Artificial Intelligence for Ecommerce: KPIs and Metrics Leaders Need To Track Success

Artificial intelligence (AI) for ecommerce is here to stay. According to Statista, 55% of companies already use conversational shopping assistants driven by generative AI (GenAI), and 41% are considering these tools.

If your ecommerce brand is just getting started with artificial intelligence, check out the seven-step AI implementation roadmap. But if your AI framework is up and running, or you’re looking ahead to next steps, it’s time to track key performance indicators (KPIs) and essential metrics.

While ecommerce AI tools offer the potential to improve customer engagement, boost conversion rates, and keep buyers coming back, it’s not a fire-and-forget function. Instead, brands need to identify KPIs that matter, measure relevant metrics, and take action to ensure AI for ecommerce lives up to expectations.

This piece breaks down some critical categories and addresses potential pitfalls to help you maximize metric measurements.

Customer Engagement Metrics

First up are customer engagement metrics. As noted by Forbes, investments in digital customer engagement deliver 123% increases in revenue on average.

For ecommerce brands, AI-enabled digital assistants are on the front lines of engagement. These assistants can answer questions, provide context, and escalate questions as necessary to human agents.

Three metrics can help measure the impact of AI-driven engagement.

1. Total Number of Assistant Interactions

The more customers using your assistant, the better. Measure the number of assistant interactions over a set period to see if usage is trending up or down.

Pro tip: Don’t start measuring right away. Newly deployed tools often see an initial burst of interest before settling into more stable patterns. Wait four to six weeks, and then start tracking.  

2. Referrals to Human Agents

Another relevant metric is the number of referrals to human agents. Higher numbers may mean that AI assistants are struggling to meet customer expectations. Track referrals over a two- to four-week period to see if any patterns emerge.

Worth noting? While this information is useful to have on hand, don’t jump to conclusions. Because AI tools are designed to learn over time, referral rates may peak and then begin to decline. 

3. AI-Connected Conversion Rates

Conversion drives ecommerce revenue. Track how many conversions began with AI conversations or were impacted by AI-created product pages.

Operational Efficiency Metrics

AI tools can also help improve ecommerce efficiency. Here, the metrics to track include:

Total Time Savings

The adage holds: Time is money. If AI tools take on redundant or data-heavy tasks, they can save your company time and money. One common way to measure time saved is in staff working hours. For example, if creating a new product page typically takes two people four hours, but an AI application does it in an hour, you’ve saved seven hours in total.

Automation Rates

Automation is another benefit of AI. For example, you might use AI to automate marketing emails. Track how many emails AI tools send over a set period, then compare this number to standard marketing campaigns.

Cost reductions

AI can also help reduce total spending. One example is tracking and anticipating potential downtime. For ecommerce companies, even an hour of downtime means thousands of dollars in lost revenue.

Given that you’re tracking something that didn’t happen, this can be a more difficult metric to manage. Ideally, you want to set up alerts on AI tools that notify IT staff of potential problems and possible impacts to get a sense of avoided costs.

Revenue Impact Metrics

AI should boost your bottom line. Not sure if tools are working as intended? Keep tabs on these KPIs.

Average Order Value (AOV)

The more customers order in a single transaction, the better for your bottom line. Measure average order value (AOV) before and after the implementation of AI to determine the impact.

AI-Influenced Sales

To track AI-influenced sales, make a list of all posts, blogs, emails, and other sales and marketing efforts that leverage AI. Then, measure the number of sales that started with (or interacted with) one of these efforts. Compare this sales data to the success rates of non-AI interactions.

Time From First Engagement to Purchase

Measure the average time between first contact with customers and their purchase. If this time gets shorter when you introduce AI tools, it indicates that the tools are effective.

Content Optimization Metrics

Generative AI tools are capable of creating content, including blog and social media posts, product detail pages (PDPs), and marketing emails. To measure the impact of your content, regularly evaluate:

AI Versus Non-AI Content Performance

Compare your AI-generated or improved content against your traditional content by tracking how often each type comes up in search results, how often customers click through, and how many sales are tied to each content type.

Comments on AI Content

Comments are another way to track the impact of AI content. If the majority of comments are positive, this indicates success in customer engagement. The total number of comments is also relevant. Given that most commercial and business blogs receive few (if any) comments, an uptick in user responses can indicate an improved impact.

GEO Impact

Generative engine optimization (GEO) now plays a key role in successful content creation. GEO-friendly content answers common user questions clearly and authoritatively. As a result, it appears in the AI-generated overviews that now appear at the top of Google search pages.

More frequent references by GEO algorithms means your content is reaching the right audience.

Customer Satisfaction Metrics

Satisfied customers come back and often tell other potential buyers about your business. Measure satisfaction by tracking:

AI Assistant Satisfaction Scores

It’s worth including an opportunity for customers to rate their experience with your AI assistant. Compare these scores with previous satisfaction rates for non-AI tools and regularly reevaluate these scores every few months.

Email Interactions

Measure the number of AI-created emails opened by customers and those that drive action, such as a direct response or a newsletter signup. More emails opened means customers like the message you’re sending.

Consumer Sentiment

While it’s challenging to assign a “score” to customer sentiment, it’s possible to track the overall public perception of your brand. For example, by using AI to seek out mentions of your company across review and social sites, you can compile a list of the most-used words.

For example, if the most common terms used by customers are “frustrating” and “difficult,” your AI may need a refresh. Words such as “personable” and “knowledgeable” suggest a more favorable sentiment.

3 Common Metric Measurement Pitfalls

While metrics are a powerful tool in AI for ecommerce, measurement does come with potential pitfalls. Here are three of the most common.

1. Measuring Everything

If you measure everything, you can’t track anything. Align metrics with key goals, such as increasing your market share or boosting average order value (AOV). By narrowing your focus, you can increase the impact of your measurements,

2. Measuring One Thing

As AI evolves, new metrics are constantly taking center stage. GEO rankings are a good example — the more often your content appears in AI-generated summaries, the better. These metrics, however, are small parts of a larger story. Measuring one thing comes with the same problem as measuring too many — you don’t get the big picture.

3. Measuring Without Analyzing

Measurement sets the stage for analysis. If AI assistant scores are low, in-depth analysis drives relevant insight. Taking metrics at face value, meanwhile, may lead to AI missteps.

Consider a digital assistant with consistently low satisfaction scores. Without analysis, brands might assume that the issue lies with engagement — the solution isn’t personable or accessible enough. An in-depth analysis, however, could reveal that the tool was redirecting users to the wrong product pages. A lack of analysis could lead to costly action that doesn’t improve ROI.

Making the Most of AI Metrics

Artificial intelligence for ecommerce offers a way for brands to connect with customers, personalize the shopping experience, and increase consumer satisfaction.

The key to consistent success? Metrics. But measurement alone isn’t enough. To maximize AI metrics, brands must target multiple categories, create consistent comparison frameworks, and align measured metrics with business goals.