Twitter still has 320 million monthly active users, but its monthly active user totals in the United States went down.
Hurdle rates help e-retailers identify problems, and that’s the first step toward solving them.
While web analytics and site optimization can be complex, there are straightforward ways online retailers can use these tools to improve their customers’ experience and boost sales. One is to establish hurdle rates, which set success criteria for a given data point, such as conversion rate, and, if handled properly, can produce significant paybacks.
Online retailers often know there are problems with their sites, but are perplexed about how to organize the data they have to understand what’s wrong.
Ask the right question
The challenge is to organize data in a way that lets retailers easily scan it, spot problems and quickly act to resolve them.
You might hear a merchandiser detailing the success of a promotional campaign, noting that a specific web page merchandising spot pushing a free shipping promotion produced $10,000 in sales during the two weeks that it ran. But the key question to ask-and the one that is often missed-is: “Is that good?” Or even more specifically: “Is that good for a free shipping promotion advertised through the home page A-spot for two weeks in July and that was clicked on 5,000 times?”
Hurdle rates help answer a question like that. They set a number, based on past performance on a retail web site, that allows an e-commerce manager to quickly answer the question: Is this good or bad?
Hurdle rates let you spend more time improving problem areas and less time identifying them. For example, an analyst for an apparel retailer notices that in the past month site visitors searched 1,000 times for the term “sun dress” and 1% of those searches led to a purchase. Previously, the same analyst had established a set of hurdle rates for on-site search and knew that, over the past two years, terms searched on between 500 and 1,500 times in a month converted, on average, at 3%. With that hurdle rate information on hand, the analyst has, in a manner of seconds, targeted an item for investigation and potential optimization.
Clearing the right hurdles
While hurdle rates can be very useful, retailers should be careful to set them within the right context. For example, creating a hurdle that identifies an average conversion rate for search keywords and ranks all searches relative to that average might be a good starting point. But it isn’t optimal.
It’s better to segment search terms in ways that are meaningful to the retailer. For example, a retailer that carries various types of footwear should develop different hurdle rates for branded queries like “Nike cleats” or “Reebok shoes” as opposed to generic queries such as “football cleats” or “basketball shoes.” That’s because branded queries convert at a significantly higher rate. Applying a single hurdle rate to both would result in unrealistic expectations for the generic keyword searches.
For a retail site with typically high shipping rates, a free shipping promo could produce unusually high conversion rates. That hurdle rate for that promotion probably should not be the same as that for a buy-one-get-one-free promo.
The root of the problem
Once you’ve established hurdles and identified optimization targets, you need to search for performance gaps, then develop a hypothesis for what may be causing a problem.
Look for common problems-such as ineffective product presentation or unclear messaging on pricing, availability or shipping-that may prevent your customers from completing a shopping task. Validate your analytics findings with both consumer insights and the competitive landscape.
Consumer electronics retailers, for example, often struggle to communicate pricing and availability for products offered in a variety of configurations, such as computers. Hurdle rates for cancellations, customer service calls and product abandonment would help to identify a potential problem-with a quick review of the shopping experience producing the pricing/availability messaging as the leading suspect. The retailer can then quickly perform competitive analysis to see how other electronics retailers are communicating the same information to consumers, with the hope of finding a better, alternative communication strategy.
Additionally, a focused usability study could help shed light on why customers are struggling with the messaging and how they might interact with alternative approaches.
Once you have determined the hurdle rates, set a timeframe to review and monitor them. Constantly hunt for gaps and test to make sure results improve once changes have been made.
One retailer’s strategy
A large national retailer realized that the product conversions on its web site were lagging. As a starting point, the GSI optimization team analyzed the retailer’s most frequently viewed products. From there, the analytics team looked at both the add-to-cart ratio and sales conversion for those products.
The team established hurdle rates for both of these metrics. Then they segmented the products by those that cleared the hurdle in relation to the add-to-cart ratio but fell below the hurdle in regards to product conversion.
This process helped the retailer identify products that customers were very interested in but bought at a lower-than-expected rate. Nearly 20% of the original set of frequently viewed products fell into this category. But there was no clear answer as to what was causing such low product conversions.
Then the optimization team found that shipping costs on the products in the group amounted to more than 60% of the retail price. That led to a hypothesis that these products weren’t clearing the conversion hurdle rate because of disproportionately high shipping rates. Further investigation, including a competitive analysis and a review of customer insights, validated the hypothesis.
Because the retailer was calculating shipping costs based on weight, those costs were unexpectedly high for some products, turning off potential customers. A study of competing online retailers revealed that their shipping rates were not as high. As a result, the optimization team recommended that the online retailer reassess its shipping policy.
60% boost in conversion
The online retailer eventually moved to a shipping table that set rates based on value, rather than weight. Within the first month, overall site conversion increased more than 15% and cart abandonment decreased materially. Cart conversion in specific product categories surged between 50% and 60%.