Personalization in e-commerce offers companies a competitive advantage in crowded online marketplaces. When applied successfully, algorithms trained on behavioral data automatically produce tailored recommendations that reduce a customer’s time from landing to checkout. Personalization has challenges, such as matching an implementation strategy with customer needs, business context, and the appropriate technology. As customers’ expectations grow, these tailor-made online experiences and personalization are fast becoming a necessity for e-commerce businesses.
The goal of personalization is to recommend the right products to the right people, improving customer experience and increasing sales. Depending on the sophistication of the tool, personalization can range from general targeted promotions to very granular recommendations based on customer segmentation. Customers expect seamless, quick online transactions, and the more well-tuned the personalization is, the less friction there is between browsing and buying. A consumer study by McKinsey & Company found that 71% of shoppers expected a personalized experience and preferred to shop on sites that provided it.
Personalization also gives retailers insights into customer buying behavior and real-time trends, helping them stock the right volume of relevant products and reducing waste and cost. This inventory control is accomplished through automated processes and artificial intelligence (AI)-powered algorithms. As more customer browsing data is collected and fed back into the models, the quality of the personalized content improves.
The Power of Clickstream Data
Understanding who customers are and what they need is critical to personalization. To achieve this, retailers gather sets of behavioral data known as clickstream data. Clickstream data is the continuous stream of information people create as they spend time online: the sites they visit, the ads they view and their scrolling behavior. After gathering this data, the information is aggregated into a unique customer identity. Behind the scenes, a personalization engine creates a custom profile, compares clickstream data with a product, and calculates a relevancy score. The higher the relevancy score is, the more applicable the product is to a person. Over time, as more customer data is stored and more relevancy scores are calculated, the personalization engine fine-tunes itself to provide highly relevant content.
The company is responsible for informing its customers about what data is being collected and allowing them to opt out. Transparency between a retailer and a customer over personal data collection is crucial for maintaining customer trust. Data privacy regulations like Europe’s General Data Protection Regulation (GDPR) require companies to make the customer aware of what data will be collected and how it will be used, obtain customer consent before collecting data, and secure the data they collect. Companies that prioritize security mitigate the risk of data breaches and the resulting loss of customer trust.
Practical Challenges and Big-Picture Concerns
Personalization looks different for every business. The challenge is matching the right personalization tools with the business context and the customer. For example, B2B commodity retailers might not need to invest in a site with recommendations the way clothing retailers would. In their case, a more strategic use of personalization would be to offer custom pricing bundles personalized to returning customers based on past orders. Location-based personalization can be a valid approach when no clickstream data are available.
Another significant challenge for companies is acquiring a starting dataset large enough to train a personalization engine. When personalization is first introduced, processing the vast amount of data needed to improve the initial results takes time. The cost is also significant, and not every company has the resources to build an in-house personalization engine. Smaller companies have begun to recognize that they need to find cost-effective ways to embrace personalization or risk being squeezed out of the market. The current best option for small to medium-sized companies is to join an e-commerce platform that offers personalization tools for a percentage of revenue. Retaining the competitive advantage, in this instance, is often worth the cost for smaller online retailers.
For platforms with the most sophisticated personalization, there is a risk that customers could begin to over-rely on customized content in a way that blurs the lines between choice and being chosen for. In other words, instead of shopping, customers get shopped. Researchers from a 2024 study on customer trust in e-commerce surveyed 500 online shoppers and found that while personalization enhanced customer trust, it raised concerns about manipulation and privacy. Ethical concerns like these are critical for companies to take seriously and approach with foresight.
Even as technology changes, personalization will continue to play a vital role in the online retail marketing landscape. Innovation may not come in the form of improved algorithms but rather in creative business strategies that leverage these powerful tools in new ways. As chatbots change how people search, new and emerging AI functionalities will be increasingly essential to the e-commerce experience of the future.
Strategic and Secure Personalization
Customized recommendations are the new normal in online sales. Whether companies have a single data point or large volumes of clickstream data, personalization engines can be applied to enhance recommendations. AI-powered algorithms drive improvements in personalization as they continue to train on behavioral data. With so much data being collected, trustworthy and secure data collection and handling methods are more vital than ever. Careful use of personalization in e-commerce elevates customer experience and offers a competitive advantage, keeping the customer in mind and making their purchase decision easier.
