The Importance and Challenges of Behavioral Data Enrichment

Today, analysts, marketers, product managers, and even security professionals are expected to drill through massive amounts of data and come up with actionable insights.  Being able to integrate different data sources and generate a unified view that allows for more comprehensive data analysis is a must for every online business.

Revealing ROI

How many users have you had that generated tremendous amounts of data moving through your online sales funnel? We’re guessing a lot. To demonstrate just how much data we’re talking about, let’s look at the possible steps a user may take before making a purchase. By first browsing through and trying free trials of online solutions, the user may visit dozens of different pages and interact with vendors across multiple digital channels. This could include posting on social platforms, watching product demo videos, filling out a ‘Contact Us’ form, and more. But it doesn’t end there, after making the purchase, the user needs to be nurtured to gain the benefits of using the product.

Online application vendors need to analyze user behavior by monitoring each user’s purchasing path, helping vendors understand what needs to be done in order to convert and retain users.  The best way to do this is by integrating behavioral data with elements such as user acquisition campaign costs (e.g. Facebook Ads, Google Adwords). Analysts are required to report the amount of users that are acquired, calculating CAC, LTV and ROI. In addition, vendors need to incorporate these costs with real time data of user behavior.

Data Enrichment

Collecting enormous amounts of behavioral data can be very challenging. In some cases, software vendors (internal or third party) prefer to collect only the most significant pieces of information to keep their databases concise and to maintain system performance as well as quality user experience. Ideally, in order to enrich baseline data, analysts want to merge their own collected data along with external sources and internal logic. Structured data enrichment can result in a rich data model that offers an enhanced analysis of user behavior.

For example, it’s possible to enrich data by tracking affiliate performance metrics with unique codes to segment data items. This segmentation allows the system to track, collect and analyze small amounts of enriched data, allowing full reports to be retrieved quickly.

Analysts may also want to filter out traffic that’s not relevant to their analysis. For example, if a site is scraped from suspicious IPs, those IPs need to be tracked, and data needs to be filtered accordingly.

As shown below, you can find the behavioral data (in the blue rectangle) side by side with SaaS metrics such as CAC per user.



Build vs. Buy

In order to accomplish any of the cases mentioned above, companies have to build their own systems that can link data from multiple sources, including third parties and their own behavioral databases.

Imagine running multiple campaigns, you’d have to find a way to merge all of the campaign reports along with your behavioral database into a table for further analysis, in order to enrich it with data that exists within your company’s operational database.

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