Netflix and chill, right?
Yeah, Netflix would even recommend the next episode to you based on your interests! No doubt they’ve managed to gather 220Mn+ paid subscribers!
Even Zomato has figured out what food are you going to order today, tomorrow and for eternity! They’ve also smartly used the data of our behaviour and patterns of ordering food, and now they just tempt you to order every single time they have an interaction.
One thing they’ve nailed is understanding customer expectations. And they’ve gone way too far with that.
However, with brands taking the bar so high on customer expectations, other brands also need to match the same expectations & bring something unique to the table!
But how will brands jump to this level of proactivity? How will they anticipate needs, trends & behaviours? Let’s learn from Netflix predictive analytics case study!
What is predictive analytics?
Predictive analytics analyzes current & historical data to make predictions using various statistical techniques- usually data mining, predictive modeling, and machine learning. Historically, it has helped brands understand customers and is also used to identify risks and opportunities and guides in the decision-making process.
Companies today are swamped with data stored and collected from various mediums and sources. To gain insights from this data, data scientists use deep learning and machine learning algorithms and make predictions about future events, and plan necessary strategies. Learnings gained through predictive analytics can be used further within prescriptive analytics to drive actions based on predictive insights.
87% of customers require active communication from companies. ~ inContact.
What does predictive analytics offer?
Predictive analytics allows businesses to look into the future with more accurate and reliable insights. At a macro-level, predictive analytics provides a lens into consumer behavior and purchase patterns, but businesses use it at a micro-level as well.
For example, retailers use predictive analytics to forecast inventory requirements and manage shipping schedules. Airlines use predictive analytics to set ticket prices based on past ticket trends. Marketing departments used predictive analytics to optimize product development, advertising, distribution and retailing, or marketing research. Predictive analytics can help attract, retain, and nurture customers at the most opportune moments.
It can also be used as a preventative measure. For example, in her interview with Engati CX, Tyler Cohen Wood mentioned that predictive analytics detects and halts malicious activities and criminal behavior in cyberspaces. When the model notices an unusual behavior pattern from the cybercriminal attempting to infiltrate a system, it fires an alert to cybersecurity teams immediately to resolve the issue.
But, what’s the difference between Predictive Analytics & Data analytics?
Data Analytics is the process of finding the logical patterns by applying various filters & models on the raw data.
On the other hand, Predictive Analytics is about making predictions about the future outcomes by understanding the past & the current data trends.
What data does Netflix capture?
For one of the shows, House of cards, Netflix captured
- 30 million “plays”
- 4 million ratings
- 3 million searches
Take a minute to digest that.
Now, let’s look at the data points that Netflix collects:
- Customer interactions on the app
- Responsiveness to shows/movies
- Date, time, location & the device being used to watch
- When & where you paused / resumed
- How many shows you complete / leave midway
- How many minutes / hours / days / weeks you take to complete a series / movie
- How many times you search before choosing the show / movie
- Queries you use to search your shows / movies
- Shows preferred by men / women / children / teenagers
- Feedback & ratings of subscribers
- Scrolling behaviour
And many more…
Based on the research conducted by Netflix, their personalized recommendations have turned out accurate for 75% of subscribers!
How Netflix uses data analytics?
Netflix uses AI-powered algorithms to make predictions based on the user’s watch history, search history, demographics, ratings, and preferences. These predictions shows with 80% accuracy what the user might be interested in seeing next.
1. Personalized recommendation engine
We’ve already seen the data points that Netflix captures in the above section. A series of algorithms are applied on this data & based on the subscriber’s viewing preferences, Netflix is able to predict what you’re likely to watch next!
Some examples of the algorithms that Netflix uses are Personalized Video Ranking, Trending Now Ranker, Continue Watching Ranker, etc.
2. Content Development Analytics
Based on the shows / movies that are performing really well and are picking up with the audience, Netflix uses a projection model to select which projects to invest and work on!
Stranger Things, Squid Game, and other shows that touched the sky - have all gone through the grind (and marketed phenomenally)!
3. Operations Optimization
Netflix uses data analytics to optimize the ground logistics for the shoots of the shows / movies. Just like projecting the shows that’d turn out to be successful, they have built algorithms that help them project the costs of filming in one location v/s another location.
Even the post-production activities are analysed using data, and performed with best productivity.
4. Customized marketing
For the show - House of Cards, Netflix created more than 10 versions for the show’s trailer. If you watched a lot of shows centered around women, you’ll be shown the trailer that’s focussed on female characters, and so on.
Mind blown, right?
5. Artwork & imagery selection
Netlfix uses the AVA tool (Aesthetics Visuals Analysis) that checks the entire video & identifies the frames that can be used as artworks.
How can predictive analytics be used in business?
Now that you know how to create predictive analytic models, here’s how they can drive business performance.
1. Highly-personalized marketing
Imagine using a model that can monitor customer behavior at both a micro and macro level. Customers expect this kind of service as it makes the experiences more convenient and enjoyable. Predictive analytics enables you to carry this out. Personalization can only be effective when it’s based on quality data. Use this data and these insights to deliver hyper-personalized messages to the right customer at the right time and place. Analytics consulting services or agency can help organizations with this in an efficient way.
2. Forecasting needs
Building on this, predictive analytics can anticipate the needs of your customers before your customer does. Predictive analytics makes it possible for businesses to forecast customer needs based on purchase history, search history, interests, demography, and more. This is what makes Netflix so successful.
3. Reduces churn
As we’ve mentioned above, predictive analytics is marvelous at identifying malware and abnormal, risky behavior. But this mechanism can also be applied to flighty customers. Leaders can use analytics to predict when a once-promoter may turn into a detractor before your agents can. Once the abnormal behavior is detected, the model can alert your customer service leaders to pay extra attention to these customers. It enables businesses to take a proactive approach to reduce churn and customer attrition.
4. Better efficiency and resource allocation
Predictive analytics can significantly improve internal operations efficiency to enhance the customer experience. The smoother the operation, the faster the service. Having efficient internal operations can help ensure that the customer receives quality service without any fuss. The model can help staff within the contact center by forecasting inventory needs, for example.
By introducing a predictive analytics model, you can further boost employee productivity to give you an edge over your competitors.
5. Pre-emptive support
The model can predict significant events in the customer life cycle to increase revenue in these critical times. For example, an insurance company will send out alerts for car insurance or driver’s tests when they’re aware of a family’s child coming of age. Providing recommendations at these turning points of a customer’s life can give you an edge.
6. Handling feedback
As the model becomes more sophisticated with the data being fed into it, it can act on real-time feedback to deliver ultra-personalized recommendations. The customer’s actions, such as jumping from one category to another on an e-commerce website, immediately impact the model and will affect the following recommendations they receive. These trends can easily be identified and acted on by the model.
7. Developing pricing models
Insurance companies typically use predictive models to determine the optimal pricing model for their clients. There’s a telematics program called Snapshot that uses in-car sensors to determine to price. The data from the model personalizes the rate for each customer based on their skill level when it comes to driving. Someone who drives less often and stays close to home is likely to have a lower rate than someone who’s always on the road and likes to speed.
8. Providing a lens into the future
We’ve spoken about predictive intelligence at a fairly micro-level, but its scope is endless. Organizations can use it to track and predict customer behavior trends to create an experience like never before. The current trends suggest that because of the forced digital transformation and migration into our devices, customers demand speed and agility, and care. Customer experience is going to dominate each industry. So, how can you get started?
Impact of data & analytics?
Well, Netflix managed to get a 93% retention rate. That’s beyond imagination.