Do you have a problem in your e-commerce? Data analysis will help you solve it effectively!

Low Retention Rate – Survey Research Using Typeform

Customer retention is undoubtedly a challenging task – the market is highly competitive, with brands constantly improving their offerings. Therefore, in the process of building loyalty, the product or service alone is no longer the only key factor. Consumers expect more from a brand. When the percentage of repeat customers on your website is low or, even worse, is drastically declining, you need to react effectively.

In such a situation, it’s crucial to understand your actual audience and discover their needs. Meeting these needs skillfully will increase the percentage of returning customers. How can you find out what would encourage your customers to make regular purchases? Just ask them directly! Typeform is an excellent tool for this purpose – it allows you to embed a pre-prepared survey on your website.

We implemented this solution for one of our beauty industry clients. Over 8,000 website users filled out the survey. The results clearly showed that respondents needed support in choosing the right cosmetics for their daily care. A large product selection can overwhelm a user, leading them to seek information elsewhere. If they don’t find it in the content provided by the brand, they will leave the site and likely be captured by competitors who invest in consumer education. Returning to the survey – about 40% of website users indicated that blog posts and expert articles would help them choose products, and nearly 35% of respondents expressed a desire for a shopping assistant on the website.

As you can see, Typeform is a great tool for gathering feedback from your website users. You can ask anything you want and use the collected data to make effective decisions – effective because they will address your customers’ needs.

Pro Tips:

• When planning your set of questions, focus on your problem – the goal of the questions is to obtain answers that will guide you toward solving it.
• Place the survey on your website during high-traffic periods – for example, during an attractive promotion.
• Encourage users to complete the survey by offering a small reward.

High traffic but relatively few purchases – Google Analytics 4 Data Analysis with UX Analysis Elements

A common issue is a high number of website users but a low e-commerce conversion rate – simply put, a small number of purchases. In this situation, I suggest checking the data in Google Analytics 4 to locate your problem. You can then expand your research to find possible reasons for the unfavorable statistics.

For one of our clients, we identified a problem at the purchase pathway stage. During the analyzed period, 100,737 users viewed product pages, but only 9.2% of them added a product to their cart. So, what causes such a large percentage of users (90.8%) to abandon the purchase path at the product viewing stage?

Of course, there could be many factors – better prices from other sellers or a less attractive loyalty program than the competition. However, with access to site data, it’s worth focusing on factors that can be improved quickly. We checked the bounce rates for product pages and found that they were very high (80-95%). This means that not only do users not add products to their carts, but they also leave the site from those pages.

What should you analyze next in this situation? It’s certainly worth conducting a UX analysis that includes a comparison with competitors. This will help you identify how your product pages differ from those of well-performing competitors and catch smart persuasion techniques that you can implement on your site.

Pro Tips:

• When analyzing the customer’s purchase path, pay attention to whether you’re examining a closed path (a specific scenario where events occur directly one after another) or an open path (events don’t need to occur directly one after another – you’re looking at a larger number of users).
• When you find unfavorable statistics in several places, focus on single, small areas one at a time – trying to address everything at once can create chaos.
• When checking the highest bounce rates for specific pages, remember to consider the weighted average – a 98% bounce rate for 10 sessions will be less significant to you than the same bounce rate for 20,000 sessions.

Low Demand for products offered in Cross-Selling  – Sales Analysis in Excel Based on Store Data

Cross-selling is a technique that encourages the purchase of products or services that complement the customer’s selected offer. An example of cross-selling would be suggesting a mouse to a website user who added a laptop to their cart. These practices are designed to effectively increase the average order value and build awareness of a rich product offering that anticipates the buyer’s various needs.

However, it often happens that the suggested products don’t attract the attention of online store visitors. After all, if someone buys a laptop, it seems they would likely need a mouse. So, what should you do in such a case? This is another situation where the first step should be data analysis – analyzing the existing data you have in your store.

I suggest exporting all available data from your store to Excel. After doing so, check what indicators you have at your disposal. Valuable information includes age, gender, location, type/category of purchased products, list of ordered products, loyalty program membership, purchase frequency, and order amounts.

With this data, you can use pivot tables and formulas to identify patterns, trends, or habits. The foundation of successful cross-selling is identifying actual needs, enabling you to effectively meet the expectations of the average website user.

Pro Tips:

• Before starting the analysis, determine whether you want to increase the sales of bestsellers or boost the popularity of rarely chosen products.
• When exporting data to Excel, select an analysis period of at least one year – this will allow you to see which products returning customers combine in their orders, as they are the most valuable to your brand.
• During the analysis, also pay attention to the characteristics of products chosen within a single order. Is there something that connects them? Remember to consider chemical (composition and properties), organoleptic (e.g., taste, smell), and aesthetic (e.g., shape, style) characteristics.

Key Takeaways

• Data analysis is an essential step in responding to problems in your business. Through it, you’ll locate the problem, understand its cause, and find a solution.
• If your e-commerce site has a low percentage of returning customers, find out what would motivate users to make repeat purchases. Use tools that allow you to embed forms and surveys on your website (e.g., Typeform). Data analysis will help you identify the specific needs of potential customers and use them to increase sales.
• When your site has high traffic but a very low percentage of purchases, analyze user flow on the purchase path and their behavior on the site. Use Google Analytics 4 data, then expand your research with UX analysis elements. Data analysis will help you identify areas on your site that need improvement to engage users more deeply and ultimately lead to purchases.
• If you notice low demand for products offered through cross-selling, conduct an analysis of your store’s data. Using pivot tables and formulas, you’ll find specific patterns, similarities, trends, or habits. Data analysis will help you refine your offering to better meet the actual needs of your customers, leading to increased cart value and customer satisfaction.
• There are at least as many potential problems in e-commerce as there are data analysis tools. This article presents only three common cases. Don’t be afraid to explore new possibilities, combine different sources and tools, and explore further. Data will strengthen the effectiveness of your business strategy.