Every frustrated shopper is costing your business money. Forbes cautions that as many as 80% of customers will leave your site due to a bad search experience. Not good. So how do you keep your bad data from spiralling 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).
What is data enrichment?
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 you sell 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.
Why accuracy and completeness rule
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.
10 KPIs to measure and improve customer experience
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. Page Views: The number of pages a user visits on your site, including reloads. This number can show you if people are clicking through, looking around and are engaged enough to stay.
3. 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.
4. 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.
5. Average Order Value (AOV): The average dollar amount spent per individual order. This gives you an understanding of customer purchasing habits.
6. 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.
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.
How to radically improve your data?
A good data enrichment strategy ensures your product data is accurate, complete and relevant to your demographic. It learns and adapts to your customer needs so the right products will appear in the right place at the right time to drive conversions. And you don’t have to handle all of this yourself. We can build a strategy tailor-made for your business. We’ve helped some of the world’s top companies improve search and CX, increase order values, drive conversions and boost their bottom line.