A Growth Hacking Case: Netflix & Facebook
What is product growth?
A lead converting to an active user & regularly taking action around the core value of the product, is the outcome of product growth. Further, users also impact the revenue model of the product as they stick to product values that satisfy their needs and experience.
The onset of product growth hacking
The concept of growth hacking flourished primarily post dot-com bubble. Snapchat, Burbn (former Instagram), Paypal, Dropbox, Airbnb, Facebook are a few names that initially grew massively through growth hacking. Organic growth was not enough for them to onboard millions of users without virality and incentivizing the users. Unlike traditional marketing approaches, for example, billboards, positioning, TV/Newspaper/online ads and press releases, growth hacking was data-driven, cost-effective, and consists a small test window and iterative in nature that help growth hackers to assess many ideas quickly and pick a few that suited best.
A growth hacker role?
A growth hacker is a balanced combination of a product manager and a marketer.
Growth Hacking in Nutshell
Growth hacking is data-driven, rapid testing & experimentation that is run on various channels to validate certain user behaviours and ideas that can be seized for product growth. One or multiple experiments are run simultaneously for short time frames. The risks associated with the cost and time remain low as compared to traditional marketing, and there is always an opportunity to drop experiments quickly that don’t meet the expected success criteria. The approach keeps the cost of acquisition (CAC) low, and thus, overall affects gross margin positively. As explained, the base of product hacking is rapid testing and experimentation which makes users stick to the product amidst several similar substitutes in the market.
How does user behaviour affect growth hacking?
Every morning people scroll through their smartphone feeds of LinkedIn, Facebook, Instagram, or they randomly bump on the landing pages of several sales promotions, but finally, opt for an option that meets their expectations and experience. Also, to escape the weekend boredom, an Instagram user posts a reel/story and further boosts the post to reach out to more users/followers or a number of likes, this is growth hacking example. Not to mention the ongoing competition in India’s tier 1 cities over digital supply chain innovation of Ola, Zepto, Blinkit to deliver goods in 10 minutes are some aggressive hacking examples. Multiple experiments are done to conclude 10 minutes delivery, most frequently placed items by average household every day, and the weight or number of items that can easily be delivered in 10 minutes. Additionally, the growth hacking strategy contributes to uplifting of the UX and the core values of the products based on the people behaviour exhibited through pass or fail results of the experiments.
💡Tip: Growth hacking works well when the product-market fit is qualified. When a product grows organically, it gets a product-market fit that gives enough assurance of the demand of the product. This is the right time to build your growth hacking team. Secondly, avoid spreading the growth hacking experiments that are tightly coupled on multiple channels as it becomes tough to analyze the results. For example, simultaneously running A/B test (type of experiment) on the landing page for changing the colour of the CTA (call to action) button along with running the pre-order promotion on the landing page would give hard time to growth team to recognize the right source that affects the conversion rate.
Before I jump to frequently asked product growth cases, let’s understand growth hacking through a real-time example. Also, I am sharing the 6 pillars of the growth hacking fundamentals that can be used to frame and solve any case using it. These are-
- Brainstorm ideas
- Categorize by channel & behaviour
- Cost & time
- Prioritization
- Rank ideas by risk/reward
- Test result
Let’s solve a real-time growth hacking problem from a day-to-day example.
Case: How can a content creator organically grow x% of subscribers WOW (week on week) for published articles on Medium?
The expected answer:
I’ll brainstorm a few ideas to acquire users and try to run the experiment based on the 6 pillars of growth hacking (Image 1). I would finally pick and apply only those ideas which passed the experimentation stage. The pass and fail criteria will be decided based on the Test results.
Note: To demonstrate how simple growth hacking decisions are made using data, I’ve collected below data from Medium stats board (Image 2). For bigger experiments, data comes from multiple sources — A/B testing, landing page, digital promotions, these collectively help in decision making.
The above insights help a content writer to evaluate the frequent readers’ interests, effective traffic sources to acquire subscribers, MOM growth, view stats, read ratio by articles. Hence, the insights help in deciding the channel and topics the writer should focus on to get subscribers easily without investing in paid inorganic growth.
💡 By now you must have a fair idea of tips & tactics for solving a growth hacking case. Let’s move to more business cases asked during the interviews.
Case 1: The Netflix marketing team observed that there are many people ordering food when they are watching movies on Netflix. In what ways Netflix growth lead can use this opportunity?
Tactics: Quickly spread these ideas across 6 pillars and explain interviewer your ideas and how will you implement them through testing and prioritization.
💡Tips: In the example case (Image 1), I have considered costs in terms of opportunity cost, but if the interviewer gives any hint about marketing budget and manpower effort, it is easy to allocate monetary budget across each idea hypothesis.
Candidate:
1- User behaviour: People like watching a movie with food
Test hypothesis/Idea 1: Incentivise users with food coupons (e.g Swiggy/Zomato) for the first few subscription renewals or a new subscription.
Test hypothesis/Idea 2: Incentivise users with personalized food coupons for their choice of food preferred during movie time at the time of subscription renewal or buying a new subscription.
Priority: [Mid value, Mid effort]
Measure: Conversion rate, engagement, retention, viewership time
2- User behaviour: People like watching movies on Netflix with their favourite food
Test hypothesis/Idea3: Provide pay per watch experience (Flexi subscriptions) with food combo/coupon too while buying the Flexi subscription.
Priority : [High Value, High effort]
Measure: conversion rate through Flexi, Conversion rate, LTV, CAC, viewership time, retention, engagement rate through Flexi
Case 2: How would you increase the number of comments in Facebook groups?
Candidate :
1- User behaviour: people like being recognized
Test Hypothesis 1: Introduce group contributor ranking & badges
Test Hypothesis 2: Special Highlights to impactful contributions made by group members
Priority : [Med Value, Mid effort]
Measures: #/% of no of comments increased weekly/monthly, group engagement rate, daily active users via groups
2- User behaviour: More likes give a sense of fulfilment and pride
Test Hypothesis Idea 3: Highlight trending comments which have more likes/share
Priority: [High Value, Med effort]
Measure: increased comment share, #/% of no of comments increased weekly/monthly, group engagement rate, Daily active users via groups
3-User behaviour: People like group engagement activities
Test Hypothesis Idea 3: Allow users to create Incentive-based group engagement through win hampers, masterclass, or influencer connect
Priority : [High Value, High effort]
Measures: Increased comment share, #/% of no of comments increased weekly/monthly, group engagement rate, DAU via groups
4-User behaviour: People like viewing live videos
Test Hypothesis/Idea 4: Allow users to conduct live influencer video engagements around meaningful content towards the goals of the goal
Priority : [High Value, low effort]
Measure: % of comments increased daily, group engagement rate, average share rate, DAU via groups
5- User behaviour: People like to remain updated with group activities and discussion insights
Test Hypothesis/Idea 5: Users like receiving emails on the group activity
Test Hypothesis/Idea 6: Users like to see the views, stats about the trending group activities and comments
Priority : [Med Value, Mid effort]
Measure: Click-through rate, group engagement rate, average ahare rate, DAU via groups
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