Measuring user engagement improvements from personalization

We deliver value to our customers through providing their users with better experiences. We enhance the user experience and increase engagement rates because we provide them with personalized data and information on financial markets, financial content, and user behaviors.

It’s natural for prospective customers to want to know how we measure that user engagement that provides the proof that we do what we say we do.

AlphaStream uses a wide range of metrics to measure user engagement. These include measure of valuable and non-valuable interactions, diversity of interactions, number of valuable (or engaged) sessions, durations of session, and qualitative feedback.

Value Interactions Index

This index shows the valuable interactions as a percentage of total interactions across all sessions.

We decide which interactions are and are not valuable using our historical understanding of user behaviors within the context of financial markets. We understand that certain research behaviors are more relevant and therefore have more value than other interactions.

Because we’re able to define the value of interactions we can build an in-depth view of intent levels each user has to aspects of the financial domain, and the level of engagement they show towards each aspect. 

Interaction Diversity Index

This details the number of unique interactions out of total interactions. Users make a brand range of interactions when researching financial markets. The breadth of these interactions is shown through this index. It gives us a deeper view of how the actions of each user in each session.

We can define which of these interactions engaged with personalized content and data, and which were not.

Value Session Index

This is similar to the Value Interactions Index however the focus is on the percentage of engaging sessions. This helps illustrate long-term engagement levels for each user.

Using this index we can see changes in daily, weekly, and monthly engagement levels, and therefore understand if user engagement levels are changing over time. 

Session Duration Index

This metric shows the average session duration and is defined as a score given between 0-1 based on the length of time, in minutes a user’s session lasts.

It helps to show how long users are engaging on our customers’ platforms. Using the data points above we know the levels of engagement taking place for each session duration. This gives a view on user engagement per session, and therefore shows daily, weekly, and monthly user activity levels.

Feedback Index

This is created from qualitative feedback in survey questions. Responses are quantified as positive or others per session. The index estimates the percentage of positive feedback on content and data relevancy, and the value of the features (e.g., a personalized newsfeed) they are interacting with.

Total engagement

We create this as an additive combination of the composite metrics used to measure engagement and value.

This helps us to understand multiple factors including;

  • Personalized content and data’s effectiveness in engaging each use
  • Percentage of user interactions directly and in-directly related to personalized content and data
  • Changes in user engagement levels since introduction of personalized content and data
  • Changes to overall Customer engagement benchmark metrics across their user base
  • Examples of engagement metrics are Daily Active Users (DAU), Weekly Active Users (WAU), Monthly Active Users (MAU), daily sessions per daily active user, average session length, average number of instruments researched, average number of instruments per watchlist, average number of traders per user
  • We continually look to find new methods and practices to review and validate the value we create for customers through our personalization technology and the engagement it delivers to their user base.

We will continue to mature and innovate the capabilities of our products, bringing users content and data relevant to them.

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