Heavy users are often described by the Pareto principle. This means that 20% of users consumed 80% of the volume of a product. In literature some researcher and marketers also call heavy users as "Heavy half”, but the main assumptions and definition stay these same. In 1964, Twedt was the first propose a division into three user segments divided by frequency of purchase:
- Non-user of a product - users who doesn't use this product;
- Light users (also: light half) - users who used product only occasionally or not more than medium used;
- Heavy users (also: heavy half) - users who used the product the most and generated the most revenue. They make most of the volume purchased;
Rebecca Heath in 1997 in her book devoted to this subject, describes them directly like "frequent flyers" and "best customers". Garet Hallberg in 1995 in his book "All Consumers Are Not Created Equal” point Heavy users like may be in some firms the source of most the profit. In the other hand Clancy and Shulman in 1994 in “Marketing Myths that are Killing Business” show cons of Heavy users like:
- good knowledge of the market price and competition;
- low brand loyalty;
- deal prone;
- in their in categories they can be similar like other demographic and media use profile;
- in consider their as segment, they may be more heterogeneous than homogeneous;
Information about heavy users can help understand needed these not as large but much profitable group. For product developers, managers and marketers theory about heavy users can help in development and project of marketing strategy. It is good to remember that marketing strategy based on heavy users have got a scenario to not only bring but also keep users what can be not so easy cause low loyalty of heavy users.
The biggest challenge is identification of heavy users. Much research suggests that demographic method of identification is not the best way to discover and describe Heavy user. They often not so different in these field than non-user or light user. Better way is analysis of use rate and search a correlation and difference between user then use the most of the product and rest of user. In present days we can use every data provided by analytic tool like Google Analytic or similar.
Ronald E. Goldsmith in the publication also point the demographic profiling is not the best way to get to know profile of heavy users: “More consistent results could be achieved if the program content were based on a sound theory of heavy user behaviour. Relationship marketing is a worthy goal; knowing what motivates heavy users may be more important than profiling their demographics.”
Great candidate to be Heavy users are product enthusiast, early adopters and innovators. They are often an opinion leader who not only can transform in heavy user but also spread their opinion by the world about product and brand. In the other hand that can be threat if in their opinion product be not so good, as marketers wish. What important segment of heavy users can need much specific segmentation by for example:
- different needed;
- different impulses to get contact with product;
- different requirements;
This example is well presented by Brian Wansink and Sea Bum Park: "One soup heavy user might be a heavy user for convenience, while another might be one for price. These two might have very different profiles. For example, even if our results indicate that heavy users are socially active, creative, optimistic, and witty, we believe there may be significant differences among heavy users that we have overlooked.“
We should not treat them as one homogeneous group, but several segments of different users with different outlets and characteristics. In this case, we do not really deal with one group of heavy users, but with a few subgroups that make up a group of heavy users.
The main point of interest in heavy users is benchmark and identify a main difference heavy users from light users. Brian Wansink and Sea Bum Park indicate one more important thing in the topic of heavy users, summarizing their publication: "The most effective market segmentation generates the most accurate, detailed, diagnostic, and in-depth profiles of heavy users.“
- Twedt, D. W. (1964)
- Heath R. P. (1997)
- Hallberg G. (1995)
- Clancy K. J., Shulman R. S. (1994)
- Goldsmith R. E. (1998)
- Wansink B., Park S. B. (2000)
- Wansink B., Park S. B. (2000)
- Chudzian J. (2014), Impact of Advertising on Behaviour of Consumers of Low and High Level of Consumption of Dairy Products, "Acta Scientiarum Polonorum. Oeconomia", Vol. 13 No. 4.
- Clancy K. J., Shulman R. S. (1994), Marketing Myths that are Killing Business, McGraw-Hill, New York.
- Goldsmith R. E. (1998), Toward a Theory of Heavy Usage: The Case of the New Fashion Buyer, "Association of Marketing Theory and Practice Proceedings", 272-277.
- Hallberg G. (1995), All Consumers Are Not Created Equal, John Wiley, New York.
- Heath R. P. (1997), Loyalty for sale: Everybody’s doing frequency marketing - but only a few companies are doing it well, "Marketing Tools", Vol. 4 No.7.
- Hiebing R. G., Cooper S. W., Wehrenberg S. (2011), The Successful Marketing Plan: How to Create Dynamic, Results Oriented Marketing, McGraw-Hill Education, New York.
- Pleshko L. P., Al-Houti S. (2011), CA Preliminary Study of Heavy and Light Users of Retail Services, "Academy of Business Research", Vol. 2.
- Twedt, D. W. (1964), How Important to Marketing Strategy Is the “Heavy User”?, “Journal of Marketing”, Vol. 28 No. 1.
- Wansink B., Park S. B. (2000), Comparison Methods for Identifying Heavy Users, "Journal of Advertising Research", Vol. 40 No. 4.
Author: Gabriela Lupa