Customer expectations are higher than ever. As a retailer, how can you ensure that they have a shopping experience that is up to scratch?
According to Statista, global retail sales are expected to hit around $28 trillion by 2020, up from approximately $22 trillion in 2016. That’s an increase of roughly 27%, making an already massive market even bigger than before.
But as consumers continue to shop offline and online, their expectations on the kind of shopping experience they feel they deserve are also growing. In fact, research by Epsilon shows that 80 percent of consumers are more likely to purchase from companies that offer personalised experiences.
This begs the question: how can you offer a personalised shopping experience to your customers?
The answer involves harnessing data through data analytics.
Enter Product Recommendation
Product recommendation is a system that filters, predicts, and shows the products a consumer is most likely going to purchase. It’s by no means 100 percent accurate, but it still goes a long way towards helping customers know and decide what to buy, or, at the very least, what to buy alongside a product they’ve already decided to purchase.
In e-commerce, product recommendation helps bridge the gap between the online and in-store shopping experience. Just as a salesperson in a brick-and-mortar shop provides shoppers with recommendations on which products they might want to buy, recommendation systems help streamline your online shopping experience by showing you the products you’ll be most likely interested in (more on this later).
As business intelligence and data science platforms continue to mature, recommendation systems have also become increasingly popular. They can be found working behind the scenes on:
- Video platforms like Netflix, YouTube, and Amazon Prime
- Music streaming services like Apple Music and Spotify
- Online food delivery platforms like Grab, Uber Eats, and Foodpanda
How Product Recommendation engines work
While not all product recommendation systems are built the same way, they more or less function in the same basic manner. For starters, we know that e-commerce platforms like Amazon and Alibaba use a proprietary engine, which in turn, runs on algorithms and filtering tools that recommend relevant products to shoppers.
We also know these engines use forecasting and predictive analytics to provide these recommendations. By leveraging past and present retail data, statistical modelling, and other mathematical models, a product recommendation system can spot patterns in a customer’s browsing and shopping behaviours and detect sales opportunities.
For example, suppose that John purchased a new iPhone on an online shopping site. After taking note of that purchase, the recommendation engine will recommend a complementary product, such as a case or a screen protector. These recommendations “follow” John around as he explores the site. He might even get a follow-up email pushing these items.
Benefits of Product Recommendation engines for Retailers
Increased revenue is the obvious benefit of product recommendation. According to McKinsey, 35 percent of Amazon’s revenue is generated by its product recommendation engine.
Apart from online shopping, product recommendation systems can also be used to influence in-store shopper movements and increase conversions. Through data-enabled heat maps, retailers can detect patterns in foot traffic, which they can use to design store layouts that maximise sales.
For example, small items offered at discount prices in a fashion boutique, such as socks, coin purses, hair clips, and handkerchiefs among other things can be placed near the counter to encourage last-minute purchases. Admittedly, this is an old psychological trick, but the difference here is that product recommendation systems use actual data to identify products most likely to elicit a purchase decision.
If set up and configured appropriately, product recommendation systems can be the key to increasing conversions, sales, and other important sales metrics. Their ability to create a personalised retail experience also has positive effects on ‘soft’ metrics, which are harder to measure but are no less important, such as customer satisfaction and retention.
Types of Recommendation Systems
There are basically three important types of recommendation engines:
1. Collaborative Filtering
Collaborative filtering is hinged on collecting and analysing data around a user’s behaviour, activities, and preferences, and coming up with a prediction on the products they like by comparing it with data from other users.
Collaborative filtering assumes that what happened in the past is probably what will happen in the present. To be more specific, it assumes that because Group B shares similar characteristics with Group A, then it is probably safe to say that Group B will also like the products purchased by Group A.
Collaborative filtering, in turn, usually depends on two types of algorithms:
- Item-item collaborative filtering — Recommendations are based on items and their similarity to other items. Amazon uses this algorithm to recommend products visually or categorically similar to what a user is viewing, adding to the cart, or adding to the wish list.
- User-user collaborative filtering — This algorithm is more resource-intensive as it looks at customer behaviours and preferences. Think of it this way, if Group A likes products x, y, and z, and Group B likes products w, x, and y, then the engine will assume that Group A will like product w and Group B will like product z.
2. Content-based filtering
This filtering method is based on recommending products to users based on their previous purchase choices or stated product preferences. Recommendation algorithms then interpret the keywords used to describe a product and connect it with a user’s browsing/shopping history.
In other words, this system shows a user a list of products that are similar to the ones they liked in the past. If you bought a down jacket last week, you may want to add gloves, a scarf, or hat to complete the ensemble. Because the recommendation engines knows you liked the jacket, it assumes that you will also share a similar interest in items related to that purchase. Find out how Zalora uses its advanced recommendation systems to cross-sell products to its existing customers here.
3. Hybrid recommendation systems
As the name suggests, hybrid systems combine collaborative filtering and content-based filtering to leverage both of their strengths to produce more accurate and relevant product recommendations. Netflix is perhaps the most recognizable user of a hybrid system, making recommendations based on:
- The searching and viewing patterns of users that share geographical location, age, and other demographics (i.e. collaborative filtering)
- The genre, themes, and leading actors of films the user gave a ‘thumbs up’ to
The challenge with hybrid recommendation systems is the sheer amount of data needed to make accurate recommendations, which includes descriptions of your products, ratings on products, and contextual information about your customers among other things.
Bringing it all together
Fully understanding how product recommendation systems work requires going deep into data science.
For the average business owner, what’s important to remember is that recommendation engines provide a smooth and automatic way to help your users and buyers find the products they care about. The key is having access to quality data analytics, which will directly impact the quality of your recommendations, which, in turn, shapes the quality of your customers’ experience.
At DataVLT, we provide companies with a seamless, easy and affordable way to implement data analytics and improve their business. If you’re looking to give your customers a more in-depth and personalised shopping experience, get in touch with us today to learn more.
Secure, Affordable, Easier — DataVLT is an on-demand data science platform integrated with blockchain. We exist to help enterprises accelerate their growth through digital transformation by way of data analytics.
Learn more at www.datavlt.com.