The Insanely Useful Guide for Using Machine Learning to Growth Hack

A deep dive into no-code AI for growth hacking.

There’s a saying in the startup world that goes something like, “In business, if you’re not growing, you’re dying." However, we're going to tweak that expression a bit.

As a fellow startup, we know this to be true. The challenges Obviously AI faces are probably different from your company, but the way to approach it is the same: Data.

We've learned that if you're not collecting data, you're dying. If you're not collecting data and taking action from it, you're wasting time and resources—and inevitably—will run out of time and resources. If you are collecting data, it's time to use your data to growth hack with machine learning.

Now that data predictions are available to everyone, there’s no excuse not to use ML to add some sugar and spice to your growth hacking. The best part is you don’t even need a technical background.

‍You just need a dataset.

You can make predictions on competitor data, marketing campaigns, build customer personas, and predict user behavior—all ground level stuff that contributes to growth.

In this post we will cover how to use machine learning for:

Finding your PMF
Customer Acquisition
Personas
Customer Retention
Content Optimization and Personalization

Come on, let’s learn how to grow. Here are ways to use data and inspire creativity for growth marketing. 🌱

Using Competitor Data to Predict Product Market Fit

This is one of the most valuable things you can do right out of the gate, and you don’t even need a personal dataset.

With resources like Kaggle, and Google Datasets, you can use public data to get answers of how to position your product.

We already have clean datasets inside our Data Store to get some ideas, but for a more specific approach, we will use an app store dataset to determine what factors go into a 5 star rating.

You can predict the factors that contribute to a 5 star rating and see the likelihood of receiving a 5 star rating based on app genre, supporting devices, and more. This is great for the ideation process and begins the PMF thought process of how to position your product. With a dataset like this, you can identify gaps in competition and where your product fits to have the most success right out of the gate.

Read more about how to guide your app building with no-code data predictions.

Depending on what space you’re in, you can make any type of prediction. Remember, this whole blog is dedicated to unleashing creativity with data predictions.

Here, we used retail store data to predict what contributes to sales. These kind of predictions will help you ideate what kind of product you want to sell and a baseline of what metrics to look for.

Based on the product, you can predict sales for each category. This instantly shows what categories of products are the most profitable in this retail store with this dataset.

Another thing you can use public data for is to find price equilibrium. We will use a publicly available dataset on Airbnb homes and predict what your price should be based on the factors in the dataset.

This helps set a competitive price right from the beginning.

Machine Learning for Customer Acquisition

Measuring the customer LTV is insanely valuable for knowing which kind of customers to target for marketing.

From the very beginning of the customer acquisition phase, you should be collecting data on your customer’s attributes so you can see which have the highest impact on customer LTV. Knowing this is integral to guiding your marketing and customer acquisition efforts.

Take this Prediction Report for example. This is a publicly available dataset from an auto insurance company. We can see the top drivers for LTV and predict how much value each customer with 2 policies will have compared to 3, 4, or 5.

You can also see which demographics are your best customers by gender, income, employment status, location, and more.

Creating Customer Personas

Building personas are perhaps the most exciting thing you can do with machine learning. You can build employee personas and predict the likelihood of a customer performing an action.

In your Prediction Report, you can click on the “Personas” tab and begin to predict the future.

You can toggle through attributes in the dataset to see the likelihood of a customer performing an action.

This is useful for marketing campaigns as well and comparing customer probabilities side by side. You can simply save the persona to use for next time you make predictions as well.

Read more on creating customer personas.

Machine Learning for Customer Retention

In the past, we’ve talked about churn a lot because it’s one of the most valuable predictions machine learning can provide. Customer retention is the key to growth. If you can get your customer to engage and be loyal to your product, they will have higher LTV and are more likely to refer your product.

You can predict when a customer will cancel their subscription and see what attributes impact cancellation.

Here you can see the customer’s likelihood of churning if the monthly charges were $61.22. Exploring a Prediction Report like this is a genius way to get in the head of your customers and seeing who you need to target to prevent them from churning.

Machine Learning for Content Optimization

There are so many ways to get insight out of content data, and it’s one of the easiest areas to collect data on if you’re using Google Analytics or an SEO tool like Ahrefs.

Take this Udemy dataset, exploring the number of subscribers. If you don’t know Udemy is an online education platform. This dataset is taken from 3,682 course listings from 4 different subjects.

Here you can quickly see what kind of courses have the highest predicted number of subscribers.

You can also see the optimal content duration to get the maximum number of subscribers with this one prediction.

Content metrics are abundant online. To make predictions using your content metrics there are tools like Hotjar and Ahrefs that allow you to export data into a CSV file. From there you can start making predictions using Obviously AI.

A dataset that has columns like this (domain rating, referring domains, traffic, traffic value, Twitter and FB shares) are key to seeing what makes your content successful.

You can explore ways to personalize your messaging by cross referencing content data and customer personas.

Read more on personalized messaging here.

Use Custom Algorithms and Automate Preprocessing Data

Mediocre machine learning doesn’t equal great insight. We want your data predictions to be as accurate as possible and we do whatever in our power to make sure you don't make an inaccurate prediction. That's why we set some data requisites to ensure your data prediction is as accurate as possible.

Inside Obviously AI, before you make a prediction, you can customize your algorithm by using filters and taking out columns from the dataset you don’t want to use.

This can all be done in our Data Dialog. You can tailor the prediction to your needs without ever looking at code.

After pressing the “Start Predicting” button, we preprocess your data, train your model, and test for accuracy in under a minute.

We also allow you to see the tech specs of the algorithm to know the accuracy of your prediction.

Additionally, you can see and download your custom decision to see how you arrived at your prediction.

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Read more on how our no-code algorithms work.

These Are Just Some of the Things Marketing with ML Can Do

Yes, it’s hard to believe. We didn’t cover everything you can do with marketing data and machine learning. But, this blog post is over 1,000 words now and your attention span might be waning.

If you want more inspiration, read our marketing case study to see what else you can do with Obviously AI.

Also some worthy reads related to marketing and machine learning

Lastly, if you know other ways machine learning can help with your marketing, feel free to reach out to us and tell us about it!