No observer of the TV industry in 2019 doubts that D2C (direct to consumer) is having its day. Driven by the extraordinary success of Netflix and its rivals such as Hulu, lots of long-established programming groups are now offering their own D2C services instead of or as an accompaniment to the linear TV experience offering shows to watch rather than channels. Pundits are counting on Disney's forthcoming service to shake-up the industry, but the reality is that D2C is already a shake-up in itself.
Implementing an D2C service represents a significant change for programmers, who are effectively now in a new business, something they often underestimate. While the hype around D2C and D2C generally might make it look like an easy way to success, the reality is that launching a successful D2C business is not as simple as Netflix have made it seem.
Our data science consulting team says that with D2C, these companies now own the customer relationship, and, because of this, need to communicate to their customers to discover what they want from their TV experience. They are also now directly responsible for doing an initial data audit, ensuring accurate reporting, creating a Customerr Lifetime View (CLV) and delivering the critical goals of customer acquisition and retention for subscribers to their D2C services. All these key activities need to be grounded in the data the company has, which once the D2C service is up and running will be considerably more than the data they had (or still have) from any linear services. Unquestionably the great attraction of D2C, apart from its high popularity among customers who like to choose what they want to watch and when, is the data. However, to get the best out of these data, companies need to have a firm grasp of best data sciences practices in using data and in the technologies which generate and process the data as well as develop a data strategy and its own data initiatives.
Data audit and reporting
Doing a data audit is the first step companies should take when implementing or planning to implement D2C, with the long-term goal being to improve customer acquisition and retention. This audit should begin by looking at business-as-usual (BAU) reports and then working out how ideal BAU reports would look. These ideal reports can then represent the goals the company should have for how its data functions, stating which technologies it will use, followed up by seeing what the company would like to achieve with these new OTT data. If the team can identify the gaps between their ambitions and the reality of the data, this is the essence of the data audit.
What factors drive customers to engage with a particular business? Or to decide to no longer do so? Companies need to define which metrics are the key to understanding customers and to assess what reporting the company is already doing and how they can improve upon their reports by using the data to answer these questions about customers. The reporting is particularly important because in the vast majority of businesses it is not the data scientists who make the critical decisions which will affect customer metrics, it is the high-level executives who make all the crucial decisions. They need the best and most comprehensive possible reports. To a large extent, the job of the data scientists is to give these executives easy-to-read reports which allow them to use the data to help them in making these critical decisions. The best place to start, assuming reporting is already happening, is to review the mandatory reporting and look for easy ways to improve it. By implementing a BAU review of the complete dataset, there may also figure out further ways to improve on the reporting.
Customer lifetime value
D2C data fundamentally helps companies understand their customers, but this needs a framework so everybody in the business can better comprehend what factors are making customers sign-up to your service or stop subscribing. This is also where having a Customer Lifetime Value (CLV) formula. The CLV is an estimate of the monetary value of the worth of an average single customer is going to be to a company during the entire period that they are customers of that company. The CLV can then be applied to each acquired customer and can be an advantageous asset, especially for communicating with customers. With this CLV in place, we recommend starting an initiative which encourages users to sign-up for the service on offer by the company and that this initiative is then used to drive forward a vision of a Single Customer View, where the company can easily view the data on each customer on a single page.
Customer acquisition and retention
The next task is to build models which can help the business acquire and retain customers. Examples of these models include building a model to identify customers, both actual and potential, who are likely to experience churn or a model identifying the main reasons why customers have signed up for the service in the past, noting the reasons, with percentages of customers giving each reason who then left the service. So if 40% of those who don't stay signed-up for your service because of the sports coverage and only 10% because of the dramas then the company likely needs to focus on improving its sports coverage to retain the maximum number of customers. Another critical area to look at is the process which drove a customer to choose your service. Did they want an D2C service and then pick you from a range of possibilities? Or was there some other factor than the D2C which brought them to your D2C, such as loyalty to your brand, where they use your linear service and then switch to your D2C service.
Taking another example, if a customer comes to your website and then signs-up for S2C from a Facebook ad, a simple explanation would attribute that customer to the Facebook ad, with all the ROI implications this brings, but the reality of what brought the customer to your service via the Facebook may be more complex. Perhaps they had been thinking of subscribing to a TV service, and the Facebook ad got the customer to act. Also of interest, is seeing what percentage of acquired customers through Facebook ads then don't renew their subscriptions or how many customers who signed up through brand loyalty or through wanting a TV service then subsequently don't renew their subscription. The clearer the picture the data science team can build of customer motivations, the better the understanding will be for the business. The key elements are learning where the customer came from, why they signed up, following the conversion rate, i.e., what converts someone who sees say your Facebook ad into a paying customer, or the person who visits your website (whether from Facebook or not) into a paying customer. Finally, there is the retention rate (or usage rate), which tries to identify who are the most loyal customers and then looks at where they came from and why they signed up.
Any data strategy should be pro-active and include data initiatives, particularly when an established company is making its first entrance into STC. By focussing on the customer and developing the models and formulas which we have outlined in this article, companies can give themselves a fighting chance of offering a successful DTC service that customers will want to sign-up to and won't want to leave.