Data science for ecommerce

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Are you wondering what types of problems data science teams can help ecommerce companies solve? Or maybe you are more interested in hearing about specific examples of data science projects for ecommerce? Well either way, you are in the right place!

In this article, we tell you everything you need to know about data science for ecommerce. We start out by discussing what types of problems data science teams can help ecommerce companies solve. After that, we provide specific examples of data science projects for ecommerce. Finally, we provide examples of datasets that are important for data scientists that support ecommerce companies to have access to.

Note that in this article we will focus on problems that are unique to ecommerce and selling products online. We will not focus as much time on problem spaces that are generalizable to a wide range of industries like marketing and new customer acquisition. The reason for this is that we cover these topics in separate articles.

How can data science be applied to ecommerce?

Problems data science teams can help solve in ecommerce

What are some examples of problems that come up in ecommerce that data science teams can help solve? Here are some examples of common ecommerce problems that data science teams can help ecommerce organizations solve.

  • Stocking the right products. Data science teams can help ecommerce organizations ensure that they are stocking the right products in the right places in the right quantities. This helps to ensure that products are ready and available for customers when they need them.
  • Show the right products to the right customers. Data science teams can also help to ensure that the right products are being shown to the right customers. This helps to ensure that customers see products that are relevant to them rather than products that they do not have an interest in.
  • Enabling customers to make a purchase. Data science teams can also help ecommerce organizations ensure that customers have all of the information and incentives they need in order to make a purchase. This applies to purchases that are made in the current browsing session as well as future purchases that are made when customers return.

Metrics data science teams can move in ecommerce

What kind of metrics can data science teams help ecommerce organizations improve upon? Here are a few examples of metrics that data science teams can help ecommerce teams improve upon.

  • Checkout conversion. The first business metric that data science teams can help improve is the checkout conversion rate and the percentage of sessions where customers purchase a product. Data science teams can help to ensure that the right products are stocked and shown to the right customers and also that the customers have all of the information they need to make a purchase decision.
  • Average order value. Data science teams can also help to increase the average order value and the average number of products that are being ordered in each successful checkout. This is achieved in large by showing the right products and the right products to the right customers.
  • Profit margins. In addition, data science teams can help to improve profitability and operational efficiency of ecommerce brands. This is done in part by helping teams that order inventory to ensure that they are ordering the right amount of inventory to the right locations.

Data science projects for ecommerce

In this section, we list some examples of data science projects for the ecommerce domain. We break these data science projects out into different sections based on the main problem that each project aims to solve.

Problem 1: Stocking the right products

The first problem that data science teams can help ecommerce teams solve is stocking the right products in the right places at the right times. The benefits of having the products stocked at appropriate levels are manyfold. For one, ensuring that products are stocked at appropriate levels helps to reduce stock out rates while also ensuring that there is not an excessive amount of capital tied up in inventory. Having the right products stocked in the right locations also helps to increase shipping speeds and decrease shipping costs.

Problem 1: Stocking the right quantity of products

Determining how much of a product to order is always a tricky task because it requires striking a careful balance. On one hand, you do not want to order too little inventory and lose sales due to stock outs. On the other hand, you do not want to order too much inventory and have a lot of capital tied up in inventory (and especially in inventory that will not sell).

So how can data science teams help ecommerce teams determine how much inventory they should keep in stock for each product? The best way that data science teams can help ecommerce teams determine how much inventory to stock is by building demand forecasts that estimate how much demand there will be for a product over a given time horizon.

These types of demand forecast models are usually built using statistical time series methods (such as ARIMA models) or machine learning models that are developed for sequential data (such as LSTMs). They are usually created using historical data on product sales as well as other attributes that may affect the sales patterns of specific products.

Problem 2: Stocking products in the right places

While having the right products in stock somewhere in one of your warehouses is a good first step, it is even better to have the right products stocked in the right locations. What is the right location? Usually, it is best to have products stocked close to the customers that are going to order them. It is also preferable to have products that are frequently sold together stocked in the same location.

What is the impact of stocking the right products in the right locations? The benefits of this are two-fold. First, it helps to reduce shipping costs and increase profit margin. Second, it helps to ensure that products can get to customers faster and more efficiently. These reduced shipping times make it easier to convert customers that have high expectations.

Demand forecast models that determine how to distribute inventory can also be built using similar time series methods that are used for baseline demand forecasts.

Problem 3: Identify emerging trends and new product opportunities

Data science teams can also help ecommerce teams to identify emerging trends and identify new products or product categories that they should stock. This helps to ensure that the company stocks the products that their customers are going to want next week rather than the products that their customers wanted last week.

There are many different types of data and models that can be used to help identify these trends. These range from segmentation models that help businesses to better understand who their customers are to time series models that look at changes in purchase patterns and search trends. These modes can be built using internal data on customer purchasing habits, external data on macroeconomics and industry trends, or a combination of both.

Problem 2: Showing the right products to the right customers

The next problem that data science teams can help ecommerce teams solve relates to showing the right products to the right customers at the right times. Being able to show the products that are the most relevant and enticing to customers helps to increase many metrics ranging from checkout conversion to average order value.

Project 1: Determine which products a specific customer will like

The first way that data science teams can help to ensure that the right products are being shown to the right customers is by predicting which products a given customer will be interested in. There are many different ways you can use information on which products a customer is likely to be interested in in order to surface products to the right customers.

For example, if you have a list of the top three products that a customer is likely to be interested in then you can feature these products at the top of your website homepage whenever that customer goes to your website. You can also show those products as examples of other products that the customer might be interested in when they are about to check out.

There are many different approaches that can be taken to building models that help to determine which customers will be interested in which products. In some cases these models may focus primarily on the previous items the customer has added to their cart to help determine what items they might be inserted in. Other times, the models will also consider attributes that are known about the customer to help guide decisions.

Project 2: Recommend products that are related to a specific product

You can also approach this problem from a different angle and build a model that helps to determine which products are related or similar to one another. This approach allows you to add some level of personalization to the customer experience without having to know any specific information about your customers.

Once you understand which products are frequently bought together, you can do things like include lists of similar or related products at the bottom of your product pages. You can also recommend related products to customers that put a given product in their cart.

This type of analysis is often called a market basket analysis or an affinity analysis. The main information you need to conduct this type of analysis is historical data on customer purchases.

Project 3: Segment customers based on important attributes

Another way that data science teams can help ecommerce teams succeed is by building models that help to segment users into different categories based on important customer attributes. This will increase the amount of information that the team has about individual customers, which will make it easier to target them with products that are relevant to them.

As a simple example, a data science team might build a model to determine which customers have kids in certain age groups. This type of information can be used in many ways. For example, it might be used to highlight collections of products that are popular with parents throughout the shopping experience.

There are different ways to formulate models that solve this problem, but one simple way is to build a simple classification model where the outcome is the attribute you are interested in. The input data might include information about a customer’s previous purchases and demographics. The most difficult part about building this kind of data science model is obtaining labeled data with examples of customers that fall into each group.

Project 4: Improve search and filtering algorithms

Data science teams can also help improve the search and filtering algorithms that customers use to find products. This makes it more likely that customers will be able to find a product they are interested in purchasing.

There are entire families of data science models that are created with the purpose of ranking responses based on their relevance for a specific query or search term. The exact type of model that should be used will depend on the type of data that is allowed to be entered into the product search.

There are many different types of data that can be used to enhance search and filtering algorithms. As a starter, outcomes of previous searches such as details around which items were and were not clicked on in a given search are typically used.

Problem 3: Enabling customers to make a purchase

The next problem that data science teams can help ecommerce teams with is providing customers with the information and incentives they need to make a purchase.

Project 1: Determine the right prices for items

The first way that data science teams can support ecommerce teams is by helping to determine the prices that should be set for specific items. Price can be an important factor that plays into a customers decision around whether to purchase a given product. As an ecommerce business, you want your items to be priced high enough that they are profitable for the business without being priced so high that the price dissuades the customer from purchasing.

There are many different types of pricing optimization models that can be used to set the best price for both the customer and the business. Dynamic pricing models can also be used if you want to facilitate a pricing strategy that responds to things like economic trends and current market demand.

Project 2: Determine the right discounts to provide

In addition to determining the base price for a given item, data science teams can also help to determine what types of discounts should be offered to customers. Discounts are often compelling incentives that encourage customers to buy products they might not have bought at the base price.

Data science teams can build models that help to determine what types of discounts to offer to what types of customers. They can help to optimize the value of your discounts as well as the structure of the offers.

Note that there may be an interplay between the base pricing models that you use and the discount models you use. There are some situations where it might make sense to set a slightly higher base price so that you can offer a higher discount without eating away at your profit margin.

Project 3: Enhance the customer review displays

Many customers are only willing to purchase a product online after seeing positive reviews from people who previously purchased the product. Since customer reviews are such an important part of the purchasing journey, making enhancements to your consumer review display can help enable customers to find the information they are looking for and make a purchase.

One example of an enhancement that can be made to consumer review displays is adding filters that allow customers to filter to reviews that mention a certain feature of the product. Another popular example is adding a display to summarize the features customers are saying positive and negative comments about.

Data science teams can train natural language processing models that enable ecommerce teams to enhance their review display by annotating different portions of the text in review with topics and sentiment. These tasks are often accomplished using deep learning models that are designed specifically for text processing.

Project 4: Automate claims and returns

Returns are another important aspect of the purchasing experience that is top of mind when customers are making online purchases of items that they have not seen or touched. Customers are often more likely to make purchases if they know that they are purchasing from a company with efficient returns and claims processes.

Data science teams can help to build modes that can determine whether a claim or a return should be accepted or denied. By helping to automate parts of the claims and returns process, data science teams can help ecommerce teams respond to customers more quickly.

There are many types of data that might be used in these types of models. Information on previous customer purchases and claims is a good starting point, as well as information on other claims and returns associated with the same product. In some cases, photos of the product involved can also be used.

Data that is importance for ecommerce

What types of data are most important for data science teams that support ecommerce and online shopping businesses? Here are some of the most important data points for data scientists that support ecommerce organizations.

  • Previous purchases. Data on previous purchases that have been made is one of the most important types of data that is used in ecommerce. This data is used to forecast future demand for products as well as to determine which products should be shown to which customers.
  • Previous product interactions. It is often just as useful to see which products a customer did not purchase as it is to see which products a customer did purchase. For this reason, it is also important that data scientists have visibility on what products a customer viewed, clicked on, and added to their cart, regardless of whether they actually made a purchase.
  • Customer attributes. It is also useful to have access to customer attributes like age, gender, location, and interests that might have been collected at sign up.
  • Macroeconomic and industry trends. Data related to macroeconomic and industry trends can also be useful for data scientists who are working in ecommerce. These external datasets can help you understand the broader environment within which your company is operating.
  • Product searches. Finally, information on product searches and interactions that users have with your search and filtering capabilities can be very informative. This can help you understand what kinds of products your users are interested in and how you can better optimize the products that are shown to them.

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