How to analyse data in e-commerce properly? – Ecometrixo

How to analyse data in e-commerce properly?

January 28, 2021

Data analysis plays a key role in the functioning of modern enterprises. Thanks to the proper interpretation of data generated every day, it is possible to quickly and efficiently control and monitor essential processes in the company. However, the mere fact of having data is not everything. It is necessary to analyse them correctly and draw conclusions. Fortunately, modern tools such as Ecometrixo come in handy. How do they support correct data analysis in e-commerce?

Data analysis in online shops – why is it so important?

E-consumers leave a lot of digital data in the virtual world every day. It comes from their visits to the shop’s website, interactions with our brand in social media, mailing campaigns or paid advertisements. Based on the data collected, we can get to know the people who visit our e-shop and precisely define their preferences. This, in turn, allows us to adjust the sales offer to their expectations right, increase the shopping cart’s value, and build a positive image of our brand.

To do this, a thorough interpretation of the data is needed. Starting with their appropriate aggregation, segmentation and presentation in the form of clear charts and reports. With Ecometrixo’s help, all data from advertising sources is automatically collected in real time and converted into key e-business KPIs, such as shop conversion, traffic, ROI and many more.

How should data in e-commerce be analysed?

Data analysis in online shops provides constant insight into the condition of our business and makes making business decisions much more comfortable. Proper data analysis should include:

1. Aggregation of data from all communication channels – today, everyone who runs e-business communicates with customers through multiple channels. Through an e-shop, mailings, newsletters, social media, loyalty programmes, price comparison engines and much more. That is why it is so essential to aggregate data from each of the selected channels. In this way, we can determine which channels are useful and should be developed and which ones should instead be eliminated. We also get to know better the recipients of our products and services – their demographics, preferred hours of activity or content format that engages them.

2. Easy segmentation this issue is significant in online shops, as our visitors are at different stages of the purchasing process. Some are just looking for possible products to buy, others have made an initial verification and are at the model comparison stage, while others are already willing to complete the transaction. Therefore, correct data analysis must also include segmentation. By doing so, we can send out personalised messages to each of the selected customer groups and thus increase the likelihood of placing an order.

3. Constant 24h/7 insight we must remember that online shops are open 24 hours a day, also for foreign customers. For this reason, we should make sure that we have constant insight into the data in real-time. This allows us to react quickly to crises and meet the needs of customers in the shortest possible time. In combination with the automation of sales and promotional activities, real-time data analysis supports e-business development and increases its profitability.

What data is worth analysing in e-commerce?

Among the critical areas of data in online shops are those concerning: 

  • sales,
  • users,
  • service. 

By analysing sales data, we gain information on the products and services that generate our online shop’s highest revenues. Therefore, we can better match the assortment to the preferences of our customers. What is important, the analysis of sales data also allows us to determine purchasing trends, which is particularly important in industries offering seasonal products. User data, in turn, will give us an idea of the channels through which they have reached our offer, which products or services they are most interested in, and we will also be able to trace their entire shopping path. On the other hand, data about the service allows us to answer questions about the number of visits, length of sessions, most popular categories and general preparation in UX terms. 

The analysis of data in e-commerce is worthwhile using dedicated tools. Automation of the aggregation process not only saves our time but above all, eliminates the risk of mistakes.