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- low spenders (less than \u00a350 in the product category of electronics and home appliances) tend to buy additional product up to 120% of the price of the main purchase,<\/li>\n
- medium and high spenders (\u00a350-\u00a3200) tend to buy additional products which are priced not higher than 20% from the main purchase,<\/li>\n
- top tier spenders (\u00a3200 + per a purchase) spend no more than 10% of their main purchase on each impulse product.<\/li>\n<\/ul>\n
The idea<\/strong> The main idea of the research was rooted in the psychology of decision-making. It simply says that each potential customer assesses their gains and losses in relation to their main reference point and not the actual value of the purchase. It\u2019s worth mentioning that reference points are very different for different people and change dynamically over time. For example, decision-making around Christmas is mostly driven by emotions and feelings with few people able to resist the mass hysteria of the Christmas season. So, the main reference point \u201chow much to spend on a Christmas present to your auntie\u201d is highly dependent on the nature of your relationship with her.
![\"analytics](\"https:\/\/ambergreeninternetmarketing.com\/wpe-content\/uploads\/2015\/10\/graph.png\")
Therefore, impulse sales for online and offline retailers, hospitality services, airlines, and anything else which could be marketed as “treating yourself” or \u201ctreat a loved one\u201d peak around Christmas. The question is: How can you stimulate impulse purchases while leaving your customers delighted with their purchases despite the extra spend?<\/em> The answer is in that simple concept illustrated above, the excitingly named Prospect theory value function<\/em> developed by Kahneman (Nobel Prize in Economics, 2002) and Tversky in 1979. Despite the age of the concept it remains relevant even now, particularly when it comes to online shopping. Some data mining is essential to identify the patterns in customer purchase behaviour in order to increase the likelihood of additional impulse shopping.<\/p>\nMain research hypothesis <\/strong> In this research I assumed that: For users who already have some planned purchases, the main reference point would be the first product visitors demonstrate a significant interest in, or, even more reliably, the first product the add to their shopping cart. In both cases the value of a product can be tracked with Enhanced e-commerce in Universal Analytics. You should find the right balance between the value of planned and impulse purchases so the price of the “impulse” purchase (suggested as a recommended product) doesn\u2019t exceed a certain percentage of the price of the main product in the cart. Let me illustrate this with an example. When a customer intends to buy a computer for \u00a31,000, spending an additional \u00a350 on speakers seems insignificant. However, a standalone purchase of speakers for \u00a350 can feel quite different and considered with more caution. Thus, spending more seems insignificant only relative to the price of overall basket value. Following this, when consumers aggregate spending, diminishing sensitivity makes it much less painful to spend additional funds in the presence of more expensive items. Customers who buy relatively expensive products should be more exposed to impulse shopping if an extra product is cheaper and can be related to the customer\u2019s needs. So, low, medium and high price of the main products would define the limit on the price for impulse purchases.<\/p>\n
Data<\/strong>\n
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