Linda, a primary school teacher, eagerly waits for her payday so that she can indulge in her favorite sport – shopping. With the increasing number of e-commerce websites, it has only become easier for her to access a wide variety of products. However, it often becomes difficult for her to find a specific product or a combination of products she is looking for. For instance, if she is looking for a particular t-shirt, she can go to a store and ask the sales representatives for it, and they can provide her with several options. That doesn’t happen with e-commerce websites.
Linda needs to break it down into specific searchable keywords to find something she likes. This process can get tedious without any guarantee that she will find what she is looking for, making her abandon her shopping midway. Not only does it disappoint her and tarnish her online shopping experience, but it also costs e-commerce retailers heavily in the long run.
If Linda could easily find products without breaking down her requirement to searchable keywords, she wouldn’t have abandoned her shopping. Customers like Linda who turn to online shopping expect a human experience on the website, sans the presence of an actual salesperson. They want websites to identify their needs and make useful recommendations based on their preferences. In order to create an in-store experience on a website, e-retail merchants are going beyond the usual tech efforts to provide tailored product suggestions, even if they are based on a single data point. Personalized experiences are the key to driving business performance and enhancing customer outcomes. Adopting a strategy that integrates customer behavior information from all data points to replicate and build personalized retail experiences can unlock Pandora’s box of opportunities for retailers.
The online retail industry is a perennial source of valuable data that can be used to provide personalized experiences for shoppers. Users, especially millennials, are now more forthcoming to share some amount of personal data with retailers to avail discounts and offers. The small amount of data revealed to retailers has enabled retailers to create unique customer experiences. However, taking advantage of a huge amount of data is not an easy feat. To provide a highly customized experience, the data at hand needs to be streamlined to obtain a unified view of the customer across multiple devices and purchasing channels. This means that if a person switches between online and mobile applications, the retailer should be equipped to leverage the customers’ location and search history to send personalized deals straight to their respective devices. Since there are many ways that a customer can begin and end their journeys, it becomes complex for retailers to fully understand their customers.
Data integration needs to be point-to-point to establish a one-on-one relationship with the customers. This, however, is an extremely brittle process as the number of endpoints and applications increases. The IT architecture has to be future-proof so that customers can adopt all kinds of technology to interact with retailers. To make this experience seamless, retailers are now approaching API-led connectivity that facilitates plug-and-play integration and seamlessly connects assets by packaging them as manageable APIs. It enables retailers to easily personalize and customize their platforms.
Application Program Interfaces, or APIs, are increasingly popular tools that allow customers to interact with retail websites by pulling data from online retailers to add shopping capabilities to the app. To help retailers drive sales and brand, Google Cloud Platform has built a Retail API suite to enable e-commerce customers to improve their shopper’s experience across all channels, driving sales and improving customer satisfaction.
Google Cloud’s Retail API provides an end-to-end personalized user experience by powering your website’s product search and recommendation capabilities with user-event data and product catalog information. It is a compilation of state-of-the-art AI algorithms coupled with the scalable infrastructure of the Google Cloud Platform. The suite includes:
Quantiphi has implemented Retail API in multiple projects across different stages viz. data ingestion, engineering, modeling, and end-point integrations. Quantiphi has used Retail API to enable enterprises to deliver exceptional customer experiences.
With Retail API, Linda’s journey through the website can be hyper-personalized, enhancing her engagement with the brand on its website. Say, Linda is looking for a shirt she saw influencer wear on TikTok, and she lands on the retailer’s website. With Vision Product Search enabled, she only has to upload a picture of the shirt, and the website will present a range of similar outfits. She starts looking for a pair of bottoms on the search bar to match the shirt.
With Retail Search, relevant results of pants are displayed on the screen. All Linda has to do now is add the products to the cart. Recommendation AI will suggest to her a few “frequently bought together” options such as accessories to pair with the outfit. Linda adds the desired products to her cart and checks out, happy with the website experience.
On an individual level, customers receive a seamless experience on the website. Businesses benefit from adopting Retail API-led personalization on their website as real-time predictions increase the user’s interaction with the brand. Faster-growing companies derive 40% more of their revenue from personalization than their counterparts. 71% of consumers expect businesses to deliver a personalized experience, and 76% are disappointed at the lack of such interaction. Companies have seen a positive impact on the following parameters.
With a dedicated team of certified professionals, Quantiphi leverages in-house accelerators to help clients accelerate the development of Retail API on Google Cloud.
In partnership with Google Cloud, Quantiphi has helped several high-profile clients implement Retail API to provide a personalized shopping experience to their customers. For one such client, a leading global IT solutions provider, Quantiphi leveraged Retail API to build a robust recommendation system with Google’s recommendation AI model. It used various data sources and provided dashboarding and reporting capabilities, and combined the ERP platform data used in the dashboards. It provided dashboarding and reporting capabilities to the client by combining user-event data, product catalog, and offline transaction data available across different geographical regions. The resulting recommendation system gave two types of suggestions – frequently bought together and recommended for you.
Personalization has moved beyond traditional methods of interaction. In order to provide a delightful experience to their customers, retailers need to collect, integrate and analyze data from different sources including social media. As demonstrated, an effective way to achieve seamless integration is through API-led connectivity.
Quantiphi’s expertise in Google Cloud Platform’s Retail API tool enables retailers to connect with their customers and orchestrate programs that increase customer loyalty. To explore the possibilities of Retail API for your business and take your website performance to newer heights, get in touch with our experts.