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      Real-Time Personalization

      Why Product Recommendations are Key to Winning With E-Commerce Personalization

      By Megan Warhurst

      Mar 21, 2022

      16 min read

      Real-Time Personalization

      Why Product Recommendations are Key to Winning With E-Commerce Personalization

      Welcome to part four of our “The Way to Data” series, where we discuss the combination of data and e-commerce personalization. Follow along for best practices, insights, and tips to create winning business strategies and better customer experiences. 

      There are countless ways businesses can utilize personalization for success. But, one of the most effective and compelling ways to use personalization throughout your customer experience is through 1:1 product recommendations — just look at the data.

      56% of customers are more likely to return to a site that offers product recommendations and 74% of customers feel frustrated when website content is not personalized.

      To fully grasp the importance of product recommendations, let’s first look at the principles that they borrow from real-life interactions between sales representatives and customers.

      Brick-and-mortar stores have a clear advantage when it comes to establishing a bond between the customer and the brand. A salesperson can learn about the customer’s interests, intents, and needs through live conversation and then make recommendations from that interaction.

      Recommendation engines provide your business with the opportunity to have these types of interactions with your customers throughout their online shopping journey. So, let’s dive into exactly how recommendation engines work, where recommendations can be leveraged across the customer journey, and why they are the key to winning with e-commerce personalization.

      First Off: What Are Product Recommendation Engines?

      Recommendation engines filter and sort your online store’s product offers on the basis of a set of rules. This process uses data about your products, such as the number of views, sales, or even reviews, to present the most popular products. The presentation of these results can be as simple as the order the products appear on the category page.

      User-specific data, such as the customer’s most-viewed categories, products, and purchase history, allows recommendation engines to find the most relevant offers for your customers. The resulting recommendations are capable of fueling your personalized advertisements, email marketing campaigns, or special offers within search results, category pages, landing pages, and more.

      Stage One: Using Recommendations to Gain New Customers

      Top Seller Recommendations

      Customers crave shopping experiences that let them encounter relevant products without needing to search high and low to find them. Personalized, AI-powered recommendations achieve this by continuously identifying top-selling, curated products geared towards shoppers new to your brand, thus bringing them to the best your brand has to offer and closer to conversion.

      The theory that fuels this commerce strategy is Pareto’s 80/20 rule of marketing. This rule alleges that 20% of your products are most likely driving 80% of your sales, and so these top 20% of your products will most likely speak to (and drive brand affinity for) new customers.

      A/B testing various recommendation models, each using a different combination and weight of input parameters — such as the number of views, purchases, time spent on a page, clicks, add-to-cart events, and so on — will help you identify which model will drive sales most effectively and then leverage these recommendation blocks across landing pages with a powerful headless CMS (content marketing system) like Bloomreach Content.

      Rating-based Recommendations 

      Nothing your marketing team could write will ever be as convincing as a 5-star review from your customers. Humans are social creatures, so social proof is one of the best ways to convince your new customers of your positive brand reputation, solid customer experience, and product quality. Using reviews as a basis for product recommendations will make the product come alive in the mind of your customer and also answer key questions or concerns they might have about the product.  

      Researchers at Boston University conducted a study that examined how reviews impact purchase decisions, and found that reviews that give a detailed account of the product’s quality and usage contribute significantly to higher sales and lower returns.

      The best part? These reviews can be used across the commerce experience to help nudge customers towards conversion. Just a few examples include:

      • Adding positive reviews in abandoned cart email campaigns
      • Attaching star ratings and review highlights in “You Might Also Love” sections of product pages
      • Giving precedence to highly rated items (that also fit the customers’ needs) in search results

      Cross-sell Recommendations

      Cross-selling is the practice of selling additional items that are complementary to a customer’s purchase objectives. This practice mimics the common offline experience one would get from a salesperson using recommendation engines. For example, retailers might cross sell by grouping particular product categories together, such as socks and shoes.

      Cross-sell banners leverage this sales opportunity by presenting relevant items during a customer’s shopping journey that are based on the contents of their cart, brand interest, or last-seen items.

      The most successful cross-sell recommendation models make use of collaborative filtering. This method makes predictions about the interests of a customer by analyzing the preferences of the customer base and finding the preference patterns that are most similar to those of the targeted customer.

      Stage Two: Using Recommendations to Keep Customers Coming Back for More

      Remarketing Recommendations and Relevant Search Results

      Today’s budget-conscious and shopping-savvy customers know to shop around multiple sites and consider plenty of options before making a purchase decision. Remarketing is a technique for presenting advertisements that display your offers and recommendations throughout the customer’s shopping journey. This method motivates shoppers to change their habits and consider your online store offers.

      The type of recommendation depends entirely on the product category. High-value products, such as electronics, benefit from display advertisements that motivate customers to spend more of their consideration process on your website. These advertisements may present products that reflect an awareness of their search intent and makes it possible to make comparisons within a product range.

      Other categories, such as food and groceries, would benefit from a different recommendation approach. Shoppers are more likely to use their shopping cart as a collection basket and check out once they’re finished with their collection process. Suitable display advertisements present a combination of cart items, product recommendations, and complementary products.

      Leading grocery chains, like Albertsons, also recognize that their shoppers interact with their brand’s site mostly as a jumping-off point: They might place an order online or choose to buy in-store at one of their locations. So, relevant and helpful search results are key in ensuring customers ultimately purchase with Albertsons. In fact, they saw a 25% increase in customer basket-building speed after implementing Bloomreach Discovery’s AI-powered, personalized search results on their site.

      Personalized Email Marketing Campaigns

      Personalized emails convey to your customers that they’re understood and valued, and result in significant increases in click-through rates. Emails with personalized subject lines are 26% more likely to be opened. Moreover, emails with personalized recommendations improve click-through rates by 25-35%.

      There are several great recommendation models that can be leveraged within emails. Whether you want to use the customer’s profile data (such as most frequented categories, purchase history, or interests) or product data (such as popularity and reviews) depends on the type of email marketing campaign.

      Is your email campaign news-centered, such as the introduction of a new fashion season, the latest product releases from a brand, or a Black Friday sale? In that case, your recommendation strategy may well benefit from reviews, user stories, and the popularity of your offers. These recommendations are eye-catching and informative — and will keep your customers engaged.

      On the other hand, weekly deals and seasonal offers are ideally suited for personalized recommendations. A customer’s purchase history and interests are a good source for weekly deal recommendations, while their previous season’s activities will help you align your offers with their seasonal interests.

      Stage Three: Using Cart and Checkout Recommendations

      Recommending Accessories

      Customers zero in on a specific purchase, like a dress or suit they may need for an event. Recommending accessories during the checkout process, such as shoes or an evening bag, will help your customers round out their purchase while also increasing your average order value (AOV).

      These recommendation models require careful annotations of your product data, which means you have to answer the question: “What products are compatible?”

      And let’s be real — your team might not have the time to go into that much detail. Having reviews and links to commonly bought accessories on product pages, within abandoned cart emails, and more can serve as a valuable alternative to maintaining detailed records about your products.

      Frequently Bought Together

      Package deals help customers purchase quicker and inform them about the items they might need to complement their main purchase. It’s a successful form of cross-selling that takes place seamlessly within the checkout process.

      Successful recommendation models show alternatives to what’s currently in the customer’s shopping cart. These alternatives can be varied colors, styles, and product combinations. Presenting reviews and user stories about the package deals will make these offers a compelling and confident option for your customer.

      These recommendations boost your business’s bottom line by offering alternatives that increase your AOV and motivate your customers to choose package deals that offer you a higher gross margin.

      Wrap Up: Why Product Recommendations Are Essential to Success

      To put it simply, recommendations not only improve the overall customer experience, but they’re also the catalyst you need to boost revenue for your business.

      But, today’s businesses have to tread a careful line between thoughtful personalization and creepy customization. To do this, it’s essential that marketing, site, and search teams partner with ethical technology providers that understand which recommendations will instill confidence in consumers instead of fear.

      The Bloomreach Commerce Experience Cloud enables a sophisticated, yet ethically minded level of personalization by combining the power of unified customer and product data with the speed and scale of AI-optimization, helping your brand deliver customer journeys so personalized, they feel like magic. We achieve this through the prioritization of consent within your data-gathering practices that put the customer in the driver's seat of their experience.

      If you’d like to learn more about how recommendations across your site, search, and marketing experiences can make a difference for your business, reach out to one of our commerce experts today. 

      Be sure to read more from our “The Way to Data” series: 

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      Megan Warhurst

      Content Marketing Manager at Bloomreach

      Megan collaborates with Bloomreach experts and customers, as well as industry leaders, to create content for customer-obsessed marketers, digital transformers, and data-driven professionals. Megan is a CDP evangelist who believes in turning the power of data into relevant, authentic experiences across the digital landscape.

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