Storytel’s Danish subsidiary runs its business using advanced analysis from SAS Institute

Audio and e-book company Mofibo, recently acquired by the Swedish audio book company Storytel, has built its business around data-driven advanced analysis since it was founded in Denmark in 2013. The strategy has paid off, both in terms of market share and value per customer. 

The book industry has fundamentally changed in the last decade as a result of several great technological breakthroughs. First E-commerce arrived with sellers such as Amazon.com, redrawing the distribution logistics and power balance in the book industry. Then, the ability to distribute books in a new format – as e-books and audio books – was fully exploited. And now the industry is seeing another huge shift – as their business models shift towards those pioneered by Spotify and Netflix. In other words, we now prefer to consume books as a subscription service rather than as individual purchases. 

But just as there are many different books by many different authors, there are many different subscription services. Mofibo is one of the most successful and insists that its success is attributable to having better information than its competitors from the start. Martin Jonassen, Head of Business Intelligence and Analytics at Mofibo, agrees. 

“Digital business models provide a lot of data, but you also have to do something useful with the data. Through data mining, we at Mofibo understand our customers’ behavior better. This in turn means that we can improve communication with them through Marketing Automation, by choosing the exact right message for each individual customer, in the exact right channel and media for the message at the exact right time.”

 

Martin Jonassen - Storytel/Mofibo

Martin Jonassen
Head of BI and Analytics
Storytel/Mofibo

We see behavior both on an overall level and on an individual level

Readers behavior

We see behavior both on an overall level and on an individual level. For example, we have learned how major public holidays impact reading, which weekdays people read most and other trends like that. But we also see how users can be categorized into groups such as “family readers,” “retirees” or “commuters.” Finally we see what a individual’s behavior looks like, and we can take steps if we get indications, for example, that a customer might be about to stop using Mofibo

Thanks to the analysis tool Visual Analytics, Mofibo has obtained such a good picture of readers’ behavior that it has built its conversion strategy around these insights. For example, it has learned to identify different signals indicating that a certain reader may need a little push to continue being a customer.

“It’s if a loyal customer doesn’t read anything for several days in a row. We can then encourage them to return to the book by send them a tip or reminder or even an offer. We also give tips on other books that a certain customer might like. Those tips are also based in advanced analysis where the customers’ previous reading habits play an important role.”

According to Martin Jonassen, it is especially critical to act on different signals when a customer has started using the service.

Data mining of user data has, for example, taught us how important it is that users do things within a certain time

Data mining of user data has, for example, taught us how important it is that users do things within a certain time. A user who is profitable in the long run should preferably read the first book within two days from starting to use the service, and then start on the next book within two weeks. We see it as a warning signal if a new customer doesn’t read for two or more days during the first two weeks of a new subscription

But just as there are many different books by many different authors, there are many different subscription services. Mofibo is one of the most successful and insists that its success is attributable to having better information than its competitors from the start. Martin Jonassen, Head of Business Intelligence and Analytics at Mofibo, agrees. 

“Digital business models provide a lot of data, but you also have to do something useful with the data. Through data mining, we at Mofibo understand our customers’ behavior better. This in turn means that we can improve communication with them through Marketing Automation, by choosing the exact right message for each individual customer, in the exact right channel and media for the message at the exact right time.”

“We see the same kind of behavior as, for example, Facebook do. New users are almost always very active just when they have created their account, but then a pretty large portion of new users lose their initial excitement and become passive. Facebook says that most of its new users invite about 10 friends in the first hour. The ones who then continue and invite more friends often become loyal Facebook users while the others stop logging in. That is true for our users as well and that is why it is very important that we act on this type of data.”   

Mofibo’s work with such data has given results – it only took one year to win over 50% of the market for audio books in Denmark. Information about reading habits also strengthens the company’s relationship with the publishers they work with.   

“We see how quickly the readers get through different parts in a certain book, which, in the future, could give publishers insights about how a real bestseller should be structured. That kind of information is good for everyone – readers, authors and publishers.   

 

Would you like to read more about Storytl /Mofibo - Check out their presentation paper from Nordic Business Forum 2017.

Mofibo logo
Storytel logo

CHALLENGE

To take advantage of all the information created by readers’ behavior when they read or listen to books through Mofibo’s service

SOLUTION

Data-driven analysis of customer behavior and actions to encourage users to stay as well as to get them to read even more.

THE RESULT

 Rapidly increasing market share, better communication, increased customer loyalty and profitability.

The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.

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