“Watch” out for these apps
A take on movie/tv review and rating prototype using the “Hook” model
Netflix, Amazon Prime, HBO, Hulu, Youtube, etc. have become household names especially during the pandemic. Netflix and Chill now has a literal meaning of watching tv shows and movies on Netflix(or any other OTT platform for that matter) from the comfort of our homes.
But one of the biggest questions that we all face in our daily life is “What do I watch” which leads us to hours of listicles, recommendations from friends or some random picks, just to be disappointed at the end of half an hour.
What if there was that one app that can predict what we would like depending on our mood, the occasion, our preferences and our watch history. Ofcourse this would be location specific and cater to people across age groups and demographic.
The Hook Model
The Hook Model is a four-phase process that businesses can use to create products or services used habitually by customers. The goal is to result in voluntary, high-frequency engagement. At its core, the Hook Model is about creating a customer habit. It also seeks to connect a customer’s problem to a company’s solution with enough frequency to make the engagement an ongoing practice.
Let’s take this model to design a prototype of what the perfect TV/Movie Recommendation App would look like.
Trigger
Trigger is the first step of the model and it cues the user to take a certain action.
Triggers can be of two kinds:
a. Internal Triggers : Internal triggers tell the users what to do next through associations stored in the user’s memory.
In this case, internal triggers for a TV Show/Movie recommendation app can be :
→ Boredom : When a user experiences boredom, an internal trigger sets them to pick up the phone/tab/laptop and search for TV/Movie show recommendations.
→ FOMO : When a user has heard of recommendations or rave reviews about a show or movie, they feel left out for not having watched it and hence would pick up their phone/tab/laptop to research on the tv show or movie
b. External Triggers : External triggers tell the users what to do next by placing information within the user’s environment.
→ Paid Triggers : Since the intention is to create a habit forming app, we will not rely on paid triggers such as search engine marketing for the TV/Movie Recommendation App
→ Earned Triggers : A good recommendation app will not require earned triggers in the form of social media mentions, viral videos, etc.
→ Relationship Triggers : This is a very powerful trigger when it comes to a recommendation app. Shows that peers recommend and rate highly can lead to a high retention rate for the app
→ Owned Triggers : Owned triggers are the most important type to bring back repeat users to the app. Frequent newsletters , nudges and reminders from the recommendation app could ensure that users are hooked to the app
Now that the users are triggered through internal or external sources, the next journey is the action that they will perform
Actions
Now that I’ve picked up the app to look for a good recommendation, the next step is the “Action” that I should be able to perform. This can be defined by Fogg’s Behavioral Model :
B = M *A *T
Not GMAT silly, B=MAT
Behavior = Motivation * Ability * Trigger
a. Motivation
There are 3 core motivators :
- Seeking pleasure and avoiding pain
- Seeking hope and avoiding fear
- Seeking social acceptance while avoiding social rejection
The highlighted motivators are necessary in order for the user to perform an action on the recommendations app
b. Ability
Ability of the user to perform an action depends on 6 key factors :
1. Time: how long will it take for the user to find a good TV show/movie to watch
2. Money: Do they have to pay a subscription or a rental fee to watch their desired show
3. Physical Effort: Do they have to go to an external location or walk to the TV to watch something?
4. Brain Cycles: Are they mentally prepared to try out some new movie or show?
5. Social Deviance: Do other people approve of what they are about to watch ? (Cue : Guilty pleasure reality tv shows or movies :P)
6. Non-routine: Will staying up late and watching something affect their routine on that day or the next?
c. Trigger
As discussed, internal and external triggers are necessary for users to perform a particular action
Another factor that contributes towards a user performing an action is :
Heuristics and Perception
Heuristics is mental shortcuts we take to make decisions and form opinions. A few of these effects are :
a. Scarcity Effect :
When a user browses the app and sees an infinite list of all the tv shows/movies available vs the first thing they see is 5 curated shows personalised for them based on multiple factors, which option do you think they would prefer?
b. Framing Effect :
Consider a movie with a multi star cast streaming on youtube for free vs the same movie premiering on a subscription platform such as Netflix. Which option would the user choose. The way a recommendation is framed as a deep effect on the decisions that the user makes
c. Endowed Progress Effect :
What if the app adds a star or a tick mark each time the user watches a recommendation with a reward at the end of the ladder? Would this encourage the user to watch more recommendations?
The answer is yes
The next stop in our journey is :
Variable Reward
To keep users “Hooked” to the product, a Variable Reward can be used to reinforce motivation for the actions that the user takes.
There are 3 types of variable rewards :
a. Rewards of the Tribe : Our brains are adapted to seek rewards that make us feel accepted, attractive, important and included. As a result, features in the app like forums, discussion threads, reviews or shares with friends makes us feel socially relevant and part of a community.
b. Rewards of the Hunt : In today’s day and age, information and knowledge is the reward of the hunt for more content . It is important that the recommendations are personalised enough for the users to feel rewarded after watching the content
c. Rewards of the Self : We are rewarded with a mental switch off and satisfaction of time well spent when we watch a good show/movie
Investment
Unlike the action phase, which delivers immediate gratification, the investment phase concerns the expectation of a future benefit. Investments increase the likelihood of users returning by improving the service the more it is used. Investments “store value” in the form of :
a. Content : Every time users of the recommendation app, mark a show/movie as “watched”, they are strengthening ties to the service. The shows on the “Watched” list are an example of how content increases the value of a service
b. Data : The more information users enter into the site, the more are they actively invested in it
c. Followers : Nobody wants to abandon a platform in which they have spent effort and time building and curating an audience or a network
d. Reputations : Will it be the Goodreads for content recommendations?
And thus, investing in good features and content on the app will act as triggers for user retention and re-engagement on the app.
