Every frustrated shopper costs your eCommerce business money. And eCommerce site search is one of the biggest friction points -- research from Google shows that not only do 82% of consumers avoid sites where they've experienced search difficulties, but that search difficulties result in a lost sale 3 out of 4 times! And the average cost of each lost sale for retailers is $72.
Fortunately, search is relatively easy for eCommerce retailers to fix. And in fixing search, you also inherently improve the eCommerce customer experience, increase customer loyalty, and boost revenue. In this blog post, we'll dive into the relationship between customer experience and site search, before listing our 10 favorite KPIs you can track to determine the quality of your customer experience and your site search experience.
Over the years, the eCommerce customer journey has shifted as technology has evolved -- and customer expectations along with it. Purchasing decisions, which used to be rather straightforward, are now complex and broken, taking place across multiple channels and spanning many customer touchpoints.
Meanwhile, despite omnichannel experiences becoming the norm, 69% of consumers typically use the search function on retail websites, making it the most common way to find products. And 96% of those consumers are at least somewhat likely to return to a website if it has a good search function. These customers who use the search bar to locate items display high purchase intent, and are most likely to make an online purchase, proving search's essential role in the eCommerce customer experience.
As such, potential customers have come to expect a lot from search. Their baseline standards for site search have been set by behemoths like Google and Amazon, who lead the pack in user experience, forcing online retailers to keep up.
There are two main problems that prevent eCommerce retailers from driving more revenue from search: legacy search technology, and bad data.
Legacy Search Technology
Online shoppers expect a lot from their eCommerce customer experiences today, and search is a key part of that. When a customer types a query into the search bar, they expect to see, at a minimum, relevant products. Ideally, they want to see the exact product they're looking for, preferably with a detailed product description that contains all the information they need to know in order to make a purchasing decision. If that product is personalized based on their previous purchases and site history? All the better.
Legacy search technology attempts to do this using keyword matching technology, searching blindly through the catalog for relevant products that match the search query. These older engines often layer artificial intelligence over top of their engines to improve the engine's understanding of user intent and deliver more personalized search results and product recommendations.
Unfortunately, these legacy platforms are limited by their underlying technology. eCommerce retailers have layered feature after feature over top of these engines in an attempt to close the gap and deliver better online shopping experiences, but have only gotten so far.
Next-generation search technology, however, has rebuilt search from the ground up to be AI-first. This means there is no legacy technology and the engine runs entirely on AI. Trained on vast arrays of data -- far more than legacy systems -- a next-generation search solution has a superior understanding of user intent. It can deliver relevant, personalized, buyable search results that are also displayed in revenue maximizing order -- increasing the amount of revenue from search that eCommerce retailers generate.
eCommerce businesses who want to improve their online experience should consider upgrading to a next-generation, AI-first eCommerce search and product discovery solution like GroupBy's platform powered by Google Cloud Vertex AI Search for Commerce. By upgrading from legacy to next-gen technology, you solve one of the core problems that prevent eCommerce sites from delivering excellent search and customer experiences.
Bad Data Creates Bad Experiences
There is an old saying in search: garbage in equals garbage out. As mentioned before, when a customer inputs a search query, they expect to see relevant products. Data quality directly impacts which products get returned after a customer search query.
For example, if a product catalog only has limited product attributes, that can greatly hinder eCommerce retailer's ability to deliver relevant products. If, as is the case in many B2B applications, a customer search query is in centimeters, but the product data only contains measurements in inches, then the product is unlikely to be returned in the search results because the search engine cannot find the right matches for the query. That data does not exist, and so the search engine cannot bring it back for the customer.
This also happens for things like product attributes -- if the only color programmed in your system is blue, it won't recognize a search for "cerulean" or "navy." Which, of course, is a very memorable experience -- in a bad way.
So how do you keep your bad data from spiraling into shopper attrition and lost revenue? By enriching your data, cleaning it up, injecting it with words that speak to your market, tracking how it affects shopping patterns and making adjustments to optimize the customer experience (CX).
Data enrichment, aka data augmentation, is the process of normalizing, standardizing and classifying product information to make it clearer, more detailed and more relevant. Shoppers rely on your data's ability to call up the right products during a search. To make this happen, products need to be put into categories. Then attributes need to be applied to describe each product and further defined by values. Together, categories, attributes and values enable accurate filters and searches. For example, say your eCommerce store sells vintage concert T-shirts. A category could be ‘'90s Grunge Bands', an attribute could be ‘Nirvana' and the values associated with it could be shirt size and color.
There are two key things you should focus on when it comes to augmenting your data: accuracy and completeness. Think of accuracy and completeness as making up a matrix with the values and attributes you assign to an item. You need to assess how accurate your values and attributes are and how complete they are – and then fix them and fill in the gaps. For example, if Nirvana is spelled ‘Nervana' in your database, you've got an accuracy problem. If your Nirvana shirts don't even appear as an option, then you have a completeness problem, and you'd better fix it or you'll never move that stock.
While assessing accuracy and completeness, you also need to consider the language you're using and its relevancy to your market. For example, for some demographics certain color names resonate better than others, such as using ‘Natural' rather than ‘Beige'. Your data needs to speak the specific language of your shoppers to be effective. With so many moving parts it's important to check-in and track how your data is working for your customers. There are important business and behavioral key performance indicators (KPIs) that can reveal a lot about your data quality and the resulting customer experience.
Business KPIs
Once you've upgraded your search experience and cleaned up your data, you're ready to start generating more revenue from search and improving the online shopping experience for customers. To track your progress and see how you're doing, here are the key business metrics we recommend. Positive changes in these metrics should correlate to an increase in revenue, making them more important for the business overall.
1. Add-to-Cart Rate: The percentage of visitors that place one or more items in their cart during a session. This can help you gauge the success of your search, site usability, product selection and merchandising.
2. Conversion Rate: The percentage of visitors that take a desired action, such as hitting ‘buy'. Take the average conversion rate in your industry/market as the benchmark to beat.
3. Bounce Rate: The percentage of visitors that go to your site and leave before viewing any further pages. This is a red flag that something is turning shoppers off right out of the gate.
4. Average Order Value (AOV): The average dollar amount spent per individual order. This gives you an understanding of customer purchasing habits.
5. Revenue Per Visitor (RPV): The revenue generated each time a customer visits your site. This is useful in estimating the value of gaining unique new visitors.
6. Revenue Per Search (RPS): RPS is exactly what it sounds like -- how much revenue you generate per search. This is distinct from RPV in that RPS tracks customers who have actively made a search on your website, and tells you your revenue from search, whereas RPV measures all visitors. RPS is most effective at showing how much your highest intent customers spend and how frequently they convert.
Behavioral KPIs
Behavioral KPIs are a bit different from business KPIs. These metrics do not have a direct impact on revenue, but are a great indicator of the online customer experience as a whole. Factors like null results (when a customer search query brings back "no results'') directly impact your online customers' satisfaction and user experience.
7. Null Results: The percentage of searches that return zero results. This KPI can show you how data quality affects CX. If a shopper can't find a product, they will abandon the search and likely leave frustrated.
8. Average Searches Per Order: The number of searches carried out per order. This can show you that customers are engaged, finding what they like, searching your recommendations and hopefully adding them to their cart.
9. Cart Abandonment Rate: The percentage of shoppers that exit before purchasing. This could indicate that they're not ready/able to pay, they're running out of patience with their search results or even that the steps to check-out are too cumbersome.
10. Search Bounce Rate: The number of customers that leave the site because they're frustrated with their search results. I don't think I need to explain why this one is a good KPI for assessing data quality.
Data enrichment has the power to improve all of the KPIs above. It shouldn't be overlooked, like some ‘90s T-shirt stuck at the back of a drawer. Enriching site data should be a business priority.
As eCommerce retailers, improving the customer experience of your eCommerce store should be your top priority. Customer experience directly correlates to increases in revenue and improved customer retention. Two factors that are holding online businesses back from delivering outstanding customer experiences are legacy search technology, and bad data.
As such, upgrading your eCommerce tech stack to include an AI-first search and product discovery solution (like GroupBy's) and having a good data enrichment strategy will go far to increase the customer experience of your eCommerce site. We've helped some of the world's top companies improve search and CX, increase order values, drive conversions and boost their bottom line. For more information about GroupBy's Search and Product Discovery platform powered by Google Cloud Vertex AI Search for Commerce, as well as our Enrich product, book a demo.
