A Complete Guide to Product Recommendations on WhatsApp

Consumers now demand brands to realize their tastes and purchasing preferences in advance. That's why integrating product recommendation systems in the sales model has gained traction over the years. Explore how to use WhatsApp for product recommendations

A Complete Guide to Product Recommendations on WhatsApp

Conventional businesses typically depend on customers to visit the retail outlet to discover their product inclinations, but a tech-powered purchasing ecosystem banks on the potential of data, analytics, and customer-specific personalization.

Consumers now demand brands to realize their tastes and purchasing preferences in advance. That's why integrating product recommendation systems in the sales model has gained traction over the years.

Statistics suggest that visitors who browse product recommendations have 4.5 times more chances to add these products to their cart and are 4.5 times more likely to finish the purchase.

As a result, numerous multinational brands are already offering tailored product recommendations to create relevant customer experiences for increasing conversion rates.

What are Product Recommendations?

Fundamentally, product recommendations are just products that a business suggests to consumers. It is, at its core, a filtering mechanism that anticipates and promotes products that your customers are likely to purchase.

Product recommendations can be provided in several ways, such as by an experienced shop assistant, in a promotion email, on a product page of an online website, or using instant messaging platforms like WhatsApp to deliver personalized messages to entice customers.

With the eCommerce boom, as product sales have moved online, product recommendation engines have surfaced to leverage the huge volumes of user data to serve customers with personalized product recommendations.

A product recommendation engine is a robust solution that produces tailored recommendations based on user analytics, machine learning algorithms, and consumer-specific data, including:

✅Product reviews and testimonials

✅Product page view frequency

✅Purchase and return history

✅Shopping cart events

✅Click-through and search activity

Read also

O2O Marketing: What’s Next?
O2O marketing has revolutionized the way customers shop from brands. This newmodel allows retailers to find customers online and drive customers to make apurchase at their nearest store. Customers opting for this model can be due to multiple reasons such asconvenience, competitive pricing, fr…

Three Prominent Types of Product Recommendation Engines

1. Collaborative Filtering

This filtering strategy gathers and analyzes users' purchasing behavior and preferences data and projects which products they would like based on their buying patterns in relation to other users.

2. Content-Based Filtering

These filtering engines rely on a product's description and a customer's profile of top choices. In this recommendation engine, algorithms attempt to offer products similar to those a customer has previously liked.

3. Hybrid-Recommendation System

This engine integrates collaborative-based and content-based techniques, generating a recommended products list based on data from customers with similar interests and an individual customer's previous preferences.

Read also

7 Definitive AI Chatbot Trends and Predictions for 2022
AI chatbots have the potential to offer a personalized and seamless experience that can ultimately help boost revenue. Learn more about AI trends and predictions in this article

How Does a Product Recommendation Engine Work?

An effective and powerful product recommendation engine goes through four stages of data processing-

1. Data Collection

It is inclusive of both explicit and implicit data. The explicit data refers to the details offered by customers, such as product reviews and ratings. In contrast, implicit data includes characteristics, such as order and return activity, cart transactions, product page views, click-through rate, and keyword search logs.

2. Storing Data

Humongous volumes of user data are fed into a product recommendation engine as the data quality and quantity input into the models and algorithms directly influence the performance and reliability of the recommendation engine. The data type and format utilized can assist you in deciding what sort of storage to use.

3. Customer Data Analytics

The next step is to refine the data using numerous analytical methodologies. You can analyze the gathered information in multiple ways, including-

✅Real-time systems

✅Near real-time analytics

✅Bath analysis

4. Data Filtering

The last phase is to select a screening mechanism. You can pick the most suitable recommendation system from the three options for filtering: collaborative, content-based, or hybrid.

4 Real-World Examples of How Top Brands Leverage Recommendation Engines

1. Amazon

To achieve the personalization of the online marketplace for each consumer, Amazon employs item-to-item collaborative filtering recommendation algorithms as a targeted advertising strategy throughout the platform.

When a user clicks the "your recommendations" link, he is taken to another product page where he can sort recommendations by product relevance, category, reviews, and past orders. The buyer could also discover why a specific product got recommended.

According to statistics, Amazon's recommendation engine generates 35% of its revenue.

2. Best Buy

The recommendation system of the consumer electronics retailer is centered on query and keyword search and clicking metrics. Best Buy has leveraged user data to anticipate what products buyers may be willing to engage with. The query-based and product-to-product approach generates clustered models, which enable the business to provide product recommendations to visitors.

The algorithm predicts which product a shopper is inclined toward based on their platform activity and buying history.

A Complete Guide to Customer Experience
Great customer experience equals increased customer retention. Here’s all you need to know to achieve it

3. Spotify

Spotify's renowned Discover Weekly playlist is one of the company's most prominent use cases of ML-powered recommendation engines. This application refreshes customized playlists weekly so that people don't miss freshly released content by their favorite artists.

The platform uses three recommendation models -

  • Collaborative filtering- Sorting music by analyzing individuals' past listening data and comparing it with the listening history of other users.
  • NLP- Scraping online for gathering data on certain artists and music, then assigning each to a dynamic list of top expressions that constantly changes and is weighed by relevance. The algorithm then evaluates if two work samples of the artists depict some similarity.
  • Audio analytics- The system analyzes each person's audio file's attributes, such as volume, tempo, tone, etc., and offers content suggestions accordingly.

4. YouTube

The online video streaming platform employs recommendation engines to provide personalized content recommendations, allowing users to hop on to relevant videos seamlessly.

The content recommendations get updated regularly according to changing user content consumption preferences, tastes, and browsing activity on the platform while also spotlighting the huge set of alternative content.

The content recommendation engine on YouTube is powered by deep learning algorithms and composed of two neural networks. The first one analyzes and aggregates data on users' viewing patterns and leverages collaborative filtering to filter user-relevant content among several videos. The second one ranks the filtered content pieces to offer useful suggestions.

Using Product Recommendation Engines on WhatsApp

Brands consistently keep exploring new channels to promote their products. If you are also searching for such mediums, undoubtedly, WhatsApp is the most popular messaging platform to establish your presence. You can use recommendation for WhatsApp to offer product recommendations to your customers derived from their past purchases.

product recommendations
Product Recommendations on WhatsApp

This approach helps you pique your consumers' interest in the products and persuade them to complete a purchase. Furthermore, WhatsApp allows for seamless AI-powered chatbots integration, which helps streamline the customer purchasing behavior tracking process. You can also use recommendation for shopping to provide an exceptional shopping experience.

These are some marketing strategies that you could deploy using WhatsApp for recommending products-

  • Customer buying history- according to the recent product purchases, you can suggest similar or 'frequently bought together' products through a personal WhatsApp message.
  • Customer browsing pattern- It refers to the activities of your customers on your shopping platform. You can deliver a catalog of their recently viewed products with a CTA to the products purchasing page through WhatsApp.
  • Occasions- You can deliver personalized product recommendations, discounts, and deals, especially for the customers on special occasions like birthdays.

Read also

10 Powerful Ways to Use WhatsApp Commerce
WhatsApp has become an ally to create exceptional experiences and boost customer loyalty. Whether it’s an ecommerce or a retail brand, the possibilities of using WhatsApp commerce are limitless. Explore 10 powerful ways to use it to your advantage

Conclusion

Businesses must deploy tailored product recommendation strategies to achieve scale and increase profits, sales, and revenue figures while streamlining a customer's purchasing experience.

Numerous industries, including eCommerce, healthcare, online gaming, media streaming services, etc., can harness product recommendations to gain a competitive edge over their peers and outperform them by providing seamless customer buying experiences.

Top 10 Brands that are using Big Data
Big data is no longer a trend but a necessary tool that can be utilized to boost revenue outcomes. Are you ready to revolutionize your customer’s experience using big data?
A Complete Guide to Conversational Marketing
Conversational marketing can be defined as a new way through which brands can engage with consumers in real-time across multiple channels. Explore the complete guide to learn more
WhatsApp Promotional Messages: Guidelines and Use cases
Recently, WhatsApp has launched its recent feature that allows businesses to send WhatsApp promotional messages to customers who opt in to receive their updates.

Ready to grow your business?

We’d love to discuss your marketing plans and tell you how we’re the best partner you could choose