During D-Congress at The Swedish Fair in Gothenburg, 3bits had a booth where we among other things presented the new service. In addition to this we had a little competition where we asked the following question to the visitors in the booth: “What is the most important thing to know about your e-commerce customers?”, and we gave them three different answer options:
1. What are they doing on the site.
2. What do they think about the product/service.
3. Which different customer groups are visiting the site, and where do they come from.
46 % of the visitors answered option 1, 31 % option 2, and 23 % option 3. There wasn’t anything that was right or wrong, everyone who participated in the contest, hade the same chance to win despite the answer, the winner was decided by a final question. The reason that we asked the question, is that there are different types of data analysis that is used to manage the different options, and it’s therefore interesting to find out what is interesting for the visitors.
Option 1 – what the customers are doing on the site, is the core of web analysis. The natural start point when you want to know that the customers are doing on the site is to add tracking code in the site and send up click data to for example Google Analytics to see which pages that are the most visited. In today’s dynamic web pages it’s often not enough to know which pages that are visited, but you need to add the standard code to get more detailed information of what the users are doing on respective page. For example if they are adding products in the cart, or looking at product details such as images or videos. You should also put extra focus on the register page, and checkout so that it’s clearer to see where in the process users tend to leave.
Option 2 – what the customers think about the product/service, is a question that typically can’t be answered by traditional click data. Google Analytics has although measures of customer’s engagement, for example how many pages that are visited, and how long they stay on the site, but a high engagement doesn’t have to mean that they are happy with the site or the products, it could just as well mean that they can’t find what they are looking for. To really answer this, you need to add qualitative data from for example customer surveys or usability testing to the quantitative analysis.
Option 3 – which different customer groups that are visiting the site and where they come from, is a more complicated question. The traditional, and not directly data driven, method is to set a number of target groups or personas and divide the customers in these (often based on geographical or demographical data). By segmenting their data for these groups the hope is to find differences in purchasing behavior between the groups, and use this in marketing campaigns. With the help of machine learning, you can instead turn the problem and automatically group the customers based on the purchasing behavior in order to really make a data driven division of your customers, ending up with groups where the purchasing behavior really differs, and is more useful for making for example targeted campaigns.
We want to thank everyone who visited our booth at D-Congress and entered our competition, or just stopped by to talk e-commerce or data analysis. Did you miss the opportunity to meet us there, or want to continue talking, you are welcome to get in touch with us.