Machine learning has been popular in marketing and sales for several years, and now it is transforming growth hacking. Why is machine learning important for growth? Because it enables smart and efficient growth. Machine learning can help you give your customers a better experience and improve your company’s bottom line.
Machine learning has many use cases – chatbots, self-driving cars, recommendation engines, credit scores, stock price predictions.
Professor Arthur Samuel first coined the term “machine learning” and defined it as a field of study that gives computers the ability to learn without being explicitly programmed.
The machine learning system learns from the data you feed it. You provide it information; it learns to make predictions. This is called model training.
You can use machine learning to solve many problems, including:
- Fraud detection
- Segment prediction
- Image classification
The Machine learning algorithm can identify patterns in the data it is fed. It then uses these patterns to provide predictions and probabilities.
For example, the algorithm can tell you if a customer will churn or retain based on an analysis of their past behavior. If you have these predictions, you can take action to avoid customer churn.
If there is a high probability, a customer will churn, you can try to engage them with a call or an email.
If you want your machine learning predictions to be accurate and effective, you need to update it with new data regularly.
There are two main types of machine learning – supervised learning and unsupervised learning. What is the key difference between the two? In unsupervised learning, there aren’t any labels.
Machine Learning Use Cases in Growth Hacking
Let’s take a look at how machine learning can be put to use for growth hacking.
As acquisition is the beginning of the funnel, it affects all your KPIs, so it is critical to get it right.
Machine learning can help to build more complex attribution models. It can help you find out the actual contribution of each of your acquisition channels. It allows you to calculate the contribution each ad has on converting a potential customer.
Using this information, you can build more efficient budget optimization algorithms and see which channel is most efficient for generating profit. Once your algorithm has enough data on customer sign-ups, it will also predict fraudulent sign-ups.
Machine learning can even help you to forecast new user numbers. This is useful for your financial and strategic planning and prioritization.
This is how the model for forecasting KPIs works:
You train the model using historical data and evaluate the model; this allows it to make predictions.
Machine learning can predict your customer activation probability. You can use this information to your advantage and inform your marketing strategy.
If a customer’s activation probability is just 5%, for example, you can send them an offer or information about products that are more relevant to them. You can use these methods to encourage activation amongst less engaged customers.
The more information you have about a customer, the easier it is to create a strategy to engage them.
Retention and Revenue
To improve your retention rate, you can make use of churn prediction. This shows you the likelihood that a customer will churn, and it can help you treat them differently. You will also be able to see what drives churn.
You want to focus on retaining your high lifetime value (LTV) customers. How does machine learning fit into this? With predictions about when you can expect the next purchase from a customer. This allows you to manage the frequency of your communications with customers.
Say someone is predicted to make a purchase, but it has been a few days since they should have made the predicted purchase. You can send them a reminder or offer to encourage them to make that purchase.
Recommendation engines are another excellent way to improve revenue and change customer behavior.
Why is this helpful? If you can predict your ROI, you can run campaigns that are focused on cost efficiency. Remember that retaining a customer is much more effective than acquiring a new one.
Another helpful input from machine learning in protecting your revenue? Fraud detection. If you use machine learning for fraud detection, you have visibility on activities that negatively affect your bottom line.
Machine learning allows you to segment your customers to see the frequency and monetary value of each one.
One of the pioneers of customer retention through machine learning, Netflix’s algorithm is used for personalized movie recommendations and the auto-generation of relevant thumbnails. Based on your viewing history, you are shown the thumbnail, which is most likely to get clicks.
Two other uses Netflix has for machine learning: to help improve their attribution and the stream quality for customers.
Supply and demand match. Predicting the demand can immensely improve the user experience.
Pre-prediction of demand helps to match the supply and to reduce the ETA of cars. Machine learning is also used for Uber’s route optimization.
Getting your algorithm right will take a lot of testing. You need to start with preparing the data that you will feed into the machine learning model. You can’t feed the data directly into the model; it will need lots of data cleanup beforehand.
There will be many iterations; you will build many models, do lots of experimentation, and A/B testing.
This will help you understand the accuracy of your model and whether or not it is solving your problem. You will need to build an API so your platforms can start consuming the data. Then you can integrate your CRM so it can automatically execute campaigns.
After a lot of testing and many iterations, you will have built an algorithm that allows you to efficiently market to potential customers, acquire those potential customers and retain them.