Creating segments of one has been a longstanding mantra for marketing. (1) With the advancements in big data analysis, it is now possible to cluster users into smaller segments as opposed to macro segments. This type of an analysis is called microsegmentation and has been increasing its popularity among eCommerce marketers.
Microsegmentation is in itself a targeted kind of segmentation. Stereotypic segmentation uses demographical data (age, income level, gender, job etc.) to classify users and forms giant and static macro segments whereas microsegmentation focuses on individuals.
Methods of Microsegmentation
Microsegmentation methods may include context, location, demographics as well as behaviours. In today’s eCommerce environment, behavioural microsegmentation can as well translate into segmenting customers by analysing their online activities such as clickstream, transaction and social media behaviours.
eCommerce enables thorough analysis of an individual’s online activity. Coupled with cloud computing and big data analysis, this online activity is used as a basis for microsegmentation. An explosion of interest in big data has occurred in the eCommerce industry as eCommerce firms that inject big data analytics into their value chain experience 5–6 % higher productivity than their competitors. (McAfee and Brynjolfsson 2012). A recent study by BSA Software Alliance in USA indicates that big data analysis contributes to 10 % or more of the growth for 56 % of eCommerce firms. (2)
For an online retailer creating millions of pageviews every month, microsegmentation can easily yield a few hundred segments. With the help of huge databases and advanced computing competencies, these segments can also be transformed into segments of one.
Inspirational microsegmentation examples can be found in many online retailers, today. A huge part of online conversion potential involves transforming anonymous visitors into loyal customers. By segmenting both anonymous and registered users in a consistent fashion, online retailers can capture valuable consumer impulses guiding registration, initial purchase, and loyalty decisions. (3) Clickstream and transaction data can be combined with the user’s shipping habits, reviews, special days, social sharing and level of engagement to reach a coherent understanding of the user. (4)
As the number of segments grow, it may be hard for the datascientists or marketers to analyse these segments and develop valuable campaigns, manually. This is where eCommerce personalization engines using microsegmentation makes a difference. With the help of a personalization software, identifying users and taking automated actions is possible. These actions can then make a great impact on conversions and loyalty.