Online selling
How eCommerce analytics can bring customer success
Online selling

How eCommerce analytics can bring customer success

Written by


November 4, 2022

Table of contents

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Customer expectations are evolving rapidly. Companies can use eCommerce analytics to identify emerging trends and make better business decisions

Key takeaways: 

  • eCommerce analytics collects customer behavior data across the online store
  • They help with measuring the effectiveness of campaigns, personalizing customer journeys, understanding customer trends
  • Immersive 3D and AR analytics can provide greater insights into customer behavior

Casper, a direct-to-consumer (DTC) mattress company, earned a profit of USD$750 million in 2018, only four years after its launch. Their secret - a customer-centric business strategy. In 2022, the brand has carved a name for itself in the e-commerce space by catering to its customers' every need and offering an excellent on-site user experience. The approach echoes the old adage ‘the customer is king.’ However, in 2022, knowing what the king wants is often a difficult task.

With access to the internet, consumers can trial the user experience offered by different brands quickly and easily. This is one of the driving forces behind the contemporary buyer’s ever-changing preferences.

Companies that want to stay ahead must adapt their strategies to match these constantly evolving consumer expectations. Making business decisions based on past performance, anecdotal evidence, or gut instincts won't cut it. Brands need to make data-backed decisions ASAP. Data obtained from eCommerce analytics can help with this task.

What are eCommerce analytics?

eCommerce analytics involves collecting customer behavior data across the online store. Such data helps sales and marketing teams make more customer-centric business decisions.

There are four main types of eCommerce analytics currently being used by businesses; audience, acquisition, behavior, and conversion analytics.

Audience analytics data helps businesses understand the demographic of prospective buyers. It includes age, income, occupation, gender, and even the device used to view the content. Such data helps companies optimize their outreach based on the type of customer.

Acquisition analytics helps companies understand how users have landed on the website. They show the results of the outreach efforts. Common examples of this include tracking website traffic generated by an online ad campaign, and blog post conversion rates.

Behavior analytics reveal customer behavior insights. This data is collected every time a customer visits a website and interacts with its different components. Ultimately, such data helps sales and marketing teams make more customer-centric business decisions. Some examples of behavioral analytics include interactions with Augmented Reality (AR) objects and website visit time.

Finally, conversion analytics allow companies to understand how and why potential buyers make a purchase decision. Businesses that use them regularly ask questions like “do customers repeat their purchase?” and “how much time does it take for a prospect to become a paying customer?”

Types of eCommerce analytics

How do businesses use eCommerce analytics?

In 2020, marketers allocated around 11% of their revenue to data analytics. The most common uses include the following;

  • measuring the effectiveness of marketing and sales campaigns
  • evaluating business trends
  • optimizing pricing
  • personalizing customer journeys
  • bolstering marketing and sales strategy with data-backed insights

Such granular insights about the store can help businesses create a better consumer experience for buyers from start to finish.

How can companies analyze their eCommerce?

Businesses that want to start measuring eCommerce analytics must choose the right metrics. Organizations can choose from five broad categories of metrics;

  • Discovery metrics that measure the success of the advertising efforts. Reach (the number of people viewing an advertisement), and impressions (the number of times a set of people viewed an ad) are examples of discovery metrics.
  • Acquisition metrics that measure the number of people visiting the online store on the basis of an advertising campaign.
  • Conversion metrics measure both the number of people who made a purchase from the website and those who didn’t. 
  • Retention metrics that help measure customer churn and win-back.
  • Advocacy metrics that measure the quality and quality of brand advocacy from regular customers.

Once companies choose their metrics, they can move on to creating goals as per the SMART framework. Doing this ensures that the goals are well-defined and achievable.

Next, businesses should collect relevant data from platforms using the right tools. This will show how the marketing efforts perform and which strategies might get them better outcomes.

Businesses should collect relevant data from platforms using the right tools

Organizations can further personalize customer journeys by checking user reports and consumer search histories. They can then alter their outreach efforts with such granular information and boost their chances of converting consumers.

Companies must also consider automate processes. This way they can save valuable time, avoid data entry issues, and focus on bettering their analysis and insights.

Lastly, businesses must avoid sticking to a single approach to boost customer journeys. They must use the insight data to tweak their campaigns and boost conversion rates continuously.

How companies can find success with eCommerce analytics

56% of marketers admit having missing or incomplete data in their CRM systems. This is an even bigger problem with eCommerce analytics because companies often do not have a system of compiling data in place.

The first thing businesses should do is use a data-connector tool to pull data from various platforms into a centralized database. Since the process is automated, there are fewer data entry issues.

Next, companies should remember to correlate data insights to customer behavior and market trends. This adds context to the collected data and helps businesses make more accurate data-backed decisions.

While customer behavioral data is a great starting point for understanding customer preferences, companies must ensure that customers have a smooth customer experience. They should regularly look at customer flows on the website to identify any hiccups while browsing or shopping for products. This goes a long way towards ensuring seamless and enjoyable customer journeys.

Lastly, businesses should look at the popularity of individual products on their eCommerce site. This helps to understand broad customer preferences and informs companies looking to identify products that may need tweaking to boost sales.

Today, brands implementing 3D and AR product simulations onto their websites have a greater edge over their competitors. This is thanks to the swathe of immersive customer data they can obtain from them.

For instance, launching an AR projection could tell companies that consumers are keen to see products in real life before they buy them. Similarly, actions like rotation, altering color, and more may imply consumers care as much about the product's physical characteristics as its functionality.

Above all, companies employing 3D and AR projections on their websites can gain a whole new perspective on consumer behavior by combining immersive analytics with traditional ones.

For example, when coupled with high view time, a higher number of customer interactions with an AR simulation could imply that the customer is actively considering buying the product. It shows more customer interest than a traditional metric like viewing time.

56% of consumers demand personalized experiences today. And to provide it, businesses must understand customer preferences and keep on top of them. eCommerce analytics help to do the latter. And companies that get started with this earlier are more likely to see business success.

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