Cloud Retail Search, part of Discovery Solutions For Retail portfolio, helps retailers significantly improve the shopping experience on their digital platform with ‘Google-quality’ search. Cloud Retail Search offers advanced search capabilities such as better understanding user intent and self-learning ranking models that help retailers unlock the full potential of their online experience.
Retailers are always working on trying to keep up with the ever changing consumer expectations and trying to forecast the next trend that can impact sales and revenue.
The pandemic brought its own (and largely new) set of challenges which further complicated the issue over the last two years. The retailers were forced to adapt to the new consumer (low physical touch) behavior in which the browsing and product research was largely digital (endless aisle) and accelerated other trends such as buy online and pick up in stores (BOPIS), curbside pick up and pick up lockers. According to a McKinsey Global Survey from early last year, the pandemic has accelerated the pace of digital transformation by several years.
The National Retail Federation (NRF) estimates that retail sales are expected to grow between 6% and 8% in 2022 (slower growth rate than in 2021), as consumers spend more on services instead of goods, deal with inflation and higher food & gas prices due to geopolitical disruptions in the world.
And the competition continues to be fierce as ever. Amazon continues its dominance in the U.S. retail world and new PYMNTS data shows that Amazon’s share of US Ecommerce sales hit an all-time high of 56.7% in 2021.
Customers now have more choices than ever on how they want to engage with the retailers, where they want to spend the money and make their purchase. They also have increased expectations from the retailers around providing a high quality product discovery experience, which is forcing the retailers to invest heavily on improving customer engagement on their digital platforms to boost conversion rate and overall customer loyalty.
This is where Retail Search can help by providing an enhanced search experience that uses Google-quality search models to understand the customer intent and takes into account the retailer’s first party data (such as promotions, available inventory and price) for ranking results.
How is Google Cloud Retail Search Different
The Ecommerce platform on-site search use case is not new and retailers have been trying to solve it effectively for the last two decades. Most retailers recognize that search is a critical service on the platform and have spent countless resources to improve and fine tune it over the years. Yet the challenge remains. According to a Baymard Institute study in late as 2019, 61% of sites still required their users to search by the exact product type jargon the site uses.
However, users now expect the same robust and intuitive search features as is offered by Google.com and other popular web platforms, who seem to have the uncanny ability to intelligently interpret and yield relevant results to complex search queries.
Google’s decades of experience and research in search technology benefits Cloud Retail Search solution and that is what differentiates it from the competition.
- Advanced Query Understanding: Retail Search can provide more relevant results for the same query due to better query understanding features and knowing when to broaden or narrow the query results. While most search engines still rely largely on keyword based or matching tokens results, Retail Search has the advantage of being able to leverage Google search algorithms to return highly relevant results for product listings and category pages.
- Semantic Search: Intent recognition is a key requirement for semantic search and identifying what the customers mean when they enter the query is a key strength of Retail Search. This is critical for retailers since this has a direct impact on Clickthrough rate, Conversion rate and the Bounce Rate.
- Personalized Search Results: Another key differentiator for Retail Search is its ability to leverage user interaction data and ranking models to provide hyper personalized search results. Retailers are able to optimize search performance to deliver desired outcomes: better engagement, revenue, or conversions.
- Self-Learning and Self-Managed Solution: Retail Search models get better over time because of the self-learning capabilities built into the solution. In addition, the service is fully managed, which saves precious resources needed to keep it running and managing its set up.
- Strong Security Controls: The service runs on Google Cloud and follows security best practices to keep our customers’ data secure. Google never shares model weights or customer data across customers using the Retail API or other Discovery Solution products. For more details about this data use, see a description of Retail API data use.
High Level Conceptual View
Here is a simplified high-level view of Retail Search API. Retailers can call the API for the given search query and get back the results which can then be displayed on their digital properties.
The returned results contains two types of information:
- Search results: Query search results including product listings and category pages based on advanced query understanding and semantic search.
- Dynamic faceted search attributes: Faceted Search is a feature that allows further refinement of the search by providing ways to apply additional filters while returning results.
Retail Search needs the following datasets as input to train its machine learning models for search:
- Product Catalog: Information about the available products including product categories, product description, in-stock availability, and pricing.
- User Events: This is the clickstream data that contains user interaction information such as clicks and purchases.
- Inventory / Pricing Updates: Incremental updates to in-stock availability and pricing as that information is updated.
(Keeping the product catalog up to date and recording user events successfully is crucial for getting high-quality results. Set up Cloud Monitoring alerts to take prompt action in case any issues arise).
Retailers also have the ability to set up business/config rules to customize their search results and optimize for business revenue goals such as Clickthrough rate, Conversion rate, Average size order etc.
How to get started
- Establish a Success Criteria: It’s important to establish a success criteria for measuring the effectiveness of Retail search. Get a consensus on which factor(s) you want to include in scope for measuring the effectiveness of Retail Search. This could include one or two from the following: Search Conversion Rate, Search Average Order Value, Search Revenue Per Visit and Null Search Rate (No Results Found).
- Initial Set Up: Create a Google Cloud Project and set up the Retail API. When you set up the Retail API for a new project, the Google Cloud Console displays the following three panels to help you configure your Retail API project:
- Measuring Performance: Retail dashboards provide metrics to help you determine how incorporating the Retail API is affecting the results. You can view summary metrics for your project on the Analytics tab of the Monitoring & Analytics page in Cloud Console.
- Set up A/B Experiments: To measure the performance of Retail Search with another search solution, you can set up A/B tests using a third-party experiment platform such as Google Optimize.
As retailers try to navigate through the post-pandemic world where supply chain failures and digital transformation acceleration are major focus areas, they now also have to keep a close eye on the recent geopolitical challenges resulting in rising inflation and costs.
While we can all agree that in-store shopping will continue to be a major source of revenue, it is also important for retailers to tweak the in-store experience for the digital world. Trends such as buy online and pick up in stores (BOPIS), curbside pick up and pick up lockers are here to stay.
Given all the above, consumer engagement and digital experience is more important now than ever before. The cost of search abandonment is way too high and has both short and longer term impact. Retail Search is a great solution to help reduce churn, improve conversion and retention. It provides Google-quality search models to help understand customer intent and the retailers have the ability to set up business/config rules to optimize search results for business revenue goals such as Clickthrough rate, Conversion rate and Average size order.
By: Vikas Saini (Principal Architect, Google Cloud)
Source: Google Cloud Blog