Defining and Measuring E-Commerce Success
Does your company have a successful online presence? It probably depends on who you ask. In the land of e-commerce, “success” is a fairly vague term that can be measured in many different ways.
Metrics like the number of app downloads, site visits and online sale revenue are often discussed, but provide little actionable information on their own. Questions remain such as how your most valuable customers are behaving online, where the online customer experience shines, where you are losing the most customers and what channels, devices or pages drive the most revenue. Monitoring and acting on these findings can be a gateway to business success and improvement.
To have more meaningful discussions about what’s working and not working online—and how to address both scenarios—the right key performance indicators (KPIs) must be established.
There are four important steps to creating a stronger measurement strategy:
Step one: Identify the issue(s)
In order to know what to measure, you must start with clear goals. KPIs should tie into your business priorities, so that everyone involved understands how progress will be tracked and reported. For example, if the goal is to increase customer loyalty, KPIs might look at online customer engagement and repeat business. If the goal is to improve online sales, measuring the biggest drop-off points during the online customer journey would make sense.
Step two: Segment the big spenders
Understand who your biggest customers are online and segment them in web analytics reports. Their actions should be closely monitored to understand:
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What influences customers to return to the site
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What channels are the most popular
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What prompts customers to buy
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Where customers experience frustration
Step three: Measure what matters
The metrics that matter most to each organization will vary, but there should be agreement within the organization about what is being measured, by whom and how often. Among the more common KPIs for websites:
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Conversion rate – the percentage of visitors who took a specified action on your site (e.g., ad click, email sign-up)
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Average order value (AOV) – the average dollar amount spent each time a customer places an order on a website or mobile app
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Shopping cart abandonment – customer who abandon their cart before checking out
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Bounce rate – people who immediately leave the website after arriving
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Referral sources – what is driving people to your site (e.g., organic search, ads, social media)
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Keywords – what search terms are bringing people to your website
These metrics will likely be refined over time as goals and customer behaviors change.
Step four: Devise an action plan
Intelligence gathered from metrics should feed into reports across the organization—from sales to marketing to IT—so that it can be acted upon. Let’s look at a couple of examples to better understand how to turn metrics into action:
Finding: Keyword monitoring shows that some of the most frequently used search terms are misspellings of the company’s name (e.g., Model instead of Modell’s).
Action: Company decides to buy the domain names for the top two misspelled searches so that customers can be automatically re-directed to the appropriate site.
Finding: The percentage of customers abandoning their shopping cart is on the rise.
Action: Company starts sending customers abandoned cart reminders and adds language to the cart page that clarifies product guarantees and the return policy.
While some solutions may seem relatively straightforward, it’s important to weigh the potential rewards against the risks of taking a particular action:
Source: NYU Web Analytics Report
Keep the end in mind
It’s easy to get weighed down in minutia when weeding through web analytics. Regularly revisit the kind of information that’s actually being used by the business and what is being tossed to the side. Consider how shifting priorities and goals influence metrics. It’s not a “set it and forget it” process; continuous testing, learning and revising is key to a strong measurement program.
Information in this post is repurposed from NYU student presentations on web analytics.
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