Data Science Increases E-Commerce Profitability

Data science is no longer a cryptic term that has nothing to do with your industry. It is now widely applied across fields ranging from medicine and security to sales and e-commerce.

To stay on top of your own e-commerce game and improve your business’s profitability, you can leverage data science breakthroughs in numerous ways. They can help you improve everything from the way you store your stock to the way you market your business. Read on to learn how.

Offer Recommendations

Cross-selling is a vital part of e-commerce success. By recommending products your customers are likely to be interested in, you can increase the value of each sale and provide a better (and more personalized) shopping experience.

Recommendation engines based on machine learning components and deep learning algorithms can literally learn which products a certain type of shopper will like. They’ll then show the customer these personalized recommendations via a simple widget. The entire process involves a lot of data gathering and filtering, but all you have to do is install the appropriate plugin or other software solution.

Optimize Prices

Ensuring the price is right is another crucial aspect of e-commerce. After all, if your competitors are selling the same product at a lower price, you won’t be making many sales. You also need to calculate into your prices things like the cost of shipping, production, and storage, as you don’t want to inadvertently end up demanding less for products than is feasible.

Data science is again here to help. AI will analyze a wide range of parameters for you, from price flexibility and competitor pricing to customer location and potential shipping costs. You’ll then use the compounded data to determine the best price for each product.

Analyze Market Baskets

Another way AI can help you determine whether a customer is likely to purchase a product is by the use of market basket analysis. This is a tool that has been available for years, and most e-commerce stores use it.

The premise behind this algorithm is a simple one: if a shopper purchases a certain group of items, they are more or less likely to purchase a set of different, related items.

As online shoppers often buy on impulse, market basket analysis can help you predict what a specific customer’s likely purchasing behavior will be. This helps you devise better marketing campaigns for your products, as you can use this knowledge for your remarketing and other paid ads campaigns.

Manage Inventory

Inventory management can turn out to be one of the most challenging aspects of running an e-commerce business. Staying on top of all of your stock, ensuring that it is organized in a way that enables quick and simple shipping, and preventing any damage to your items can quickly become a very demanding task.

You also need to be mindful of sudden increases in demand and how they would affect your supply chain. That’s especially important in these (post) pandemic times when replenishing stock can take much longer than it used to.

Data science has the power to keep a watchful eye on sales trends and patterns and forecast likely future purchases. You can also use these large data sets to come up with your own inventory management strategy. As the quality and condition of your stock directly impact your business value, you want to do your very best to stay on top of it.

Predict Customer Lifetime Value

The lifetime value of a customer is the total value of the profit they bring you. The higher it is, the easier it will be for your business to grow. However, determining it can be rather complicated without the help of data science.

An algorithm will collect and classify all the data relating to a specific customer. This will include:

  • recent and lifetime purchases
  • rate of shopping
  • product and delivery preferences
  • the amounts they usually spend

When this data is processed, it will provide a clear estimate as to the possible value this customer can have for your business.

However, bear in mind that this forecast may turn out to be false. You can never actually predict what an individual’s future purchasing habits will become. Never forget that all the data you collect pertains to living and breathing human beings. The more you can treat them as individuals and less like numbers, the better their experience with your brand will be, and they will be more likely to keep doing business with you for a long time to come.

Improve Your Marketing and Sales Strategies

Finally, all this data you’ve gathered about your customers and subsequently analyzed can help you improve both your marketing and your sales strategies.

The more you can learn about each individual customer, the better your campaigns served specifically to them will become. You will show them better ads, and you can tailor your email marketing campaigns practically infinitely.

You will also be able to offer more intelligent discounts and perks, making a conversion that much more likely. For example, you may know that a customer has been looking at a specific item for a long time. If you then send them a personalized email offering 20% off, they’ll jump at the chance to make a purchase.

Your social media campaigns can also be improved. You will be able to serve the kind of content your target audience is actually interested in. No more guessing and playing things by ear. All of your decisions can be based on real-life data, which has infinite potential.

Final Thoughts

Machine learning and data science are powerful tools that the e-commerce industry will continue harnessing to improve profitability. However, the deployment of these tools can become rather costly. That’s why you’ll want to first determine which element of e-commerce you need the most help with first, and only then choose the the technology to help.

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Sarah Kaminski

Sarah Kaminski

Sarah Kaminski is a freelance writer and social media marketer. She works with a number of small businesses to build their brands through more engaging marketing and content.

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