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Learn data science best practices

  • April 18, 2017

Four Ways Data Science is Valuable for eCommerce Businesses

The nature of business in the eCommerce industry involves many trackable customer touchpoints like clicking an ad, viewing an item, making a purchase, or submitting a product rating. The resulting volume of information can be overwhelming to navigate, but it’s a data science gold mine. That’s because data scientists excel in sifting through big data to identify causal patterns or make predictions about what’s in store in the future for your business.

With the proper systems in place to act on these insights, data-savvy eCommerce business are sure to observe a positive impact on their bottom line. Below, we’ll explore four ways that online retailers can leverage data science to achieve business value.

Identify Your Most Valuable Customers

The biggest way that data science is useful in eCommerce is through customer lifetime value (LTV) modeling. With this approach, you can predict the future revenue that each customer will bring to your business in a given period as a function of the length of time an individual is likely to remain a customer, how often they’re likely to make purchases, and the average value of each purchase. Understanding how your most valuable customers are typically introduced to your business allows you to focus your marketing efforts on the right channels. Alternatively, LTV modeling can also shed light on the types of customer engagement that are usually associated with higher-value or more-frequent purchases so you know what kind of user behavior to incentivize.

Discover Which Customers Are Likely to Churn

Acquiring new customers is often much more expensive than maintaining relationships with existing customers. A churn model can provide the insights you need to implement a data-driven customer retention strategy. This type of model can help you understand the general reasons your customers stop using your products or services, and can even provide an individual-level churn risk score than measures the probability that any given customer will decide to take their business elsewhere in a given period. This information is critical for retention-focused email campaigns or prioritizing product or service changes.

Drive Sales with Intelligent Product Recommendations

With companies like Netflix valuing their recommendation engines as high as $1 billion per year, it’s no surprise that these systems have become a classic example of how data science can provide business value for the data-savvy organization. Outside of the media industry, recommendation engines allow eCommerce businesses to recommend products their customers are likely to enjoy based on their browsing and purchasing behavior, driving the value of the customer’s average order in the process.

Automatically Extract Useful Information from Reviews

When a company receives hundreds or even thousands of product reviews on a weekly basis, the task of sifting through all of this feedback can be daunting, if not impossible. Natural language processing is a set of techniques that allows data scientists to scrape user reviews for the underlying reasons why people tend to give 1-star or 5-star reviews. With this information, eCommerce companies can efficiently maximize user satisfaction by prioritizing product updates that will have the greatest positive impact.

While there are many ways that data science can potentially bring value to your eCommerce business, it’s critical to set your quantitative team up for success in order to maximize the return on your investment. Learn more about data science best practices before you get started to ensure you don’t fall into common pitfalls of predictive modeling.

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