with this goal with passive crowdsourcing, e.g., products that automatically collect data from all customers.

Additionally, it can be tough to combine active usage with the passive crowdsourcing seen in paywalled data networks. Paying customers are often reluctant to share their activity data (e.g., sales intelligence or contact information) directly with others. After all, they’re paying to use your product, and privacy will likely be a purchase consideration. When used to inform a recommendation algorithm (e.g., XANT), enhance the capabilities of an established product (e.g., Mapbox).

Complementary Products

They are individual features or capabilities that increase the value a customer receives from the product. This is difficult to apply to “background” products as it does not improve data quality or access. Instead, users need to actively use the product to derive value from these plugins. And, as we’ve seen in advertising, this is most effective where engagement generates more engagement, meaning it’s more suitable for data networks that rely on active crowdsourcing.

Derived Products

A derived product leverages interactions and participation on a network to produce a directly monetizable asset. This means making data generated by free users a standalone product for third parties in data networks. However, data users must consume it for a feedback loop (and data network effect) to exist.

Since monetization is entirely separate from the users of the data network, data collection and product usage have no impact.