eCommerce personalization is making new strides every day. While customers may not understand exactly how recommendation engines work, they do understand the importance of personalized recommendations, thanks to Amazon's customized shopping experience.
In this article, we will understand how eCommerce brands can leverage an AI-powered personalized recommendation engine to customize the user experience from start to finish. Before we jump into the, 'how,' let's get to know the 'why.'
Why use an eCommerce Recommender System?
Recommender systems empower eCommerce marketers to personalize the shopping experience. Moreover, they enable marketers to analyze eCommerce performance by leveraging two key metrics --Recommendation Revenue and Conversion Funnel.
Quite simply, the Recommendation Revenue metric is the revenue from recommended items. According to Google, it is signified by the sum of the original item price, as shown in the catalog, for every item that was selected from a recommendation panel and ultimately purchased.
Note that it does not include shipping, tax, or any discount applied at the time of purchase.
The eCommerce Conversion Funnel, on the other hand, also known as the sales funnel, is a series of actions a visitor takes to turn into a buyer--from the moment they land on your website site to the moment they purchase something. Typically, it comprises five stages-- Discovery, Interest, Intent, Purchase, and Engagement.
Now let's get to the meat and understand how to use personalized recommendations as a service.
Top 4 Ways to Leverage Personalized Recommendations as a Service
#1: Understand the Costs of Using a Recommender System
Before you invest in a robust AI-powered recommender engine, you need to understand the costs involved. Contrary to popular opinion, using a recommendation engine is not a costly affair. A SaaS recommendation engine with a monthly subscription cost can easily allow the eCommerce brand to make use of personalized AI-driven recommendations and target new customers.
The learning: Do your market research, conduct an internal survey, and speak to your Sales, Marketing, and CX teams about your eCommerce brand's personalized recommender needs, and zero in on the top-3 AI-recommender tools for your organization. Make sure to factor in your user base type, budget, and business goals while making the decision.
#2: Do a Conversion Funnel Analysis
When it comes to eCommerce, the conversion rates differ from sector to sector. Here's what the data tells us:
Let's say your brand experiences 100K visitors across the homepage, landing page, etc. Of these, only 1000 customers end up making a purchase, driving the conversion rate to be 1%. This is where using an AI-powered tool such as Argoid can lend a helping hand. It can engage in a funnel analysis to demonstrate the key bottlenecks where users might be dropping off. In the following case, primarily at the Product and Add to Cart pages:
The valuable insights and data extracted from the tool can then be used to optimize the “Add-to-Cart” conversions and improve the overall conversion rate for the eCommerce company by 20%.
The AI-driven tool makes use of real-time user data such as user activity, catalog information, user buying behavior, shopping preferences, etc. to drive personalized product recommendations at the Add to Cart page.
Some popular examples of Cart recommendations include:
- Frequently bought together – If you are not featuring complementary product recommendations on the Cart page, you're losing out on customers. Amazon makes excellent use of this feature to upsell/cross-sell to customers, personalize the user experience, and boost the conversion rate:
Note that you can offer accessories for the items in the cart by reviewing the customer's past purchases.
The learning: Identifying the pain points and gaps within your conversion funnel can help you to pivot your eCommerce strategy and bridge the gap between sales and buyer's intent. This can only be done if you have real-time customer data, at the click of a button, so invest in the right technology to optimize your sales funnel and boost your bottom line.
#3: Drive Product Recommendations on the Home Page and Product Page
Product recommendation is the cornerstone of successful eCommerce sales. Luckily, there are numerous ways to drive product recommendations on the Home page as well as the Product page:
A. “Customers who bought this also bought…” – This algorithm makes use of collaborative filtering and works by figuring out the key similarities between the customer's shopping preferences and identifying how often two products are browsed/purchased together. Here's a wildly popular example by Amazon:
B. Similar Products – The type of recommender system leverages a simple category and integrates it with the price or product title. This data is then used by the recommendation engine to demonstrate relevant product suggestions based on the brand or the color and act as a digital shopping assistant for the customer for all intents and purposes. Flipkart makes use of this algorithm effectively to cater to first-time customers who may need a helping hand while shopping:
Here's an example of a home page product recommendation that allows the eCommerce brand to set the stage for new customers and provide them with the opportunity to know your brand's offerings a little better:
You can display your best-selling products and boost conversions organically.
The learning: Product recommendations work like a charm--be it for new customers or existing ones--as they can personalize the user's experience, making it more convenient and productive for your users and leading to a happier, more loyal user base.
#4: Drive Category Recommendations
If you have customers who haven't visited your website in a while and you wish to win them back, you can send them a personalized email/push notification about category-specific product recommendations and incorporate best-sellers within the email's content, based on the user's favorite category to boost the chances of a sale:
Alternatively, you can show category recommendations for existing customers to convert them better:
Pro tip: This strategy is especially useful when targeting first-time customers who may not be exceptionally familiar with your brand and will appreciate the 'added' help.
The learning: This strategy sits well for win-back campaigns to target lost customers as you expand the user's knowledge of your brand by showcasing complementary products from the categories that they already like. Additionally, it also works well for new customers who may have limited knowledge of the brand. So you can drive a customized marketing campaign to get the new users hooked. Alternatively, you can show category recommendations for existing customers to convert them better:
The Bottom Line
There's no “one-size-fits-all” strategy that can be used to drive personalized sales for new customers. The strategy you choose will ultimately depend on your brand's end goals as well as the type of user base you cater to. Make sure to sit with your team to brainstorm the degree of personalization your customers deserve, and wow your users with the intuitive services of AI-recommender engines--every time a user lands on your website.
FAQ's on product recommendations
What are personalized product recommendations?
Personalized recommendations are suggestions given to a customer based on their buying behavior. It is fundamentally a filtering system that predicts, displays products based on the intent of the customer. Product Recommendations can help retailers grow their revenue and improve customer retention.
How do you recommend new products to customers?
Product recommendation tactics like trending, similar products and new arrivals can be used in order to convince a new customer. You can make use of social proofing with a beleif that people will follow the action of masses. Popularity messaging ,creating a sense of urgency to your customers and adding trending icons can be used to impress customers whose preferences you don't know yet.
What factors would you consider for Product Recommendations?
In order to offer hyper-relevant product recommendations you need to consider demographic, geographic and behavioral data as high-value data points. With these relevant data about every customer create buyer personas and recommendation rules that will help better to tailor site experiences.