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eCommerce analytics

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How to use ecommerce analytics for your online store?

What is eCommerce analytics?


eCommerce analytics refers to the process of exploring, decoding, and reporting data patterns related to e-business. eCommerce analytics measures user behaviour, performance trends and ROI (return on investment).

eCommerce analytics is a process that involves collecting data, analysing it, setting recommendations, and addressing specific actions. This is supported by analytical tools such as Google Analytics which allow monitoring of what is happening on and around an on-line store website. A visit to a website by each user provides the opportunity to collect relevant data on their behaviour, the choices they make, where they access and leave a store and the shopping decision areas. Based on the information obtained, including what type of device a user is using (desktop, laptop, smartphone), appropriate conclusions can be drawn by implementing measures to improve the business scalability process. eCommerce analytics is therefore one of the key issues. The website features publications on relevant components of analytics, such as key KPIs in eCommerce, Google Analytics in an on-line store, eCommerce module and targets in Google Analytics, traffic sources and sales channels in an on-line store, or attribution models and attribution in an on-line store, including how to measure them.

Why is eCommerce analytics important?

The eCommerce and DTC (Direct-To-Consumer) industry is where your customers have all the power. They are kept up to date with reliable product reviews from their peers, the option to quickly compare prices between online stores and the freedom to vote with their wallets. Their expectations are high and to provide them with a reason to buy from you, you need to offer them the best possible online shopping experience. No matter how you imagine it, there is only one thing that will help you do it right: data

And this is where ecommerce analytics enters into play. 

A good marketing analytics software stores all your relevant data in one location. You can bookmark all your campaigns, ranging from social ads to emails to marketing automation. Plus, you can see real-time statistics, so you know which is performing fast and make better decisions about where to allocate your marketing dollars. Analytics helps measure marketing performance and streamline the decision-making process, allowing one to become a more strategic organization.

Today’s advanced ecommerce analytics platforms prioritize your data as an interconnected system and help you uncover trends and patterns in your business. This provides you with insights into the performance of your business in the present and the future. 

To consolidate data and render it visible in the least time possible, you can depend on marketing analytics that shows: How many visitors are accessing your website through referrals and marketing campaigns, the actions these visitors perform at your site over defined time periods, the most frequented pages during shopping seasons, and the devices on which people are visiting your store.

The beauty of marketing analytics lies in the fact that brands can gather, administer and use data on customers. Customers are able to perform certain actions on your store and marketing analytics will capture every interaction. However, unless you have the right marketing analytics and reporting, it is impossible to see who is on your site. 

Reporting on growth, engagement and revenue helps in understanding customer behavior. That way it is easy to identify who has interacted with a content and whether they have clicked, shopped or downloaded something, allowing to develop content that responds to them. 

“Marketing analytics ultimately has the potential to assist brands in reaching the right audience with the right message and at the right time,” – Craig explains. ” Focusing specifically on data points and leveraging marketing analytics tools, teams can gain insights into their perfect prospects to optimize their communication. With the creation of more relevant content that will generate more engagement, brands are able to target the demands of their audience quicker and more effectively than their competitors.” 

For instance, just say you see an increase in sales generated from a campaign on Instagram featuring your footwear in an urban street setting compared to an office setting. In the future, you could position your merchandise to streetwear shoppers to attract the right customers. A retailer could collaborate with more appropriate influencers or tailor its ad targeting to create stronger product awareness.

How can you leverage data to increase sales?

The pricing of products is the most potent lever for enhancing profitability. Studies reveal that pricing management strategies can boost corporate margins by 2% to 7% within 12 months, generating ROI ranging from 200% to 350%. 

You should have an optimized price for each product that your customers agree to bear. Marketing analytics helps to better understand how price affects purchase among different customer segments. This will help you uncover the best price points at the product level, allowing you to optimize revenues.

Types of ecommerce analytics for beginners

Many different types of ecommerce analytics exist that can be used to communicate your marketing strategy and to ensure that you are one step ahead of the competition. That said, here’s a look at some of the most successful types in more detail.

  • Audience

There is just one starting point, and that is to analyze your audience data. It will provide an in-depth overview of your audience demographics, i.e. their gender, age, income, occupation, location and language they speak. 

Additionally, your audience data should provide information about the different devices that your audience uses. For example, do they mostly access your store from a mobile phone or from a desktop? Should it be the former, do they tend to use Android or Apple devices? Such metrics can give you excellent insight as to how your online store is accessed, allowing you to target accordingly.

This audience data provides ecommerce businesses with the opportunity to modify the shipping options and advertising campaigns based on their target audience’s locations.

It will also give you the power to reconsider the different areas that you cover and the way your marketing content is displayed on devices. The types of technologies you use and your audience’s session data will be of great use here.

  •  Acquisition

The other type of ecommerce analytics that can be used to drive your business forward is customer acquisition data. This is highly informative as it will provide information on how visitors have found you across the Internet and how they reached your website to start with. 

Using customer acquisition data, you will find out more information on the type of online marketing channels that attract most visitors to your website. You will also learn what channels generate most sales or conversions.

In addition, you can clearly see upfront which online marketing channels are successful and which are not. 

  • Do the majority of your website users originate from social media posts?
  • How many visitors reach your site through email campaigns?
  • What is the conversion rate for your blog posts?
  • Are paid ads appealing to the majority of your audience?

These metrics will prove crucial in understanding what marketing channels are most cost-effective for your business, allowing to determine where to focus your resources.

  • Behavior

Information about the way your consumers behave is another type of data you should pay attention to. How do people behave once they access your website?

  • What products do customers end up buying?
  • How many viewers finish leaving your site right away, rather than exploring it?
  • What page do customers click on first?
  • What marketing content do audiences click on?
  • Which products generate high interest but few sales?
  • How much time on average do viewers spend on your site?

Questions like these can provide you with an in-depth understanding of how your website is currently being utilized, so you can get a grip on the usual trip people take when interacting with your online store.

If it turns out that most visitors do not scroll through your product inventory or abandon your site fast, have a look at your page load times. It could be an indication that your website is not loading fast enough.

In case users abandon a page quickly, it is an indication is that they failed to find what they are looking for. It could mean that your type of business is not what they were hoping for, so there might be a problem with the keywords you are targeting with your marketing campaigns. Alternatively, it is possible that the content you are generating is simply as clear as mud.

Overall, behavior analytics helps uncover the parts of your store that you can refine to boost engagement rates and conversion levels.

  • Conversions

The conversion has already been mentioned a few times and this is another type of analytics you can leverage to boost your business. 

  • When do online users convert into actual customers?
  • How do online users convert into actual customers?

These are the two questions you are specifically addressing in terms of conversion analytics. Whenever you develop your marketing strategy, you just cannot disregard this part.

There are a lot of different things you can explore when you start delving into the topic.

  • How long does it take a standard user to convert into a paying customer?
  • Do customers tend to convert once a year or at multiple times?
  • Do customers tend to purchase products after one visit to your online store, or do they have to make multiple visits before they make a purchase?
  • Do customers tend to make repeated purchases?
  • How many customers abandon their shopping cart instead of converting?

Understanding such details may help affect your marketing communication, allowing you to determine how to effectively engage with users and potential customers. 

Likewise, being aware of factors like the average revenue your store generates per transaction is also crucial. Furthermore, what is the average number of products your customers purchase per transaction?

These figures are relevant as they help you identify what discounts and offers will appeal most to your existing customers.

  • Paid Marketing Activities

Alongside the four metrics we have highlighted so far, the next critical field to be considered is your paid marketing activities. It will support you in determining the exact return on investment (ROI) for your various paid marketing campaigns. 

  • How much revenue have you generated from social media ads?
  • Have you generated more revenue than what you have spent on developing your ads and promoting them?

How much revenue have you generated from your pay-per-click ads?

Finally, what about your email marketing campaigns?

Unless you evaluate your current pay-per-click marketing campaigns, you will end up overspending on marketing measures that do not contribute to your bottom line.

The key focus for ecommerce start-ups should be achieving product-market match, i. e. providing an effective response to a problem or an unmet need that customers are willing to pay for. Nothing else matters at this stage.

Only after achieving product-market match is a business prepared to be scalable – to invest time and resources in marketing to boost sales and generate a profit. Scaling up prematurely can be hazardous, ultimately resulting in financial loss and even bankruptcy.

However, when planning to use analytics to make data-based marketing decisions for your business, confusing concepts such as ‘products that customers like’ will not do the job. There are five metrics you can objectively track to ensure your store avoids the problems in the example above and scales at the right time:

  • Customer lifetime value (LTV). What benefit will you gain from an average customer in the time it stays a customer. For example, if your average customer returns to your store three times to buy something, spends an average of $100 per purchase, and your profit margin is 10% ($10), then the LTV of that customer is $30. This is an important piece of information because LTV is directly related to profitability. A business that has a high overall LTV will have the capacity to spend more to attract customers and will have a higher margin.
  • Returning visitors. The proportion of users returning to your site after their first visit. This figure provides a clear indication that people liked what they saw. Based on our studies, a good ratio of returning visitors to new visitors is anything above 20%.
  • Time on site. The average time users spend on your site per visit. As we have seen above, what length of time is a good length of time depends on what you sell. But generally speaking, as long as people spend time on your site, it demonstrates that they have a good browsing experience. Based on our analysis, a good average time on page is over 120 seconds.
  • Pages per visit. The average number of pages that users navigate in a single visit to your site. High number of pages per visit (around four) indicates that people are interested in what you sell.

eCommerce analytics – sectors

 eCommerce analytics is divided into several core sectors:

  • data collection – compiling traffic data, referring sites such as Facebook, and technical aspects.
  • data analysis – compiled data are processed, bundled and analysed in detail.
  • reports – based on pre-compiled and processed data, reports are generated containing the relevant operations performed in an analysed on-line store.
  • recommendations – based on the generated reports, we identify the best ways to enhance performance of a store, increase traffic and conversions.

 eCommerce analytics – Google Analytics

 A range of analytics content is available in the eCommerce analytics category. A comprehensive article on one of Google’s best-known free tools for analysing traffic statistics and user behaviour on a website is available in the blog, among other things. It is about Google Analytics. The tool is divided into sections (reports) that are dedicated to specific areas of site monitoring. These sections include:

  • real time – i.e., information on who is visiting the store at a given moment and which page is viewed most frequently. This feedback provides data on which of the ongoing campaigns is effective and generates traffic on a website, as well as the conversions.
  • audience – this section provides information on who exactly is accessing a store. Verifies the gender, age, interests, and preferences of users, including which devices they use (mobile, desktop, tablet) and which browsers they use to access the site.
  • sourcing – i.e., where the user came to the site from. This information provides us with the information we need to determine from which channel a visitor reached the store (Organic, Social, Direct, Referral), what were the direct results of Google Ads campaigns, or what are the page views in the organic search results of Google Search Console behaviour – i.e. a set of data about which pages are most frequently viewed, which products are most popular, which keywords are most frequently searched for in an on-line store, and what the site loading speed is on different browsers.
  • conversion – i.e., operations performed on a website. This feedback provides insight into the objectives pursued by a user on a website (purchase, Newsletter subscription, e-book download). We also learn about a customer’s entire customer journey, which products convert best, which paths are multi-channel, etc.

 The article available on the website provides detailed information on how to implement and correctly set up a Google Analytics account in an e-store. Useful tips for implementation into Shoper and Shopify are also provided. eCommerce analytics is also about reporting, so the blog answers how to monitor data in Google Analytics, explains what information we will get in a given report and how to read it correctly, defines what a session is, average session duration, bounces, and bounce rate. This article has been written for people who want to better understand what eCommerce analytics should be and are taking their first steps with Google Analytics.

eCommerce analytics – attribution models and attribution in e-commerce

 Selling on an on-line store requires an understanding of consumer buying behaviour and an understanding of the customer journey. eCommerce analytics also covers these issues. Attribution and its models are relevant here. That is why an article dedicated to this has also appeared on the blog. What exactly is attribution? In the simplest terms, it is monitoring and attributing a share of conversions to a specific traffic source. An ideal attribution model separates the values from the different sources of in-store traffic, accurately determining their share. Attribution therefore allows to identify which marketing campaigns – both channels and traffic sources – are producing results and gives an idea of what the impact is. By introducing attribution models, we can get answers to these questions: 

  • Which marketing campaigns translate into the highest revenues?
  • Which traffic sources are worth investing in?
  • Has the recent investment translated into revenue growth in an on-line store?
  • Which campaigns and traffic sources are better to abandon to acquire extra advertising budget?

 The article provides an insight into what the attribution of Facebook Ads, Google Ads and Google Analytics is and why the total of sales from the Facebook Business Ad Manager and Google Ads is often greater than the analysed actual total in the Google Analytics account. eCommerce analytics is based on an analysis; hence I suggest how to measure conversion attribution in the article, and I also introduce the issue of attribution models, which include: 

  • the Last Non-Direct Click model – the default attribution model in Google Analytics
  • the last interaction model
  • the last indirect click model
  • the Google Ads last-click model
  • the first reaction model
  • the linear model
  • the time-distribution model
  • the positioning model

 I explain when it makes sense to use a particular model, and which one is best for eCommerce analytics.

eCommerce analytics – traffic sources and sales channels

eCommerce analytics is inextricably linked to traffic sources and sales channels. What exactly are eCommerce traffic sources? These are the locations from which the traffic directing users to our website originates. The article in which I explain, among other things, the differences between channels and traffic sources, has been written to explore this topic. I also distinguish specific traffic channels, which include: 

direct – i.e. directly entering the URL into a search engine or accessing it from a saved bookmark
organic search – are all traffic sources from organic search results for the available browsers
social – the social network from which an action on a website (Facebook, Instagram, Twitter, LinkedIn, and TikTok) originates
e-mail – traffic generated by e-mail marketing campaigns
referral – Sources from static pages, blog, posts, newsgroups, price comparison sites, campaigns with links marked as UTM
paid search – campaign sources labelled as CPC, PPC and Paid Search, i.e. campaigns on social networks – Facebook, Instagram, Google Ads
other advertising – campaigns with traffic sources labelled CPV, CPA, Content
display – banner campaigns in the Google ad network or a traffic source from other ad networks, e.g. a blog campaign with a medium labelled as a display CPM banner
other – all sessions that match the above traffic sources, which have not been flagged as referral and dropped into other. 

Alongside explaining the sources, I share, across the article, which sales channels convert best, I also explain which channel will be best for a particular business and which channels are worth investing in.

 eCommerce analytics is a process that requires tracking more statistics to understand customers’ purchasing behaviour, to learn more about their needs and to verify that the actions undertaken are delivering the desired results. It is worth remembering that eCommerce analytics also allows uncovering the shortcomings of an on-line store that result in less traffic to the site, as well as the number of purchases. It is also a field to explore the space that has not been exploited yet but has a lot of potential and could translate into profits. The articles prepared will not only give you a better insight into eCommerce analytics issues but will also allow to understand how specific tools work, how to correctly read the data we have at our disposal and to better match sales channels, thus streamlining an on-line store performance and profitability. So, if you want to scale your business, better understand your potential customers by tailoring your actions to your audience’s demands and have your finger on the pulse by properly monitoring and interpreting the data acquired, take a look at the content available in eCommerce analytics where you can find comprehensive articles and guides prepared by experienced professionals.


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