Week 2

Analyze trends for an online store

You will examine how to gather and analyze data for an online store. You’ll learn how to use the data you’ve gathered to improve conversions and increase sales. You’ll also learn how to identify which products are performing well or underperforming. Finally, you’ll discover ways to improve product performance based on data.

Dedication to study

Videos: 28 min
Leitura: 3 h 10 min
Teste: 1 Teste com avaliação

Learning Objectives

Understand how Shopify analytics works.
Identify different e-commerce analytics tools.
Monitor an e-commerce store’s performance.
Update an e-commerce store based on data.
Evaluate product performance.
Make updates to product listings.
Suggest new merchandising based on data

Content

Learn about e-commerce analytics tools
Monitor e-commerce stores for growth and revenue
Make update to an e-commerce store based on data
Identify and update listings based on data
Review: Analyze trends for an online store

1. Learn about e-commerce analytics tools

Welcome to week 2

Video Duration: 1 minute

Welcome back. So far you’ve learned the basics of selling online, including how to create and update an e-commerce store, how to engage customers online, and how to build loyalty. Now we’re going to explore how data relates to e-commerce. You’ll learn how to analyze data for an e-commerce store, make decisions based on that data, and improve the store’s performance. You’ll start by learning the basics of e-commerce analytics, including how to set goals, evaluate metrics, and measure success. Then you’ll learn how to use the built-in analytics tool in Shopify. Next, you’ll be introduced to some of the most important metrics you use to monitor the overall growth and revenue of an e-commerce store. Then you’ll learn how to apply the insights from that data to improve the performance of the e-commerce store. You’ll also learn how to track metrics related to product performance. Plus, you’ll learn a few ways to improve product performance based on the data you uncover. As an account partner, I discovered how important analytics are when I worked with a direct-to-consumer brand on their strategy for the holiday season. In the past, they had only focused on acquiring new customers. However, when we took a look at their analytics, we discovered that returning customers had a significantly higher conversion rate in cart value than new customers. This insight lead to a successful holiday remarketing strategy and helped the client meet their annual goal. Ready to learn about e-commerce analytics? Let’s get to it. I’ll be here to guide you along the way.

Analyze data to measure e-commerce success

Video Duration: 5 minutes

Ready to learn about analyzing data for an e-commerce store? In this video, we’ll discuss some of the basics of analytics, including how to set goals, evaluate metrics, and measure success. You’ve learned about analytics in a previous course, but now we’re going to consider how analytics applies specifically to e-commerce. Analyzing data is one of the best ways for an e-commerce store to figure out what’s working and what isn’t. That’s true whether a business is a major online retailer or a small business just starting out. With the insights gained from analytics, an e-commerce store can determine which sales and marketing tactics are the most effective. They can also use analytics to better understand customer behavior. All of these insights help a business discover which tactics provide the best results. These are the areas where a business should invest their time and money because they’ll deliver the most return on their investment. The data a company analyzes can come from multiple sources. For example, you’ve already learned about Google Analytics. Many e-commerce platforms include their own built-in analytics as well, plus e-mail marketing, social media, and other tools sometimes include built-in analytics. Companies can also gather data from other sources, such as customer surveys, A/B testing, and heat maps, which you’ll learn about later. How does an e-commerce store measure success using data? They start by setting goals for each area of their business. As you learned in a previous course, these goals should be SMART, which stands for specific, measurable, attainable, relevant, and time-bound. Each of these SMART goals contributes towards achieving the ultimate goal of success in e-commerce. Part of setting goals for an e-commerce store is deciding which metrics to track to measure the success of a business or marketing goal. As you learned in a previous course, these metrics are a company’s Key Performance Indicators, or KPIs. A KPI is a measurement used to gauge how successful a business is in its effort to reach a business or marketing goal. The metrics that a company monitors might be different depending on their specific goals and how much data they’ve accumulated over time. Newer e-commerce stores will find it helpful to compare the results of their metrics quarter-over-quarter. A quarter is a three-month time period based on a company’s financial calendar. Each year includes four quarters. Comparing results to the previous time period helps a company determine whether their metrics are improving over time. Companies that sell high-priced products, such as diamond jewelry or upscale furniture, need to be aware that the conversion process will take longer. Customers might need more time to research their options and make a decision. That’s when it’s helpful to track micro conversions, which you learned about in a previous course. Micro conversions indicate that a potential customer is moving towards a completed purchase transaction. Stores that have been around for at least a couple of years will use both quarter-over- quarter and year-over- year comparisons to measure growth and revenue. When an e-commerce store has been around for a couple of years or longer, they have access to more data and longer-lasting relationships with their customers. This means the company can track metrics over a longer period of time. They can also focus more heavily on metrics that relate to customer loyalty. Let’s use an example to demonstrate how data relates to a company’s goals. Imagine an online store that sells office supplies. The store sets a goal to increase their conversion rate by 1 percent in the next six months. To measure the results, they’ll track the conversion rate metric in their analytics tool. They might also go deeper into the conversion rate by separating mobile and desktop visitors to find out how they behave differently on the site. If mobile visitors convert at a lower rate, for example, the company can work on improving the mobile experience. Another tool the company might use to gather data is a heat map. A heat map demonstrates how visitors interact with the website. This can help an e-commerce store make improvements to their website. For example, do customers get stuck on the promotion code field during checkout and end up leaving the checkout process to search for promotions? The company can use this data to make improvements to the checkout process. They might make the promotion code field less prominent, to reduce friction in the checkout process, and improve conversion rates. Now that you have a better idea of how to set goals and analyze data, let’s consider how analytics applies to the entire marketing funnel. Starting from when a customer first discovers a brand to when they become a loyal brand advocate. E-commerce analytics helps companies discover important information about their customers, such as where their traffic comes from, and which channels attract the most visitors and sales. Companies can also learn information about their customers’ geographic location, interests, behavior, and other data. As customers move through the marketing funnel, they arrive at the checkout process. Here, companies can use analytics to measure sales and shopping cart metrics such as conversion rate, average order value, and cart abandonment rate. We’ll go deeper into those metrics later. Finally, analytics helps e-commerce stores measure customer loyalty. The customer lifetime value is a helpful metric for this because it estimates the total amount of money that a customer is expected to spend with the business over their lifetime. The higher the number, the better, because retaining existing customers is more cost-effective than acquiring new ones. For some e-commerce stores, the customer lifetime value might be based on a subscription service, such as meal kits, or a repeat purchase, such as water filters. For others, it might be based on a one-time purchase, such as a musical instrument. Analytics also helps companies measure brand advocacy, which is the strongest form of customer loyalty. Brand advocacy measures the number of customers who promote a brand through word-of-mouth marketing. The Net Promoter Score, or NPS, is a metric that measures brand advocacy by asking how loyal customers are to a company. The NPS data is gathered through a survey that asks customers how likely they would be to recommend the company to a friend or colleague. Customers then rate the company on a scale from 0 to 10. The results help companies form an overall picture of how customers view their brand. To recap, you’ve learned why analyzing data for e-commerce is important and how to set goals and measure success. You’ve also learned how analytics relates to the marketing funnel. Coming up, you’ll learn more about e-commerce analytics.

William - The power of data

Video Duration: 2 minutes

Hi, my name is William and I’m a Lead Strategist. So a Lead Strategist is a team that works with the fastest growing businesses and helps return more money for their investment. What’s most exciting about the job today is that a lot of these businesses really do want to grow and really have good intentions for their services that they provide to the people, but they just need a little bit of help in making sure that their investment goes a lot further than that what they’re spending on today. So I’m really happy that I can unite my expertise with the businesses and make sure that they can also focus on what they do best, which is helping the people. We’re looking at data, and we’re trying to create a picture for the advertiser. So my job is to package the data and make it as clear as possible to the end client so that they can make decisions whether to move faster or move slower. That creates the best business outcome for everyone. On a consistent basis, we are almost like the eye when it comes to where the dollars are being spent. And so what we do is, we’re looking at, if this amount that is spent today, the week, the month is in the right places, and that’s our job as a team to help brands continue to grow. During the peak of the pandemic, I worked with a vitamin brand, and part of my job was to make sure that they were spending in the right buckets. And so I did data analysis, and during that time period, vitamin C, as you can imagine, had the highest return on ad investment. And so when I look at the actual overall data landscape for other vitamins, my job was to recommend them to move spends. So for example, if they were spending $100 a day on vitamin B, to move those $100 a day to vitamin C spending because they would have a much higher yield than if they continued to spend it on vitamin B. If you’re not making decisions with data early on, you could be creating a new product, you could be investing into R & D, and all that money could potentially go to waste because it’s not selling as good as the rest of your products. It’s very crucial from the e-commerce side of things that you’re looking at the data, so as you continue to iterate on products, you’re iterating in the right direction.

Shopify analytics and reports

Video Duration: 2 minutes

Now that you’ve created your own e-commerce store in Shopify, you’re ready to learn about the analytics and reports available in this platform. Although you won’t have actual data for the store you created, it’s helpful to learn how to navigate the analytics dashboard in an e-commerce platform. Shopify’s analytics help companies learn about their sales and customers in greater detail. Using this knowledge, a company can make changes to its e-commerce store to improve the customer experience and increase sales. The types of analytics and reports available within Shopify depend on a subscription plan that a company chooses. Upgrading to a different plan unlocks access to more analytics and reports. Companies could also integrate their Shopify store with Google Analytics to receive further insights and find more ways to improve their website. You’ll learn more about using Google Analytics for e-commerce later. Shopify’s analytics allow companies to review their store’s recent activity, get information about their customers, test the site speed, and analyze the store’s transactions. All Shopify stores include access to the overview dashboard, reports, and Live View, which provides a real-time view of the store’s activity as it happens. Let’s go over these in more detail. The overview dashboard allows companies to get an overview of how their store is performing over time across all sales channels. It includes the most important metrics for the store, including key data about the store’s visitors, sales, and orders. You can change the date range to a specific time period, such as results over the last quarter or year. The dashboard allows you to monitor metrics like site traffic, total sales, conversion rate, average order value, and best-selling products within that time period. You can get more details about a metric by viewing reports. You can access multiple types of reports including finances, customer acquisition, inventory, customer behavior, and marketing. The reports available will depend on the company’s subscription plan. You can filter the data, edits columns, and change the date range. You can also print, export, or save a report. Some subscription plans allow you to create custom reports as well. You can also access Live View, which includes maps to visualize where customers are coming from in key metrics that indicate what’s happening right now. For example, Live View shows the number of visitors and total sales for the day. It also shows the number of customers who have added items to their cart, reached the checkout, or made purchases in the last 10 minutes. Now you know how to access analytics reports in Live View and Shopify. This data helps companies understand who their customers are and how well their online store is performing. Based on this data, companies can make decisions on how to improve the customer experience and increase the success of their business. You’ll learn more about making database decisions later, which is an important skill to learn for an e-commerce role. That’s all for now.

How to use Shopify analytics

Reading Duration: 20 minutes

Using analytics is an important skill for working in e-commerce because it helps you understand how an online store is performing. Earlier, you learned about the analytics and reports available in Shopify. In this reading, you’ll learn more about how to use the features available in the Shopify analytics dashboard.

Basic features in Shopify analytics

Shopify analytics allows you to access an overview dashboard, reports, and live view. To view the analytics for your mock e-commerce store in Shopify, clickAnalyticsin the navigation menu. Since your e-commerce store isn’t live, there won’t be any data available. However, you’ll still be able to review the metrics and reports available.

Overview dashboard

The overview dashboard provides data about the store’s sales, orders, and online visitors. You can adjust the date range to review data within a specified time range, such as the last quarter or year. You can also compare the data to a previous time period, which allows you to analyze the results quarter-over-quarter or year-over-year.

Reports

If you want to share analytics data or save it for future reference, you can use the reports feature in Shopify. This feature allows you to print, export, and save reports. Shopify includes built-in reports for analyzing a store’s sales, orders, customers, finances, inventory, marketing, and more. You can also create custom reports that include metrics and data specifically related to your performance goals.

Live view

The Live View feature in Shopify analytics displays how many visitors are currently in your store and where they are located in the world. It also displays key metrics, such as the total sales and orders received since midnight.

Using Live View can help you monitor activity during high-traffic periods, such as peak times for holiday shopping. It can also help you monitor the results of marketing activities such as limited-time discounts or promotions.

Live view for the Bath EcoShop e-commerce store in Shopify

Integration with Google Analytics

If you want access to more data, you can integrate your Shopify store with Google Analytics. As you learned in a previous course, Google Analytics gives you access to advanced analytics reporting for your e-commerce store.

Key takeaways

Shopify analytics makes it easy to track the performance of an e-commerce store with a built-in analytics dashboard, reports, and a live view of activity on your site. You can also integrate your e-commerce store with Google Analytics for advanced data tracking and reports.

Resources for more information

Learn more about Shopify analytics and reports by visiting the help center on Shopify’s website.

Shopify analytics: Learn more about dashboards and reports.

Analytics tools used in e-commerce

Reading Duration: 20 minutes

Analytics tools provide insight into what’s working for an e-commerce business and what isn’t. Throughout this program, you’ve learned how businesses can analyze data using spreadsheets and analytics tools. This reading will cover some of the most popular analytics tools used in e-commerce. However, there are many other helpful analytics tools for e-commerce besides the ones mentioned in this reading. As the field of e-commerce continues to change and grow, the list of analytics tools available will continue to change as well.

Google Analytics

Used by a majority of e-commerce sites, Google Analytics is a web analytics service that tracks and reports website traffic. It offers detailed information about the activity on a website, including the following:

How many visits the website gets
Where its traffic comes from
How visitors engage with the site content
Which products generate the most revenue
The total number of conversions and sales

This screenshot demonstrates how the main dashboard in Google Analytics appears:

Shopify analytics

Many e-commerce platforms include their own built-in analytics, including Shopify. Online stores that use Shopify as their e-commerce platform have access to analytics and reports that provide the following information:

Recent activity in the store
Insights about the store’s visitors
Website speed for the store
Analysis of the store’s transactions

This screenshot demonstrates how the Shopify analytics dashboard appears:

Email marketing analytics

Besides tracking the performance of your e-commerce store, it’s also important to track the performance of your email marketing campaigns.

Google Analytics tracks certain details about your email campaigns, such as how many visitors came to your site by clicking a link in one of your emails. However, you can get more detailed analytics about your email campaigns by using the analytics tool in your email marketing platform, such as Mailchimp or Constant Contact. You may also be able to integrate your email marketing platform with a Customer Relationship Management (CRM) tool, such as HubSpot or Salesforce. This allows you to track analytics from multiple sources in one location.

Email marketing analytics can help you improve your campaigns by providing detailed information, such as the following:

Open rate: The percentage of users that open your email
Click-to-open rate: The percentage of email recipients who clicked on one or more links in an email
Unsubscribe rate: Percentage of email recipients who unsubscribe from your send list after opening an email
Conversion rate: The percentage of email recipients who clicked on a link in your email and took a desired action, like making a purchase
Email bounce rate: The percentage of emails sent that could not be delivered to the recipient’s inbox
Complaint rate: The percentage of complaints recipients send to mailbox providers about receiving your email

Along with email marketing, it’s also important to track your social media marketing efforts. Social media analytics allows you to track, collect, and analyze data from social media platforms, such as Twitter.

Many social media platforms have built-in analytics tools. You can also analyze your social media marketing across all channels by integrating with an analytics tool like Sprout Social or Hootsuite.

Social media analytics can help you better understand your brand, your audience, and your competitors. It provides information such as the following:

Brand awareness: Measures the attention your brand received across all social media platforms during a reporting period
Impressions: The number of times a piece of content is displayed to your target audience
Potential reach: Measures how many people have potentially seen a post since you published it
Applause rate: The number of approval actions—such as likes, mentions, retweets, or favorites—that a post receives relative to your total number of followers
Referrals: The number of times someone was guided to your website from another site
Conversion: The completion of an activity that contributes to the success of a business

Paid advertising analytics

If you’re running paid ads in search engines or on social media sites, analytics can help you monitor the performance of your advertising campaigns and improve your return on investment.

You can view analytics for your paid ads in the advertising tool itself, such as Google Ads or Twitter Ads. You can also integrate these tools with Google Analytics to view all your data in one place.

Paid advertising analytics can help you improve your campaigns by providing detailed information, such as the following:

How many people viewed your ads online
How many people clicked on your ads
The number of conversions
The conversion value per cost

This screenshot demonstrates how the advertising dashboard in Google Analytics appears:

Big data

Most of the analytics tools you learned about in this reading can only handle a certain amount of data. But what if you need access to more data? You’ll need a database that’s designed to store big data.

Big data is the field in analytics that systematically mines and extracts information from very large datasets for insights. Big data is changing what’s possible in e-commerce by allowing marketers to monitor data in real-time and make immediate adjustments to a campaign. Big data also helps marketers use predictive analytics to predict how a webpage or ad will perform.

Here are a few solutions for analyzing big data:

Structured Query Language (SQL): The standard language used to communicate with databases developed by different vendors and hosted on multiple platforms. SQL queries enable you to pull data from databases for analysis.
BigQuery: Google’s cloud-based data warehouse solution. It helps you manage and analyze your data with built-in features like machine learning and business intelligence. You can use SQL queries to find data that answers your business questions.
Python: A programming language that’s become popular for data analysis. It can also be used for data visualization.

Key takeaways

Data analytics provides insight into how your website, marketing campaigns, and paid ads are performing. Using analytics tools, you can monitor data and find ways to improve your website or campaigns.

Test your knowledge: E-commerce analytics tools

Practice Quiz. 5 questions. Grade: 100%

2. Monitor e-commerce stores for growth and revenue

Monitor growth and revenue for an e-commerce store

Video Duration: 5 minutes

Now that you understand why it’s important to use analytics to measure the success of an e-commerce store, we’re going to cover some of the most important metrics to monitor for growth and revenue. I should mention that some of these metrics might be called by different names depending on the analytics tool you’re using or the company you’re working for. Knowing which metrics to focus on is important. In this video, we’ll concentrate on a few key metrics that make the biggest difference for most types of e-commerce businesses. Ready to get started?

Let’s cover some of the key metrics an e-commerce store might want to monitor. Imagine you’re working for an e-commerce store that sells leather phone cases. The company has been working hard to grow their sales and revenue, but they want to find out how they can make better marketing and sales decisions based on the data they’ve accumulated. Where do they start? Which metrics are important for them to monitor? First, the company might focus on the store’s overall traffic and revenue by comparing the results of the previous time period, such as quarter-over-quarter or year-over-year. Traffic is especially important to a new e-commerce store because if there are few visitors, there will be even fewer sales. The company will want to monitor the total number of visitors, and determine which sources deliver the most qualified traffic, or traffic made up of visitors who are likely to become customers. Some traffic sources will deliver more qualified traffic than others. The majority of the company’s advertising and marketing budget should be focused on increasing traffic from those sources. Total revenue is also an important metric for the success of an e-commerce store. Besides tracking the overall revenue, it’s helpful to break out the revenue by traffic source, such as social, organic search, paid search, and referral. Which traffic sources produce the most revenue? Also, are there any trends for when customers choose to buy? For example, is a certain day of the week busier than others? Companies can use information to plan the timing of their marketing and advertising campaigns. When analyzing traffic, the company might also explore the demographics of their users. Companies can use this demographic information to create customer personas, which you learned about in a previous course. Another important metric to track is the conversion rate. As a reminder, the conversion rate is the percentage of users who complete a desired action, such as making a purchase. An e-commerce company might track the conversion rate for a number of different actions that users take, such as adding a product to their shopping cart, entering the checkout process, and completing a purchase. Monitoring conversion rates is important because it demonstrates whether users are taking actions that contribute to the success of a business.

The cart abandonment rate is also a key metric to track. The cart abandonment rate is the percentage of customers who add a product to their shopping cart and leave the store without completing their purchase. If the company is able to capture an email address before these customers leave, they can try to recover lost sales by sending out email reminders to customers who abandon their cart. Another significant metric for e-commerce is the average order value, which tracks the average amount of money a customer spends each time they complete an order. By increasing the average order value, a store can increase their revenue, regardless of whether they are able to acquire new customers or improve conversion rates. It’s also important for e-commerce stores to understand how much it costs to gain a potential customer. This is where tracking the cost per acquisition comes in. The cost per acquisition is the average cost of acquiring a potential customer. The customer lifetime value needs to be greater than the cost per acquisition. Otherwise, the company is losing money to gain customers. The company should also track the customer acquisition cost, which is the average cost of acquiring a paying customer. If the cost of acquiring a paying customer is higher than the average order value, this means that the amount a company is spending to get customers is greater than the amount of money they’re receiving in return. In most cases, that’s not good news for the business. In some cases, however, it could be an acceptable trade-off, as long as the customer lifetime value is high enough to make up for the cost of acquiring customers. For example, let’s say the company that sells leather phone cases spends $75 to acquire a paying customer. However, the customer only spends $60 on their first purchase. Not so good, right? The company spent more than they received in return. But let’s say the customer loves the product so much that they come back a few months later to buy another $60 phone case as a gift. The customer lifetime value is now $120.

Then a couple of years later, the customer spends $70 on a phone case for their brand new phone. Now, the customer lifetime value is $190, which is more than enough to make up for the $75 the company initially spent to acquire the customer. As you learned in a previous course, the customer lifetime value is the average revenue generated by customers over a certain period of time. It’s an important metric to track because it helps companies measure customer loyalty to their brand. Loyal customers are a significant source of revenue for many companies, especially ones that sell subscription-based products or services. Customer retention rate is another metric that helps companies measure loyalty. The customer retention rate is the percentage of customers that a company retains over a certain period of time. It gives companies an idea of how satisfied their customers are with the brand. If the retention rate is low or begins to decrease, the company needs to evaluate the customer experience and find ways to make improvements.

An e-commerce store will also want to track their NPS scores, which you learned about earlier. Tracking NPS scores help companies go beyond customer loyalty to measure brand advocacy. Customers who are passionate about a brand will naturally spread the word to other people they know. They’re one of the company’s greatest assets. Great work. You’ve learned a lot about the most important metrics used to monitor growth and revenue for an e-commerce store. Later, you’ll learn how to apply the knowledge gained from this data to improve an e-commerce store’s performance. Meet you again soon.

Identify: Monitor e-commerce store performance

Ungraded Plugin Duration: 30 minutes

Key metrics for monitoring an e-commerce store’s performance

Reading Duration: 20 minutes

Monitoring an e-commerce store for growth and revenue is important for the success of any e-commerce business. In a video, you learned some of the most important metrics to monitor for growth and revenue. This reading will review the key metrics you learned and demonstrate how they would appear in an analytics tool. Keep in mind that how a metric appears might differ based on the analytics tool you’re using. Also, not all metrics are available in every analytics tool, and some metrics you may need to calculate using a formula.

Comparing metrics over time

When it comes to tracking metrics, it’s helpful to compare the results to a previous time period, such as the previous quarter or year. This comparison allows you to measure whether the results are improving over time.

An e-commerce store that’s less than two years old should compare the results quarter-over-quarter. A quarter is a three-month time period based on a company’s financial calendar. Each year includes four quarters.

E-commerce stores that have been around for at least two years should measure their results both quarter-over-quarter and year-over-year. The chart below demonstrates an example of how to measure revenue by comparing each quarter to the same time period for the previous year.

Two-year quarterly revenue comparison chart

Traffic and revenue

Attracting more traffic to an e-commerce store is important, because if few people are visiting the store, there will be even fewer sales. However, the type of traffic a store receives makes a big difference. The majority of a store’s traffic should be qualified traffic, which is website traffic made up of visitors who are likely to become customers. If lots of people visit the site but don’t end up making a purchase, that doesn’t help the business grow. Qualified traffic is more likely to lead to a boost in revenue.

Google Analytics makes it easy to discover which types of traffic bring the most visitors and how much revenue each type of traffic generates.

The screenshot below demonstrates a quarter-over-quarter comparison of an e-commerce store’s traffic.

This Google Analytics dashboard shows a quarter-over-quarter comparison of an e-commerce store’s traffic

The screenshot below demonstrates a quarter-over-quarter comparison of revenue from each type of traffic.

Tracking the total revenue for an e-commerce store is also important.

The screenshot below demonstrates how Google Analytics displays the total revenue for an e-commerce store, with a quarter-over-quarter comparison.

This Google Analytics dashboard shows a quarter-over-quarter comparison of total revenue

Conversion rate (CVR or CR)

The conversion rate (CVR or CR) is the percentage of users who complete a desired action, such as signing up for a software trial or making a purchase from an apparel retailer. The conversion rate is a key metric to track because it demonstrates whether users are taking actions that contribute to the success of an e-commerce store.

Cart abandonment rate (CAR)

Sometimes visitors add a product to their cart but don’t buy anything. The cart abandonment rate(CAR) measures the percentage of customers who add a product to their shopping cart and leave the store without completing their purchase.

If the company gets the customer’s email address before they leave, they can try to recover lost sales by sending email reminders to these customers.

An increase in the cart abandonment rate might indicate that customers are having issues completing the checkout process.

The screenshot below demonstrates how the cart abandonment rate appears in Google Analytics.

This screenshot shows the cart abandonment rate in Google Analytics

Average order value (AOV)

Another key metric to track is the average order value (AOV), which is the average amount of money a customer spends each time they complete an order. A higher average order value can increase a store’s revenue regardless of whether they’re able to acquire new customers or improve their conversion rate.

Cost per acquisition (CPA)

The cost per acquisition (CPA) is the average cost of acquiring a potential customer. It’s important for e-commerce stores to know how much it costs to gain a potential customer, so that they’re not spending more than it’s possible to make in return.

Here is the formula to calculate the cost per acquisition:

Total cost of conversions / Number of conversions = Cost per acquisition

The number of conversions could be the number of email subscriptions added, the number of times a product was added to cart, or another micro-conversion that’s important to the business.

For example, if a company spent $10,000 on campaigns over 90 days for their software product, and 5,000 people added the software product to their cart, the cost per acquisition would be calculated as follows.

10,000 / 5,000 = $2

The cost per acquisition is $2.

Customer acquisition cost (CAC)

The customer acquisition cost (CAC) is the average cost of acquiring a paying customer. Unlike the cost per acquisition, which tracks potential customers, the customer acquisition cost tracks actual paying customers. As with the cost per acquisition, the customer lifetime value needs to be high enough to make up for the cost of acquiring a paying customer.

Here is the formula to calculate the customer acquisition cost:

Total cost of sales and marketing / Number of customers acquired = Customer acquisition cost

For example, if a company spent $10,000 on sales and marketing over the last 90 days for their software product, and 2,000 people bought the software product, the customer acquisition cost would be calculated as follows.

10,000 / 2,000 = $5

The cost per customer acquisition is $5.

Customer lifetime value (CLV)

Customer lifetime value (also called lifetime value) is the average revenue generated per customer over a certain period of time. It’s a key metric for companies to track because it helps measure a customer’s loyalty to their brand.

The screenshot below demonstrates how the customer lifetime value appears in Google Analytics.

This screenshot shows the lifetime value in Google Analytics.

Customer retention rate (CRR)

The customer retention rate (CRR) is the percentage of customers that a company retains over a certain period of time. It’s a key metric for measuring customer loyalty, especially for businesses that rely on recurring purchases or subscriptions. If the retention rate begins to decrease, this could indicate that the company needs to find ways to improve customer satisfaction.

Here is the formula to calculate the customer retention rate:

[(E-N)/S] x 100 = Customer retention rate

E is the number of total customers at the end of a given time period.
N is the number of new customers added within a given time period.
S is the number of existing customers at the start of the time period.

For example, an e-commerce store that sells subscription-based software online might want to measure their customer retention rate over the last three months. At the beginning of the three-month time period, the store had 5,000 customers. During these three months, they added 500 new customers. Three hundred customers canceled their subscriptions during this time period. By the end of the three months, the store had 5,200 customers remaining.

Now you can plug these numbers into the formula.

[(5,200-500)/5,000] x 100 = 94%

The store has a 94% retention rate, which means most of their customers are satisfied.

Key takeaways

Monitoring an e-commerce store’s performance is important because it reveals what’s working and what’s not. Knowing which metrics to monitor can help you discover how to improve the performance of an e-commerce store.

Activity: Analyze an e-commerce store’s performance

Practice Quiz. 5 questions. Grade: 100%
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On Step 1: Access supporting materials

The following supporting materials will help you complete this quiz. Keep them open as you proceed to the questions below.

To use the supporting materials for this course item, click the link below and select “Use Template.”

Link to supporting materials: E-commerce performance data

Test your knowledge: Monitor an e-commerce store’s performance

Practice Quiz. 5 questions. Grade: 100%

3. Make update to an e-commerce store based on data

Use data to improve the performance of an e-commerce store

Video Duration: 4 minutes

You’ve learned about some of the most important metrics to monitor for an e-commerce store. Now let’s go deeper into how to use the insights gained from this data to improve an e-commerce store’s performance. There are many updates a company can make to their e-commerce store based on data. The specific updates that a company chooses to make will depend on their goals, as well as how much their e-commerce platform allows for customization. The updates they make can help attract more qualified traffic, increase sales, and improve the customer experience. In this video, we’ll cover just a few of the ways a company might use data to improve the performance of their e-commerce store. First, let’s consider an e-commerce store that’s experiencing a steady increase in traffic, but the additional traffic is not leading to an increase in revenue. There could be a number of reasons for this. Let’s look at a couple of them. One reason could be that the additional traffic is coming from customers who aren’t as likely to buy. The company’s goals should not just be more traffic, but qualified traffic. To attract more qualified traffic, the company can analyze all of their traffic sources to determine which ones generate the most revenue. Then they can create a plan to attract more traffic from those sources. For example, if email campaigns generate qualified traffic, the company can work to grow their email subscriber list. If organic search generates qualified traffic, the company can optimize their website for SEO, which you learned about in a previous course. This might include optimizing product pages by including SEO keywords in the title, URL, and description. It might also include publishing regular blog posts or articles that feature helpful information for customers. Another possible reason that the increased traffic isn’t leading to more sales can be a low conversion rate. By finding ways to improve the conversion rate, a company can increase their revenue. Conversion rate optimization is the process of increasing the percentage of website visitors who complete a desired action. The goal of conversion rate optimization is to remove any barriers to the customer making a purchase. A company can discover the barriers to purchase by using analytics to determine at what point customers are leaving the site or abandoning their cart. They can also use heat maps and record how customers interact with the site to better understand how customers navigate the website. Then they can make improvements based on this information. For example, are customers able to easily find their shopping cart and begin the checkout process? If not, the company needs to update the location of their shopping cart or make it available in multiple locations. Or, are customers landing on a product detail page for an out-of-stock item? If so, the company can add a feature for product recommendations on the page. They can suggest similar products that might meet the customer’s needs and hopefully avoid losing the customer to one of their competitors. The checkout process is one of the most important areas to optimize, as you learned earlier. Allowing guest checkout, removing unnecessary fields, and including flexible payment options are just a few ways to optimize the checkout process. There are multiple ways a company can update their website. To find out which updates will have the biggest impact, the company can use A/B testing to compare two versions of a web page. At the end of the A/B test, the version with the higher conversion rate wins. In an entry-level e-commerce position, you might be involved in suggesting ideas for an A/B test for a marketing campaign or other project you’re working on. Now let’s cover another important strategy a company might use to improve their store’s performance. As you learned earlier, the average order value is a significant metric because it can increase revenue even without attracting more customers or improving the conversion rate. Two common ways to increase the average order value are cross-selling and upselling. Cross-selling is a sales technique used to encourage customers to spend more by purchasing a product that’s related to what they’re already buying. For example, a custom suits retailer might encourage customers to add a dress shirt, neck tie, or cufflinks to their order. This increases the average order value by increasing the number of products a customer buys. Upselling is a sales technique used to encourage customers to spend more by upgrading to a more expensive product. For example, the custom suits retailer might recommend a premium suit sold at a higher price point. This increases the average order value by increasing the amount a customer spends on a single product. Other common strategies for increasing the average order value include creating an order minimum for free shipping, offering bundle deals, and providing incentives through a rewards program when customers spend a certain amount. All of these strategies are helpful for improving the performance of an e-commerce store based on data. In an entry-level e-commerce role, you may be involved in gathering data, making recommendations, or updating an online store based on data insights. Great work. You’re making progress.

Conversion rate optimization

Reading Duration: 20 minutes

You’ve learned about the importance of using data to improve the performance of an e-commerce store. One of the ways you can use data to improve performance is to focus on increasing the conversion rate. In this reading, you’ll learn about conversion rate optimization, which is the process of increasing the percentage of website visitors who complete a desired action.

The desired action for an e-commerce store could be signing up for email offers, watching a product video, or adding a product to cart. These are called micro conversions, which are actions that indicate a potential customer is moving towards a completed purchase transaction. If the customer continues through the buying process, these micro conversions will lead to a macro conversion, which is a completed purchase transaction. That’s the most important conversion for an e-commerce store to track.

Using data to increase conversion rates

Analyzing data can help you figure out how to increase conversion rates for an e-commerce store. It can also reveal opportunities to improve the customer experience and the checkout process.

Using data, you can discover the following insights:

Which traffic sources convert at the highest rate
Which elements on the page have the biggest impact on conversion
At what point in the buying process customers tend to leave your store or abandon their cart
Where customers get stuck during the checkout process

You can gather data and discover insights by using analytics tools and other tools designed for conversion rate optimization.

Tools for conversion rate optimization

Analytics tools, such as Google Analytics, can give you insight into how users behave on a website and at what point they leave the buying process. Other conversion rate optimization tools can help you gather more data and test how users will respond to changes on the website.

Here are some examples of the types of tools used for conversion rate optimization:

Heat maps

A heat map is a data visualization tool that demonstrates how visitors interact with a website. It uses color variations to represent users’ behavior. For example, if a large number of visitors clicked on a specific link, the heat map would show darker, more intense color in that area.

Session recordings

A session recording captures a visitor’s actions as they navigate a website, including mouse movement, clicks, taps, and scrolling. It’s also known as a session replay or user recording.

Session recording makes it easy to see how visitors interact with a website. This information provides valuable insight into how to optimize a website and increase conversion rates.

A/B testing

A/B testing is an online test of two variants to determine the better performing option, with 50% of testers directed to one variant and 50% of testers directed to the other variant. An A/B test is also known as a split test or bucket test.

After you decide which changes to make to an e-commerce store to increase conversion rates, you can use A/B testing to find out if those changes will make a positive difference. If the change leads to a higher conversion rate compared to the original version of the website, you can update the website to reflect these changes.

Web traffic splitting to the two different versions of an A/B test

Ways to increase conversion rates

Based on the data collected using the tools above, you can decide which changes to make to a website to increase conversion rates. There are lots of different changes you could make. The specific changes that will make the biggest impact will depend on a business’s products or services, as well as what their customers want.

Here are a few ideas to get started:

General tips

Remove distractions, such as elements on a webpage that customers don’t notice or interact with.
Make sure elements that customers interact with most are in a prominent location on the webpage.
Make your call-to-action buttons clear and easy to find.
Test out different call-to-action button copy.
Remove unnecessary form fields for creating an account, subscribing to emails, and completing the checkout process.
Test out different sales promotions.
Add live chat.
Make your website mobile-friendly.
Improve the speed of your website and/or app, especially on mobile.

Product pages

Offer a product guarantee.
Personalize the customer experience by offering a virtual try-on experience.
Improve your product detail pages by including a detailed product description, using high-quality images that show the product from multiple angles, and adding product videos.
Add a product recommendation engine to your website.

Emails

Increase the number of product reviews by asking customers to review a product after they purchase it.
Send abandoned cart emails.

Checkout flow

Offer free shipping.
Allow guest checkout.
Add flexible payment options.
Make sure the return policy is clear, easy to understand, and easy for customers to find.

These are just a few of the types of changes that can help increase conversion rates. You can also use A/B testing to test out your changes before making changes to the website.

Keep in mind that conversion rate optimization is a continuous process—not something you only do once. There’s always room for improvement. Even a small increase in conversion rates can make a noticable difference in revenue for an e-commerce store.

Key takeaways

Conversion rate optimization helps you increase sales by making it easier or more enticing for customers to buy. Using data—along with tools such as heat maps, session recordings, and A/B testing—can help you determine what changes you need to make to improve conversion rates.

Case Study: The importance of ROI and CPC for a small business

Reading Duration: 10 minutes

In this case study, you will learn about how a small business acts strategically to effectively use every marketing budget dollar.

Company Background

Dr. Lyn and Terry Lam are the CEO and CFO of Kapa Nui Nails — the only 100% fume-free, toxin-free, and odor-free, vegan and cruelty free nail polish that doesn’t damage your body or the planet. The innovative and environmental justice-driven minds behind Kapa Nui Nails say their mission is to keep people’s nails safe, protected, and simply beautiful.

Before they started Kapa Nui, neither Terry or Lyn had experience in the beauty industry. But they both come from the industry of medicine, and they knew about the negative effects of toxic nail polishes on the body and the planet.

So, they sought out to create a product that was safe for every person to use. They also knew that nail polish is a large contributor to o-zone depletion and environmental pollution, so they created a nail polish that didn’t contribute to climate change.

The challenge

The Kapa Nui team is small but mighty. The two owners handle everything from sales to operations to marketing. Terry has taken on the role of digital marketer, and she says it’s been a journey. She’s working with a relatively small budget, so she has to be incredibly thoughtful about the way they budget their marketing dollars — especially since 90% of their budget goes to marketing.

The challenge for Kapa Nui is to create successful ad campaigns on a small business’ budget, ensuring their return-on-investment and cost-per-click metrics indicate they’re spending their money wisely.

Terry and Lyn wrapping nail products to send to customers

The approach

Kapa Nui’s approach to marketing includes the use of targeted Google and Facebook ads, robust email marketing strategies that send traffic to the website, and using targeted keywords which they find through Google analytics.

They know their target audience is people in the US within the broad age range of 21-65+. They know they need to prioritize social media marketing just as much as they prioritize the other marketing avenues.

The approach for the Kapa Nui team is to execute trial and error to figure out what marketing their target audience responds to (and how), and above all else, they know they’ll need to track analytics to gain valuable insights using Google Analytics, because this will tell them if they’re being smart about their marketing spending or not.

Their approach is and always will be to track which marketing strategies they see the best return on. The approach is to find out which types of marketing provide the highest return-on-investment (ROI) and lowest cost-per-click (CPC).

The results

Using Google Analytics, Google Search Console, Google Ads, and Google Tag Manager, Kapa Nui Nails are experts at tracking metrics and turning them into insights.

Kapa Nui has been running shopping ads which are integrated with Shopify, keyword search campaigns, and social media campaigns.

By tracking the ROI and CPC on each of these types of campaigns, they’re able to see which types of digital marketing campaigns have been the most successful.

Currently, they are finding their integrated shopping ads are performing the best, based on their ROI and CPC metrics. On these types of campaigns, their ROI is 3-to-1 and their CPC is under $1. Those numbers are more impressive than those of their keyword campaigns, which have lower ROIs and higher CPCs.

Conclusion

Since Kapa Nui is a small company with a small budget, they have to use their marketing dollars wisely. Thanks to analytics and tracking tools, they now know they should be putting most of their resources into shopping ads because it has the highest ROI for their business. Focusing on ROI is a concept that is integral to every company’s success.

Understand visitor behavior with heat maps

Reading Duration: 20 minutes

Earlier, you learned about tools used to measure webpage performance and improve conversion rates, including heat maps, session recordings, and A/B tests. In this reading, you’ll explore heat maps in more depth, and how they can help you optimize an e-commerce website’s content and organization.

What is a heat map?

As you’ve already learned, a heat map is a data visualization tool that uses a color scale to represent how visitors interact with an individual webpage. Most heat map tools use a rainbow (or thermal) scale to demonstrate patterns of user behavior across different parts of a page.

A rainbow gradient color scale labeled “cold” at the blue end and “hot” at the red end

Areas of greater focus or engagement are considered “hot” and indicated by colors at the warm end of the scale (e.g., red, orange). That’s why elements with high click or tap rates are known as hotspots. In contrast, areas with less engagement are “cold” and expressed through cool colors (e.g., blue, cyan).

Heat maps are useful tools because they do more than just track metrics. They provide detailed data on how visitors are responding to specific parts of a page. Heat maps can tell you what visitors are reading, where they’re clicking, and what they might be missing entirely. The insights you gain from this data can help you determine how to test or adjust each page to improve the visitor experience and reach performance goals.

Types of heat maps

There are several types of heat maps businesses use to optimize e-commerce websites. Each provides slightly different information, so they are often used together to gain a more detailed understanding of visitor behaviors. In the rest of this reading, you’ll learn how to interpret three common types of heat maps: scroll, click, and hover.

Scroll heat maps

Scroll maps track what percentage of visitors scroll down to specific points on a webpage. They tell you what areas of a page visitors are likely to see, rather than how they respond to individual elements. Often, the “hottest” area is above the fold—the area displayed before scrolling—with the map becoming cooler further down as visitors click away (as in the graphic below).

A scroll heat map with the hottest area at the top of the page

Insights gained from scroll maps allow you to:

Put key content where it will get the most attention
Identify false bottoms (places where visitors stop scrolling prematurely because they think the page has ended)
Propose changes to encourage scrolling (if additional scrolling is the goal)
Optimize pages for both web and mobile experiences

Click heat maps

Click maps (or touch maps on mobile devices) track where visitors click or tap on a webpage. Clusters of activity appear as blobs, or spots, of color across the page, as shown in the graphic below. This data lets you identify patterns and trends in visitor behavior, which can help you increase future click rates.

A click heat map with irregular spots of color, demonstrating higher and lower engagement with page elements

Click maps can tell you if visitors are clicking (or tapping) where you want them to—for example, on certain links or calls to action (CTAs). If a link isn’t getting enough engagement or visitors are clicking unclickable elements, it could mean the page is overcrowded or confusing.

Click map data can also help you:

Find out which page elements visitors are responding to
Gauge the effectiveness of your CTAs
Decide whether to run A/B tests on certain elements
Clarify page content or structure

Hover heat maps

Engagement can’t always be measured in clicks, particularly when it comes to written or visual content. Hover maps (also known as mouse maps or movement maps) can capture these patterns of visitor attention by tracking mouse movement across a page. Mouse movement isn’t a perfect indicator of attention, but it does provide a sense of where visitors are focusing.

Hover heat map with irregular spots of color, demonstrating differing amounts of attention paid to various parts of the page

The optimal amount of attention paid to specific elements depends on the goals for a given page. If visitors are focusing on a specific paragraph, that may mean they find it interesting or helpful. However, it could also mean the text is confusing or difficult to read.

Hover maps can help you determine if visitors are:

Paying attention to the right (or wrong) elements
Becoming distracted by unimportant page details
Struggling with page content

Key takeaways

Using metrics to understand visitor behavior can be a challenge. Fortunately, heat map data allows you to understand the impact of specific choices on metrics like clicks and conversions. When used together, scroll, click, and hover maps provide actionable insights to improve performance and help you reach your goals.

Resources for more information

For more on heat maps and what they can do for e-commerce businesses, visit the following resource:

The complete guide to heatmaps: A history of heatmaps and how they’re used today

Activity: Use heat map data to optimize a landing page

Practice Quiz. 1 question. Grade: 100%
- Access Quiz:

On Step 1: Access the template

To use the template for this course item, click the link below and select “Use Template.”

Link to template: Landing page ideas
On Step 2: Access supporting materials

The following supporting materials will help you complete this activity. Keep them open as you proceed to the next steps.

You can download the supporting materials directly from the attachment below.

Landing page heat maps

Activity Exemplar: Use heat map data to optimize a landing page

Reading Duration: 10 minutes

Here is a completed exemplar along with an explanation of how the exemplar fulfills the expectations for the activity.

Completed Exemplar

To review the exemplar for this course item, click the link below and select “Use Template.”

Link to exemplar: Landing page ideas

Assessment of Exemplar

Compare the exemplar to your completed email. Review your work using each of the criteria in the exemplar. What did you do well? Where can you improve? Use your answers to these questions to guide you as you continue to progress through the course.

Note: The exemplar represents one possible version of the landing page ideas email. Yours will likely differ in certain ways. What’s important is that your email makes specific, data-driven suggestions to improve the landing page.

Let’s review each idea in the email:

Problem: Most customers leave the page without scrolling down far enough to find the form.
Heat map data used: This idea draws on data from both heat maps to conclude that elements near the top of the page have greater engagement.
Solution: Move the signup form up the page, so site visitors are more likely to find it.

Idea 2: Reduce the number of fields

Problem: Site visitors are abandoning the signup form at the halfway point.
Heat map data used: This idea uses the click heat map, which indicates that engagement decreases with each field in the signup form.
Solution: Reduce the number of fields in the signup form and find another way to collect the rest of the information.

Idea 3: Make the form and CTA stand out more

Problem: The signup form is too light and blends into its surroundings.
Heat map data used: The click map indicates that visitors are engaging more with nearby page elements. Some of those who are using the signup form are clicking in the wrong places.
Solution: Make the form larger or bolder (or both) and turn the CTA from a link into a more noticeable button.

Idea 4: Reduce overall page clutter

Problem: There are so many page elements that visitors’ attention is divided.
Heat map data used: The click map indicates that visitor engagement across most of the page falls on the lower end of the heatmap scale.
Solution: Focus visitor attention on the most important areas by removing some of the less important page elements.

Test your knowledge: Data analysis

Practice Quiz. 5 questions. Grade: 100%

4. Identify and update listings based on data

Evaluate product performance

Video Duration: 5 minutes

We’ve covered the importance of using analytics to measure the success of an e-commerce store. Now we’re going to discuss the importance of using analytics to evaluate product performance. In earlier lessons we focused on the performance of the e-commerce store as a whole. In this video, we’ll be focusing on the performance of specific products. First, let’s talk about why product analytics are important. Product analytics involves monitoring and evaluating data to gain insights into how users interact with a product or service. This data helps companies evaluate whether a product is successful. If a product isn’t meeting expectations, analyzing the data can help a company figure out why the product is underperforming and identify opportunities for improvement. Product analytics can also help a company avoid selling products that don’t benefit their business or that can harm their brand’s reputation. A product that lacks quality leads to negative customer reviews or has a higher than normal return rate isn’t worth the hassle or the risk to a brand’s reputation. Another reason product analytics are important is because they help a company plan its inventory needs. Knowing which products are the most popular and understanding changes in seasonal demand helps a company know when to purchase more inventory and how much to purchase. Product analytics also help companies evaluate the impact of marketing campaigns. For example, do orders and revenue increase when there’s a campaign centered around certain products? How do promotions impact sales and is the impact large enough to make up for the discounted price? A company can use this information to plan its future marketing campaigns. Now let’s consider some of the metrics a company might use to analyze product performance. A good place to start is with the number of times a product is viewed. This indicates whether visitors are able to find the product. If the number of views is low compared to similar types of products, the company will need to find ways to increase traffic to the product. For example, the company could highlight the product in their marketing campaigns. They could also feature the product on the homepage or category page or include it as a product recommendation on their website. Next, it’s important to consider the product conversion rate, which is the percentage of customers who purchase a product after viewing it. Increasing the conversion rate even a small amount can make a noticeable difference in the total revenue generated by a product. What are some of the ways the company might improve the product conversion rate? First, the company might update the product detail page. They could upload higher quality photos and allow customers to zoom in for more detail. They could add photos of the product in all available colors and from multiple angles. For example, if the product is a t-shirt, they’ll want to include photos of the front and back or even a video of a model wearing the t-shirt. Including a photo of the product packaging can also be helpful. They might also update the product description to include the benefits of the product, technical specifications, a size chart, and any information that’s available on the product packaging. The product photos and description need to make up for the fact that customers may not be able to try on or inspect the product in a store. If the product doesn’t have customer reviews enabled, the company might consider adding this feature. It can help convince customers who are undecided about purchasing the product. If the product has no reviews or only a few, the company can encourage customers to review the product by sending email reminders. Another metric to consider for product analytics is the number of unique purchases compared to the number of recurring purchases. This is especially helpful for products with a short lifespan or for subscription-based products and services. For these types of products, a high number of recurring purchases will lead to stronger growth. Net profit margin and return on ad spend are two important metrics that we covered in the lesson on product research. It’s a good idea to evaluate these metrics on a regular basis to track any changes and uncover opportunities for improvement. In some cases, a product might have a negative profit margin but still be worth selling. For example, consider a business that sells printers at a loss but then makes up for the difference over time through recurring sales of ink and toner. The business loses money upfront but eventually makes up for it in the end. It’s also important to consider how a product affects the average order value. For example, is a product priced significantly higher than the average order value? If so, it may be priced higher than customers are willing to spend. Another aspect to consider is how a product affects the purchase of other products. For example, do customers who purchase a bicycle also tend to purchase a helmet? When customers readily purchase both products together, it increases the average order value. To increase the average order value, a company could include the product in their cross-selling and upselling strategies. They might also include the product in a bundle offer to increase sales. For example, the bundle offer for bicycles and helmets could be “Save 10% on a helmet when you buy a bicycle.” Finally, it’s important to consider the return rate, which is the percentage of products sold that are returned by customers. Is the return rate for a specific product higher than the average for its category? If so, the company will need to dig into why this is happening and find ways to lower the return rate. For example, is there a flaw in the product’s design? Is the product description on the website accurate? It’s important for a company to ask customers why they’re returning a product so that they know how to make it better. After making updates to improve a product’s performance based on data, it’s important for the company to track any changes to the product’s performance over time. This will help them evaluate whether the updates are making a difference. If not, it may be time to try something else or stop selling the product. Alright, you’ve learned a lot about how to evaluate a product’s performance. You’ve also learned ways to try to improve the performance of a product that’s not selling as well as expected. These will be valuable skills to help you in an entry-level e-commerce position. In your role you may be asked to provide updates on how a product is performing or offer suggestions on how to improve a product’s performance.

Identify: Underperforming products

Ungraded Plugin Duration: 30 minutes

Key metrics for product performance

Reading Duration: 20 minutes

You learned earlier about product analytics, which involves monitoring and evaluating data to gain insights into how users interact with a product or service. In this reading, you’ll learn more about the key metrics used to evaluate product performance.

Introduction to product analytics

Monitoring product performance is important because it helps a company evaluate the success of a product and identify opportunities for improvement. It also helps the company plan their inventory and avoid selling products that consistently underperform.

For a product that’s launched recently, such as in the past six months, it’s a good idea to monitor performance over the entire lifespan of the product. If the company has been selling the product for six months or longer, it can be helpful to compare the results quarter-over-quarter or year-over-year to discover how the product’s performance has changed over time.

It’s also helpful to compare a product’s performance to similar products, such as several backpacks in different styles. Another helpful strategy is to compare the product performance of different variations, such as a gray and blue backpack in the same style. Certain colors or sizes might perform better than others.

Number of product views

One of the basic metrics to monitor is the number of times a product was viewed. This metric gives you an idea of how many visitors were able to find the product on the website. This metric also gives you an idea of whether the business’s customers are interested in this type of product.

Here is an example of how the number of item views (the number of times the item details were viewed) appears in Google Analytics:

Quarter-over-quarter comparison of the number of item views in Google Analytics

Number of add-to-carts

Another basic metric to monitor is the number of times a product was added to cart. This metric is a strong indicator of how much a business’s customers are interested in buying a product.

Here is an example of how the number of add-to-carts appears in Google Analytics:

A quarter-over-quarter comparison of the number of add-to-carts in Google Analytics

Number of units purchased

It’s also helpful to monitor how many units of the product were purchased. This indicates that customers were interested enough in the product that they decided to buy it.

Here is an example of how the number of number of units purchased appears in Google Analytics:

Quarter-over-quarter comparison of the number of units purchased for a specific product in Google Analytics

Product revenue

Product revenue describes the amount of revenue generated by a product. It gives you an idea of how much the product benefits the business, although you’ll also need to consider other product metrics for a more complete understanding of the product’s performance.

Here is an example of how the product revenue appears in Google Analytics:

Quarter-over-quarter comparison in Google Analytics of how much revenue a product generated

Product conversion rate

The product conversion rate is the percentage of customers who purchase a product after viewing it. You can calculate the conversion rate using this formula:

(Product conversions / Unique visitors to the product page) × 100 = Product conversion rate

For example, a store sold 50 units of their best-selling coat in the last 90 days, and 2,000 people viewed the product page during this time period. This means there were 50 product conversions and 2,000 unique visitors to the product page.

This is how the store would calculate the product conversion rate for this coat:

(50 / 2,000) × 100 = 2.5%

The product conversion rate for the coat is 2.5%.

Unique vs. recurring purchases

Another key metric is the number of unique purchases compared to the number of recurring purchases. A unique purchase means the customer only bought the product once. A recurring purchase means the customer bought the product twice or more.

This metric is especially important for products with a short lifespan or for subscription-based products and services. For example, there should be a high number of recurring purchases for electric toothbrush heads or meal delivery kits.

Net profit margin

Net profit margin is the percentage of revenue left over after expenses are paid. It allows you to compare the profitability of different products, no matter how much they cost.

You can calculate the net profit margin using this formula:

(Net profit / Total revenue) × 100 = Net profit margin

For example, imagine the store mentioned earlier wants to find the net profit margin for their best-selling coat. They know that the coat generated a net profit of $1,500 and a total revenue of $5,000 within the last 90 days.

This is how the store would calculate the net profit margin for the coat:

(1,500 / 5,000) × 100 = 30%

The net profit margin for the coat is 30%.

Return on ad spend (ROAS)

The return on ad spend (ROAS) helps measure the success of advertising for a specific product.

You can calculate ROAS using this formula:

(Number of units sold × Cost per unit) / Ad spend = ROAS

If the store mentioned above wanted to measure the ROAS on their best-selling coat, they could analyze the numbers for the last 90 days and enter them into this formula.

This is how the store would calculate the product’s ROAS:

(50 × 100) / 1,250 = $4

The ROAS for the coat is $4, which can also be expressed as a ratio (4:1) or a percentage (400%).

Average order value (AOV)

The average order value tracks the average amount of money a customer spends each time they complete an order.

If an underperforming product is priced higher than the average order value, it may not be selling well because it’s priced higher than customers are willing to spend.

In other cases, a product might increase the average order value. For example, if customers who purchase a coat often purchase accessories, such as a hat or gloves, these accessories increase the average order value for the site.

Return rate

The return rate is the percentage of products sold that are returned by customers. If a product’s return rate is high compared to similar products in the same category, there may be issues with the product quality or how the product is represented online.

However, keep in mind that some product categories may naturally have a higher return rate than others, such as clothing or shoes, because customers aren’t able to try them on before buying.

You can calculate a product’s return rate using this formula:

(Number of units returned / Number of units sold) × 100 = Return rate

If the store mentioned above wanted to measure the return rate on their best-selling coat, they could analyze the numbers for the last 90 days and enter them into this formula.

This is how the store would calculate the product’s return rate:

(5 / 50) × 100 = 10%

The return rate for the coat is 10%.

Quarter over quarter performance

The quarter over quarter performance is the percent change of quarterly results. A quarter is typically a three month period. A digital marketer can use this percent change to compare the quarterly performance of most metrics.

You can calculate the quarter over quarter performance change by using this formula:

(most recent quarter metric) - (prior quarter metric) = (metric change)

Then, take the metric change and divide by the prior quarter’s metric:

(metric change) / (prior quarter metric) = (quarter over quarter percent change)

This is how you would calculate quarter over quarter performance for the number of units purchased metric listed above:

188 units purchased - 101 units purchased = 87 units purchased

87 units purchased / 101 units purchased = 86.13% quarterly change

Key takeaways

Product analytics makes it easier to monitor and evaluate a product’s performance over time. Tracking key metrics can help companies evaluate the success of a product and discover opportunities to improve a product’s performance.

Activity: Analyze product performance for an e-commerce store

Practice Quiz. 7 questions. Grade: 100%
- Access Quiz:

On Step 1: Access supporting materials

The following supporting materials will help you complete this quiz. Keep them open as you proceed to the questions below.

To use the supporting materials for this course item, click the link below and select “Use Template.”

Link to supporting materials: Product performance data spreadsheet

Optimize product offerings based on data

Reading Duration: 20 minutes

Earlier, you learned about the key metrics used to evaluate product performance. Now it’s time to apply the insights from this data to make changes that can improve a product’s performance. Product analytics can help companies increase the number of sales for a product, discover new product ideas, and decide which SKUs to retire, or stop selling.

Increasing the number of sales for a product

In order to increase the number of sales for a product, a company needs to make changes to improve key metrics, such as the number of product views and the product conversion rate. Below are a few ways that a company can improve these two metrics.

Increasing the number of product views

The number of times a product is viewed indicates whether visitors are interested in buying that product. If a product has a low number of product views, visitors might not be able to find the product on the site. Or, visitors might not be interested in buying that type of product.

If a product has a low number of product views, a company can try to increase views by making it easier to find the product on their website. If this strategy doesn’t work, it may mean that customers aren’t interested in the product. In that case, it could be time to stop selling the product on the website, or “retire the SKU,” as it’s often called in e-commerce.

Here are some changes a company could make to try to increase the number of product views:

Make sure the product is included in the navigation menu in the place(s) a visitor might expect to find it.
Make sure the product is displaying in the search results on the website.
Feature the product in a more prominent place on the website, such as the homepage or category page.
Include the product in the product recommendations engine.
Highlight the product in email marketing, social media, and advertising campaigns.
Bundle the product with a related product that has strong sales—for example, a bicycle and helmet package.

Increasing the product conversion rate

The product conversion rate is the percentage of customers who purchase a product after viewing it. If the company’s goal is to increase the product conversion rate, they can make changes to the product page that might encourage more customers to buy the product.

Here are some change the company could make to their product pages to try to increase the conversion rate:

Improve the product images:

Use high-quality product photos.
Allow customers to zoom in on the photo for more detail.
Include photos of the product from multiple angles and in all available colors.
Include a photo of the packaging, if it features information about the product.
Include a photo of the product in use.

Upload product videos:

Add videos that demonstrate the product in use.
Include a video that features a 360-degree view of the product.
Include instructional videos, if applicable.

Update the product description:

Highlight the benefits and features of the product.
List any technical specifications, such as product size and weight.
Include a size chart, if applicable.
Use a comparison chart to explain the difference between similar products.

Increase the number of product reviews:

Enable product reviews, if the store doesn’t currently have product reviews on the website.
Encourage customers to review products by sending an email request after the customer receives their product.

Consider pricing:

Make sure the price of the product is competitive when compared to similar products in the market.
If the product is priced too low, customers may question the quality of the product.
If the product is priced too high, it may cost more than customers are willing to spend on this type of product.

Discovering new product ideas

Product analytics can help a business make changes to improve a product’s performance. It can also provide insight into new products that might perform well on the site.

One way to find new product ideas is to analyze the keywords customers use to search on the website. If a lot of customers search for a product that the store doesn’t currently sell, it may be time to consider adding that product to the website. Keep in mind that the company will also need to do product research to determine if the product is worth selling. One method for researching products is to reference the best sellers report in Google Merchant Center, which provides information about the most popular brands and products used in Shopping ads and free listings.

Another way to find new product ideas is to figure out which products are the best-sellers on the company’s website. Then use those products to brainstorm ideas for related products that customers might also be interested in buying.

For example, if a store sells a lot of mattresses, customers might also want to buy sheets, pillows, a mattress pad, or a bed frame from the same store. Product research can help companies determine the demand and viability—or sales potential—for these products. They can also gather data by conducting surveys or interviews with customers to find out if they are interested in buying these types of products.

If the company typically sources products and handles the shipping and fulfillment process in-house, they might consider dropshipping the new product to test out the product’s performance before committing to a supply of inventory. Or, the company could start out by purchasing a limited supply of inventory to avoid the risk of dead stock, which is inventory that remains unsold for a long period of time and has little chance of selling in the future.

Deciding which SKUs to retire

Even after making changes to the product detail page and website, sometimes a product’s performance doesn’t improve—or, it doesn’t improve enough to make the product worth selling. In that case, it may be time to retire the SKU.

In some cases, retiring a SKU might mean continuing to sell the product but no longer offering a certain variation, such as a specific color. For example, if a company sells a lunch box that comes in multiple colors, and lots of customers buy the blue lunch box but very few customers buy the green lunch box, they might choose to continue selling the lunch box but retire the green SKU.

Retiring SKUs that are underperforming can free up more time to focus on products that customers really want. No one can predict for sure how a product will perform before it’s launched. If it turns out that a SKU doesn’t perform well, despite the effort put into it, then it’s better to retire the SKU than continue trying to sell a product that isn’t adding value to the business.

Key takeaways

Product analytics can help companies discover how to optimize product offerings based on data. Insights from this data can help companies increase the performance of a product, discover new product ideas, and decide which SKUs to retire.

Activity: Suggest a new product category based on search data

Practice Quiz. 1 question. Grade: 100%
- Access Quiz:

On Step 1: Access the template To use the template for this course item, click the link below and select “Use Template.”

Link to template: Internal site search data

Activity Exemplar: Suggest a new product category based on search data

Reading Duration: 10 minutes

Here is a completed exemplar along with an explanation of how the exemplar fulfills the expectations for the activity.

Completed Exemplar

To review the exemplar for this course item, click the link below and select “Use Template.”

Link to exemplar: Internal site search data

Assessment of Exemplar

Compare the exemplar to your completed site search spreadsheet. Review your work using each of the criteria in the exemplar. What did you do well? Where can you improve? Use your answers to these questions to guide you as you continue to progress through the course.

Let’s review the completed site search data spreadsheet:

Categories for each search term

The Category column identifies the category for each search term. For example, “soy wax for candle making” belongs in the candle making category, and “velvet yarn” belongs in the yarn category.

Search terms that don’t belong in the yarn, jewelry making, or candle making category are grouped together in the other category. For example, “photo frame kit” belongs in the other category, since it doesn’t relate to yarn, jewelry making, or candle making.

Category with the most searches

According to the search totals in the pivot table, the yarn category received the highest number of unique searches. This category is highlighted in yellow. The company will begin researching this category to determine whether these products are worth selling.

5. Review: Analyze trends for an online store

Wrap-up

Video Duration: 1 minute

Awesome job. You learned a lot about e-commerce analytics. Let’s review what we covered. First, you learned about how data applies to e-commerce. Then you got an overview of how to use the built-in analytics tool in Shopify. After that, you learned about some of the most important metrics used to monitor growth and revenue for an e-commerce store. You also learned how to use that data to find opportunities for improvement. Finally, you learned about product performance, including the metrics used to analyze products and the steps that a company can take to improve a product that’s not performing as well as expected. How does all this relate to an entry-level role in e-commerce? Even if your role in e-commerce doesn’t directly involve data analysis, it’s helpful to understand how data informs the decisions that an e-commerce store makes. Making updates to a store based on data can help increase sales and improve the customer experience. You may also need a basic understanding of data if you’re asked to help put together a report on the performance of a product or marketing campaign that you worked on. I’m proud of how far you’ve come. You’re that much closer to completing the program. You’ve got this.

Week 2

Analyze trends for an online store

Dedication to study

Learning Objectives

Content

1. Learn about e-commerce analytics tools

Welcome to week 2

Analyze data to measure e-commerce success

William - The power of data

Shopify analytics and reports

How to use Shopify analytics

Basic features in Shopify analytics

Overview dashboard

Reports

Live view

Integration with Google Analytics

Key takeaways

Resources for more information

Analytics tools used in e-commerce

Google Analytics

Shopify analytics

Email marketing analytics

Social media analytics

Paid advertising analytics

Big data

Key takeaways

Test your knowledge: E-commerce analytics tools

2. Monitor e-commerce stores for growth and revenue

Monitor growth and revenue for an e-commerce store

Identify: Monitor e-commerce store performance

Key metrics for monitoring an e-commerce store’s performance

Comparing metrics over time

Traffic and revenue

Conversion rate (CVR or CR)

Cart abandonment rate (CAR)

Average order value (AOV)

Cost per acquisition (CPA)

Customer acquisition cost (CAC)

Customer lifetime value (CLV)

Customer retention rate (CRR)

Key takeaways

Activity: Analyze an e-commerce store’s performance

Test your knowledge: Monitor an e-commerce store’s performance

3. Make update to an e-commerce store based on data

Use data to improve the performance of an e-commerce store

Conversion rate optimization

Using data to increase conversion rates

Tools for conversion rate optimization

Heat maps

Session recordings

A/B testing

Ways to increase conversion rates

General tips

Product pages

Emails

Checkout flow

Key takeaways

Case Study: The importance of ROI and CPC for a small business

Company Background

The challenge

The approach

The results

Conclusion

Understand visitor behavior with heat maps

What is a heat map?

Types of heat maps

Scroll heat maps

Click heat maps

Hover heat maps

Key takeaways

Resources for more information

Activity: Use heat map data to optimize a landing page

Activity Exemplar: Use heat map data to optimize a landing page

Completed Exemplar

Assessment of Exemplar

Idea 1: Move the signup form higher on the page

Idea 2: Reduce the number of fields

Idea 3: Make the form and CTA stand out more

Idea 4: Reduce overall page clutter

Test your knowledge: Data analysis