A type os exposé for current churn rates in streaming services, for example Netf

A type os exposé for current churn rates in streaming services, for example Netflix, Disney and HBO Max. What is the current
situation? What are the reasons for the problem? Will churning get worse? How can this be prevented?
Netflix
is one of the best places to watch movies, TV shows, and
documentaries. But it is also a place where you can cancel your
subscription anytime. This has been termed Churn Rate, and it has
been increasing on Netflix for the past few years. There are
several
reasons behind this increase; the most obvious is that many people
are turning towards other similar services, such as Hulu or Prime
Video (Seal, 2022). A major reason could be Netflix’s lack of new
content, because of which people have decided to turn towards other
streaming services. Another reason could be that people don’t want
to pay for Netflix anymore because they have
already watched all
their favorite shows on that platform. Some customer has also pointed
to price hikes and poor content selection as reasons for the surge in
churn rate and consumer exodus. Some analysts suggest that Netflix
has made some unpopular decisions in recent years, but it’s a
mistake to blame these things for its declining subscriber base
(Arkenberg et al., 2022). Their failure to
meet the expectations of
modern consumers is fueling their churn problem. Netflix has always
been on top of trends in technology, from streaming video to content
distribution deals. Still, it’s time for them to catch up with the
way people watch TV today by introducing new features that give
subscribers more control over their viewing experience (Fitzgerald
2022). The paper intends to
provide the necessary insights for the
management of Netflix to understand the churn problem and help them
conceive a strategy
that will reduce the churn rate and help them
retain more subscribers. But can this danger be also a problem for other streaming
services like Disney Plus, since competition is rising?
When
a subscriber decides to terminate their subscription, the term
“churn” describes the resulting situation. These are challenges
that subscription Video on Demand (SVOD) firms such as Netflix, which
may pay up to US$200 per member to attract them,
though acquisition
fees vary by area, must overcome (Kweon,2021).
Because the number of SVOD providers is expanding while
untapped
users are diminishing, customer retention is becoming even more
crucial nowadays.
To replenish lost subscribers, the acquisition engine continues to grind and expenses mount. As services reach for customers
beyond the core segments they may have initially targeted, the service offerings may not resonate with new subscribers over
the same durations of time or may overwhelm the ability of these subscribers to consume, reinforcing the overall problem of
subscriber churn.
Churn management is a prominent analytical marketing tool to fight attrition, inform resource allocation decisions, and
eventually increase firm performance. A churn model predicts whether a customer will defect in the near future. The firm then
targets the most risky customers with retention campaigns to prevent attrition (e.g., Ganesh et al. 2000; Libai et al. 2009;
Shaffer and Zhang 2002). Targeted marketing actions toward risky customers can significantly reduce churn rates and increase
firm profits (Burez and Van den Poel 2007).IESEG Working Paper Series 2013-MARK-06 Marketing analysts can choose from a
variety of forecasting methods to build churn models. The choice of the modeling technique is important because it has a
direct impact on prediction quality and thus on the profitability of all subsequent targeted marketing efforts (e.g., Neslin et al.
2006; Risselada et al. 2010). Many studies have thus compared different methods to identify a ‘best’ churn modeling technique; Verbeke et al. (2012) provide a comprehensive overview. This approach seeks profit improvements through more accurate
forecasts that, in turn, result from better methods. In this work, we advocate a more direct link between churn modeling and
campaign profitability.
In general, churn models can be grouped into two categories, explanatory and predictive. Approaches of the first category
develop models to explain churn patterns on the basis of various constructs, including the firms’ marketing activities (Lewis
2004), customer knowledge (Capraro et al. 2003), or attitudinal concepts such as satisfaction (Bolton 1998; Gustafsson et al.
2005) or perceived quality (Zeithaml et al. 1996). Understanding the sources of defection is important to improve customer-
centric business processes and reduce attrition rates in the long run. However, these models are less suitable to support
operational business decisions in the short run.

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