E-commerce

How to use personalization and recommendations to improve conversions
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Thelma and Louise…Batman and Robin…Macaroni and cheese. These and many more famous pairs have left their entertaining, delicious, or other type of mark on the world.

One secret of their success: differences and similarities that let them complement each other.

When it comes to having an ecommerce business, that goes for the duo of personalization and recommendations as well. While these may sound like similar concepts, they’re not quite the same. Each revolves around delivering tailored experiences and suggestions to individual online shoppers, and they have distinct nuances. Most importantly, though, when it comes to enhancing the customer experience in order to boost conversion, these two types of artificial-intelligence-driven features are a dream team.

Complementary conversion partners

Here’s how recommendations and personalization are aligned while also being unique.

Similarities

Both personalization and recommendations:

  • Have the potential to seriously improve conversion
  • Are data driven, relying on information collection and analysis
  • Make visitor experiences more relevant and engaging
  • Improve user satisfaction through providing a personalized experience
  • Are made more effective by ongoing optimization and refinement

Differences

  • Objective: The primary objective of shopping personalization is to tailor a great user experience in response to unique preferences and behaviors of each shopper. The role of recommendations — one form of personalization — is suggesting products and services to shoppers based on their past user behavior, individual consumer preferences, and similarities to other customers.
  • Scope: Personalization encompasses a broad range of customization options and touchpoints on the shopping journey, including everything from customized email marketing campaigns to dynamically adjusted ecommerce content. By contrast, content recommendations are focused exclusively on suggestions, typically within the context of product pages, search results, and shopping-cart pages.
  • Engagement: Personalization entices shoppers by tailoring any part of their journey on the site to resonate with their individual customer preferences and interests. Recommendations also drive engagement, but specifically by offering appealing item suggestions.
  • Implementation: Setting up personalization can be a comprehensive project involving data, segmentation, and advanced AI algorithms to customize a shopper’s experiences across multiple touchpoints. Recommendations are implemented fairly narrowly, with items suggested on product pages and in search results.

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Strategies for success

By effectively integrating both members of this dynamic pair, you can create online shopping journeys that make people feel at home and inclined to buy. Here’s how to put personalization and personalized recommendations into practice together.

Collect and analyze your data

Successful personalization and recommendation strategies both rely on gathering and making sense of the right data. There’s a wealth of information available at every interaction point; you can tap into by tracking people’s website engagement, looking at their purchase history, and paying attention to their social-media interaction. Having this eye-opening insight will let you offer both expertly personalized experiences and accurate recommendations to drive conversion and enhance your online sales figures.

Let’s say an online fashion marketplace tracks a shopper’s browsing history, noting their clicks on a variety of product categories and brands. Through AI-aided analysis, they can gain critical insight, like discovering that the shopper likes browsing dresses in the “casual chic” category and clicking on those from a brand that offers free, easy returns. Based on this data, the retailer could curate a selection of dresses from this particular brand, potentially building up shopper trust and increasing the likelihood of repeat purchases.

Use AI to create targeted recommendations

When it comes to personalized product recommendations, AI naturally lets business owners better connect with shoppers. And when they feel catered to, they’ll be more inclined to stay loyal to your brand.

Advanced algorithms can uncover intricate patterns in vast datasets and predict, with remarkable accuracy, which products a shopper will want next. This level of predictive intelligence lets a company enhance the customer experience by delivering precisely what people have their hearts set on, whether they’re on a smartphone or their computer. Plus, AI-powered recommendations can be seamlessly integrated across touchpoints on an ecommerce website. On the homepage, in search results, or alongside the checkout process as online purchases are being finalized, the right suggestions can instantly guide shoppers to products they’re seeking.

Let’s say you run a sports-memorabilia online store and you’re leveraging AI technology to streamline your recommendation engine results. By analyzing a shopper’s past purchases and browsing habits, along with the user preferences of people who have similar profiles, AI can generate on-target personalized sports-gear recommendations. If data points to a shopper likely purchasing products that affirm their status as a proud Seattle Seahawks fan, the AI will curate recommendations to align with that and also favor suggesting NFL-related products in general.

Enhance your user profiles

Effective personalization strategies require continuous optimization. Over a period of time, building and refining your online user profiles is akin to sculpting a masterpiece: each interaction and data point can mean a more nuanced shopping experience. When your site visitors create accounts and save preferences, you can amass a trove of data that can serve as the cornerstone of an effective personalization and recommendation strategy.

On Amazon.com, every shopper has a profile and every interaction enables the company to deliver further personalized experiences and recommendations. Let’s say someone loves buying high-end skincare products. Collecting details such as their skin type, skincare concerns, and previous purchases means being able to offer them the products they want. Whether it’s a hydrating serum or an exfoliant, personalized recommendations are a promising way to encourage profitable online transactions and build considerable customer loyalty for the long run.

Test and iterate

At the heart of online strategy refinement is A/B testing, a methodological approach used by 58% of companies that compares two versions of a web page, email message, or marketing campaign to determine which performs better. By systematically varying elements such as content, layout, and messaging, you can learn exactly what resonates most with shoppers and then roll out the best-performing option.

Let’s say an online retailer is looking to optimize product recommendations. Through A/B testing they can experiment with different recommendation algorithms, product placements, and messaging to determine which combinations drive the highest conversions. By looking at the results of these tests, they can come away with the right online-marketing to-do list.

Keep customers coming back

That’s it for how personalization and recommendations functionality can make a dynamite team for an online business. Their strengths play off of each other to create a rewarding shopping experience in an ecommerce store, and it’s safe to say that you can combine these tactics to send your conversions skyward.

Want to get started now? Algolia’s AI Search experience enhancements and AI Recommendations work beautifully together. Let’s talk soon about what these new technologies can do for your site or app.

About the authorJon Silvers

Jon Silvers

Director, Digital Marketing

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