Optimizing e-commerce marketing strategies in the digital economy: a big data approach enhanced by genetic algorithms

Smarter online shopping for everyone

The way we shop online is changing fast, and so are the tactics companies use to grab our attention. This study looks at how e-commerce businesses can move beyond guesswork and trend chasing by using data and smart computer search methods to design marketing that feels more relevant and less wasteful. The findings matter not just for marketers, but for anyone who shops online and wonders why some sites feel helpful while others feel spammy or intrusive.

From crowded digital shelves to tailored offers

Today’s digital economy lets almost any business sell to anyone with a smartphone, but that freedom also creates noise and confusion. Many firms still push one-size-fits-all ads, copy rivals’ campaigns, and underinvest in customer service, which erodes trust and clutters our screens with promotions that miss the mark. The authors argue that thriving in this environment requires marketing strategies that are genuinely customer-centered, drawing on large pools of data about what people browse, buy, and respond to rather than relying on hunches or outdated mass advertising.

Letting algorithms explore better marketing choices

To tackle this challenge, the study turns to genetic algorithms, a family of computer methods inspired by natural selection. Instead of humans manually tweaking a few campaign settings at a time, the algorithm generates many different combinations of marketing choices, tests how well each combination performs, keeps the best ones, and then “mixes and mutates” them to create new options. Over many rounds, weaker ideas are discarded and stronger ones survive. In this research, the algorithm works with three core elements that matter to shoppers: how products are organized and presented, how content is tailored to each person, and how social media is used to share information and build relationships.

Putting the approach to work in real companies

The framework was tested in three Chinese e-commerce firms of different sizes, using both survey responses from marketing staff and detailed operational records. Before and after the new system was introduced, the researchers tracked customer satisfaction, the perceived strength of advertising strategies, and how reliably promotional campaigns were executed. They also used specific scores that capture how sound a company’s overall marketing program is and how fully its promotion plans are carried out.

Clear gains in customer experience and reliability

Across all three firms, the data showed marked improvements after the genetic algorithm was applied. Customer satisfaction rose by 21 percent, suggesting that shoppers felt better served by the new mix of product suggestions, messages, and channels. Ratings of advertising effectiveness climbed by about one third of a point on a standardized scale, and the reliability of promotions improved by a similar amount, meaning fewer misfires and more consistent delivery of what was promised. Overall marketing program quality and completion of promotion plans moved from middling scores to values close to the top of the scale, and these gains were seen in both smaller and larger organizations.

What this means for online shoppers and sellers

For everyday users, the study’s conclusion is that smarter, data-guided tools can help turn online marketing from a blunt instrument into something more like a well-fitted recommendation service, with fewer irrelevant ads and more timely, useful offers. For businesses, the message is that advanced optimization need not be reserved for tech giants; with the right data and care for privacy and fairness, even modest-sized firms can use these methods to fine-tune their campaigns, strengthen trust, and compete more effectively in the crowded world of digital commerce.

Citation: Yang, J., Peng, B. Optimizing e-commerce marketing strategies in the digital economy: a big data approach enhanced by genetic algorithms. Humanit Soc Sci Commun 13, 691 (2026). https://doi.org/10.1057/s41599-026-06654-w

Keywords: e-commerce marketing, digital economy, big data analytics, genetic algorithms, customer satisfaction