Unleashing the Predictive Power of Web Data in E-Commerce

Published on December 1, 2022

Being able to predict consumer behavior and market trends is the holy grail for e-commerce brands. This is what they’re all striving towards: the tangible insights that enable them to operate more effectively and provide superior customer experiences.

Collecting and analyzing public web data is one of the best ways to uncover these insights, and it’s a strategy that more e-commerce brands are embracing. According to Bright Data’s recent survey of IT, technology, and data analytics professionals, 93% of businesses believe that web data is either “very important” or “crucial” in supporting their operations and decision-making.

Indeed, 87% said the need for web data has grown within their organizations over the last 12 months – with the retail sector leading the charge alongside travel and banking.

Ultimately, web data can deliver the predictive insights required by e-commerce brands in today’s competitive environment. But how can they practically cross-reference key datasets to grab valuable market share?

Predicting behavior with web data

Let’s take a closer look at three types of publicly available datasets from big-name retailers that are being used by e-commerce brands to predict consumer behaviors.

The first is best-selling products. Several parameters, such as the product category, subcategory or brand, can be fully customized to deliver insights about products within the company’s field of interest. Then, if a company identifies a certain product that is selling well with competitors, they can quickly move to incorporate that product into their own catalogs and price it attractively.

Secondly, businesses can take this dataset to the next level by looking at best-selling products with the lowest number of sellers. While it’s important to know the top-performing products, the likelihood of making a sale is significantly reduced if there are hundreds or thousands of vendors competing for the attention of customers.

That’s why looking beyond sell-through rates (STRs) is extremely important. By identifying the popular products that are being sold by a comparatively low number of vendors, businesses can uncover high-impact value propositions for their merchandising team to exploit.

The final example is a dataset of the most reviewed books, which enables vendors to quickly and easily determine value based on the number of reviews a book generates. Simply put, the more reviews a book has, the more popular and engaging it is. These books may not necessarily become best sellers, but they are much more likely to attract customer interest via advertising campaigns and online discussion forums. This can help a brand introduce its store or marketplace to a relevant target audience.

E-commerce brands are currently using these datasets to predict consumer behavior and receive actionable insights. However, the true value comes when this data is cross-referenced with additional information to provide even deeper insights that can help brands formulate an impactful go-to-market (GTM) strategy.

Going deeper with data

Let’s stay on the topic of customer reviews. We’ve already established that the number of reviews is a strong indicator of a product’s popularity. But what if we cross-reference review quantity with substantial, qualitative customer feedback provided across hundreds of individual reviews?

For example, a certain product may have 20,000 reviews, suggesting that it is a popular item. However, cross-referencing it with qualitative feedback could reveal common denominators among buyers, such as technical limitations or design flaws. This is valuable information that a brand could use when looking to create a new product or expand into a new vertical. By understanding the shortcomings of their competition, companies can develop a product that they know will outperform the competition. This proficiency can then be promoted on product listings and in advertising campaigns.

This demonstrates how creatively cross-referencing datasets can be a powerful strategy for e-commerce brands. On the one hand, it can empower brands to grab increased market share and dominate their respective fields. On the other hand, it can provide the informational advantage companies need to successfully localize a business or enter a new market.

The web data industry will continue to expand over the coming months and years. New data strategies are already taking hold in sectors such as healthcare and hospitality, but the data-driven nature of e-commerce makes the opportunities virtually limitless. In 2023 and beyond, more e-commerce brands will look to take advantage of web data’s tangible benefits to enhance their marketing strategies and drive revenue growth.

Or Lenchner is a Grit Daily contributor. Ever since his appointment as CEO of Bright Data (formerly Luminati Networks), Or Lenchner has continued to expand the company’s market base as an online data collection platform dedicated to delivering complete web transparency.For the past three years, under Lenchner’s leadership, the company has advanced its product offerings to include first-of-its-kind automated solutions, enabling its customers to collect and receive data in a matter of minutes.Among Bright Data’s thousands of customers are Fortune 500 companies, major e-commerce firms and sites, prominent finance firms, leading security operators, travel sites, academic and public sector organizations. Prior to his career at Bright Data, Lenchner founded and managed several web-based businesses, developing digital assets and online marketing programs. Initially joining Bright Data as head of product development, Lenchner’s career and evolvement at the company has been driven by his firm belief in a transparent, ethical-by-design web environment that contributes to an open, competitive market benefitting both, businesses and society as a whole.

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