Using Sales analytics for killer end-of-the-year clearance sales

As the new year approaches, many e-commerce businesses are gearing up for their end-of-year clearance sales. As a business owner or manager, you may be looking for ways to maximize your revenue during this busy time. 

What’s even more impressive is that the industry is expected to double in size within the next two years, growing from an estimated 3.53 trillion US dollars in retail e-commerce sales in 2019 to 6.54 trillion US dollars in 2022.

By utilizing the right analytics, you and your team can make data-driven decisions that can help boost your sales and increase your profits.

This guide will provide a comprehensive overview of the key metrics and tools you need to track, as well as some best practices for analyzing your data and using it to optimize your clearance sales. 

Whether you’re a seasoned e-commerce professional or just starting out, this guide will provide valuable insights and tips to help you make the most of the end-of-year shopping season.

What is e-commerce analytics?

E-commerce analytics is the practice of collecting, organizing, and analyzing data from your e-commerce site to help you make smarter business decisions.

 The goal of e-commerce analytics is to provide insight into how your customers are behaving so that you can improve the customer experience on your site.

The importance of e-commerce analytics for holiday sales

The holiday sales season is a great time to drive traffic, generate sales, clear out the inventory and make room for new products. A clear understanding of your E-commerce analytics is a great way to set yourself up for success during the holiday season, whether you’re an established retailer or just starting out.

Here are crucial areas where E-commerce analytics can be used to improve your sales performance:

1. Identifying which products sell best during the holiday season

You can use your analytics to identify which products are most popular during the holiday season. Then, decide whether you want to stock up on those items or focus on a different product line.

2. Tracking trends in customer behavior and conversion rates

If your business has been around for a while, you probably already know what products sell best during a clearance sale. Specific days or times of the year are better than others. But if you don’t have data on these things or don’t know how to analyze them, you won’t know what works best.

 Analyzing trends in customer behavior and conversion rates will help you optimize pricing and promotions to align with what customers want at any given time. You can also use this information when planning future campaigns so that they have the best chance of success.

3. Understanding which marketing channels drive most of your sales

If you have multiple channels for marketing your clearance sales, it’s important to know which drives the most traffic and conversions. This information can help you decide whether or not you should continue spending on each channel.

4. Optimizing product listing pages

Your product listing page is one of the most important factors when converting customers into buyers. This is because it’s the first thing people see when they visit your shop, setting the tone for their shopping experience.

To improve your conversion rates, make sure that:

• You have clear calls-to-action (CTAs)
• You have high-quality images
•  You have informative descriptions 

Why should you rely on e-commerce analytics for your holiday sales?

E-commerce companies generate a large volume of data daily.

This generated data includes essential information about your business and your customer behavior.

 By relying on e-commerce analytics, you can work on critical data such as:

1. To know what to sell

Understanding the needs and wants of your customers will help you make better decisions about the products you should be selling in the future. 

You can create an effective inventory management strategy by identifying trends in your data, such as which products are selling well.

2. To organize the supply chain and logistics

The supply chain is one of the most important parts of any business. You need to know where your products are coming from and how long it takes them to arrive at their destination. 

This will allow you to understand if there are any bottlenecks in your supply chain or if there are any issues with your suppliers that could affect your delivery times

3. For Checking website readiness

Before you launch an end-of-year clearance sale, you want to ensure that your site is ready for the rush of traffic ahead. 

You can do this by comparing your current site performance against key metrics from previous sales events to determine whether the site can handle the expected increase in traffic.

You might also want to test your site’s performance during peak hours which provides useful data about how fast your site is loading for different users around the world.

4. To optimize pricing and avoid pricing glitches

You can use your e-commerce analytics to optimize pricing by looking at how much each product sells for and then adjusting accordingly based on demand and competition.

By doing so, you’ll also avoid any potential pricing glitches that could occur if you don’t price your products correctly. The last thing you want is for customers to be confused about whether or not they’re getting a good deal when they purchase something from your store.

You can also compare your products against each other based on price and see which ones have better conversions and which ones don’t.

5. To reduce cart abandonment

One of the biggest challenges for an e-commerce business is cart abandonment. For e-commerce businesses, it’s bad news because when shoppers leave without buying anything, they’re not just losing money on that transaction; they’re losing valuable leads that could turn into repeat customers.

The good news is that there are ways to reduce your shopping cart abandonment rate. By analyzing your shopping cart abandonment data and identifying which products are causing shoppers to leave the site before completing a purchase, you can make changes that will encourage more people to complete their purchases.

E-commerce analytics can also help you identify complementary products your customers may want to buy along with the items they’ve already placed in their carts, increasing your revenue through cross-selling.

6. To improve the payment success rate

By analyzing payment data like average amount, payment type, and payment method, you can determine which payment methods are more popular with your customers and which ones are less popular. 

This way, you can optimize your checkout page and try to increase the number of successful transactions by leveraging the most popular ones.

7. To plan marketing campaigns

In order to plan marketing campaigns, you can use e-commerce analytics to determine which products are selling well, where you should focus your marketing efforts, and which channels are most effective at driving sales. 

This information will help you optimize your marketing budget by focusing on the channels that will generate the highest ROI.

8. For Understanding funnel progression to target users better

Let’s say you want to know how many users are going through the funnel and where they drop off. 

You can add a pageview goal to see the number of times a user views a particular page, regardless of whether they purchase anything. In this case, we would want to set a goal for each page in our funnel to see where people drop off.

Another important thing that you should know about e-commerce analytics is that they work with attribution models.

Attribution models help us determine which channel brought in each new customer. This allows us to allocate budgets more effectively by helping us identify which channels bring in the most revenue and therefore deserve more investment than others.

Key areas of e-commerce metrics

In e-commerce, businesses should track several key metrics to understand and optimize their performance. These metrics can be grouped into five main categories: discovery, acquisition, conversion, retention, and advocacy.

1. Discovery

Discovery metrics measure how customers find and learn about your e-commerce company’s products or services. 

Examples include traffic, referral sources, search engine optimization (SEO) performance, and social media engagement. These metrics help you, and your team understand the effectiveness of your marketing and advertising efforts and identify opportunities to improve your visibility and reach.

Here are some of the common discovery metrics for an e-commerce store:

Reach: This metric measures the number of people who see a piece of content or an advertisement. It can be calculated for a specific period of time, such as a day, week, or month. A high reach indicates that a business effectively reaches many people with its content or advertisements.

Impressions: This metric measures the number of times a piece of content or an advertisement is displayed to users. It is calculated by multiplying the number of people who see the content or advertisement by the number of times it is displayed. A high number of impressions indicates that a business effectively gets its content or advertisements in front of users.

Engagement: This metric measures users’ level of interaction and engagement with a piece of content or an advertisement. It can be calculated by dividing the number of engagements (such as likes, comments, or shares) by the number of impressions. A high engagement rate indicates that users actively interact with a business’s content or advertisements.

2. Acquisition

Acquisition metrics measure how customers become aware of and interact with your products or services. 

Examples include click-through rate, bounce rate, cost per lead (CPL), and cost per acquisition (CPA). These metrics can help you understand the efficiency of your customer acquisition efforts and make data-driven decisions about where to allocate your resources.

Here are some of the e-commerce acquisition metrics that you should be aware of:

Acquisition click-through rate (CTR): This metric measures the percentage of users who click on a piece of content or an advertisement to acquire a new customer. It is calculated by dividing the number of clicks by the number of impressions. A high acquisition CTR indicates that a business’s content or advertisements effectively capture users’ attention and prompt them to act to become a customer.

Acquisition CTR = (Number of clicks) / (Number of impressions)

Bounce rate: This metric measures the percentage of users who leave a website after viewing only one page. It is calculated by dividing the number of users who leave a website after viewing only one page by the total number of users who visit the website. A low bounce rate for a customer acquisition website indicates that it effectively engages users and encourages them to explore additional pages to become a customer.

Bounce rate = (Number of users who leave after viewing one page) / (Total number of users who visit the website)

Cost per lead (CPL): This metric measures the average cost of acquiring a new lead (potential customer). It is calculated by dividing the total cost of acquiring new leads (such as advertising and marketing expenses) by the number of new leads acquired. This metric can help businesses determine the effectiveness and efficiency of their customer acquisition efforts.

CPL = (Total cost of acquiring new leads) / (Number of new leads acquired)

Cost per acquisition (CPA): This metric measures the average cost of acquiring a new customer. It is calculated by dividing the total cost of acquiring new customers (such as advertising and marketing expenses) by the number of new customers acquired. This metric can help businesses determine the effectiveness and efficiency of their customer acquisition efforts.

CPA = (Total cost of acquiring new customers) / (Number of new customers acquired)

3. Conversion

Conversion metrics are a crucial aspect of e-commerce analytics, as they help businesses measure the effectiveness of their sales and marketing efforts. The average conversion rate for e-commerce websites is 2-3%.

Examples include sales conversion rate, average order value, and cart abandonment rate. These metrics can help you understand how well your website converts visitors into customers and identify opportunities to improve the checkout process and increase sales.

Here are some of the e-commerce conversion metrics that you should be aware of:

Sales conversion rate: This metric measures the percentage of website visitors or leads who become customers. It is calculated by dividing the number of new customers by the total number of website visitors or leads, as follows:

Sales conversion rate = (Number of new customers) / (Total number of website visitors or leads)

Average order value (AOV): This metric measures a customer’s average amount per purchase. It is calculated by dividing the total revenue generated from sales by the number of orders placed, as follows:

AOV = (Total revenue from sales) / (Number of orders placed)

Cart abandonment rate: This metric measures the percentage of users who add items to their shopping cart but do not complete the purchase. It is calculated by dividing the number of abandoned carts by the number of users who add items to their cart, as follows:

Cart abandonment rate = (Number of abandoned carts) / (Number of users who add items to their cart)

Note the average cart abandonment rate for e-commerce websites is 69%.

4. Retention

Retention metrics measure the success of your efforts in retaining and engaging existing customers. 

Examples include customer lifetime value (CLV), customer satisfaction, repeat purchase rate, and churn rate. These metrics can help you understand the value of your customers and make data-driven decisions about how to retain and engage them.

Here are some of the e-commerce retention metrics that you should be aware of:

Customer lifetime value (CLV): This metric measures the total revenue a customer is expected to generate over their relationship with a business. It is calculated by multiplying the average customer value (the average amount that a customer spends per purchase) by the average customer lifespan (the length of time that a customer is expected to continue making purchases) as follows:

CLV = (Average customer value) x (Average customer lifespan)

Customer satisfaction: This metric measures customers’ satisfaction with a business’s products or services. It can be calculated by surveying customers and asking them to rate their satisfaction on a scale of 1 to 5 or 1 to 10. A high customer satisfaction score indicates that customers are generally happy with a business’s products or services.

Repeat purchase rate: This metric measures the percentage of customers who make multiple purchases from a business over time. It is calculated by dividing the number of customers who make multiple purchases by the total number of customers, as follows:

Repeat purchase rate = (Number of customers who make multiple purchases) / (Total number of customers)

Note the average repeat purchase rate for an e-commerce business is close to 30%.

Churn rate: This metric measures the percentage of customers who stop making purchases from a business over time. The average churn rate for e-commerce businesses is an astonishing rate of 70% to 80%. It is calculated by dividing the number of customers who stop making purchases by the total number of customers at the beginning of a given period, as follows:

Churn rate = (Number of customers who stop making purchases) / (Total number of customers at the beginning of the period)

5. Advocacy

These metrics measure how your customers promote and recommend your products or services to others. 

Examples include referral rate, social media shares, customer reviews, and net promoter score (NPS). These metrics can help you understand the impact of word-of-mouth marketing and identify opportunities to increase customer loyalty and advocacy.

Here are some of the e-commerce advocacy metrics that you should be aware of:

Referral rate: This metric measures the percentage of customers who refer others to a business. It is calculated by dividing the number of referrals by the total number of customers, as follows:

Referral rate = (Number of referrals) / (Total number of customers)

Social media shares: This metric measures the number of times that a business’s content is shared on social media platforms. It can be calculated by tracking the times a business’s content is shared on social media platforms, such as Facebook, Twitter, and Instagram. Many social media shares indicate that a business’s content engages and resonates with users.

Customer reviews: This metric measures the number of reviews customers leave for a business on platforms such as Google, Yelp, and Facebook. It can be calculated by tracking the number of reviews customers leave for a business on these platforms. 

Many positive customer reviews indicate that a business provides high-quality products or services.

Net promoter score (NPS): This metric measures customers’ likelihood of recommending a business to others. 

It is calculated by surveying customers and asking them to rate their likelihood of recommending the business on a scale of 1 to 5 or 1 to 10. A high NPS score indicates that customers will likely recommend the business to others.

Benefits of e-commerce analytics

E-commerce analytics collects, organizes, and analyzes data from your online business to make data-driven decisions and optimize your performance. 

By utilizing the right metrics and tools, e-commerce businesses can gain valuable insights into their customers, their sales and marketing efforts, and the overall health of their online operations. 

Let’s look at the key benefits of e-commerce analytics and how it can help businesses grow and succeed in the digital economy.

Data-driven product development

By tracking and analyzing customer behavior data, you and your team can gain valuable insights into the products or services that are most popular, the most valued features, and the areas where there may be gaps or opportunities for improvement. 

For example, you may discover that a particular product is consistently out of stock, indicating a high demand that you can capitalize on by increasing production or offering similar products. 

This can help you make informed decisions about product development and prioritize your resources to maximize customer satisfaction and revenue.

Good inventory management

E-commerce analytics can help you track and manage your inventory more effectively. 

By analyzing product performance, customer demand, and shipping and fulfillment data, you can make informed decisions about how much inventory to stock, when to order more, and how to allocate your resources for maximum efficiency and profitability. 

For example, you may discover that a particular product is selling faster than expected, indicating a need to reorder sooner rather than later to avoid stockouts. This can help you avoid lost sales and maintain a good customer experience.

Cross-sell and up-sell to existing customers

By tracking customer behavior and purchasing history, you can identify opportunities to cross-sell or up-sell to existing customers. 

For example, if a customer has purchased a particular product, you may be able to recommend related products or accessories they are likely interested in. 

Or, if a customer has shown interest in a particular product but hasn’t made a purchase yet, you may be able to offer a discount or promotion to incentivize them to complete the purchase. This can help you increase customer satisfaction and revenue.

Gather user behavior data

E-commerce analytics can help you gather valuable data on how customers interact with your website, your products, and your brand. 

This can include information on website traffic, referral sources, search engine optimization (SEO) performance, shopping cart abandonment, and other key metrics. 

For example, you may discover that a particular product page has a high bounce rate, indicating a need to improve the page’s content or design to keep customers engaged. This data can help you understand your customers’ needs and preferences and make informed decisions.

Personalized shopping experience (product recommendations)

You can use e-commerce analytics to provide personalized product recommendations to individual customers by tracking customer behavior and purchasing history. 

For example, suppose a customer has shown interest in a particular product or category. In that case, you can use algorithms to recommend similar or related products they are likely interested in. This can help you increase customer engagement and satisfaction and ultimately drive more sales.

Engaging user experience

One of e-commerce analytics’s key benefits is creating an engaging user experience for your customers. By gathering data on how customers interact with your website, your products, and your brand, you and your team can gain valuable insights into their needs and preferences and identify areas for improvement. This can help you optimize the user experience and drive conversions.

For example, you may discover that a particular landing page is not performing as well as you would like, with a high bounce rate and a low conversion rate. By analyzing the data, you can identify potential reasons for this, such as a confusing layout, a lack of relevant information, or a slow loading time. This can help you make data-driven decisions to improve the page’s content, design, or functionality and increase the likelihood of converting visitors into customers.

Another example is customer feedback data. By tracking and analyzing customer reviews, ratings, and comments, you can gain valuable insights into the strengths and weaknesses of your products or services. This can help you identify areas for improvements, such as product features that are lacking or issues with the customer experience, such as shipping delays or poor customer service. This can help you make data-driven decisions to enhance the customer experience and increase customer satisfaction.

Optimized product portfolio

Another key benefit of e-commerce analytics is optimizing your product portfolio. By tracking and analyzing customer behavior data, you and your team can gain valuable insights into the performance of your products or services. This can help you make data-driven decisions about which products to keep, which to discontinue, and which to add.

For example, you may discover that a particular product is consistently out of stock, indicating a high demand that you can capitalize on by increasing production or offering similar products. Or, you may discover that a particular product is not performing well in terms of sales or customer feedback, indicating a need to improve the product or discontinue it altogether.

Optimizing your product portfolio can ensure that you are offering the right products to your customers and allocating your resources to maximize revenue and customer satisfaction. This can help you stay competitive and grow your business in the digital economy.

Maximize return on ad spend (ROAS)

E-commerce analytics can help you maximize the return on your advertising spend by tracking and analyzing the performance of your marketing and advertising campaigns. This can include metrics such as cost per acquisition (CPA), conversion rate, and return on investment (ROI). 

By analyzing this data, you can identify which campaigns are performing well and which are not and make data-driven decisions to optimize your ad spending and drive conversions. 

For example, you may discover that a particular advertising channel is driving a high number of clicks but a low number of conversions, indicating a need to improve the targeting or messaging of that campaign.

Satisfied customers

The ultimate benefit of e-commerce analytics is satisfied customers. By tracking and analyzing customer behavior and feedback data, businesses can gain valuable insights into their customer’s needs and preferences and make data-driven decisions to improve the customer experience.

For example, by analyzing customer feedback, businesses can identify common issues or pain points that customers are experiencing and take steps to address them. This can include improving the product or service, streamlining the checkout process, or providing better customer support.

Your business can increase customer loyalty, drive repeat purchases, and reduce churn by satisfying customers. This can help you build a loyal customer base that can drive long-term growth and success. Additionally, satisfied customers are more likely to leave positive reviews and recommendations, which can help you attract new customers through word-of-mouth marketing. 

Overall, e-commerce analytics can help businesses create a positive, engaging customer experience that can drive growth and success for your e-commerce business.

Amp up your holiday sales strategy with online data analytics

The holiday season is here, and it’s time to amp up your holiday sales strategy!

Online data analytics can help you run a successful holiday campaign that drives more traffic to your website, converts them into customers, and ultimately increases your overall revenue.

Here are some tips for using data analytics during the holidays:

Learn Lessons from Last Year

The best way to improve your holiday sales is by reviewing your past performance. A good starting point is looking at last year’s data from the previous holiday season. This includes information like how much was spent on each product category and which products sold the most. You can also look at customer patterns, such as who bought what product and when they made their purchases.

For example, let’s say you didn’t sell as many toys as expected last year, and now you want to increase toy sales in 2023. Reviewing last year’s data will help you identify trends about who buys toys and when they buy them — both online and in-store — so that you can develop strategies for boosting toy sales this year.

Be Prepared For New Challenges

The holiday season is always a time for new challenges, such as finding the right balance between supply and demand regarding inventory management and staffing levels. While you can’t predict events like weather or natural disasters, you can prepare by analyzing historical data on past seasons. Hence, you know what to expect — and adjust accordingly as needed.

For example, if your website starts getting more traffic than normal, you might need to upgrade or scale up your hosting infrastructure. You might also want to consider hiring more seasonal employees or outsourcing tasks like customer service and order fulfillment if necessary. It’s better to get ahead of these challenges now than deal with them later on down the road when things start getting chaotic!

Create a marketing plan

Planning is the key to any successful holiday campaign. Before you start, create a plan that includes the following:

For many reasons, you should use data analytics to drive your holiday marketing strategy. Here are just a few:

• It helps you understand what your customers want and how they purchase.
• It allows you to make better decisions about where to spend your budget.
• It can help you predict what products will be popular with your audience, allowing you to plan ahead.

The most important aspect of marketing plans is that they should be flexible enough to allow for adjustments as new information becomes available throughout the campaign period.

Once you have a plan, you can use tools like Google Analytics and Facebook Pixel to collect data from your website and social media accounts. The collected data will help guide your future marketing efforts, helping you get the most out of every dollar spent on advertising.

Prepare to handle huge user traffic

During the holiday season, many businesses see a significant increase in website traffic and online sales. 

To ensure that your website can handle this increased demand, you can use data analytics to identify the peak times and traffic patterns from last year. 

This will allow you to plan and make any necessary updates to your website or e-commerce platform to ensure that it can handle the increased traffic.

Offer a Personalized Retail Experience for Customers

By analyzing customer data, you can create a more personalized shopping experience for your customers. This includes personalized product recommendations, targeted promotions, and email marketing campaigns. 

You can increase customer engagement and conversion rates by tailoring your marketing efforts to your target audience’s specific preferences and behaviors.

Data-Driven Pricing Strategy

By analyzing sales data and tracking your competitors’ prices, you can create a pricing strategy based on data and more likely to drive sales. 

For example, you might use data analytics to identify the most popular products during the holiday season and adjust your prices accordingly. You can also use data to identify when demand is highest and adjust your prices to maximize your sales during those periods.

Factor-in returns

Returns are common during the holiday season, and it’s important to factor them into your sales strategy. 

By analyzing data on returns from previous years, you can identify the products that are most likely to be returned and create a strategy to minimize the impact of returns on your bottom line. 

This might include offering free returns, providing detailed product descriptions, or offering refunds or exchanges.

Manage expectations and improve communication

Customers often have high expectations for delivery times, product availability, and customer service during the holiday season. 

By analyzing customer behavior and expectations, you can create a strategy to manage those expectations and improve communication with your customers. 

This might include providing regular updates on order status, offering flexible delivery options, or providing clear information on your return policy.

Prepare customer service

The holiday season can be a busy time for customer service, with many customers reaching out with questions or concerns about their orders. 

By analyzing data on customer behavior and common customer service issues, you can create a strategy to prepare your customer service team for the increased demand. 

This might include increasing staffing levels, providing additional training, or implementing new technology to streamline the customer service process.

Ecommerce Analytics Tools

E-commerce analytics tools are software solutions that help e-commerce businesses track and analyze their website traffic, sales data, and customer behavior. This information can be used to improve the customer experience, increase sales, and make better business decisions.

Some of the most popular e-commerce analytics tools include:

• Google Analytics: Google Analytics is a free and powerful analytics tool that can be used to track website traffic, user behavior, and conversions. It offers a variety of reports and features that can be used to analyze e-commerce performance, such as the E-commerce report, the Product Performance report, and the Shopping Behavior report.
• Hotjar: Hotjar is a heatmapping and analytics tool that can be used to track how users interact with your website. It offers a variety of features that can be used to analyze e-commerce performance, such as heatmaps, session recordings, and conversion funnels.
• Crazy Egg: Crazy Egg is another heatmapping and analytics tool that can be used to track how users interact with your website. It offers a variety of features that can be used to analyze e-commerce performance, such as heatmaps, scrollmaps, and confetti mode.
• Mixpanel: Mixpanel is a customer engagement and analytics tool that can be used to track and measure how users are interacting with your website or app. It offers a variety of features that can be used to analyze e-commerce performance, such as user flows, funnel analysis, and product analytics.
• Kissmetrics: Kissmetrics is another customer engagement and analytics tool that can be used to track and measure how users are interacting with your website or app. It offers a variety of features that can be used to analyze e-commerce performance, such as customer profiles, cohort analysis, and revenue attribution.