50% increase in e-commerce conversions with smart personalized recommendation algorithms

Would you say yes to more revenue, more profits?

I know that’s a silly question. Anyone would say yes, of course. Esp when the revenue channels are ethical and legal.

But still, 75% of the e-commerce entrepreneurs are not leveraging smart recommendation algorithms, which yields up to 50% revenue growth via cross-selling, and upselling.

I feel smart entrepreneurs must tap into the power of machine learning to optimize their sales.

Why?

Reports suggest that Amazon clocks in 35% of its revenue from personalized product recommendations. 

And the magic of recommendation algorithms is not just limited to e-commerce. It’s equally useful for multimedia streaming services, social media platforms, and content discovery platforms like Netflix, Hotstar, Medium, Facebook, and Youtube. In fact, Netflix reports 75% of its engagement roots in recommended watch items. Read this insight to find how you can start & grow your own OTT startup.

In this insight, let’s thoroughly understand what are recommendation algorithms, their types, which ones you should try, how to implement recommendation algorithms in your existing eCommerce sites, and everything in between.

What are recommendation algorithms?

Before I answer that, let me ask you a question. Who is your favorite person in the real life? Keep the answer in your brain’s cache memory. We’ll come back to this. By the way, cache memory is a temporary storage system on servers and is often used for quick information storage and retrieval for delivering a better user experience with improved performance.

Generic explanation:

The true essence of recommendation algorithms is rooted in customer behavior, interests, and personal inclinations. It’s a data-backed methodology to understand a user’s persona and accordingly match them with the right product, services, and other recommendations that feed their wants/needs.

So, the “people who bought this also bought” section on Amazon or “Products you may also like” section on any e-commerce store with product suggestions for you are examples of recommendation algorithms.

eCommerce recommendation algorithms are no different than a friend recommending a movie to another friend, a salesperson recommending the best shoe that would go well with particular jeans, or a bookaholic recommending another book to a bookworm. We humans recommend anything to a person/group based on our natural intelligence (gratitude to god!), and online websites recommend using mathematical algorithms powered by machine intelligence.

Ultimately, be it humans or machines, the food for highly personalized recommendations is good-quality data observations about the target user.

Let’s try to understand recommendation algorithms from a technical lens.

Technical definition:

eCommerce recommendation algorithms, on a broad level, make use of statistical and knowledge discovery mechanisms like data mining, neighborhood formation, dimensionality reduction, matrix factorization, and machine learning to predict user preferences in a highly accurate manner.

And the eCommerce websites then use the processed information to recommend products to buyers/users. It falls under an information filtering system.

Recommendation systems have witnessed widespread success in eCommerce. But still, we see a low adoption rate because non-technical eCommerce teams with a size of 5-10 members find it difficult to implement these algorithms on their own.

But my human intelligence is compelling me to recommend this to all the eCommerce entrepreneurs out there. And don’t worry, we’re here to do the technical heavy-lifting for you i.e., you can depend on us from developing to deploying, maintaining, and improving the recommendation algorithms & models. We are experts at handholding our clients when it comes to technical matters.

Now, the big question for you as an eCommerce entrepreneur should be “but what’s in it for us?”

Why do you need a recommendation algorithm for your eCommerce store?

Businesses across the world have advocated for capturing customer loyalty. One-to-one marketing is one of the ways to achieve the same. One-to-one customer personalization has proved to yield great results for eCommerce ventures. It has tasted its fair share of success, and personalized commerce continues to evolve further with advanced neural-networks-led recommendation engines.

Broadly speaking, you need to integrate recommendation muscles into your eCommerce because:

– It’s an effective marketing mechanism to convert browsers into buyers.

– You can revitalize relevance for the end-users by winning the eCommerce personalization game.

– It holds the potential to surge your sales and generate way more revenue, of course, only if it is done right.

Coming back to the question at the beginning of this section, Who is your favorite person in the real life? I can bet, this person is your favorite because s/he understands what’s best for you and provides (recommends) highly personalized advice/suggestions to you. This person understands you to the core! I hope am not wrong. Same way, 91% of consumers would fall in love with your eCommerce platform if you can perfectly match them with their needs before they know it. Consumers love personalization.

But how do you add the recommendation features into your eCommerce?

How are recommendations generated?

As discussed briefly, a typical recommendation generation involves-

• Data mining techniques (for association rules),
• Data analysis for top-N recommended products, and
• Collaborative filtering (or your preferred recommendation generation algorithmic approach).

Data mining techniques for the discovery of association rules:

• Tree projection algorithms
• FP-tree algorithms
     

Basically, data mining is to find out how two sets of products are related to each other. We also have, traditional data mining for association rules-

• Let’s say there are two distinct product sets- X & Y. X & Y do not have any products in common. And there is a set of product purchases P.
• Now to associate products of X & Y, P is a set of product purchases from the set X, and products purchases from the set Y. Remember, X & Y have no products in common.

The quality of data mining is dependent on-

• Support metrics of association rules i.e., the occurrence frequency of P.
• Confidence metrics of association rules i.e., the strength of implications or conditional probability of occurrence of product item Y being purchased when X is already purchased in the same transaction.
• The more your recommendation generation approach avoids artifacts and prioritizes behavior that defines large populations, the better the outcome.

To further improve the quality of recommendations, your algorithms can make use of dimensionality reduction to filter redundant data variables around the product. There are several approaches to dimensionality reduction in machine learning.

For generating high-quality recommendations, the most widely used dimensionality reduction methods are-

• Principal component analysis (PCA) – uses covariance matrix, Eigenvalues of these matrices, and their corresponding eigenvectors.
• Latent semantic indexing/analysis (LSA)- It uses singular value decomposition (SVD) for transforming a m x n matrix into 3     component matrices with USV* factorization.
  • U is an m x p matrix,                    
  • S is a diagonal matrix i.e., it’s a p x p matrix, and                
  • V is an n x p matrix.                
  • V* is transpose of m x n matrix.   
  • V* will be a conjugate transpose of V if the m x n matrix contains any complex values.   

What are different types of recommendation algorithms?

The most common recommendation methodologies include-

1. Collaborative filtering

2. Content-based filtering

3. Risk-aware recommendations

4. Session-based recommendations

5. Hybrid recommendations, and

6. Reinforcement learning led recommendation systems

The most popular ones are collaborative & content-based filtering. Hybrid and reinforcement learning-led recommendation algorithms are also popular because of their high performance.

By the way, the outcomes for all of these approaches converge to-

1. Recommendation quality- highly personalized recommendations with minimal false negatives, and minimal false positives.

2. Recommendation performance- for better user experience

Most of these recommendation methodologies involve three segments-

1. Representation of data

• To alleviate sparsity (reduced coverage) challenges that often result in poor recommendations to customers.        
• Reduced dimensional representation indirectly improves performance and helps tackle scalability challenges.
• Improvises latent association between products in different transactions and helps solve synonymy problems too.

2. Neighborhood formation

• It’s a model building & learning phase.
• Makes     use of proximity measure for neighborhood formation by utilizing Pearson correlation and/or cosine of two user vectors.
• Center-based neighborhood formation and aggregate neighborhood formation are two widely used methods for this.

3. Recommendation generation

Different conceptual recommendation types-

Popular– The most popular products on your eCommerce store. It could be all-time popular, popular in a month, or around a specific festival or event.

Trending– It could be new launches, or a trending product because of an increase in demand. For example, sports merchandise during big tournaments.

Context-based recommendations

Browsing history– Based on the product views, view frequency, search history of a user on your eCommerce store.

Purchase history – Based on the transaction history of the customer.

Interest-based– Same as browsing history but can make use of third-party data to sharp segment user interests and recommend based on the same.

Predictive recommendations– It could be a hybrid approach to come up with recommendations for products that are not directly discoverable using simple methods.

Collaborative Filtering based recommender system

The collaborative filtering approach is one of the most successful approaches to recommendation generation. It makes use of user’s behavior i.e., live customer interactions to build memory-based or model-based recommender systems. The model generates recommendations based on a customer’s explicit and implicit data. These data points include purchase history, customer preferences, search history, product ratings, product reviews, product views, cart items, cross-device activity, social network likes and dislikes, etcetera.

With data mining, statistical filtering, nearest neighbor formation, and dimensionality reduction, collaborative filtering led recommendation engines to generate millions of potential neighbors in real-time.

The three stages to recommendation generation in collaborative filtering:

• Representation: model the already bought products, or some other implicit/explicit data point.
     
• Neighborhood formation: identify similar profiles based on reviews products     similarly, products bought in a similar pattern, and so on
     
• Recommendation generation: find top-N recommended products for the consumer

User similarity empowers these algorithms to recommend accurate products without any need to understand the products.

But generating these many neighbors at times also becomes a bottleneck for collaborative filtering. It’s called a cold start problem where you don’t have any initial data for a new eCommerce website or a product or category. And as mentioned earlier, this also introduces the problems of false negatives, and false positives impacting the quality of recommendations. Besides, long rows of customer data points can potentially slow down the model performance as well.

How does Codewave help you implement or integrate (leverage) recommendation systems to grow your e-commerce business?

We help you implement microservices-led recommendation architecture into your existing eCommerce website. This makes use of predictive analytics, experimental validation, eCommerce data, content data, sparse data sets, and practical circumstances to minimize the performance & quality challenges of a recommendation engine.

We build solutions for the magical knowledge discovery in your database, which helps you-

• Save extra by optimizing your efforts and investment into efficiency-focused channels
• Boost your eCommerce sales revenue by discovering more sales opportunities
• Personalize the experience for your customers and match them with their purchase     needs, including well-timed offers.

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