Academy blog

How to handle fraud using overlapping metrics

We’re living in a post-Cambridge Analytica world where tools that can capture and analyze user behavior are in the interest of both Saas businesses and their users. The world of user behavior has been broadcasted to the public and as a result, we’re seeing a great interest from Telcos, media services and OTTs in tracking user behavior when it comes to video.

There’s a really interesting discussion to be had regarding the study of user behavior so let’s talk a bit about how this topic has started to come up more and more.


Why now?

Data is becoming very important for media companies to the benefit of end-users due to an increasingly crowded market full of OTTs and telcos stepping in, all competing for the same audience and limited leisure time. User behavior insights are crucial to build personalized experience with appealing content, certain levels of quality, or innovative pricing plans. To keep up with the pace, media services need insights to stay competitive.

So, before your Telco, OTT or media service is going to commit to a Business Intelligence (BI) tool, you’re going to need to know the ways in which you can analyze behavior using overlapping metrics along with other tools, and how this can benefit you.


How does it work?

Overlapping metrics is a really simple tool that can be used in a few different and effective ways:

  • See when the same user is coming from different geo-locations
  • Identify and appropriately deal with account sharing
  • Use consumer trends to redesign payment options based on usage

The benefit of seeing if the same user is logging in from different locations at different times is that you will be one step ahead when it comes to the fraudulent use of your service. It may be that you are aware of account sharing and actively overlook or allow fraudulent activity because you have seen that it increases engagement. But allowing this to go on outside of your knowledge could also have a negative effect on user churn and make your MAU(monthly active users) statistics invalid.

As for simultaneous use, put simply, if you’re able to see the same user interacting with your content from two locations at the same time, you can be certain that there is some sharing of login data going on. Again, if you’re aware of this there’s no need to start canceling accounts and losing customers, but this is the perfect time to see if it’s time to reevaluate your price plans.

How to use overlapping metrics to deal with fraud


Do your price plans reflect your customer usage?

There have been plenty of studies looking into why tiered pricing options are going to boost your ROI but if you can’t be convinced by the research, overlapping metrics and other consumption trends data are going to reveal the truth in its effectiveness when you see the different ways your users behave with your product.

Whether you notice a large demand for expensive products and want to charge users for premium content or see you’re getting a lot of dropouts during ads from a certain location and want to offer an ad-free version, you’re going to benefit infinitely from using behavior analytics, including overlapping metrics.


Here’s an example…

Let’s say you’re running a SVOD business and you’re charging $10 a month for unlimited access for a single user. Using overlapping metrics you find that this account is being shared between two or more devices in different homes (different IP addresses) at the same time. This suggests it’s time to implement a multi-user package similar to Netflix’s strategy. Not only will this bring in more revenue, it’s going to save time spent on tracking down unauthorized use. They may even be more inclined to pay just a tad more to make everything right legally, especially in countries like the US where there are hefty fines for fraud.


What do we do now?

It is now the duty of big players to distinguish their content by how seriously they take fraud. If you’re looking for a way to broadcast your commitment, investing in a tool like overlapping metrics is going to make your customers feel safe and reduce churn.

NPAW’s team of video data engineers have developed SmartUsers, a powerful AI-driven module that will grant you visibility on user behavior, churn and suspicious activity using overlapping metrics to reduce churn rates. Have a look here.

Research & Editorial Team on May 07th 2018

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