Product recommendation for more sales based on purchase history

Product recommendation helps reduce the time it takes to find a relevant product and increases the likelihood of conversion.

But a good product recommendation doesn't just suggest a product; it anticipates the user's future needs.

The Basics of Product Recommendation

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Recommendation systems use precise analysis of purchase history, a key asset for understanding what each customer really likes. By collecting information such as products purchased, their frequency, and the amount spent, an overview of purchasing preferences begins to emerge. It's a bit like a puzzle, where each piece (each purchase) provides an additional clue about the consumer's habits.

Its operation relies on sophisticated algorithms that mine data sets to recognize behavioral patterns. For example, if a customer often buys a certain type of product in combination with another, this correlation can be used to suggest similar or complementary items on future visits. A simple purchasing habit, such as buying coffee and cookies together, thus becomes a powerful indicator for guiding future recommendations.

The other fundamental aspect is the ability to anticipate. By integrating data on frequency, seasonality, and changing preferences, recommendation systems constantly evolve and refine their suggestions. For example, if a consumer gravitates toward sporting goods at the beginning of the year, it is possible to suggest products in that same category, but diversify the offering to maintain their interest.

Well-crafted recommendations go beyond simple product pairings: they add relevance and appeal to the customer journey. When the algorithm identifies products often associated with past purchases or items in popular price ranges, it increases the likelihood of conversion. It's almost as if each recommendation is a personalized response to the customer's tastes, making the shopping experience not only enjoyable but also more efficient for the business.

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Strategies for implementing product recommendations

Developing an effective recommendation strategy begins with accurate customer segmentation. This segmentation allows customers to be grouped into categories that reflect similarities in behavior, preferences, or demographics. By gaining insight into segments—whether repeat customers, seasonal buyers, or customers with specific tastes—each recommendation can be better tailored to the expectations and purchasing habits of each group. For example, customers who frequently purchase sporting goods would benefit from targeted recommendations for products that align with that area of interest, increasing the relevance of each suggestion.

The use of algorithms and filtering technologies adds an extra level of personalization and efficiency. Collaborative filtering, for example, is a method where recommendations are based on the purchases and behaviors of users with similar profiles. In concrete terms, if two customers share similar habits, items purchased by one can become relevant suggestions for the other. This method creates a network of influence between customers with similar tastes.

Behavioral analytics further refines these recommendations. By examining data such as purchase frequency, time spent on certain product pages, or specific actions (add to cart, cart abandonment, etc.), recommendations become highly tailored responses to each customer journey. For example, if a customer frequently browses a range of products without making a purchase, offering slightly different alternatives or offers on those specific products can encourage them to take action.

Content-based filtering is another technique that leverages intrinsic product characteristics to make suggestions. If a user has purchased a book in a certain genre, content-based filtering can recommend other titles in the same theme, or even expand suggestions to include accessories or complementary products. This approach is particularly effective for retaining customers with well-defined interests.

To maximize the impact of these strategies, it's wise to turn to machine learning, which allows for continuous improvement of recommendations. By integrating learning algorithms, recommendation systems become smarter with each new customer interaction. Each click, each addition to the cart, or each purchase enriches the database and refines the suggestions. This way, not only do recommendations adjust to the evolving tastes of each user, but they also help anticipate future purchasing trends within each segment.

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Types of product recommendations‍

Recommendations based on purchase history

Using a customer's purchase history, we can identify recurring trends in their product choices and suggest items related to their previous purchases. For example, a customer who has purchased several houseplants might be interested in decorative pots, specific fertilizers, or even care accessories. By taking into account what has worked in the past, this method leverages familiarity and previous satisfaction to encourage repeat purchases and build customer loyalty.

Recommendations by browsing behavior

Website visitors leave behind a wealth of valuable clues through their browsing actions. By observing the pages viewed, the time spent on each product, and the items added to the cart and then abandoned, highly personalized suggestions can be created. For example, if a customer has browsed several shoe models without making a purchase, a targeted recommendation with those same models or similar alternatives can rekindle their interest. This approach can also be extended to a follow-up email with an offer or discount, capturing their attention after their visit to increase their chances of conversion.

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Cross and complementary recommendations

Known as cross-selling, these recommendations consist of suggesting products that complement those added to the cart, increasing the overall value of the purchase. If a customer adds a smartphone to their cart, it's relevant to suggest a case, a portable charger, or even screen protector insurance. These suggestions aren't random: they're based on the purchasing habits of other customers who have made similar purchases. Cross-selling, when relevant, adds value to the main product and increases customer satisfaction by meeting their anticipated needs.

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Recommendations based on expressed preferences

Seasonal trends and new products are also important levers for suggesting products. Here, the goal is to introduce customers to popular items or items that are in line with current market trends. For example, during the holidays, highlight gift suggestions based on popular categories. This type of recommendation, in addition to guiding choices, can also stimulate impulse purchases by offering popular or limited-edition items.

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Recommendations inspired by general trends

Seasonal trends and new products are also important levers for suggesting products. Here, the goal is to introduce customers to popular items or items that are in line with current market trends. For example, during the holidays, highlight gift suggestions based on popular categories. This type of recommendation, in addition to guiding choices, can also stimulate impulse purchases by offering popular or limited-edition items.

Integrating recommendations into the customer journey

Integrating product recommendations into every step of the customer journey, both on and off-site, transforms the shopping experience into a personalized and seamless exploration. Here's how to leverage each touchpoint to maximize the impact of recommendations and boost sales.

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On the site:

• Homepage: From the moment visitors arrive on the site, carefully chosen recommendations can capture their attention. Highlighting popular products, new arrivals, or seasonal items quickly captures their interest. By personalizing this space with suggestions based on their preferences or past purchases, visitors already feel they're in a tailored experience. For example, a returning customer after a gardening purchase might find recommendations on outdoor decorating trends.

  
  

• Product pages: When a customer is exploring a specific product, this is an opportunity to offer alternatives or complementary products. For example, on a laptop page, show accessories like a case or a wireless mouse. This allows them to see a complete package and makes the purchasing decision easier. These suggestions enrich their experience, offering other options without them having to search themselves.

  
  

• Add to cart and checkout: As the customer adds an item to their cart or is about to complete their purchase, integrating recommendations can increase the average order value in a subtle and natural way. Suggestions for small or practical accessories are ideal at this stage: they can be easily added to the purchase without distracting the customer from the checkout. For example, when purchasing a smartphone, offering a charging cable or a screen protector can seem like a logical extension of the initial purchase.

  
  

• Qstomy Chatbot: Integrating an intelligent chatbot like Qstomy allows for real-time, interactive recommendations. Qstomy uses customer purchase history, preferences, and AI to suggest relevant products as soon as a customer engages in a conversation on the site. For example, if a customer is searching for sporting goods, the chatbot can suggest related accessories or new products based on their previous choices. This direct, personalized interaction not only helps meet customer needs immediately, but also guides their purchasing journey smoothly and naturally.

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• Personalized Emails: Emails are a great way to rekindle interest in a personalized way. After a purchase, sending an email with recommendations based on that purchase—for example, complementary or similar products—extends the relationship with the customer and generates new purchase opportunities. For abandoned carts, a reminder with the items left in the cart, along with alternative suggestions, can convince the customer to return and complete their order.

  
  

• Advertising campaigns: On social media and other platforms, targeted advertising campaigns are a powerful way to attract returning visitors. Recommendations can include products viewed but not purchased, items related to previous preferences, or new items in their interest categories. This approach keeps customers connected even when they're not on the site and strengthens product recall.

Persuasion techniques to optimize recommendations

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Using Social Proof and the FOMO Effect

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Social proof is the idea that people rely on the choices of others to guide their own decisions. To capitalize on this principle, displaying customer reviews, ratings, and testimonials alongside recommended products can reassure potential buyers of the quality and popularity of those products. For example, a customer who is unsure about a recommendation might be convinced by seeing that a large number of other users have given the product a 5-star rating.

The FOMO (Fear of Missing Out) effect reinforces this idea of social proof by creating a sense of urgency. By specifying that the recommended product is in limited stock or that a special offer is about to end, the customer is encouraged to make a quick decision. Statements like “Only 3 items left” or “Offer valid until tonight” encourage immediate action, for fear of missing out on a good deal.

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Visual design and strategic placement of CTAs (Call to Action):

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Attractive design plays a key role in persuasion. Recommendations should be both visible and harmonious with the rest of the page, without getting lost in the content. Using subtle color contrasts to highlight recommendations and call-to-action buttons draws the eye without disrupting navigation. For example, an “Add to Cart” button can be a bright color to stand out from the rest of the page and encourage a click. CTA placement is also strategic: recommendations arranged in a carousel at the bottom of the page or as suggestions to the right of a product page help avoid cluttering the reading space while still providing quick access to recommended products. Adding demo videos, explaining the features or benefits of a recommended product, can increase engagement. This brings the item to life and allows the customer to visualize its usefulness, thus reinforcing their desire to purchase it.

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Message personalization and storytelling

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To increase the impact of recommendations, it's helpful to personalize messages based on customer behaviors. For example, a message like “This product is popular among gardening enthusiasts like you” or “Discover these new products that perfectly complement your last purchase” creates a more personal connection and increases engagement.

Incorporating storytelling elements—such as the story behind a recommended product or its manufacturing process—adds an emotional dimension. The customer feels engaged, transforming a simple recommendation into a memorable and engaging experience. This approach can also help justify higher prices by emphasizing quality or craftsmanship, which adds perceived value.

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Smart retargeting to extend engagement

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After a visit or purchase, retargeting techniques, via email or social media, can reinforce recommendations. For example, sending a reminder of similar or complementary products via email with a message like “You might also like these items” prolongs the shopping experience and helps maintain the connection with the customer.

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Measuring the effectiveness of your recommendation strategy

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A/B testing: experiment to optimize

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A/B testing is a powerful tool for testing multiple approaches and identifying which ones generate the most engagement and conversions. For example, you can compare recommendations based on browsing behaviors to suggestions based on past purchases. This type of testing can also include different layouts, CTA wording, or placement of recommendations in the customer journey. By measuring the performance of each variation, you can identify which recommendations best capture attention and encourage clicks.

A well-conducted A/B test focuses on a single parameter at a time (such as recommendation type or CTA placement) to provide clear and actionable results. Testing visual elements, such as button color or the size of recommended product images, also allows you to see how simple adjustments can impact click-through and conversion rates.

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Tracking essential KPIs: click-through rate, conversion rate and average basket size

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Key performance indicators (KPIs) play a key role in measuring the effectiveness of recommendations. These include:

• Click-through rate (CTR) on recommendations: This metric reveals how well recommendations are capturing visitors' attention. A high click-through rate indicates that the recommendations are relevant and well-placed. If the CTR is low, it may be worth testing other types of recommendations or adjusting their presentation to capture more interest.

  
  

• Suggested product conversion rate: Measuring the percentage of clicks on recommendations that lead to purchases helps assess the effectiveness of suggestions. A high conversion rate means that recommendations are perceived as relevant and motivate customers to take action. Comparing this rate with the overall conversion rate can also provide insight into the direct impact of recommendations on sales.

  
  

• Average order value after interaction with recommendations: Observing the average order value after an interaction with recommendations allows you to measure their impact on the average order value. If this KPI increases significantly, it shows that recommendations encourage customers to add additional items, thus reinforcing the value of each sale.

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Analysis of customer feedback and browsing behavior

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Customer feedback and browsing data are valuable sources of insight for refining recommendations. By analyzing customer comments or post-purchase survey feedback, you can identify the types of recommendations perceived as most useful. For example, if customers mention that they particularly appreciate the additional suggestions on the product page, it may be wise to reinforce this approach.

Analyzing browsing behaviors, such as time spent on specific recommended product pages, cart abandonment after viewing recommendations, or the pages on which recommendations generate the most interest, also provides insights for optimizing strategy. These insights allow you to adjust not only the types of recommendations, but also their placement and presentation, making the shopping experience more seamless and personalized.

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Continuous adaptation and constant improvement

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Finally, to remain efficient, regular monitoring and adaptation to market trends are essential. Customer preferences evolve, and recommendations that were effective yesterday may no longer be so tomorrow. By conducting frequent analyses, adjusting recommendations, and tracking KPI changes, your strategy remains agile and relevant.