Machine Learning for Personalizing Digital Experiences

Personalization has always been a key aspect in almost all kinds of digital experiences. Some examples of commonly found personalisation use cases are: allowing users to customise their dashboards or user interfaces, showing content based on explicit user-defined criteria, showing content based on implicit criteria or even that based on user behaviour. All these required complex personalization systems, with processing and rules engines for creating and managing personalization rules. As a result, it has always been a non-trivial exercise to implement personalization in a resource effective way.

Artificial Intelligence (AI) and Machine Learning (ML) techniques have evolved and it is now much easier than ever to use these to implement personalization now. At a very simplistic level, personalization is about “predicting” what a user will like to see and then offering that to the user. You can make this prediction based on a complex hierarchy of rules or use historical data to make this prediction. The latter is exactly what machine learning based techniques can do for you.

Delivering Right Content to the Right People

Consider this common scenario: You want to show content that is relevant to the user. For example, let’s say you run an events site and want to show events that are relevant to the user. To do this, you could create multiple rules such as rules that match a user’s and event’s locations, or show events based on user interests and so on. This works great, with may be 5 rules. But consider a scenario where your users have 100s of profile and behavioural attributes and your events also have similar large number of attributes. So as you come up with more criteria, this rules based business becomes really messy and difficult to manage.

But with machine learning based techniques, you now have alternatives. Plus you no longer have to procure sophisticated personalisation systems. Instead, you can start writing very simple programs that can help you predict what kind of events a user would like to view depending on the events that other users with similar profiles viewed. You could use the same logic to display targeted news, movie recommendations or books. Some of these machine learning techniques are really simple and you can get started very easily.

Here’s another example for the same events web site. As an event organizer, you create a new event but are not sure what kind of pricing would work best. Again, if you think of this problem as a prediction problem, as in “predict price of new event given pricing of past events”, you could again use a simple prediction algorithm to recommend pricing based on pricing data for past events. Instead of events, you can use the same logic to price your new offerings or whatever. In addition, you can use this new data point as another input for your next prediction.

Start Small and Experiment

In addition to personalization, Digital Experience Management use cases can have several aspects for which you can start using machine learning. And there is no need to wait for your vendors to start offering additional AI/ML capabilities. Almost all programming languages provide APIs and libraries for all kinds of machine learning algorithms for clustering, classifications, predictions, regression, sentiment analysis and so on. The key point is that AI and ML have now evolved to a point where entry barriers are really low. You can start experimenting with simpler use cases and then graduate to more sophisticated use cases, once you are comfortable with basic ones.

If you would like more  information or advice, we’d be happy to help. Please feel free to fill the form below or email.

 

Blockchain for Information Management

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Blockchain is best known for its use by the alternative and controversial currency market and most notably Bitcoin. But Blockchain is not Bitcoin nor is Blockchain an alternative/crypto currency. Rather Blockchain is an underlying distributed ledger technology (DLT) that can be used in many different contexts other than Bitcoin.

In fact, we are already seeing the experimental use of Blockchain in many non-currency related situations – for example, the management of healthcare records and the processing of shipping manifests. Specifically, in the context of Information Management, Blockchain can address a number of use cases. However, as with anything new in tech industry, Blockchain is also being used for information management use cases for which it is not a suitable platform.

This recently released report by analyst firm Deep Analysis (written by Alan Pelz-Sharpe and me) looks at the use of blockchain for information management. It explores the structure of this market and its future impact and growth potential.

The report’s ToC is as follows:

  • About this report
  • Methodology
  • Introduction and a brief history
  • Executive Summary
  • How does a blockchain actually work?
  • The key attributes of blockchain
  • Blockchains in action
  • Public versus Private Blockchains
  • The Market Structure
  • Market Drivers
  • Market Realities
  • Our Advice
  • Summary

You can read more details, including exec summary as well as purchase the report here.