Driving 5X Conversions With Personalized Product Recommendations In Mobile Apps

Have you ever gone to a restaurant and struggled to decide what to order?

When the server asks if you're ready, you ask on the spur of the moment, "Which do you prefer, the holubtsi or the deruni?" (You've arrived at a Ukrainian cafe.)

When you do this, you are asking for a product recommendation.

And the reaction, whether you're craving pierogi or shopping online, is more powerful than you might think. We tried to figure out why that is the case !

Here’s what we found, 

Buyers are 40% more likely to spend more than planned when experiences are highly personalized.

Makes sense to collect all that customer buying behavior data you have and put it to good use.What you’ll end up with is data-driven and robust product recommendations.

Without meaningful customer data, your product recommendations will be subjective, generic, and less powerful than they can and should ideally be.

Let us now look into the dynamics involved in product recommendations in detail.

What Are AI Driven Product Recommendations?

According to a recent study by Orbis Research, AI-based recommendation engines are "data-filtering tools that use various algorithms and data to recommend the most relevant items to a specific customer."

These are a component of an eCommerce personalization strategy in which products are dynamically populated to a user on a Mobile app, web page, or email based on data such as customer attributes, browsing behavior, or situational context, resulting in a personalized shopping experience.

They're the targeted suggestions that cause you to stop and think, "Huh! That looks like something I'd like!"

And guess what? They are effective.

Product recommendations are beneficial for organizations with large and diverse product catalogs. The catalog may be diverse due to a wide range of products (e.g., a department store), or it may have a small number of product categories but a wide range of product feature sets (e.g., a garment store).

Artificial Intelligence Revolutionizing The eCommerce Industry

Did you know?

AI can boost business productivity by 40%.

Artificial intelligence (AI) has made significant advances recently, but most people still need to figure out what it is.

To begin, you must understand what AI is: "Artificial Intelligence" is a machine's ability to mimic intelligent human behavior. It has been defined as machines that can respond to their surroundings with little or no human intervention.

Artificial intelligence is more than that. It is a collection of various technologies that work together to accomplish specific tasks. Other computer programs are intended to automate human endeavors by learning from previous experiences and establishing criteria.

Examples of typical artificial intelligence applications incorporate web search engines, language conversion, AI driven product recommendation etc.

Artificial Intelligence Is Being Used To Match Consumers To Products

Predictive analytics, a type of AI used for correlating disparate data and making predictions based on it, will power most product recommendation applications. Once the software's algorithm has been trained on all necessary customer and product data, it can begin recommending products to specific customers.

Let's take the example of Alibaba.com And Netflix

The world's largest e-commerce platform, selling more than Amazon and eBay combined. Artificial intelligence (AI) is used to predict what customers might want to buy in Alibaba's daily operations. The company uses natural language processing to automatically generate product descriptions for the website.

Another popular AI recommendation example is from Netflix, which recommends shows and movies to the user based on their watch history.

Having said that, what still remains at the core of effective product recommendations is data aggregation. You've probably heard of the three main types of data that are commonly collected:

Data aggregation

Product/category, cart and purchase information, and internal search queries.

Static Product data

Information is statically pulled from the product feed.

User specific Data

Information statically pulled from product feed.

These types of data are critical to the operation of any eCommerce application, but they are also merely surface-level. They leave the search and information submitted to the visitor, who may need to be more familiar with your product offerings. You must be able to communicate with your customers, understand their problems and needs, and then provide them with hyper-personalized product recommendations that address those needs.

How Do AI Product Recommendations Work?

The concept of recommended products may appear simple, but behind the scenes are essential. 

Artificial intelligence (AI) plays a significant role in collecting data and displaying products based on buyers' preferences, and delivering them a personalized shopping experience. 

The well-sorted and precisely analyzed product recommendations that appear for customers result from complex algorithms that rigorously study user behavior and then produce personalized product recommendations.

It employs specialized AI algorithms and techniques to support even the most extensive product catalogs. The recommendation engine, powered by an orchestration layer, can intelligently choose which filters and algorithms to apply in any given situation for a specific customer. It enables marketers to increase conversions and average order value.

A recommendation engine typically processes data through the four stages listed below-

Data Collection

Data collected here can be explicit, such as user-supplied data (ratings and product comments), or implicit, such as page views, order history/return history, and cart events.

Data Storing

The type of data you use to generate recommendations can help you decide whether to use a NoSQL database, a standard SQL database or object storage.

Analyzing

The AI recommender system filters and finds items with similar user engagement data using different analysis methods such as batch analysis, real-time analysis, or near-real-time system analysis.

Filtering

The final step is to filter the data to obtain the pertinent information needed to provide recommendations to the user. To enable this, select an algorithm suitable for the recommendation engine from the list of algorithms explained in the following section.

Strategies Concerning Related Products

In the case of Related Products, it is critical to identify the right prospects and recommend appropriate products that meet the expectations of each customer.

This improves the user experience and encourages customers to buy. Below are some strategies based on which products can be recommended to the buyers. 

What do the below pointers indicate?

• Automatic: It automatically recommends appropriate products to customers based on available customer data or context at a given point in time.
• Most popular: It displays suggestions for the products that are currently the most popular in a store.
• User Affinity: Based on the browsing approach, these product recommendations match each customer's preferences.
• Bought together: These are suggestions showing matching products that can be purchased with the product(s) the customer is viewing.
• Similar products: It provides recommendations based on what each visitor has recently viewed.

• Upsell Product Recommendation: Upsells are designed to do exactly what they sound like: sell more to customers. You could upsell a customer to purchase a more premium version of a product, a larger quantity, or even a subscription to generate long-term recurring revenue.

• Cross-sell Product Recommendation: The cross-sell product recommendations will allow you to choose which products should be related to one another. This could be as simple as matching a scarf to a pair of mittens being considered or as complex as highlighting a few different lenses designed for a specific camera brand.

• Recently purchased: Displays product recommendations that were purchased with a product that the customer is viewing.
• Last purchased: Recommends items based on a customer's most recent purchase.

Case Study: How AI Product Recommendations Help Increase Sales

As business owners, we are primarily responsible for incorporating features that encourage user engagement while making the purchase process easier for buyers.

As a result, the AI driven Product Recommendations feature has become essential in every mobile app.

Customers prefer a quick purchase process that saves them time and money.

As a result, business owners incorporate the Related Product section into their apps, encouraging customers to buy in bulk.

This section may display products based on previous purchases made by the customer.

Assume that the customer previously desired to purchase a Yoga Mat but did not due to its high cost.

• The same Yoga Mat is available at a 50% discount the next time the customer visits the store. Not only that, but he discovers Yoga equipment in the product recommendations section and purchases it all in bulk.

• This is the effect of mobile app-related products.

• Moving on, let us look at the factors that influence product recommendations in the MageNative mobile app.

• Customers are encouraged to buy bulk, allowing business merchants to make more money.

• Most importantly, customers can save significant time when browsing products in a store.

• Product recommendations from Mobile App Builder are a boon to online businesses. It contributes to increased store sales and conversion rates.

• Customers can go from one product page to another.
• Personalized recommendations are even more effective because they are based on previous purchase history data from customers, such as the most visited, viewed, or purchased product items and the product that the customer is currently viewing or buying.

How AI Recommender System Helps To Quicken Purchase Decisions

You don't need market research to figure out whether a customer is willing to shop at a store where they can get the most help finding the right product. They're also more likely to return to a shop like this in the future. 

To get a sense of the business value of recommender systems, consider the following: Netflix estimated a few months ago that its recommendation engine is worth $1 billion per year.

Implementing a product recommendation has two major advantages: customer satisfaction and revenue. Let's look at how it assists them in streamlining the purchase process. 

•  Quicken the purchase process by reducing the time it takes to find products and services.
• Help in the selection process for the undecided customer.
• Improve the relevance of search results.
• Contribute to increased purchase rates, user loyalty, and satisfaction. 5- Encourage users to interact with more products, increasing consumption and profits.
• Increase the order value and profit margin.
• Display newly released content to your users based on their preferences.

Brands must establish data collection and utilize it in business optimization to increase profitability. 

Why Do You Need Better Product Recommendations?

According to  Invesp, the conversion optimization oracle surveyed product recommendations.

Here are some eye-opening statistics:

• 49% of consumers say they purchased a product they did not intend to buy after receiving a personalized recommendation.
• 75% of customers are more likely to purchase if they receive personalized recommendations.
• 54% of retailers reported that product recommendations were the primary driver of the average order value in customer purchases.

Consider the numbers.

Half of those polled by Invesp said that product recommendations influenced them to buy something they otherwise would not have. That is how increased order value is defined.

Product recommendations are so effective at persuading people to buy more that nearly 6 out of 10 eCommerce operators cited them as the primary reason for people adding more to their carts.

The last statistic should come as no surprise, given that we're all consumers; who among us hasn't seen that little particular recommendation made just for us and been persuaded to click and buy?

And one more thing before you go: it's estimated that personalized product recommendations account for 35% of Amazon purchases.

Nobody is Amazon, but who couldn't use a boost like that to their monthly sales?

Product recommendations are highly effective.

What’s In It For You?

About 56% of customers are likely to return to a site that recommends personalized products.

Customers, who return? That's great news.

However, the advantages continue beyond there. Accurate, data-driven product recommendations are incredibly beneficial to your company:

• They boost conversion rates.
• They increase customer loyalty and encourage repeat purchases.
• They raise the average order value and the number of items per order.

Furthermore, your site can provide customers with product recommendations in real time because personal information is collected in real-time. This will take little time and then rely on the visitor returning once the numbers have been crunched.

This is instant conversion and long-term loyalty. When your application can make suggestions that appeal to a visitor, you establish a connection with that person. They believe they have been heard. And there's a lot of potency in that.

To provide an exceptional user experience, meet all of their users' needs, and increase sales, MageNative has integrated third-party integrations such as personalized recommendations.

Personalized recommendations enable you to deliver a highly personalized experience to your buyers. Employing MageNative personalized recommendations Integration in the app helps you accelerate conversions by making purchasing decisions easier for your shoppers.

How Can MageNative Help You Get The Most Out Of Shopify Product Recommendations?

MageNative offers a Shopify mobile app that recommends products for cross-selling and upselling campaigns using artificial intelligence. You can do a few things with Ai Product Recommendations by setting up an intelligent search bar and relevant recommendations and providing a user-friendly experience.

You can tailor your smart search bar widget to your brand's preferences with auto-complete, auto suggestions, synonyms, and other features. In addition, track performance in real-time by displaying products such as new arrivals, best sellers, and trending among different categories.

It's time to leverage your Shopify site's data to provide 1:1 content personalization.

This enables you to target, convert, retain, and grow your customer base across all onsite touchpoints while meeting your business objectives every time.

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