Apparently, customers prioritize cost and design appearance over co-branding and means of activation. It can be observed that the sampled customers preferred wearables that did not require annual subscriptions and appeared like wristwatches. Seemingly, co-branding was the least significant factor affecting customer choice and can barely be used to achieve a meaningful competitive edge by manufacturers.
With the understanding that design and cost are the most sensitive elements, designers would be compelled to tailor devices alongside the two factors. Jantti (2017) observed that between 30% to 40% of people stop wearing wearables in the first six months, and by the end of one year, only 1 out of ten people still wear them. Novelty wears off quickly, and making the devices less bulky and attractive to the eye is certain to attract more buyers and stay longer on their wrists. One way designers can meet these conditions is by making the devices sleek and compact. To achieve this goal, there is a need to define the device’s specific use case to eliminate redundant or nonessential features. In turn, that means fewer components are accommodated, and the processor is relieved of unnecessary load, making the dice even more powerful. For instance, a blood glucose tracker wouldn’t need a GPS tracker module, should it just be case-specific.
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I suggest that clustering the customers first and running separate conjoint trials would yield significantly different results. Abadie et al. investigated the optimal sample size for clustered and isolated statistical data. Clustered samples yielded more consistent data for larger iterations of statistics analysis (over 5 times) at a confidence interval of 0.99 (Abadie et al., 2017). That should imply that if the companies clustered the clients in suitable groups, then repeated the assessment, factors that appear to have an insignificant effect on purchases, e.g. branding, could have shown up in particular groups. The clusters could be based on age, sporting interests, pre-ownership of a wearable, among other economic factors like income.
Judging from the Nike + Fuelband SE fitness tracker’s surge in sales after Kanye West touted it, we can’t simply push the branding factor away. In fact, appropriate clustering will reveal the proper market segments best suited for particular design choices. Say the company identifies five categories of clients that appeal to unique combinations of the design matrix. Designers can develop five versions of the product, each suited to a particular market. Considering that some segments might exhibit less sensitivity to price, some categories might include annual charges (as a trade-off with more functionality). That makes the product differentiation option most profitable and feasible for expansion.
The sample is not representative of people who might purchase online for three reasons;
The ratio problem
A 2019 study by Outerbox revealed that many buyers prefer shopping online for electronics because of the ease in comparison, details and convenience (Vozativa, 2020). With a conservative approximation of more than 46% making purchases online, that would render the in-shop survey not entirely accurate. The unique differences in the in-shop versus online shopping experience guarantees that the responses will differ significantly. Besides, it can be argued that one in ten might not capture the exact patterns and consumer trends, but that heavily relies on the number of units sold.
The appeal factor
The temptingly appealing display of electronic products on websites and online shopping portals often overshadows some of the elements highlighted in the survey. For instance, Kaplan (2019) revealed that close to one in five online buyers of electronics would prioritize factors like appearance, mode of payment and delivery, discounts and return policy over subtle product features. That is partly supported by high return rates for online versus in-house shoppers, which is as much as three-fold. Going by the observation, we expect that online shoppers will be biased towards physical appearance; hence co-branding with teams might be more significant. Also, considering that many online purchases are driven by impulse, elements like activation might fall lower because users rarely scrutinize much beyond surface value.
The Age factor
Wearable sales are strongly tied to fairly subtle factors like age. In its 2016 survey, Forbes noted that senior citizens are the most health gear clients, at 48%. Considering that more than half of wearables have health-related functions (e.g. heartbeat tracking), we can comfortably say the target market is heavily influenced by people well over 50 (Jaspen, 2016). Coupled with the fact that the old make more purchases online (Eurostat, 2021), it is apparent that data from 1 out of every 10 in-house shoppers is less than ideal for representing wearable buyers.
What features would have been included?
Warranty period, eco-friendly materials and availability/unavailability of a software upgrade are three features that are often critical in consumer electronics that should have also been considered. Here is how they matter;
Warranty period
Like phones and other electronic devices, wearables are fragile and often break down for numerous reasons. The warranty factor could be introduced as a trade-off with an annual fee to evaluate people’s sense of trust in the brand.
Eco-friendliness
As climate change warnings become more common headlines, manufacturers are trying to keep up with sustainable technology, but that is quite expensive. In the case the wearable manufacturer has materials for both, the factor could be used vis-à-vis cost.
Software upgrade
People don’t like it being left behind when the world moves ahead with new software rollouts. However, some software ecosystems do not support such upgrades, and because they are often cheap, they could be left to the buyer to choose whether or not they would love to upgrade regularly.
References
Abadie, A., Athey, S., Imbens, G. W., & Wooldridge, J. (2017). When Should You Adjust Standard Errors for Clustering? (No. W24003). National Bureau of Economic Research.
European Union. (2021). “E-Commerce Statistics for Individuals.” Retrieved Https://Ec.Europa.Eu/Eurostat/Statistics-Explained/Index.Php/E-Commerce_Statistics_For_Individuals#
Jantti, K. (2017). “What Makes a Wearable Successful Design-Wise .” Reaktor. Retrieved Https://Www.Reaktor.Com/Blog/What-Makes-A-Wearable-Successful-Design-Wise/
Japsen, B. (2016). “Wearable Fitness Devices Attract More Than the Young and Healthy.” Forbes . Retrieved Https://Www.Forbes.Com/Sites/Brucejapsen/2016/07/11/Wearable-Fitness-Devices-Attract-More-Than-Young-Healthy/?Sh=412be56757df
Kaplan, M. (2019). “The Growing Problem of Customer Returns.” Practical Commerce. Retrieved Https://Www.Practicalecommerce.Com/The-Growing-Problem-Of-Customer-Returns#
Vozativa. (2020). “ Difference Between Online Shopping and In-Store Shopping And Which One Is Better! ” Https://Vozativa.Org/Difference-Between-Online-Shopping-And-In-Store-Shopping/
