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How to use data analytics to increase eCommerce revenue

Increase eCommerce revenue

Congratulations! Your online store has incredible traffic. You advertised, got on social media, optimized content for search engines, pushed email marketing, and more to ensure buzz on your site. But the real pot at the end of your rainbow is data that captures details about the traffic visiting your online store. Web analytics makes sense of this precious data so that you can make informed decisions. Platforms like Graas' Predictive AI Engine help in improving user engagement and conversion rate, thereby increasing eCommerce revenue, on your store using data analytics.

eCommerce stores that use statistical information collected from online marketing and sales activities have reported a 40.38% influence in revenue after just 36 months post-adoption. Clearly, they have an edge over companies not using data analytics.

Imagine you have an online footwear shop that has a lower-than-average number of page views on a Saturday. Wonder what’s up? Well, this could be because you changed Google Ads keywords. Or maybe most of your audience prefers to shop during the week. The only way to understand this is by jumping in.

Knowledge is power and you can learn to experiment with the data available and drive growth for your eCommerce business.

After you have access to data, figure out what it means and how to use it to your advantage. In the case of your footwear store, your analyst may tell you that the keywords needed to be tweaked to regain the Saturday page views. He could ask you to add “party shoes”, for instance.

Data analytics helps you make sense of the information. It’s the process that gathers data from all areas that impact your online store. The information lets you understand trends and changes in behavior of shoe buyers.

Let’s deep dive to find more about this puzzle called data analytics

According to Statista, the number of people buying goods and services online is expected to reach 2.14 billion in 2021, up from 1.66 billion global digital buyers in 2016.

The eCommerce industry is expected to double within the next two years and reach 6.54 trillion US dollars in 2022 from 3.53 trillion US dollars in retail eCommerce sales in 2019.

To increase eCommerce revenue and get your business riding this wave, it is vital that you start connecting with and developing a deeper understanding of the empowered consumer. Metrics and analytics bring consumer behavior into the spotlight.

Data from Deloitte

Consumer behavior metrics and analytics | Deloitte

But how to use tech-like data analytics?

Let’s start with figuring out one of the basic concepts of web analytics – funnels. A marketing or sales funnel describes the relationship between you and your potential buyer. Funnel analytics presents your target audience going through a set flow or funnel. Think about an online skincare store:

1. A fan sees a post about sensitive skin on the store’s Instagram page.

2. She clicks on the post.

3. She reaches the landing page, sees a sensitive-skin moisturizer and clicks on ‘add to cart.’

4. She clicks checkout

5. She inputs personal details and buys the moisturizer.

This of course is an ideal scenario. Truth is, each step will see people drop out. To know what might have led to the drops and the psychology of the customer, you need the percentage of dropouts at each stage.

While a bunch of standard tools give website analytics data, Graas' Predictive AI Engine goes further to deliver store data which is at the heart of decision-making.

Data analytics for the different ways buyers interact with sellers.


All email marketing initiatives have the same basic funnels. Let’s go back to the online footwear store for example. You design and send out an attractive email to all the ids in their database. You need to know the numbers attached to:

  • Sent

  • Delivered

  • Unique opens

  • Unique clicks on links

  • Visits to your landing page

  • Action performed

Analytics tools will tell you how many from the list reached the landing page and how many bought a product or clicked a link

Social Media :

Entire funnels for social media marketing campaigns can be tracked. Facebook offers the Page Insights section.

Facebook Page Insights

If you use Twitter cards on Twitter, you can get access to their analytics to get info related to impressions and clicks on your tweets.

Pay-Per-Click (PPC):

Your eCommerce site can benefit hugely using PPC. To use this effectively know how your campaigns are performing. Whether you do this internally or outsource this, it’s better to be on top of the terminology to decode the reports.

  • Cost-per-mille or cost-per-thousand impressions (CPM) is a pricing model that charges for every one thousand displays of an ad to a user.

  • Cost-per-click (CPC) charges for every click on an ad.

  • Cost-per-action (CPA) charges when a visitor finishes a particular action.

  • Click-through-rate (CTR) is the number of times an ad was clicked divide by the number of times the ad appeared in a specific time.

  • Average position states where your ad appeared in the search results.

Let’s optimize

Once you set up the Graas' Predictive AI Engine, and know how to read the core analytics from various marketing channels, it’s time to move on to the real magic. Your funnel needs to be optimized to increase eCommerce revenue and your marketing money has to be shifted to maximize potential.

Start your optimizing process with A/B testing. This compares two variants of a page element by testing users’ responses to variant A vs variant B to arrive at which variant is more effective. Optimizely is a popular tool for this that can help you improve conversion rate and average order value. If you are diving into optimization, consider using a separate calendar in your Google Calendar to track specific tests. This recording of tests will help you figure out the cause of sudden spikes or dips in sales.

Platforms like Graas' Predictive AI Engine fetch customer journey data from different sources such as paid ad data, information from customer relationship management platforms, and email providers, giving you a 360 perspective of the business. You understand what is working and what is not – opening up a goldmine of opportunities.

How can you use data analytics to increase eCommerce revenue?

Recommendation engines:

These are powerful tools that drive your customer towards a purchase and you get to dictate trends. Seen recommendations from Netflix and Amazon as your browse? Those are good examples. Machine learning and deep learning algorithms track every user’s online behavior. They also analyze patterns to make good recommendations.

Market Basket Analysis:

This traditional online retailer’s data analytics tool says that if a person buys one group of things, they are likely to buy another set of items related to the first. For example, if you buy shampoo, you’re likely to buy conditioner too. The algorithms predict the chances of a customer’s buying behavior.

Price optimization:

Did you know that tech can help you fix the best price for your products? The tool offers best price options after considering location, buying attitudes of customers, and competitor pricing.

Inventory analytics:

This is very useful for your eCommerce business especially in times of crises. Think about eCommerce stores during Covid-19: online grocery stores that had inventory well organized were able to thrive as their stocks were available even when demand for products spiked. Inventory data allows platforms like Graas Predictive AI Engine to offer strategies to increase sales, confirm timely delivery and manage your stock.

Customer sentiment analysis:

Machine learning algorithms listen to what your customers are saying about your brand everywhere especially on social media. For example, the skincare brand’s moisturizer buyer might write on Instagram, ‘I love this moisturizer’ or she may write, ‘This product is not great’ on Facebook. Automatically, the algorithm will pick up whether the language used on the different platforms is positive, negative or neutral.

Merchandising analysis:

Retail business and merchandising go hand-in-hand. There are algorithms that analyze merchandising information to suggest priority sets based on trends, relevance and seasonality.

As an eCommerce business owner, data is your friend, not your enemy.

Often, you may be tempted to go by gut feel rather than tech insights. It’s true there are plenty of metrics available out there but it would be best to consider what your unique requirements are and then look at the big picture. If you still feel that data analytics is overwhelming, turn to established expert online platforms like Graas for guidance on how to increase eCommerce revenue. Try it out here.


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