“If you don’t know where you are, you can’t know where you’re heading”. This saying rings true when it comes to managing and scaling your eCommerce company.

So, knowing the ins and outs of your analytics is crucial because, without a robust approach to your analytics dashboards, you won’t know:

• How many people are visiting your store
• Where they’re coming from
• Which categories and products they frequent
• What the average buyer journey looks like
• Whether your paid ads are making an impact
• And so much more.

Just master your eCommerce analytical knowledge and you’ll know what encourages customers to click “Buy Now”, and what pushes them to close the tab altogether. So, we’ve put this comprehensive guide together to help you become a data-crunching machine.

4 common Google Analytics mistakes: eCommerce edition

Before we delve into our eCommerce analytics checklist, here are four common eCommerce mistakes we see far too often within Google Analytics dashboards. If you’re making any of these mistakes, then you already know where your eCommerce analytics strategy needs attention most urgently.

1.Data discrepancies between Google Analytics, sales data, and shopping cart data

Pre-built shopping carts handle unfulfilled orders, canceled orders, refunds, and test orders. Google Analytics (GA) does not come with such a feature. GA tracks when a user visits a confirmation page, but GA doesn’t record canceled orders or refunds. This leads to discrepancies in the Google Analytics eCommerce reports. It’s important to remember that GA is an analytics platform, not accounting software.

How to prevent this issue

eCommerce platforms can handle sales information better than GA, so we advise that you rely on your eCommerce platform’s data more.

2.Running test orders without reverting them in Google Analytics

Before you launch an eCommerce website or application, it’s good practice to run test orders. When developers run test orders, sometimes they are not reversed on Google Analytics. If you don’t reverse them, you’re inflating your sales data.

How to prevent this issue

Work with your developers to make sure you deduct all test orders from your analysis.

3.Data sampling can lead to inaccuracy

Data sampling is selecting a subset of traffic data for analysis. It’s used in statistical analysis to analyze large data sets. Google Analytics has a limit on data sampling, so you can’t produce reports on large data sets.

Even if you manage to run a data sampling below the Google Analytics threshold, you could risk skewing your eCommerce data. According to Optimize Smart, eCommerce data could be off the mark by 10% to 80%.

How to prevent this issue

Shorten the data range to stay under the sampling limit or use default reports that are not subject to sampling.

4.Duplicate transactions

Duplicated transactions are a common issue. A duplicate transaction happens when the tracking code is executed more than once without a new order being placed. A lot of duplicate transactions can even reduce the company’s value sales data.

How to prevent this issue

Ensure visitors cannot access the order confirmation page more than once without submitting a new order or disable the tracking code from being executed on reload or refresh.

Functions supported by Analytics in the E-Commerce Industry

This list is in no way exhaustive but will cover broad roles in the E-Commerce industry.

1. Supply Chain Management: This includes managing the data for products right from the warehouse of the company to the customer. E-Commerce industries are used to analyze extensively to manage Inventory.  Also, some significant portion of work is in optimizing transportation and pricing of the delivery.

2. Merchant Fraud Detection: The E-Commerce company might have nothing to do with this fraud and all, they are the one who always pays for it. However, frauds are not always from the merchant side. Even though it is very rare when customers also make false claims in frauds. Initially, all these frauds were handled on manual bases, but with time E-Commerce is moving towards developing predictive algorithms to detect frauds and avoid them if possible.

3. Merchant Analytics: Merchants form the core of the E-Commerce industry. If the merchant grows, the E-commerce provider also grows. So E-Commerce players do extensive analysis for Merchants to get into new markets or set the right price for their goods. Such decisions would have been much more expensive for the vendor, had they not partnered with E-Commerce players.

4. Recommender Systems: As soon as I hear Recommender engines, I imagine YouTube. Recommender systems in the E-Commerce industry are not very different from YouTube. These engines serve as a blueprint for customers to navigate through the store of this virtual environment. Recommender engines have been the strongest contribution of analytics to technology. 

5. Product-specific analytics: These teams generally work on product-specific details for example – The satisfaction rate of customers for a product, forecast of sales for a product, etc. Their work cuts across verticals and is specific for a family of products or a single product.

6. Online Marketing Analytics: As E-Commerce provides you a virtual environment to buy stuff, they have to market in the virtual environment extensively. The online marketing team generally works on bidding for ads on Google or other websites. They analyze the funnel of new prospective customers and maximize the likelihood of a customer clicking an ad.

7. User Experience Analytics: This probably is the biggest task for analytics in the E-Commerce industry. It’s all about customer-centricity because of the ease to shift from Amazon to Flipkart. This team primarily works on creating the right architecture of the website. This will include how the product is searched across the portfolio, what decides the rank ordering of products for a particular search, what is the best landing page of a customer coming from Facebook etc. They also test what type of layout is better for what type of customers.

Endnotes

There are three basic components in the eCommerce analytics framework – channels, user experience, and products. Knowing how to pull analytics data for these three components and segmenting that data are the two core skills that are required for excelling in eCommerce web analytics.

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