December 30, 2023

Determining The Most Profitable Customer Segment In An Online Brand Introduction

With the e-commerce industry growing at an unprecedented rate, brands are further put to test on the understanding their customers outside the typical retail environment. Businesses are always striving for the piece of the market that drives the largest profit. However, each segment that brings in higher levels of profit may also have excess work required to acquire that customer to spend their money in such ways.

I had a conversation with a friend who has developed a successful online store in the female-niched fast-fashion industry. In our conversation, he told me his store was having trouble meeting typical sales targets and he was identifying new strategies for customer acquisition and profitability. At this point I saw a research opportunity to identify the highest- profitability segment in this online brand. I believed conducting research on the value of the most cash-flow driven customer segment would allow my friend to focus on this target segment that would deem most beneficial to his brand.

This granted me the opportunity to conduct primary research based on real customer data to test which customers were the most profitable and which customer segment they ended up being in. The timeframe selected was 1 month between the date of September 1 – September 30th. All primary research conducted in this report will relate to data coming from this time period, Stats about this period are available in Appendix A. Consumer Segmentation

In order to assess the profitability of each customer segment, customer segments were created to allow for a better understanding of how each customer group makes their purchases within this online store. Thus, to test the profitability, Loyal, Discount, Impulse, Need-based and Wandering customer segments were created to determine the profitability relation between each of the 5 customer segments (Hunter, M., 2019). Each customer was specified to how they will be identified within the pool of the 488 purchase sales for the month of September as presented below:

Loyal Customer – A customer from the data set who purchases from the online store multiple times in the given time-period. Rationale: According to the Department of Information Science at the University of Malaya, E-loyalty refers to desirable customer attitudes in EComm, which lead to repeat purchases (Sohrabi, N., 2013)

Discount Customer – A customer from the data set that has purchased based on a discount code in the given time-period.

Impulse Customer – A customer from the data set that has purchased based on an App/ store incentive that gives them an opportunity to purchase an item they didn’t intend to buy.

Rationale – According to the Association for consumer research, an impulsive buyer is one “who makes a purchase that is unplanned, only after being prompted by seeing the product or some other relevant cue” (Piron, 1991, p. 512).

Need-based Customer – A customer from the data set that has purchased an item based on a search query. Rationale: They have searched exactly this and purchase exactly this.

Wandering Customer – A customer from the data set who has abandoned their cart. Attempted to checkout but did not complete the purchase. Rationale: A regular online visitor would not be an appropriate number because as there have been over 113,000 site views, but not all may showcase interest towards the product. Abandoned checkout shows customer interest but lack of initiation.


Based on the associations and rationale of each customer segment above, data was stripped of all 488 customer orders in the month of September and each customer was individually identified under each category under a specific colour code; As shown in Appendix B.

After categorizing each customer segment, number figures were summed up for each customer segment in the month and put in an excel table as shown in Appendix C.

Further Understanding and limitations with Research

The research conducted was limited to several assumptions as it did not take into the long-term effects of the value of a customer, which are most essential to sustaining business. An article by the Harvard Business Review, goes in-depth about the value of a loyal customer and attributes the profitability puzzle of how much profit a loyal customer generates overtime to 5 strong attributes. A loyal customer: generates Longer-cash flows, has a reduced acquisition fee, allows business to be efficient with how it serves the customer due to understanding of customer preferences, can be charged a premium of company services, and is involved in spreading word-of-mouth(Reichheld & Sasser, 2014). A look at Appendix D shows a visual representation of customers being more profitable in the long run, as time increases each bar graph gets added more profitability components. Just like this bar graph shows, our primary data shows loyal customers in the online brand generating a higher Revenue per customer than any other target segment at $54.13/customer (Appendix C). This is simply due to loyal customers having more than 1 order/ longer purchase history, resulting in more revenue earned.

Further research was done on the loyalty and profitability correlation, and according to the Journal of Marketing Management, increased customer loyalty has a positive “effect” on customer profitability, but at a decreasing rate; As shown visually in Appendix F. (Øyvind Helgesen (2006).

These two sources bring forth a strong conclusion in customer loyalty holding a great deal of profits, however, these sources do not consider tests of online customer behaviour.

Online purchase behaviour may bring about out varying purchasing characteristics that can influence the customer segment of which has the highest profitability.


The results section in Appendix C, show a high number of customers and cash-flow arriving from one target segment, impulse buyers. These buyers were categorized by seeing which apps in the online store triggered them to purchase something they did not intend to. Paid website plug-ins like ‘Upsell’, and ‘Frequently bought together’ are examples of apps that bring out impulsive behaviour, their use is shown in Appendix E.

Further research conducted by the JAIS (Journal of Association for Information Systems) uses theoretical framework of Latent state trait theory to identify the impulsive behaviour of buyers and website quality (Wells., Parboteeah. & Valacich., 2011). The latent state theory states buying decisions are based on an individual’s traits (core values), the environment (the online store), and their interaction with both characteristic sets (Steyer et al., 1999). The conclusions based on the JAIS study show that buyers are more impulsive when introduced to a higher-website quality (Wells., Parboteeah. & Valacich., 2011). Apps used in this Shopify store are paid, tested plugins which are popular within a variety of ecommerce sites, possibly attributing to higher website quality.

The study also showed buyers are more impulsive when they are not being watched, which is a determinant of the buyer’s external environment (Wells., Parboteeah. & Valacich., 2011), as the consumer believes they are not being watched in making a purchase online. Both these conclusions have an impact on how online consumers make purchasing decisions and can be attributed to the higher sales of impulsive buyers that has been calculated.

On average, each Loyal customer are shown to spend more, have smaller acquisition cost, but make up for a smaller amount of the online stores cashflows. On average, each Impulsive customer are shown to spend less, have a higher acquisition cost, but make up for most of the online stores cashflows. Although results from the study show higher regions of cash-flow from the ‘impulsive’ consumer segment, further research should be conducted over- time to evaluate the side-by-side comparison of the Impulsive and Loyal customer segment in terms of overall profitability.

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