Online and eCommerce stores are a gold mine of data for applying AI (Artificial intelligence) 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

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 they 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.