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

Low Retention – Survey Using Typeform

Customer retention is undoubtedly a challenging task – the market is highly competitive, and brands are constantly improving their offerings. In this environment, product or service quality alone is no longer the key factor in fostering customer loyalty. Consumers expect more from brands. When the percentage of website users placing repeat orders is low or, worse, dramatically decreasing, you must react effectively.

In such a situation, it is crucial to understand your actual target audience and identify their needs. Effectively meeting these needs will correspond to an increase in returning customers. How can you find out what would encourage your customers to make regular purchases? Ask them directly! In this case, Typeform is an excellent tool, allowing you to embed a pre-prepared survey on your website.

We used this solution for one of our clients in the beauty industry. Over 8,000 website users completed the survey. The results clearly showed that respondents needed guidance in choosing the right cosmetics for their daily care routine. A wide range of products can overwhelm customers, prompting them to seek information. If they don’t find it in the content provided by the brand, they will likely leave the site and be captured by competitors who invest in consumer education. Referring back to the survey, about 40% of users indicated that blog posts and expert articles would help them choose products, and nearly 35% of respondents expressed interest in having a shopping assistant available on the website.

As you can see, Typeform is an excellent tool for gathering feedback from your website users. It allows you to ask whatever you want, and the collected data can be used to make effective decisions – decisions that meet your customers’ needs.

Pro Tips:

• When planning your set of questions, focus on your specific problem – the purpose of the questions is to get answers that will guide you toward solving it.
• Place the survey on the site during times of high traffic – for example, during an attractive promotion.
• Encourage users to fill out the survey by offering a small reward.

High Traffic but Low Purchases – Google Analytics 4 Data Analysis with UX Elements

A common issue is having a high number of website visitors while simultaneously experiencing a low e-commerce conversion rate – in other words, a small number of purchases. In this situation, I recommend reviewing your data in Google Analytics 4 to pinpoint the issue. You can then expand the analysis to uncover the potential causes of unfavorable statistics.

For one of our clients, we identified a problem during the purchase funnel. During the analyzed period, 100,737 users viewed product pages, but only 9.2% added a product to their cart. So, what is causing such a large percentage (90.8%) of website users to abandon the shopping process at the product viewing stage?

Of course, there could be many factors – better prices at other sellers or a less attractive loyalty program compared to competitors. However, with access to your website’s 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 users not only failed to add products to their cart but also left the website from this point.

What should you analyze next in such a situation? It’s certainly worth conducting a UX analysis, taking your competitors into account. This will allow you to see how your product pages differ from those of successful competitors and to spot persuasive techniques you can implement on your own site.

Pro Tips:

• When analyzing the customer journey, consider whether you are studying a closed path (a specific scenario where events happen sequentially) or an open path (events don’t have to occur consecutively – you’re looking at a larger number of users).
• When you find unfavorable statistics in several areas, focus on small, individual sections one at a time – tackling everything at once can lead to chaos.
• When reviewing high bounce rates for specific pages, remember the weighted average – a bounce rate of 98% for 10 sessions will matter less than the same rate for 20,000 sessions.

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

Cross-selling, or recommending complementary products to customers, is a strategy that encourages the purchase of additional items that complement a customer’s main selection. For example, offering a mouse to a user who has added a laptop to their cart. These efforts aim to increase the average order value and build customer awareness of your diverse offerings.

However, it often happens that the recommended products don’t attract much interest from online store visitors. And yet, one would think that if someone buys a laptop, they’d also need a mouse. What should you do in this situation? Once again, the first step should be data analysis – analyzing the existing data that you have in your store.

I recommend exporting all available store data to Excel. Once this is done, review the metrics you have at your disposal. Valuable data points include age, gender, location, product type/category, product order list, loyalty program membership, purchase frequency, and order value.

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

Pro Tips:

• Before beginning the analysis, determine whether your goal is to increase sales of bestsellers or to boost the popularity of less frequently purchased products.
• When exporting data to Excel, select an analysis period of at least one year – this will allow you to see which products are commonly purchased together by returning customers, the most valuable group for your brand.
• When analyzing products within a single order, pay attention to their characteristics. Do they share any common traits? Consider factors like chemical (composition and properties), sensory (e.g., taste, smell), and aesthetic (e.g., shape, style) features.

Key Takeaways:

• Data analysis is a crucial step in resolving business challenges. Through it, you can identify the location of the problem, understand its cause, and determine how to resolve it.
• If your e-commerce website 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 understand the specific needs of potential customers and use this information to increase sales.
• If your website has high traffic but a low percentage of purchases, analyze user flow throughout the purchase journey and their behavior on your site. Use data from Google Analytics 4, then extend your research with UX analysis. Data analysis will help you find areas on your site that need improvement to engage users more deeply and ultimately lead them to purchase.
• If you notice low demand for cross-sell products, conduct an analysis of the data from your store. By using pivot tables and formulas, you can identify patterns, similarities, trends, or habits. Data analysis will help you tailor your offering to meet the actual needs of your customers, leading to an increase in cart value and consumer satisfaction.

The potential problems in e-commerce are as numerous as the tools available for data analysis. This article covers just three common cases. Don’t be afraid to explore new possibilities, combine various sources and tools, and experiment. It’s through data that you will strengthen the effectiveness of your business strategy.