Poland | industry: e-commerce | sample size: 25 thousand measurements | time: Q4 2016
Ordinary web analytics allows analysing customer journey to a fairly limited extent – the extremely important aspects of emotions and experiences are completely ignored. When analyzing the data collected by e.g. Google Analytics, it is difficult to distinguish the customers who happily view the website with the intention of making a final purchase in a brick and mortar store from users frustrated with uncomfortable navigation, struggling with an inaccurate search engine, who most likely not only won’t buy anything but will also share negative feedback with their friends. Similar limitations, due to the lack of knowledge about the context of visits, have the effect on analyses of conversion analysis and purchase funnels.
Path analysis tool offered by YourCX enables advanced segmentation of history of visits by specific types of users, such as Detractors, Promoters or Passives, by purposes of visits, the reasons for leaving the website or by specific technical problems. Such grouping allows for verification of the hypotheses resulting from analyses of the remaining data, and for identification of bottlenecks, i.e. those places and areas where the users get confused and/or experience problems.
An example conversion analysis in investigating the purchase abandonment process shows clearly what factors have the most impact on the final purchase decision. Delivery terms appear to be decisive in many cases, although the lowest conversion level is noted for the users who wish to make a purchase at a traditional shop. Those data confirm the conclusions of other YourCX studies – offline purchase-oriented users (who drop off their carts because of that) are the group with the lowest conversion rate.
|Reasons for abandonment of purchase process||Purchases|
|Declaration of willingness to continue shopping||28%|
|Too high product price||13%|
|Change of decision||11%|
|Finding another, better offer||8%|
|No option of collection from brick and mortar store||5%|
|Too high delivery cost||5%|
|Too long delivery time||4%|
|Willingness to make a purchase in a traditional store||2%|
Analyzes of user paths and conversion funnels are useful for verifying the hypotheses. The mentioned reluctance to buy online that is characteristic to those users who visit an online store solely for the purpose of getting acquainted with the offer can be confirmed, for example, in studies aimed at acquiring consumer purchase preferences. Below is an example – online purchase declaration along with the % of purchases completed.
|Likelihood to purchase online in the future||Purchases|
|will purchase for sure||8%|
|will likely purchase||6%|
|do not know whether they will purchase||3%|
|will definitely not purchase||3%|
|won’t likely purchase||0,8%|
|prefer purchasing in a traditional store||0,3%|
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