Are You Leaving Money On The Table And Why A Monetization Strategy Is Key

 In Analytics, APIs, AutoAI, Big Data, DataStack, Digital Strategy, NewsRxR, Technology & Design, TouchPoint

Enterprises across the board have a lot of untapped potential in their data. The data is not only relevant and useful within the enterprise but can be a valuable source of insights for the enterprise partners and customers. In some cases, the value of this data can be high that partners and customers are willing to pay extra to get access to this information at a certain fidelity, freshness or scope. Enterprises that do not have a clear and coherent monetization strategy are leaving money on the table. In addition, they stand to lose customers to competitors who gain the first movers advantage by addressing this market need.

The Value of Data

The first step in determining a monetization strategy is an audit of the enterprise data assets and a determination of the customers who are interesting and willing to pay a premium for access to this data. The Value of Data is proportional to the following:

Freshness

The more “fresh” a dataset is higher its value typically. This is because there is an advantage in the early visibility provided by first access to new information. ‘Freshness’ is defined the latency between the creation of data and the delivery of the data to the consumer. Consumers of data will pay a premium for fresh data if it fits into their decision and action strategy.

Fidelity

Higher the “fidelity” of data i.e. how much detail a particular data point carries also increases the value of the data in the eyes of the data consumer. Higher fidelity data offers more information and detail enabling the consumer to design highly valuable analysis that leverages the additional details offering a deeper insight into the situation at the present or historically.

Raw

The more “raw” a data set, higher its value as it can support a much larger set of analysis scenarios that a processed data set could support. Data sets that are aggregated, sampled, filtered or transformed can have a lower value as they can severely limit the type of analysis. Raw data is worth a lot more in the eyes of the data consumer than processed data.

Delivery Channels

Delivering information and data to customers requires a multi-channel strategy to adapt to the specific customer needs. 

E-Mail

The most basic and widely used mechanism outside mail is e-mail. Attachments to email have been used regularly to deliver data and information to clients. Data management and data size are some limitations of this approach.

Files

Data files can be shared through file servers and protocols such as FTP make it relatively easy to upload, share and download files. However, the size of the data file and the quality of the network can impact performance.

APIs

APIs are the most ideal, forward looking mechanism for data delivery. APIs can offer bulk data download or selective queries enabling the consumer to control and adapt how and what information they want to download.

Push Notifications

Push notifications are another useful mechanism to deliver information in a timely manner without requiring an explicit request from the user.

Monetization Strategy

Monetization strategy is defined as the mechanism through which customers can pay to get access to the data. Monetization strategies are usually independent of the delivery channel however API based data and information delivery is typical much easier to monetize.

Free

The Free model enables users to consume as much data as they need within the service defined constraints and limits.

Usage Based

Usage based model charges users a certain amount every time the user uses the information or data

Flat Fee

Flat fee based model charges the user a flat fee for all the data and information they can consume or charges flat fees through multiple tiers offering higher volume of usage.

Other Variations

There are several other variations possible that combine the above three monetization models in different ways.

Invoices and Billing

The last part of monetization strategy requires the ability to convert usage of data and information into invoices that can be billed to the customers. Apart from integration to billing, this requires the capability to provide and backup any billing to the user with the appropriate details required to explain the billing.

Typical enterprises have a lot of data with a very high monetizable value. However, lack of good technology solutions hinders monetization. AutoLearn.ai solves your monetization problems and helps bring new products to market by enabling new APIs enabled with monetization models.

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