5 most common mistakes in the analysis of e-commerce data

Making the right decisions related to the business is possible by basing them on reliable data. E-commerce analysis is a powerful tool, providing valuable information about the condition of an online shop and the behaviour of its customers. Skilful use of data can be quite a challenge. Below we present typical mistakes in the analysis, which may affect the conclusions drawn.

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Effective e-commerce analysis

Web analytics is a valuable source of information about the condition of an online shop. Analytical tools, headed by Google Analytics, provide owners of e-businesses with data on where users come from, which subpages of the service they visit and at what stage of the order process they resign from completing it. The effectiveness of the data analysis process in e-commerce is determined both by collecting correct data and drawing proper conclusions from it. Practice often turns out to be more difficult than theory, as proof – 5 mistakes which are worth avoiding when analysing data in e-commerce.

1. Focusing on quantity, not quality

It would seem that high-volume websites views are equivalent to an increase in sales. Although these stats are enjoyable, they do not always bring the expected results. It often turns out that a large number of visits is not reflected in the products or services sold.

For example, a shop visited by 15 thousand users, out of whom only 150 will make purchases, will have a much worse result than selling 100 products in a shop with 1500 customers. 

This is due to the focus on the traffic, not it's quality. Meanwhile, for owners of online shops, such parameters as time spent by users on the website or the number of subpages visited during a single visit should be an essential determinant of sales effectiveness. Conversions obtained through specific traffic sources should also be taken into account.

2. Incorrect interpretation of data

Just collecting data is not enough. It is often a mistake to draw the wrong conclusions from it. And yes, it cannot be assumed that the time spent on the website of an online shop corresponds to the involvement of a potential customer. Even if he spends several minutes on it, he does not have to look through the products and read the descriptions attached to them. Many of the data analysed in the e-commerce analysis are average results, which in themselves say little about the real situation in an online shop. Wrong estimation of the data can also lead to a false understanding of the individual concepts under which the various indicators are hidden. Therefore, when undertaking data analysis, it is necessary to become familiar with such slogans as session, page view and entry rate.

3. Lack of external context

The second point is linked to another mistake typical of e-commerce analysis. This is to ignore the external context of the data collected. Therefore, the study of indicators such as the number of sessions or shopping cart abandonment rate must always be accompanied by a comparison with the conditions under which they were achieved. Thus, the sudden increase in website traffic does not necessarily have to be the result of marketing activities, but, for example, of mentioning a shop on a large thematic group or a channel of a famous influencer. 

It does not always make sense to look for links between the two indicators. Some values can be linked, and others cannot. Therefore, instead of wasting time on in-depth analysis and searching for wrong conclusions, it may be better to look at the data holistically.

4. Forgetting the competition analysis

Just analysing the situation in our online shop, although undoubtedly important, is not enough. One of the most frequent mistakes among owners of e-businesses is the lack of time to observe competing companies. It is worth finding some time for it – this way, among other things, we will find out what phrases appear in search results of different websites from our industry. The information collected in this way will be useful for work on an effective shop strategy.

5. Analysis without drawing conclusions

Finally, a key mistake – analysing an online shop without drawing specific conclusions later on. Although this seems unlikely, many online shop owners collect and analyse data without any particular purpose. The number of page views or time spent on a website alone is just basic statistics that need to be benchmarked and skilfully translated into further sales strategy. Only in this way can the performance of the shop be significantly improved through more substantial involvement of customers in the purchasing process.

Summary

The above mistakes are just a few examples of how to miss opportunities arising from the analysis of e-commerce data. It is worth remembering that effective data analysis requires a specific plan and skilful use of available tools, such as Ecometrixo. Basing actions on cursory, out-of-context and misunderstood data risks misinterpretation and the risk of overlooking valuable information.