3 ways you can use machine learning in your marketing today!

While machine learning and artificial intelligence are making news every now and then, it hasn’t changed the way we do marketing.

Most marketers rely on the pre-built models in tools to guide their way through machine learning. While there are challenges in terms of learning python and implementing a lot of the machine learning models due to data paucity, there are some problems that marketers can right away solve with machine learning.

So here we go;

1.Forecasting sales: When it comes to forecasting sales, and defining sales targets, marketers rely either on simple time-series forecasts, boss’s fancy, or plain old astrology. There are better models that we can employ especially when historic data is handy. One of the most popular models used is SARIMAX which means Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors

While the terminology might be difficult to grasp, SARIMAX basically takes into account the seasonal factor and makes your time series into stationary data, and tries to predict future values on the basis of autocorrelation and passive auto-correction. In layman’s terms, it tries to understand the correlation of its values (sales in a given period in this example) to its past values and also understands the effect of a time series value from its standalone previous period’s value.

You can combine this model with some exogenous variables that might affect your sales.

Apart from SARIMAX, you can also use linear regression or other supervised regression models to predict continuous variables like your sales.

Note: You can use the same concept to forecast any time series eg: stock price, cost or any other time series.

2. Customer Segmentation: Machine learning algorithms not only allow you to forecast sales but also segment your users on the basis of the parameters defined by you.

Here both supervised and unsupervised machine learning models would come in handy but I would suggest using supervised learning models as you would have an easier time explaining that to the stakeholders in your company.

Under supervised machine learning models, you can use KNN classifier or logistic regression to classify your customers into classes.

You can start with a rudimentary RFM (Recency, Frequency, and Monetary Value) model if you don’t wish to implement a complicated model in the first place. RFM model basically divides your customer data into cohorts of Recency, Frequency, and Monetary Value (revenue or profit) and gives ranks to your customers on the basis of these 3 parameters. Post that we sum up these ranks to get the total score. This total score can be thought of as a priority score that you give to your customers.

This way you can know which customers are the most frequent, least recent and give the maximum revenue or profit.

A KNNclassifier model can be used for a classification problem and essentially can predict classes on the basis of the data that you give to the algorithm. One such use case is to predict the churn (Yes/No) class. Since a customer will either convert or won’t, this is a case of binary classification. You then supply the data related to the customers and their status of Churn v/s no-churn. This is helpful for your model to learn the relationship between what makes a customer churn and what doesn’t.

3. Finding CLV: In order to understand the amount of money you can afford to spend to get a customer on board, the understanding of a Customer Lifetime Value is very important. This can be based on your profit margin or else you can base this on the revenue that you get from a customer in his lifetime as well.

Customer lifetime value = Average revenue * Retention Rate/Churn Rate

Note that there are other more complicated models as well to calculate CLV but the equation on the top can serve as a good starting point. Once you have the Customer lifetime values with respect to all customers, you can use regression models to predict it for others given variables like country, age, gender of the customer in your database.

It doesn’t stop here. Apart from the use cases mentioned above, there are NLP libraries that you can utilize to understand the intent of uses from textual data and do sentiment analysis on the reviews of your brand.

Some of these models might give you predictable results and some might give you valuable insights into your data but these models should be applied with caution. Domain expertise goes a long way when you are applying these models to practice. These models follow a GIGO (garbage-in garbage-out) philosophy, so if you plan to apply a regression model to data that doesn’t make too much sense, the results won’t make sense either.