The total number of customers has always been the signifier of what makes a company successful – the one statistic that you’ll mention when promoting your product, I’m sure. This statistic is still very important. However, subscription-based software companies have grown since the mid-1990s and the SaaS market is now growing five times faster than traditional software delivery.
As tempting as it is for a company to focus on customer acquisition – 86% of SaaS companies are guilty of this – it’s actually going to dramatically increase your ROI if you focus on customer retention. It’s 4x cheaper to upsell existing customers than acquire new customers: costing just $0.28 to acquire an additional dollar of revenue.
The key to customer retention is understanding your users.
There are various tactics and well-known methods regarding maintaining customers like offering discounts and changing your price segmentation, but this problem starts when usage begins to drop. Low usage leads to less customer loyalty, and a lack of customer loyalty is likely to result in a customer churning. Now the questions here isn’t, “how do we convince a customer to stay?” it’s “how do we stop a customer from even thinking about leaving?”
With the right knowledge of how your userbase behaves, you can not only gain visibility of how successful your campaigns are but your marketing and operations team can also use this information to make the difficult business decisions and reduce the risk of failure.
Using a recommendations platform is an extremely popular and effective approach to nurture customer retention. This approach has been discussed in our recent blog – “Personalization is the new Frontier for Media Companies”:
“Recommending content is a very commonly used technique to not only keep viewers watching, but also to learn about users’ likes and dislikes. This technique involves a media provider offering more content once a customer has finished a video. Once the user completes their view, it’s expected that the service will want to keep hold of the user and maximize watch-time. Now, offering a few random or popular shows is doing some good but it’s really stabbing in the dark. What a recommendation engine will do is remember the type of content people who watched this video normally chose next – this way your chances of catching their attention grows exponentially.”
The process goes deep and in many directions in order to adapt to each companies unique customer journey. The most popular use of this is Youtube’s sidebar, famous for various reasons, began using tracked clicks and A/B testing in order to discover which content the typical user would want to watch next. Now, this does sound a bit like stabbing in the dark and if you’d like to learn more about Personalization, a new-age approach to understanding users, make sure to read our recent blog on differences between this and recommending content.
“There’s a mountain of data that we have at our disposal, (…) Geography, age, and gender? We put that in the garbage heap. Instead, viewers are grouped into “clusters” almost exclusively by common taste” – Todd Yellin, VP of Product @ Netflix
It is common practice that a company will use A/B testing and discover the most likely customer journey that leads to retention and then try to replicate this. However, the issue here is that this is far too general. When you start to look at numbers in the hundreds of thousands it would be naive to assume every 25-year-old woman from Chicago has the same taste. Obviously, one solution is to look at the deeper information on a user-by-user basis. Without going into the realms of Personalization, it is possible to use customer segmentation, content tags, and a lucrative onboarding process in order to gain deeper information and then use an analytics tool such as YOUBORA to track the success of your efforts.
There are many different ways to segment your audience – by location, by age, by gender. But the way that’s going to be of the most use to your media service is to define who are the high-level, medium-level, and low-level users. This way you can gain clarity of your at-risk users and concentrate your marketing and operations budget at these users.
It’s also in your best interest to experiment and take risks regarding customer retention. Netflix reports that the average cost of acquiring a single customer is $100. A subscription costs $10/month so they are banking on the fact that these customers will stay for almost a year in order to make the investment worthwhile.
In order to define your success, define your KPIs and parameters for measuring the intensity of your users viewing habits. It could be you’re looking for viewers who watch at least two full sports matches a week, or a customer who can finish a series in a weekend. It’s important to define your segmentation in order to define goals and strive to impress your users with your attention to detail.
One such way of utilizing both the benefits of a compelling recommendation platform and customer segmentation is by using a tool like Spideo. This solution will allow you to take new approaches towards understanding your users by implementing features such as mood-based discovery and semantic search. The combination of a predictive content strategy and semantic profiling has been proven to unearth deeper information and give better results.
In order to drive engagement, you need to understand users’ semantic profiles. This defines their tastes and habits and provides a solution to individual anomalies which occur when we only use demographics to predict a user journey.
The onboarding process is a fantastic time to absorb user data. Use a preferences page similar to Spotify or Netflix so you can then use these initial preferences a base to then launch deeper from then on using A/B testing and customer segmentation.
Do you want your content to be easy to discover? Do you have a defined content strategy? If you answer “no” to either of these then it’s definitely time to consider using a recommendation tool partnered with an analytics tool like YOUBORA in order to get the most information from your users and drive engagement.
Thank you to the Spideo team for their extensive knowledge and supporting research into these content strategies.
Research & Editorial Team on July 05th 2018
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