Discussion and Analysis of Consumer Behavioral Patterns:
The Cell Phone Market
Executive Summary
Most wireless communications providers offer an overwhelming array of plan types, features and add-ons, each one trying to devise packages that provide better value and more services and amenities than the other companies. Here, perhaps more than in other segments of the market, consumers turn to the alternative evaluation and selection process to help work through, and winnow down, the many details. It is through this consumer behavior model that consumer impressions can be assessed and analyzed. The power of the media, the popularity of communications applications and the assigned status of various types of mobile devices however bring preference, impulse and personal opinion into the equation as it does with few other products, with the possible exception of automobiles and clothing. Thus, this report sees the measurement of consumer behavior in the purchase of mobile data devices in a much broader perspective, one that takes into account the influence of trends, fads, human conceit and ego.
Table of Contents
Creation of Decision Matrix. 6
Introduction
Selecting a cell phone and cell phone plan can be one of the most detailed and complicated “mix-and-match” (Matutes and Regibeau, 1988) decisions a consumer can make. Most wireless communications providers offer an overwhelming array of plan types, features and add-ons, each one trying to devise packages that provide better value and more services and amenities than the other companies. Here, perhaps more than in other segments of the market, consumers turn to the alternative evaluation and selection process to help work through, and winnow down, the many details. It is through this consumer behavior model that consumer impressions can be assessed and analyzed. By collecting this data, it is possible to extrapolate how cell phone brands rate based on evaluative indicators, and how brand positioning shifts according to changes in marketing priorities (Akeyene 2012, 9-10).
Perception is the driving factor in consumer behavior (Payne 1976; Wu and Rangaswamy 2003). Perception is the process of selecting, organizing, and interpreting information inputs (sensations received through sight, taste, hearing, smell, and touch) to produce a sense. The three steps in the perceptual process are selection, organization, and interpretation. Individuals have numerous perceptions of cell phones that have an effect on purchasing decision processes. Perception is an approximation of reality. The consumer’s mind attempts to make sense out of the stimuli to which he is exposed. This works well, for example, when he ‘sees’ a product three hundred feet away at his or her correct height; however, his perception is every so often ‘off’ – for example, certain shapes of ice cream containers look as if they contain more than rectangular ones with the same volume. The accuracy of assessing this behavior depends on the extent that consumers believe that one aspect of a product accurately predicts another, while consumer confidence is derived from the consumer’s ability to discern the differences between brands (Liang, Kuo, Hu and Chen 2011, 3; Chu and Eric 2003).
Price is also an important determinant, with the customer’s perception of quality based on its price level (Mehta, Surendra and Kannan 2003). Price has been used as an input to perception. It is not uncommon for consumers to equate high prices with high quality, particularly if backed up by appropriate associations like a premium brand name or an upmarket retail outlet. With several product categories, consumer the brands’ prices with those of the one they have in mind. The brand is yet another important factor, a psychologically oriented behavior that is exhibited over time (Callwood 2012). Although most research in consumer-based brand equity assumes that attitudes beget behavior, many psychological components of branding, such as awareness, knowledge and preference, are not necessarily good predictors of observable purchase behavior. Understanding the psychology behind brand-based behavior is a key to acquiring and retaining a loyal clientele, customers who are apt to create other customers. This lies at the heart of what management consultant Peter Drucker meant when he said, “The business of business is getting and keeping customers” (Ball and Douglas 1999, 434).
Creation of Decision Matrix
Perceived Perfomance of Six Mobile Phones and Plans in relation to eight evaluative criteria
| Consumer Perceptions | ||||||
| Evaluative Criteria | Vodafone | Telstra | Optus | TransACT | Southern Cross Telco | Optus Business |
| Cost/Price | 4 | 3 | 4 | 3 | 3 | 4 |
| Network Coverage | 5 | 4 | 4 | 3 | 4 | 4 |
| Promotion | 3 | 3 | 4 | 3 | 2 | 3 |
| Billing Rates | 3 | 4 | 3 | 4 | 3 | 4 |
| Free talk time, texts, and data | 2 | 3 | 3 | 2 | 3 | 3 |
| Monthly Costs | 3 | 3 | 4 | 3 | 4 | 3 |
| Service Quality | 4 | 4 | 4 | 3 | 3 | 4 |
| Service Availability | 4 | 3 | 5 | 3 | 4 | 4 |
Note that in this analysis 1=very poor, 2=below average, poor, 3= average, 4= above average, good, 5=very good.
The Importance of the Evaluative Criteria to the three consumers
| Evaluative Criteria | Charlie | Ted | Huda |
| Price | 1 (25) | 2 (15) | 1 (30) |
| Network Coverage | 2 (20) | 1 (25) | 3 (15) |
| Promotion | 6 (10) | 7 (5) | 5 (10) |
| Billing Rates | 5 (10) | 3 (15) | 4 (10) |
| Free talk time, texts, and data | 4 (10) | 5 (10) | 6 (5) |
| Monthly Costs | 8 (5) | 8 (5) | 8 (5) |
| Service Quality | 3 (15) | 4 (15) | 2 (20) |
| Service Availability | 7 (5) | 6 (10) | 7 (5) |
In this analysis, 1=most important, 8=least important, values in () = percentage
Mobile phones and plans
| Service Provider | Mobile Phones | Plans |
| Vodafone | Samsung Galaxy SIII | Offers an unlimited single line plan that starts at $ 40/month. Other features include:
· Minimum cost of as low as $ 720/year · Offer of data bundles from 200MB |
| Telestra | Samsung Galaxy Note II | Offers Everyday Connect and Everyday Connect BYO. Other features include:
· Data Packs · Premium Care Mobile Insurance · Prepaid Mobiles |
| Optus | iPhone 5 |
|
| TransACT | Samsung Galaxy 4G |
|
| Southern Cross Telco | Cricket Mercury Ice M886 |
|
| Optus Business | Samsung Galaxy SII |
|
Perceived Perfomance of six mobile phones and eight evaluative criteria
| Consumer Perceptions | ||||||
| Evaluative Criteria | Samsung Galaxy SIII | Samsung Galaxy Note II | iPhone 5 | Samsung Galaxy II 4G | Cricket Mercury Ice M886 | Samsung SII |
| Cost/Plan | 1 | 1 | 1 | 2 | 2 | 1 |
| Contract duration | 3 | 2 | 2 | 8 | 1 | 2 |
| Opearting System | 5 | 4 | 4 | 3 | 6 | 3 |
| Colour | 8 | 6 | 7 | 7 | 5 | 8 |
| Camera | 6 | 7 | 5 | 6 | 4 | 6 |
| Screen | 4 | 5 | 6 | 4 | 7 | 5 |
| Storage | 7 | 8 | 8 | 5 | 8 | 7 |
| Basic Features | 2 | 3 | 3 | 1 | 3 | 4 |
Note 1=most important rank, 8= least important rank criteria
Application of Compensatory Decision Rule
Charlie’s Analysis
| Evaluative Criteria | Importance Rank | Vodafone | Telestra | Optus | TrasACT | Southern Cross Telco | Optus Business |
| Price | 25 | 4 | 3 | 4 | 3 | 3 | 4 |
| Network Coverage | 20 | 5 | 4 | 4 | 3 | 4 | 4 |
| Promotion | 10 | 3 | 3 | 4 | 3 | 2 | 3 |
| Billing Rates | 10 | 3 | 4 | 3 | 4 | 3 | 4 |
| Free talktime, texts, data | 10 | 2 | 3 | 3 | 2 | 3 | 3 |
| Monthly Rates | 5 | 3 | 3 | 4 | 3 | 4 | 3 |
| Service Quality | 15 | 4 | 4 | 4 | 3 | 3 | 4 |
| Service Availability | 5 | 4 | 3 | 5 | 3 | 4 | 4 |
| 380 | 355 | 385 | 305 | 335 | 380 |
So Charlie would have the highest preference toward Option 3 (Optus) if using the compensatory decision rule.
Ted’s Analysis
| Evaluative Criteria | Importance Rank | Vodafone | Telestra | Optus | TrasACT | Southern Cross Telco | Optus Business |
| Price | 30 | 4 | 3 | 4 | 3 | 3 | 4 |
| Network Coverage | 15 | 5 | 4 | 4 | 3 | 4 | 4 |
| Promotion | 10 | 3 | 3 | 4 | 3 | 2 | 3 |
| Billing Rates | 10 | 3 | 4 | 3 | 4 | 3 | 4 |
| Free talktime, texts, data | 5 | 2 | 3 | 3 | 2 | 3 | 3 |
| Monthly Rates | 5 | 3 | 3 | 4 | 3 | 4 | 3 |
| Service Quality | 20 | 4 | 4 | 4 | 3 | 3 | 4 |
| Service Availability | 5 | 4 | 3 | 5 | 3 | 4 | 4 |
| 380 | 345 | 390 | 305 | 315 | 380 |
So Ted would have the highest preference toward Option 3 (Optus) if using the compensatory decision rule.
Huda’s Analysis
| Evaluative Criteria | Importance Rank | Vodafone | Telestra | Optus | TrasACT | Southern Cross Telco | Optus Business |
| Price | 30 | 4 | 3 | 4 | 3 | 3 | 4 |
| Network Coverage | 15 | 5 | 4 | 4 | 3 | 4 | 4 |
| Promotion | 10 | 3 | 3 | 4 | 3 | 2 | 3 |
| Billing Rates | 10 | 3 | 4 | 3 | 4 | 3 | 4 |
| Free talktime, texts, data | 5 | 2 | 3 | 3 | 2 | 3 | 3 |
| Monthly Rates | 5 | 3 | 3 | 4 | 3 | 4 | 3 |
| Service Quality | 20 | 4 | 4 | 4 | 3 | 3 | 4 |
| Service Availability | 5 | 4 | 3 | 5 | 3 | 4 | 4 |
| 375 | 345 | 385 | 300 | 320 | 375 |
So Huda would have the highest preference toward Option 3 (Optus) if using the compensatory decision rule.
According to the compensatory decision rule, consumers utilize all available brand information and performance data (Lantos 2010, 135). In compensatory decision rules the attractiveness of aspects of one attribute is swapped in comparisons across characteristics and options. As a result, positive aspects can compensate negative aspects. The adding up of attractiveness rule belongs to this grouping of decision rules (Svenson, 1979). A compensatory decision compares the benefits and detriments and integrates them so that one of the options turns out to be better for the other. It is obvious that decisions can also make cognitions that are at variance and not rational, as well as having effects on from lasting conflicts, regret, disappointment, and resentment (Svenson, 1979). Some of these cognitions and way of thinking are driving forces behind post-decision consolidation processes and decision result management. Thus the compensatory decision rule is how a consumer evaluates each product in terms of each important attribute, and then chooses the product or brand with the highest overall rating.
Weighted linear compensatory rules (Dawes and Corrigan 1974) are all taken into account; no un-weighted attributes (Einhorn 1971) are factored in because all elements are considered to be significant in weighting the buying decision. A refinement of this model is the finite ideal point model, through which consumers pursue moderate levels of information about a product’s relative characteristics (Lantos 2010, 135). This is the approach a consumer might take if he or she did not want to be weighted down with too much data, thus making their decision process more difficult. The buyer applies these “strategies” based on three essential decision conditions:
- The product offers desirable sensual benefits, i.e. smell, appearance, etc.
- The individual has some level of experience with the product in the past, which has created a relationship/sense of loyalty
- Most consumers approach the purchase decision from a non-cognitive standpoint, and are more concerned with personal impressions and instinct (Lantos 2010, 135).
Cellular phone purchases are no different than other consumer decisions. Brand loyalty/experience is important, as are affective factors, including the look, feel and technical capabilities of the unit. A 2011 study of consumer decision-making processes in the cell phone market found that individuals were primarily impacted by “being novelty fashion conscious,” which would seem to support the view that cell phones are considered a reflection of identity by many consumers (Smadi and al-Jawazneh 2011, 119). Another most important purchasing factors were found to be price, brand loyalty and consumer impulsiveness (Smadi and al-Jawazneh 2011, 119). This consumer purchases without much analysis or information. The impulsiveness mentioned in this context is not the impulsiveness associated with most consumers when they purchase cell phone plans on impulse. The impulsive purchaser is in a hurry to finish off his/her shopping without much planning even while purchasing expensive and risk-oriented durables. This consumer does not seem to get involved with the learning process in purchasing or decision-making. Well-known brands can reassure this kind of consumer through a distinctive advertising campaign. For example, a hi-tech brand introducing a new version of a product can bring back its signature line it used a decade back “If it’s Philips, you are sure”.
Analysis of Choices
The choices listed in the decision matrix are more or less similar in terms of size, capability, price and brand-related popularity. The interactive, versatile and highly personal Smart phone line dominates the market at present, though its prevalence among consumers and general availability of these phones does depend on the wireless provider – Cricket, for example, being an entirely pre-paid provider, offers a different kind of service and phone product menu. A CNET review article notes that the Smart phone model has fundamentally altered the entire wireless paradigm, with all-in-one technology (i.e. Wi-Fi, video, camera, instant messaging, etc.) providing the basis for consumers’ desires and perceived needs. “Smartphones have become incredibly advanced, with multicore processors and cameras that almost make us want to abandon our point-and-shoot cameras for good” (Dolcourt 2012).
In the end, the best offering for consumers, based on a basic plan format, is the Apple iPhone 5 by Verizon. This version of the Smartphone design, manufactured by the inventor of the genre, offers the best value based on compensatory decision factors, taking into account price, capabilities, “app” adaptability, performance and appearance. The preliminary problems encountered by the early iPhone users appear to have been resolved, and there now exists a certain user “cache” in owning the model that sets the Smartphone trend (TopTen Reviews 2012). “Apple Inc. has smartly added the most important features that people felt were deficits of the iPhone 4S, while making the storied design even well again and putting it all in a slimmer, lighter way than ever before” (TopTen Reviews 2012). The Apple iPhone 5 offered most of the technological advantages/amenities of the other providers’ top Smartphones, with decided advantages in terms of convenience and competitive pricing.
Heuristics are quite common in the purchasing process. Some widespread heuristics consumers use in decision-making contain: ‘the availability’ (Folkes, 1988), ‘scarcity choice’, ‘liking choice’ (Whittler, 1994), ‘judgement referral’ (Mattila, 1998), and ‘elimination by aspects’ (Tversky, 1973). Consumers use heuristic because of memory limitations. Information that was readily available at the time of purchase influences their decisions. However, the scarcity choice is used when there is an impending increase in price if the product is not brought at a particular time. In this case, the consumer thinks that a good deal will be lost if one does not make a purchase. The affect influences the liking choice heuristic as it refers to the feeling of the consumer to the seller. Consumers commonly use this heuristic when the items are available are not extremely differentiated. Judgment referral is a heuristic that relies heavily on what other people are saying about the product. Since the majority of consumers stay away from cognitive effort when making decisions or purchasing a product, they count on the input of the other people. Another simplifying heuristic is the removal by aspect proposed by Tversky (1973). Products that do not obey the rules to the criteria consumer formulates are straight away eliminated using this heuristic. As such the option is in the end limited to only a few items. This heuristic stops cognitive excess on the part of the consumer.
Rather than taking into consideration all aspects of a product, or product line, across the entire range offered by the market, a consumer is more likely to “focus on a relatively few features (color display, long battery life, etc.) and eliminate those that do not have the desired features (a ‘conjunctive’ decision rule)” (Hauser, Ding and Gaskin 2010, 211). Simplistic cognitive decision-making rules appear to predominate in “typical” purchasing environments because features that motivate most purchases tend to be highly correlated (Hauser, Ding and Gaskin 2010, 211). As such, mobile phones point up the formula in which consumers utilize a fairly simplistic screening process, as opposed to the more exhaustive weighing of all aspects of a product that has relatively few features, components, styles, and so forth. In this way, the purchaser is able to compensate for not being able to factor in all features by balancing “search/evaluation cost with the value of a higher-value ‘best’ product” (Hauser, Ding and Gaskin 2010, 212). In this way, the purchaser is able to put him or herself in the best position to make a decision that seems logical and well-considered, while satisfying the hedonic desire to select the alternative that, for no quantifiable reason, just “feels” right.
The differences in the choices made by Huda, Ted, and Charlie differ because of the two crucial consumer behavior theories: personality and demographics theories. Personality factors that lead to the difference in the choices made by the three consumers include attitude and culture based on the socialization process. The demographic factors that result into the differences in the choices by the consumers include age, family situation, income levels, gender, and ethnicity.
Conclusion
In conclusion, there are scores of factors involved in the selection of a cell phone and a mobile data plan. Consumers are driven by a wide range of factors, which necessitates a complex, agile and dynamic model for collecting, extrapolating and analyzing the data. One reason for this is the prevalence of human idiosyncrasy in the equation. It is one thing, and a fundamental principle of scientific inquiry, to assign data to complex mathematical equations, to plot graphs and to assign values to various outcomes. This rational, quantitative approach is appropriate and productive across a broad spectrum of purchasing behaviors. However, cell phones are quite another matter. The power of the media, the popularity of communications applications and the assigned status of various types of mobile devices bring preference, impulse and personal opinion into the equation as it does with few other products, with the possible exception of automobiles and clothing. Thus, measuring consumer behavior in the purchase of mobile data devices must be seen in a much broader perspective, one that takes into account the influence of trends, fads, human conceit and ego.
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