This report will mainly focus on the performance of Smith’s. In term of brand performance, the data discloses that the difference between bigger brands and smaller brands is the penetration level. It also is proved that Double Jeopardy law exists in competition among brands. That is smaller brands tend to have less customers who will expectedly buy less often than bigger brands.
Furthermore, when looking at the way brands share their customers within a category, duplication of purchase law applies through out the table 2 in section 1.
However, exceptions also exist as they are called Partitioning. The reasons to explain partitioning is due to the distribution channels. In applying these findings, manager should keep in mind that Duplication of Purchase Law co-exists with Double Jeopardy Law. That mass marketing is one of the most appropriate method to go a brand.
The data in section 2 reveals that attitude is opinions and beliefs that people hold towards brand. It is situation-specific and fickle.
On the other hand, brand salience is such a methodology to influence brand choice. Salience is the quantity and quality f cues which could be thought in buyers memory. Based on the concept of salience, the more cues a brand has in buyer’s memory, the higher likelihood it will be chosen in buying situation. Therefore, salience is more important to measure in term of brand choice.
When analyzing the proportion of people giving attitudinal responses within total sample and within brand’s users, it proves that bigger brands have more responses because they have more customers saying so, and that customers of brands have the same things to say towards brand. Smith’s has been proved to perform expectedly as it has the highest proportion thin total sample and within its users. In order to grow a brand, changing attitude of the market is ineffective because attitude does not hold the trigger to drive behaviors.
Lastly, table 4-6 shows that brands seldom differ in term of customer profile. The method that was used is the Mean Absolute Deviation (MAD). Precisely, Smith’s only deviates a little difference comparing to other brands in the market. Such small deviation is not significant enough for brand to take it in to account. As a result, it is assumed that the customer base of Smith’s is not different to that of the competition. The first implication that marketers would apply is they ought to remember not positioning their brand as a special ones.
Instead, they should consider which means of media, says, advertising, social media could attract as much customers as possible. Moreover, mass marketing is also an important approach that marketers should undertake. It is because by doing mass marketing, brands could reach to the number of non and light buyers of the category, which contributes fairly significant to the sale of the brands. Section 1 – Brand Performance Question 1 The table shows two types of different brand performance metrics of five brands within a category.
Table 1 shows that the difference between bigger brands and smaller brands is the penetration ratio, which calculates the number of shoppers who actually bought from brand once. Therefore, it means that the higher penetration level is, the bigger the size of the brand is. Additionally, penetration level will reduce gradually as the firm goes smaller. Specifically, Smith’s, which holds the highest market share of 34%, also achieves the highest penetration level of 72% whereas Jumps, the smallest brand, only hold 24% of penetration.
Furthermore, when looking at loyalty metrics, bigger brands tend to have better loyalty performance metrics. For instance, as the leader within the chip category, the Smith’s has both the highest purchase frequency of 2. 2 times and share of category requirements of 40%. On other words, these figures mean that Smith’s buyer purchases as often as 2. 2 times from them on average and 40% of their customer total purchases are from them. It seems that bigger brands tend to benefit twice due to higher metrics of penetration and loyalty.
Remembering, Goatherd & Barbwire (1 990, cited in Sharp 2010) related this pattern to a model called Double Jeopardy law. They describes this theory that smaller brands tend o have less customers than bigger brands, who will expectedly buy less from them. This results from the fact that people who are aware of smaller brands also know about bigger brands because smaller brands would have less outlets comparing to bigger brands (Sharp 2010). Therefore, they are having less physical availability in the market, attracting less customers (Sharp 2013).
Furthermore, Natural monopolize effect law also helps explaining the difference amongst brand. Sharp (2010, p. 97) states people who do not buy often from the category will choose big brands rather than smaller brands. Hence, Smith’s will share less of its customers to Jumpy while Jumpy still has an considerable amount of its buyers purchasing from Smith’s. Overall, within chip category, bigger brands, especially Smith’s, have a higher proportion of customers and their customers purchase more often comparing to smaller ones.
Question 2 Table 2 highly demonstrates the structure that brands share their customers within the category. According to penetration level, it is assumed that bigger brands share less of its customer brands, however smaller brands actually share more of its customers to bigger brand. In the example of Smith’s and Auditors, Smith’s, as a bigger brand, shares 62% of its customers to Auditors whilst Auditors shares up to 71% of its customers to Smiths. Additionally, the average duplication also reduces gradually toward smaller brands from 71% (Smith’s) to 33% Moves).
This pattern in sharing customers exists throughout the table, which is a sign of Duplication of Purchase Law. Remembering (1996, cited in Lam 2008) describes this theory in a sense that a brand would expectedly share more of its customers to the bigger brands and share less of its customers to smaller brands. Moreover, Sharp & Sharp (1977, cited in Mansfield, Romaine & Sharp 2003) also points out deviations in this pattern, which is called Partitioning. Partitioning is the exception when brands share more or less their customers to each other unusually.
For example, both Smith and Kettle share more of their customers than usual. Particularly, Smith’s share 72% of it customers to Kettle, which is higher than the average (67%) and that of other brands. Meanwhile, Kettle’s has 77% of its customer purchasing from Smith’s, which is again, higher than the average of 71 %. Throughout the table, other partitioning case would include Smith’s and Sump’s when they share less of their customers together (28% comparing to 33% and 65% comparing to 71% respectively). The reason to explain the existence of these deviations could be the category layout across retailers.
Daces (2007) argues that the in-store layout among competing brands would affect significantly to the brand choice. For instance, due to being top brands within the category, both Smith’s and Kettle products could be laid out in a larger scale and near to each other, which increases the tendency of customers buying their products. Moreover, price policy and demographic store action could also attribute to the partitioning cases. Because customers also buy from other brands, manager should consider mass marketing as one of the most applicable approach.
The aim of mass marketing is to attract all potential within category (Sharp 2013), which consequently will increase the penetration level of the brand. Secondly, by acknowledging the appearance of duplication of purchase law, brand manager should keep in mind that double jeopardy law would also exist. It is explained that by sharing more of its customer to bigger brands, smaller brands will experience a low level of share of category acquirement and average purchase frequency, meaning their customers would buy less often from them. Section 2 – Brand Attitude Question 3: According to East et al (2013, p. 29), attitude refers to opinions, beliefs that people form based on their previous experience. From a different marketing perspective, Beam (1968 cited in Sharp 1997) defined attitude as a “person’s description of their behaviors”. This means that attitude is formed based on the situation after people observe their behavior and provide their personal thinking at that moment. Hence, attitude plays as a tool to benchmark past behavior in a specific situation. Instead, the brand salience is a methodology for marketers to predict future behaviors.
According to Rumanian & Sharp (2004), brand salience is the approachability of a brand in buyer mind. It is measured by the quantity and quality of information (attributes) linked to a particular brand. These attributes could be any cues of the brand such as value for money. Therefore, in reality, salience would undoubtedly influence the propensity of a brand being chosen in accordance to the amount of cues being though in buyers memory. When considering whichever concept is more important in predicting true behaviors, brand salience seems to be a better way than attitude.
Firstly, attitude has such weak influences in term of having impacts on brand choice. As mentioned earlier, attitude is situation-specific. Additionally, Rumanian & Sharp (2004) describe attitudes as a way to evaluate the brand. Moreover, they argue that the concept of attitude depends heavily on how people think of the brand and how “motivationally strong ‘ and stable it lasts. However, attitude has been proved to be fickle. Sharp (1997) once proved that 50% people would change their attitudinal responses in the next interview regarding the external environment.
Therefore, it means that attitude does not drive consumer’s behavior. Secondly, salience is related directly about the probability of brand being bought. According to Rumanian & Sharp (2004), a brand with more cues would eventually have higher chances being chosen. Additionally, the concept of salience is subjectively based on situations where people are affected by a range of cue when choosing a brand. Consequently, it is convincing and logical for manager to use brand salience as a tool to foresee future behaviors.
Question Table 3 demonstrates the proportion of people giving their attitude towards rand within the chip category. Accordingly, it is recorded that the percentage of total sample giving either negative or positive attitudes toward brands tends to increase in-line with the size of the brand. For example, as the largest brand within the category, 68% of total respondents has a negative attitude about Smith’s and the number reduces gradually as brand goes smaller. Specifically, Kettle only has 59% of the population having a negative attitude, following lastly by Sump’s (36%).
Likewise, the same patterns also exist when people give positive responses. Nevertheless, the percentages of users giving either negative ND positive attitudes toward brands do not differ much across the category. Precisely, the amount of users having either negative or positive attitude only varies in a small range (65% to 74% and 59% to 65% respectively), which is different than that of the total population. Riley et al (1999) explains these patterns that because bigger brands have more customers, they eventually have more people giving attitudinal responses.
In addition, he also mentioned that current users of a brand are more likely to associate the brand with a attitudinal responses since they have used the products. Moreover, people tend to have name attitude toward brands especially within a repertoire market due to the indistinct personality among brands (Rumanian & Sharp, 2004). Therefore, by having the highest penetration rate, more people will give attitudinal responses to Smith’s while fewer people will say the same things to Red Rock Deli or Sump’s due to their small size.
Overall, Smith’s has performed expectedly as having the highest proportion of people giving attitudes within the total sample and its customer profile. Question 5: In order to grow a brand, changing attitude of the market is not a effective way for manager to approach. Firstly, as discuss in question 3, attitude does not have such strong influence in brand choice and could only measure past behavior. East et al (2013) also debated this statement that there is such a poor correlation between satisfaction and purchase behaviors.
He explained that it is because customer’s needs could change unpredictably, satisfaction would eventually be different. Therefore, such thing which is fickle and can be affected by multiple factors could not definitely yield a accurate prediction. In contrast, behavior actually is the factor driving attitude. Additionally, Riley et al (1999) showed that he relationship between attitude and behavior is that attitude acts as “mutual reinforcement and consistency” factor rather than a “causality” in term of brand choice.
A great example could be the research of Bird & Remembering (1996 cited in East et al 2013, p. 22), which they found that two-thirds users of a brand would be likely to express their intention to purchase in the future. It means that attitude does not hold the trigger to force customer to purchase in the future, however behavior does. On the other hand, to grow a brand, managers should focus on increase brand’s penetration level. East et al (2013) claims 80% of the market is on and light buyers while the rest 20% is heavy buyer. However, both classes account for of total purchase.
Moreover, consumers can reportedly shift from being light buyer to heavy buyer in the next period. This is why it is crucial for marketers to aim to the non and light buyer class. Overall, attitude does not play such an important role in forcing behaviors, hence changing it will not help brand growing. Section 3 – Segmentation and demographic Question 6: By comparing between the customer profile of brands and that of the entire category, deviations among brands in term of customer that brands attract old be spotted out (Kennedy & Remembering 2001).
The method of measuring these deviations here is called Mean Absolute Deviation (MAD). MAD is the average distance between each data value and the mean. The closer that MAD to 0, means that there is less difference amongst data, in this case, the customer profile. Through researches, Kennedy & Remembering (2001) showed that the average Mad’s for all of the demographic in 42 different categories are identically not significant. However, exceptions are practical as any brands having an average MAD of above 1 0%, are considered to have a totally different customer profile.
According to tables 4, the average MAD for the entire category is 2%. It proves any brand within the category only deviate 2% on average in term of relationship status segmentation. Precisely, the average MAD of Smith is 2. 3%, which is not considerably different than that of other brands such as Kettle (2. 1%). Such difference is too small to be considered to conduct a different marketing strategy. This results also appear in the other two demographic segmentations as Smith’s experiences deviations of 5. 5% and 4% in term of total household income and gender respectively, which again, are not sufficient.
Consequently, despite the different level that each brand invests in different demographic segmentation, Smith’s does not have different customer base to that of other brands due to the small deviations. Question 7: According to the findings above, marketers should keep in mind that they should not position their brand as a special ones in the market, rather they should make their brand appeal as memorable and attractive as possible to customers. As discuss above, salience measures the propensity of brand being chosen in a buying situation. Thus, the more salience a brand has in customer’s mind, the ore likely they will purchase from brand.
Hence, to increase salience, marketers should consider means of media would reach to customers in the most effective way. Moreover, one of the most proper implication is to mass marketing. Remembering, Kennedy & Long (2000) debated there would be no reasons to restrict brand’s market by undertaking a segmentation marketing strategy as brands in a repertoire market appeal the same to customers. Hence, it is because the number of non and light buyers is such an important factor in term of total sale and they could easily shift from being a light buyer to a heavy buyers.
Overall, it is crucial for marketers to prioritize their marketing strategy on increasing salience in buyer’s mind and mass marketing with an aim of gaining more customers, hence increasing the sales.
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