Personalized Product Recommendations

1.Overview

It seems as if the era of never-ending, irrelevant emails are over as companies like Netflix and Amazon, among several others have upped their game by focusing heavily on personalization due to which consumers are now expecting such services from companies of all sizes. Investing in this results in building better relationships with the customers while enhancing their experience.

A study by Accenture says that 91% of consumers expressed that they are more likely to shop from websites which offer personalized product recommendations.

Product recommendations suggest which products can be interesting for the customer. It provides suggestions for items or content a specific user wants to purchase or engage with. McKinsey estimated that 35 per cent of what consumers purchase on Amazon and 75 per cent of what they watch on Netflix come from product recommendations.

Companies across different areas of the enterprise are beginning to implement recommendation systems to enhance their customer’s online purchasing experience, increase sales and retaining customers.

Personalization online involves tailored product recommendations, offers, related products and more. Analyzing data about products and users, the system creates a connection between the two.

Visual search is one of the latest breakthroughs. It is truly making a mark, especially in the e-commerce arena. This gives suggestions to shoppers on similar products.

2.The Business Needs [Why Personalization & Recommendation]

Personalization & Recommendation (PnR) is becoming a MUST have engagement strategy for brands in customers’ journey towards conversions.

Recommendations and ads can potentially be integrated into this technology for added personalization. These are the items that a customer might be interested in, and they are based on the various customer purchase trends.

Recommendations allow customers to find their list of favourite products quickly. Once retailers have data, it becomes easier for them to provide customers with what they want and meet their expectations.

Brands are looking for ways to personalize and recommend based on customer attributes like:

• Customer location
• Customer recent views
• Recommendations based on weather
• Previous order history
• Similar items and similar colour

3.Offerings and Solution by Happiest Minds

Happiest Minds product personalization solution is built on SAP Hybris commerce. This helps in product recommendations based on transactional data available from internal (loyalty data, geo, and weather data etc.) from a different API call.

Product Recommendation provides personalized, relevant product recommendations in real-time and is well connected with API to respond appropriately.Dynamically push ad-hoc campaigns based on the customer location, weather, or any events.

On the customer home page will display product recommendations based on their weather and location like the city, region etc. Products that become especially relevant with certain weather are displayed on the customer home page—promoting cold-weather clothing in a cold snap, umbrellas if it is raining or maybe sunny.The weather is considered based on shopper’s country and either their zip code or city.

Created new custom components and service extending from SAP commerce OOTB for automatic product recommendations based on local weather and location. We have integrated JS and services to retrieve customer data based on his current location. Accurate geocoding results are an essential part of many geospatial processes. This API enables you to associate latitude and longitude with an associated address. SAP Commerce data model and code is customized to accommodate products mapping with weather and location-specific.

Product recommendation based on transaction history and recently viewed.

Product recommendation based on transaction history and recently viewed products- Customers past transaction is vital data for personalization of content and to provide the best possible recommendations.

Having historical sales or browsing data on the customer, we can also recommend a few personalized products. Again, this recommendation helps marketers to know a bit more about their consumer’s behaviour. Many people browse a ton of products which they do not buy, and purchase history leverages a far stronger signal making a high-quality recommendation. Recommendations based on browsing history also use collaborative filtering to suggest items that have compelled customers with similar histories to buy.

For returning customers, a personal strategy such as recently viewed allows to promoting their previous interested products quickly and easily.

Created carousel components on the home page and product page with frequently ordered and recently viewed products. Customised SAP commerce code base to handle user session and business logic to retrieve frequent customer orders. Extended out of box services and components to render various product recommendations.

Visual Recommendation

Visual search uses real-world images as the stimuli for online searches. These recommendations can absolutely work on the product page; when a shopper is browsing for specific product, it is logical to suggest similar products.

Visual search allows retailers to suggest thematically or stylistically related items to shoppers in a way they would struggle to do using a text query alone. Shoppers like image, they can tap on the image to find similar products. These similar recommendations help merchants to present products without compromising on the original style of the product. It gives seamless product discovery with shoppers now purchasing new but similar styles across a vast product catalogue.

A product page is the most obvious spot for recommendations; when someone is browsing a specific product, it is logical to suggest similar items.

Visual Search uses the image uploaded by customer to match similar products based on color and shape using Lire Indexing algorithm (Color Layout Descriptor (CLD), Edge Histogram Descriptor (EHD). It filters out the results to show the relevant matches for the uploaded image. It helps customers to stumble upon relative and similar products.

 

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