Nowadays firms have increasingly adopted personalization strategy to provide customers with the option to choose on important feature parameters at the price similar to the standardized products. For example, Dell is well known for its success in mass customization. (Randall, et al., 2005) Subway is also a kind of customization and received huge success. (Choi & LEE, 2015) However, the academic defines the personalization in a different way. It has been suggested as a revolutionary approach to market segmentation. The company now treat the individual customer as individual segments by satisfying their very specific need. The strategy can boost sales and in such bring competitive advantage to the company.
However, it is not necessarily always true for every company. There are many attributes can influence the attractiveness of personalization option, for example, price premium, effort and monitoring quality. If we take a closer look in the effect of effort that the customer needs to take in the personalization process, we can find that the more complex the personalized process is, the less the customer will likely to personalize the item. (Choi & LEE, 2015) Especially the company failed to convey information to the different type of customer. Just like the example of Dell, the inexperienced user cannot tell the meaning of “Memory”. (Randall, et al., 2005) Some of those customers just decided not to make the choice because the complexity exceeds the optimal level. The product utility that consumer could get through personalization is not strong enough to cover the inconvenience.
This research argues that the customers prefer standard products over the personalized product when the range of personalization is perceived as excessive. The customer is more likely to select standard products over personalized alternatives when faced with inordinately complex decision-making. In order to test the pattern of consumer responses regarding the personalized product and the standardized product, the author decided to use the attitude of customers towards product and the purchase intention as the key factor.
H3: Customers will demonstrate a more positive attitude for standard products over personalized products with a higher level of personalizing options beyond a customer-perceived optimal point.
H4: the customer will demonstrate a higher purchase intention for standard products over personalized products with a higher level of personalizing options beyond a customer-perceived optimal point.
The hypothesis is tested by using stimulation experiment. There are 195 undergraduate students in Korea are recruited. They have been randomly assigned to one of the seven sub-group according to the extent of personalization options. The basic setting is the standardized product, and the other six have the different level of personalization in an ordinal ranking. The incremental increase in the personalization level allows the author to compare between groups and examine the relationship between personalization level and customer response.
The result shows that the customer product attitude shows an inverted U shape as the number of personalizing options increased. This is also the case for the other factor, purchase intention, as the number of personalization option increase, the purchase intention increase until the optimal level and then decrease. (Figure 1 and Figure 2)
The author then conducted an ANOVA analysis on the product attitude and purchase intention respectively for the standardized product and the personalized product (with most extensive personalization options). The results support the Hypothesis 3 while the result of Hypothesis 4 was not statistically significant. To test the mechanism of complexity as a mediator, they also test the hypothesis based on Baron and Kenny (1986). The perceived complexity mediated the effect of product type on product attitude. The mediation tests show that perceived complexity is the influence factor for the preference over standard products and the personalized products when comparing the standard product with the overwhelming personalization options.
The logic of this research is very easy to follow, they successfully demonstrated the importance of setting an appropriate level of personalization to companies that wanted to implement the personalization business model. However, the question arises when evaluating the regression model. Has the author controlled enough factors to isolate the effect of independent variables on the dependent variable? For example, if we look at the product nature, the result doesn’t necessarily apply to the experienced user with the clear expected product. They will likely to appreciate the extensive personalization options to fine tune the product to maximize the fit with their expectation. In addition, the author used to watch as the testing product. It is a relatively straightforward functional product. The personalization option does not include any configuration regarding the function itself but only the design. Will the research show a different result if we test it on Dell computer? The answer is unknown. If we look at a further research in the background of web companies. There is a phenomenon called “filter bubble”. It suggests that with those filter bubbles people are restricted to the filter options and shapes our view to the worldview. If you are interested, please have a look at the following video.
Choi, J.-E. & LEE, D.-H., 2015. Customers do not always prefer personalized products: The role of personalized options range in personalization. International Academy of Marketing Studies Journal, 19(2).
Randall, T., Terwiesch, C. & Ulrich, K. T., 2005. Principles for User Design of Customized Products. California Management Review, 47(4), pp. 68-85.