5 Product Recommendations Techniques to Improve Consumer Experience
A study has proven that one third of ecommerce sales during the quartered studied resulted from product recommendations and as per Accenture report, personalization increases the likelihood of a purchase by 75%.
What is product recommendations?
Ecommerce personalization and product recommendations aren’t any lucky guess anymore. Amazon, ebay, Netflix are using data science and machine learning algorithms to mine and understand relevant data points to come up with accurate recommendations and customer experience for each customer.
Product recommendations & personalization start with capturing product meta data and website traffic. A sophisticated product recommendations engines like CanisHub uses Hybrid model where both these data is to predict the probability of a visitor buying a product. This sophisticated engine uses online behaviour, purchase history, session data, product meta data, and much more to find relevance for each visitor and thereby enhancing their digital buying journey.
An effectiveness of any product recommendations depends on three major factors:
1. measurable effectiveness of ML models
2. Timely delivery of recommendations
3. Data capturing and processing capabilities.
It is said that “A timely recommendations can lead a shopper choose one product over another.” Therefore, it is important to understand these aspects if you are out there looking for a product recommendation solution for your brand.
How Recommendation Engine Benefits E-Commerce?
There are many benefits of using accurate and hyper personalized recommendation engine apart from conversion rate and increased sales, especially for e-commerce:
- Increased Engagement: The average duration of session for any e-commerce site is around 2-3 minutes. This means that the visitor if not convinced within this duration will leave the site and there will be no conversion. A recommendation engine analyses each visitors and provide relevant products for each visitor within 100 millisecond making it more relevant for each customer and increases the likelihood of visitors spending more time on site. This ultimately increases the probability of conversions. At CanisHub, we have seen the average session duration increase by 125% over a period of time.
- Increased Loyalty: Customers tends to remember great customer experience. If an ecommerce site is able to understand the customer requirement and deliver right products at the right time, customer loyalty and longevity shoots up.
- Low Drop Off Rates: One of the main reason for low conversion rate for e-commerce brands is that the customer is not able to find products that they want to buy. Most of the customers are impulsive buyers, the moment they find the site to be flat and boring they go away. Brands with hundreds and thousands of SKUs need to understand what exactly a customer need and show products and pages dynamically to match their expectations.
So now, without wasting more of your time, let’s see that are those top technique that you can use to improve customer experience and thereby sales using product recommendations:
Have you ever noticed your Amazon homepage? Almost 70% of the homepage widgets are recommendations based on machine learning. A direct customer normally lands on the homepage to see what new or relative product your store has to offer.
It is the responsibility of the e-commerce brands to understand their customers need and customize the homepage for each customer. There are many ML models that can be used on Homepage, based on an industry, however, most commonly used models are as follows:
- Recently Viewed
- Interest Based
- New Arrivals
- Discounted Products based on Interest
User Smart Offers
Almost 90% of the e-commerce websites have snackbar on top with a single offer across website. A snackbar is a component that a retailer can use to target various offers based on customer interest.
Example: If a customer has shown interest in Tshirts and you are running flat 10% discount on Tshirts then it is time to show that offer to those people or a different offer for new visitor. Dynamic or smart offers are important to lure the visitors into becoming customers.
Show Recommendations in the first fold
Many retailers show recommendations much below the first fold, however, product recommendations takes user interest and product data both into consideration before recommending anything to a user. Therefore there is a high probability that a user would click recommended item if shown within the first fold. The result will yield in higher CTR.
Different Algorithms for Returning Visitors
Product recommendations are tricky. Most of the companies follow collaborative approach. However, if you think logically then you will realize that a visitor who has visited your site has given you enough data to personalize the experience. Therefore, returning visitors must have a different logic for personalization keeping their behaviour in the center of recommendation strategy. We at CanisHub call it as cognitive approach.
Checkout Page Recommendations
Checkout page is the most famous type of recommendations used globally. However, in my personal experience I have seen only 30% of e-commerce using them correctly. The basic logic is that you show what other products can be bought together using collaborative approach. However, almost 80% of the items are never bought together. So, what can we do in this scenario?
There has to be some fail safe mechanism that determine if the product in cart has other bought together products to recommend or can we cross-sell products from other categories that has higher probability of purchase with the product in cart. Most of the time your product recommendation data frame will be populated with either approach. This increase the average order value of cart by at least 25%.
So the next time, you think about how to implement product recommendation, do consider these advice before selecting a platform or a product.