Iliya Valchanov

Online stores are a gold mine of data for applying AI and deep learning in the retail and e-commerce business. Each data point builds a detailed understanding of customer habits. To harness this data, the most successful e-commerce companies leverage not only data science basics, but also deep learning techniques. Deep learning and AI can provide business-critical predictions like whether or not a customer will buy again. 

Any business can capitalize on deep learning techniques as long as two criteria are met: 

  1. Access to a large volume of data
  2. Investment in the infrastructure and the people who can make sense of that data

Luckily, ad providers like Facebook and Google allow small businesses to collect data with almost zero effort simply by pasting a script on their website. Additionally, Google Ads and Facebook Ads provide access to deep learning-based purchase intent models to all business customers on their platforms. So, although your team may not build the deep learning model, it can leverage the technology developed by these tech giants. This democratization of AI has reinvented marketing by creating new subfields like customer analytics. It’s also positioned machine and deep learning as a key player in e-commerce for the upcoming years. In the Udemy course Customer Analytics in Python we explain how to leverage deep learning to glean personalized customer insights.

Customer Analytics in Python 2020

Last Updated June 2020

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  • 77 lectures
  • Intermediate Level
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Beginner and Advanced Customer Analytics in Python: PCA, K-means Clustering, Elasticity Modeling & Deep Neural Networks | By 365 Careers, 365 Careers Team

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What data does the e-commerce industry use?

In the e-commerce world, customer data is abundant. Companies can leverage all types of data for a given customer — from demographics and geolocation to income range. Smartphone apps and cookies embedded into websites can recognize customers’ devices and build profiles for brands and ad platforms based on customer preferences. 

Some of this data can even be used as a proxy for more important indicators to derive customer insights. For instance, a customer using the latest iPhone model can be assumed to make a higher income bracket than a customer using a five-year-old iPhone model.

Rich data like this allows companies to fine-tune data insights to better understand and serve customers. However, a data analyst can slice and dice the data all day, but that would not allow them to generate a reliable prediction on customers’ future purchasing behavior on an individual level. This is where a nuanced use of data — built on deep learning algorithms — can play an important role. How can companies take advantage of this information about their customers? Enter deep learning — an outstanding resource for predicting purchase intent.

How does deep learning inform purchase intent?

As a subfield of artificial intelligence, deep learning has been instrumental in some of the most transformational products available today. Self-driving cars, facial recognition, and translation apps are just some examples of consumer-facing offerings based on deep learning techniques that are already available. 

But the applications of deep learning are not reserved for high-tech products only. In fact, many e-commerce companies are empowering their marketing and sales teams through deep learning technology. These techniques are used most often when predicting purchase behavior at an individual consumer level.

This brings us to purchase intent. Purchase intent shows whether a customer is ready to purchase a product. For example, when you enter a board game shop, your purchase intent to buy a board game is high simply because of the nature of the store you visited. On the other hand, visiting a grocery store implies that you want something to eat, but doesn’t reveal much about your purchase intent to buy eggs, for instance. Fortunately, in the world of e-commerce, the wealth of data available to companies lets them uncover detailed customer preferences and profiles. 

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By using data science and deep learning practices, we can quantitatively analyze purchase intent. In mathematical terms, purchase intent is the probability that a consumer will buy a product or a service. With a mathematical representation of purchase intent and enough data points about our customers, we can create deep learning models that show with near certainty whether a customer will buy our product. 

In the Udemy course Customer Analytics in Python we show you how to do this. We outline how to pair datasets with deep learning techniques to predict the likelihood of a repeat purchase from a customer. In the example used in the course, we build a dataset from the real data of a popular audiobook app. By using metrics such as number of purchases, minutes listened, last login date, reviews, and so on, we predict the probability that a customer will purchase another audiobook from the platform.

What is most intriguing, though, is that deep learning models can make a specific prediction for each customer. If you use business intelligence dashboards or other everyday data analysis tools, you would only get a general picture, but never predictions on an individual level. 

Such advanced insights are reached through ‘will buy’ or ‘won’t buy’ predictions (usually represented by 1s and 0s, respectively). However, on the back-end, we have purchase intent models that actually output a probability (e.g. we are 67.24% certain that Alice will buy again in the next 3 months; so we presume that she will purchase again). Such findings could then be used in various ways — most notably for marketing purposes. We could stimulate specific people with higher discounts, influence others with more features, and so on. This type of insight also helps marketers decide how to best allocate their advertising budget.

3 ways the retail industry uses deep learning

Measuring, evaluating, and predicting a customer’s purchase intent isn’t the only use for deep learning in the retail industry. Other deep learning applications include:

1. Predicting churn rate

Churn — AKA attrition — is a term used for subscription businesses to measure the number of people who unsubscribe from and stop using a service. You may have also seen churn used to describe the rate of employees leaving their jobs at a given company. This concept is the opposite of a growth rate. For a company to succeed, its growth rate should be higher than its churn rate. As retailers predict purchase intent, which is often used as a proxy for growth rate, they also will want to forecast the churn rate.

2. Recommendation algorithms 

Sites like Netflix, Amazon Prime, and, even Udemy are great at acquiring new customers. In order to maintain a low churn rate, these companies must understand how to retain customers by keeping them satisfied and engaged with the product. Relevant recommendations have become an essential tool to keep customers engaged. These deep learning recommendation algorithms use data from the customer’s habits on the site and with product use to recommend shows, products, or courses.

3. Fraud prevention 

While financial fraud may attract the most headlines, tiny fraudulent activity happens all the time and can disrupt your customers’ interactions with your brand. There are fake likes on social media platforms, fake emails sent by lookalike companies, fake reviews to boost a product’s profile, and fake social media profiles to make a community look more popular than it is. All of these types of fraud can be identified or prevented by leveraging deep learning algorithms. Companies that employ deep learning to prevent fraud of all kinds do so in order to improve or maintain a customer’s positive associations with their brand. 

Is your mind racing with ideas for how to use deep learning in your e-commerce business operations? Join us in the Customer Analytics in Python course and unlock data science techniques that will help your company keep customers happy by understanding which products and services matter most to them.

Page Last Updated: February 2020