Why Building a Recommendation Engine is a Good Strategy for Your eCommerce Business?

Why Building a Recommendation Engine is a Good Strategy for Your eCommerce Business?

Back in 1988, a British mountaineer penned down a book named, “Touching the Void,” narrating his real-life experience in the Peruvian Andes. The book was not much of success then. In a strange turn of events, when another writer came up with a similar book named, “Into Thin Air,” the sales of the former started to elevate, that too more than before.

How did this happen?

The credit goes to Amazon’s recommendation engine. The avid readers who searched for “Into Thin Air” were suggested “Touching the Void” too. The positive reviews and feedback, in turn, became the reason behind the surprising success of the book that was long forgotten. That is the power of product recommendation engine.

This technology invention has proved to be a blessing for eCommerce retail stores as it is a proven tool for adding up and multiplying sales like never before. In simple words, it is a tool for maximizing profits by personalizing the user experience.

This is a simple success story of any eCommerce store in a few words: Do not ask the customers what they want, instead, show them!

What is a Product Recommendation Engine?

A product recommendation engine is a system that helps offer a personalized experience to every individual customer. It mines data and filters the product listings in accordance with what they would like. It is pure calculated science and no hit-and-trial here.

Talking of Amazon alone, 35% of purchases are a result of their recommendation engine – McKinsey On the technical side of things, a product recommendation engine algorithms are used to mine customers’ purchase histories, browsing data, and reviews & feedback data to get a perspective of their mindset. Based on this information, the

online recommendation engine delivers content that aligns with the customers’ wants and needs, instead of trapping them in the paradox of choice.

This intelligence is the key to analyzing customer behavior to drive conversions in the end. Contrarily, if you avoid its importance, you are likely to lose out on business.

The Goals of Building Recommendation Engines The four goals of recommendation engine that should be your ultimate agenda, include:

1. Relevance Recommendations made should be relevant to the user, i.e., they should invoke their interests.

2. Novelty Your business will create more value if the customers are recommended items they have not seen or used before, but are similar to the items they have bought before.

3. Serendipity Your business will create more value if the customers are recommended items they have not seen or used before, but are entirely different from what they might have bought before. Only the concept is the same. Here, the probability factor steps in.

4. Diversity Recommending similar items to users is not always helpful. Consider giving recommendations that do not relate to their past purchases.

Difference Between Recommendation Engines and Personalization A recommendation engine is merely a tool that facilitates personalization. Where personalization denotes certainty, a recommendation is a prediction.

For example, a user buys groceries from Amazon every month. The next time they log-in the app, the usual grocery items get suggested. That is personalization, which means reducing the customer’s effort to look for and add items to the cart.

In short, user experience personalization is the result of their preferences, and there is no surprise factor in it.

On the other hand, if the same user is suggested an item related to their previous purchases, but something they never bought before, but are likely to show interest in, equates to a recommendation.

A recommendation is a result of predictive modeling that suggests something that users might like. It is similar to surprising the customers and making them feel empowered and boosting sales in turn.

How Do Online Recommendation Engines Work?

Here are three approaches that are followed for building recommendation engines .

1. Collaborative Filtering If a person with similar profile and preferences bought “this,” it is likely that you will like and buy it too. So, lets’ recommend it.

Collaborative filtering is about clustering groups of profiles with similar preferences, search histories, and buying habits into a single set and analyzing their behavior. Let us take an example.

Assume there are two different shoppers X and Y. X has purchased a table, a chair, and a lamp in the past. Whereas Y has bought a table and a chair in the past. Their purchase history puts them in the same group. So, the recommender system would analyze the data and the pattern and would suggest a lamp to Y as well. This is collaborative filtering.

2. Content-Based Filtering Focuses on the individual buyer. A customer’s likes and preferences are mapped with product features to offer recommendations.

In content-based filtering, two things are essential, i.e., data about the product and a customer profile. The product should have tags such as a name, description, and relevant keywords attached to it. And, a user profile should exist that is created based on their likes, purchase history, and browsing data.

Finally, the respective product and user repositories are mapped to recommend what a user would like.

3. Hybrid Recommendations Combining the power of both Collaborative Filtering and Content-Based Filtering, i.e., the CB-CBF approach A hybrid recommendation engine merges the inputs from what it gains from collaborative filtering and content-based filtering, to offer a better and a fool-proof recommendation that would not go “unclicked.� It is a product recommendation engine that is best suited for eCommerce platforms that deal with massive volumes of data and need to deal with scalability issues.

Collaborative Filtering helps form a reduced dataset of users who like and buy similar products, and content-based filtering helps match the products’ feature tags with the deduced set of user profiles. In short, there are no loopholes.

Product Recommendation Engines – Making Money or Creating Value?

What Should be: The purpose of an eCommerce recommendation engine – Bring value to the customers while making revenues in turn.

The Reality: Do what everyone else has been doing, recommend products that customers may or may not need, who cares unless we are making money.

So, if you own an eCommerce business or are thinking of building one, you need to do things differently. In simple words, do not follow the herd, be your own kind of

unique while keeping track of the best recommendation engine practices. Because, when you empower the customers, revenue growth follows without you having to stress about it.

To get a broader picture, here are some common issues with the online recommendation engines today.

1. Reading the Minds of New Users You have a newly registered customer, who maybe has heard about you from a friend or maybe has clicked on your carousel advertisement on social media platforms. What would you recommend them? There is no prior data on the user, and their preferences are unknown. In technical terms, this is known as a cold start problem.

You see, first impressions should always be right, and ignoring these first-time customers can be a massive mistake on your part.

According to a Statista report, 2.58 percent of eCommerce website visits converted into purchases as of Q2 2019, which is less as compared to Q1 2019 conversion rate of 2.72%. Have a look for yourself.

So, how do you address the cold start problem?

The basic strategy is to recommend your best-reviewed products, or let’s say the best selling products to these new guests. In this case, the probability of them liking it is high. Another approach could be to take advantage of the season & festivities.

Like, if it’s Christmas time, the chances are that they might be interested in holiday shopping.

Related Article: 2019 Holiday eCommerce Website Checklist to Boost Sales

Or you could build a recommendation engine that goes about tracking the geolocation data of users, such as their location and device used to personalize product results. Once you realize these factors, you will notice how effortlessly visitors get converted into buyers.

2. Missing the Fact that Everyone is Unique A customer is scrolling through prom dresses on let’s say some X eCommerce platform. While she is scrolling through, a collaborative filtering result pops up, which says; “This is a popular dress, 1000 something people have already bought this.” While the lady was looking for some unique dress, would she want the same dress that has already been bought and worn?

The problem with the product recommendation engine is that the recommendations are based on how people are like each other, and not on how they are unique in their own way. Everyone has a different taste and preference, and presenting them with similar suggestions might not be a good idea.

The solution is simple, focus on building a user-centric or a personalized recommendation engine that celebrates uniqueness over commonality. That might be a long journey down the road, but offering this kind of customer experience would be worth it. Think about it.

3. The Toilet-Seat Problem Yeah, it sounds absurd, right? But, it is a real problem that an Amazon customer reported. See it for yourself.

Dear Amazon, I bought a toilet seat because I needed one. Necessity, not desire. I do not collect them. I am not a toilet seat addict. No matter how temptingly you email me, I’m not going to think, oh go on then, just one more toilet seat, I’ll treat myself.

— Jac Rayner (@GirlFromBlupo) April 6, 2018

If a customer searches toilet seats on the on-site search bar and buys it, that doesn’t mean they need it again after a week. Such eCommerce recommendation engines make a customer doubt your credibility. Why commit such a mistake after all.

So, the simple fix would be to set a time interval for the recommendation so that they stay for a particular period and fade away automatically.

Or, a better approach could be to stop suggesting the items that have already been ordered. Instead, unique items should be more of a focus, like suggesting items that would even surprise the customers. That would make them feel empowered while bringing more value to your business in turn.

Tip: If the customer has bought an ordinary toothbrush, recommending them to try an electric toothbrush would be valuable, i.e., same product, but different technology.

Conclusion A practical recommendation engine is the one that mines accurate customer data and presents them with products that have a high chance of making it to their shopping cart. Therefore, it is a prerequisite to building a recommendation engine to stay ahead in this competitive landscape. It is the best and the latest technology that helps build strong relationships between businesses and customers.

And, in a world where the fifty-nine percent of 16-36 year-olds head to Amazon before any other eCommerce website, you need to start thinking differently. It is high time to build a recommendation engine that causes disruption and proves to be a game-changer. Only then can you think of making a difference that would count.

Lastly, remember to conduct a thorough R&D before you approach any recommendation engine builder. And, a builder who knows that there is always room for improvement and helps your recommendations get better day by day.

Source - https://www.netsolutions.com/insights/building-recommendation-engine/