Do Customer Profiles Change Over Time? A Two Year Investigation of into the Success of Targeting Consumers of Australia s Top Ten Banks 2009 and 2011.

Transcription

1 Do Customer Profiles Change Over Time? A Two Year Investigation of into the Success of Targeting Consumers of Australia s Top Ten Banks 2009 and Gavin Lees* & Maxwell Winchester* *Victoria University Abstract Segmentation and targeting are cited as being a core part of marketing strategy for any organisation. This paper examines whether the top ten Australian Banks have successfully managed to segment the market and target consumers with different profiles. In doing so we compare the profiles of those banks over two years, 2009 and 2011 noting any changes between the years. A sample of Australian households, from each of the two years, from the Roy Morgan Single Source Survey was utilised to profile users of Australia s top ten banks by market share. The results suggest that there has been little difference in profiles of consumers across most Australian banks. The results challenge whether financial service companies successfully are segmenting the marketplace. 1

2 Introduction For decades targeting of unique segments of any market has been seen as a key strategic activity for marketers (Dickson & Ginter, 1987; Smith, 1956; Wind, 1978). Marketers have been arguing for some time that segmentation is a crucial first step in marketing strategy as it is the basis upon which targeting and positioning strategy is developed (M. McDonald & Dunbar, 1998; Yankelovich, 1964). Many marketers commonly recall that a requirement for success of a segmentation programme is that the segmentation be measureable, substantial, accessible, different and actionable. This set of criteria has been widely attributed, including to the following authors: Wedel, and Kamakura (1998, cited in Lemon & Mark, 2006), Kotler and Keller (2009, cited in Kazbare, Van Trijp, & Eskildsen, 2010), Kotler (1984, cited in Dibb, 1999) or Brassington & Petit (1997, cited in Rees & Gardner, 2005) to name a few. This study aims to consider whether customer profiles in the Australian banking industry are different enough to be substantial (and therefore actionable). In this study, the success over time of segmentation and targeting of the Australian banking industry is considered using data from both 2009 and Most of the research on bank segmentation and targeting comes out of the United Kingdom. In this market, there is an abundance of published evidence of banks attempting to segment and target, including: age (Elder, 1988; Elliott, 1986; Fiori, 1984; Gorman, 2003; Scott, 1988; Simmonds, 1990; Stanford-Dowling, 1982; R. Walker, 1995), marital status (Scott, 1988; Simmonds, 1990), income (Elder, 1988; Fiori, 1984; Gorman, 2003; Simmonds, 1990), social class (Elder, 1988; Fiori, 1984; Howe, 1982; Simmonds, 1990; R. Walker, 1995), attitude (Gorman, 2003; Scott, 1988; Simmonds, 1990), life cycle stage (Jarvis & Brand, 1998), ethical values/consumerism (Cowe & Williams, 2000; Ryder, 1994) and profitability (Storbacka, 1997). Evidence of segmentation and targeting in business-to-business financial services markets is far less prolific, but the evidence that was available suggested segmentation and targeting on variables such as business size or turnover (Gabbott, 1992; Wingate, 1988). Aside from the European examples above, there are a small number of other examples of arguments supporting segmentation and targeting in financial services markets in North America and Australasia. An example from The Royal Bank of Canada (RBC) demonstrates how bringing big data together enables banks to target specific consumers and consumer groups. Large segments can be further subsegmented, by defining a useful sub-segment [which] generally involves doing a deep dive into the most profitable end of the segment to tease out distinct behaviour patterns (p2, Dragoon, 2006). RBC, has segmented customers based on 5 strategic life-stage segments : Youth, under 18s; Getting Started, aged between 18 and 35; Builders, aged between 35 and 50; Accumulators, aged between 50 and 60; and Preservers, over 60 (p2, Dragoon, 2006). Similarly, in the Australian context Veda Advantage identifies 45 customer segments including "suburban battlers", "working students" and "affluent young families" (VASG, 2009). However this demographic segmentation only skims the surface of segmentation. In Australia, Westpac s Programme Reach enables similar segmentation. This programme provides customer centre representatives with the ability to focus a specific campaign on a specific customer. Outbound callers are sent a Westpac Lead which matches the customer with other suitable offers. For example, an outbound caller who is targeting consumers via Westpac s Relationship Builder is sent a Lead regarding a campaign that Program Reach predicts the customer may be interested in (Harrison & Ti Gray, 2012). While there are 2

3 examples of complex segmentation methods using big data available, it needs to be acknowledged that most media is still bought using demographics, so the practicality of such targeting methods should be considered (Cannon, 1984; McDonald, 2004; Pooler, 2002; Walker, 1998; Winter, 1980). On this basis, the current study considers demographics only. While it is evident from the paragraphs above that there is a plethora of academic papers and industry case studies arguing the importance of the practise of segmentation in financial services, research on successful implementation of segmentation and targeting is rare. There have been concerns raised about the ability of successful implementation of a segmentation programme that include the inability to implement (e.g., Dibb, 1999), whether segmentation can practically work in a market (e.g. Danneels, 1996), or whether segments do actually exist in markets (Hammond, Ehrenberg, & Goodhardt, 1996; Kennedy & Ehrenberg, 2001a; Uncles, Kennedy, Nenycz-Thiel, Singh, & Kwok, 2012) or remain stable over time if they are found (Esslemont & Ward, 1989; Hoek, Gendall, & Esslemont, 1993). Further to the research above, there have also been questions on the validity of targeting (Wright, 1996; Wright & Esslemont, 1994). Very few studies have considered the success of segmentation or targeting at an aggregate level by reviewing consumer profiles of competitive brands. Previous empirical studies on consumer profiles of different markets with large databases indicate that successful implementation of segmentation is rare. Studies conducted of consumer profiles in a range of consumer markets (Hammond et al., 1996; Kennedy & Ehrenberg, 2001a; Uncles et al., 2012), television viewing (Barwise & Ehrenberg, 1988) and radio listening (Nelson-Field, Lees, Riebe, & Sharp, 2011; Nelson-Field & Riebe, 2010; Winchester & Lees, 2013) have found very little evidence of successful segmentation or targeting. As it has been empirically demonstrated, there are two types of market based on consumer purchase patterns, repertoire and subscription (Sharp, Wright, & Goodhardt, 2002). In a repertoire market any attempt at successful segmentation may be limited by the fact that any consumer is likely to buy more than one brand; meaning that the same consumers will be buying a number of brands in any market (Dawes, 2008; Sharp, 2007; Uncles, Ehrenberg, & Hammond, 1995). This alone suggests that there is less likelihood that differences in customer profile across competitive brands is likely. Subscription markets on the other hand are more likely to have consumers use one brand for a period of time (B. Sharp et al., 2002). It is on this basis that the financial services market is suitable as a potential market in which stable segments if they exist, may be found. It may seem that in the financial services market, mass marketing may actually make sense. For example, Romaniuk (2001) found little evidence of brand positions being unique in Australian banking brands, by implication suggesting that if banks had been aiming to attract unique customer segments they may have failed. This is supported by Kennedy and Ehrenberg (2001b) whose empirical analysis of customers of financial services in the UK found few differences across segmentation variables. To the contrary, there is a compelling argument against mass marketing, even in financial services: Trying to provide all things to all people is difficult, if not impossible in a competitive marketplace. Many financial institutions are selecting a few key target markets and concentrating on trying to serve them better than their competitors (p14, Zineldin, 1996) 3

4 The literature offers little clear direction for financial services marketers. To segment or not to segment that may be the question? This study considers whether customer profiles of financial services brands in Australia are different. It aims to replicate research such as that conducted in the UK by Kennedy and Ehrenberg (2001b) on segments in financial services and extends previous research such as that by Romaniuk (2001) by exploring whether the lack of positioning found with Australian financial institutions may be a function of a lack of identifiable segments in this market. In doing so it considers data from both 2009 and Method The data used in this paper was collected through Roy Morgan s Single Source Surveys in 2009 and The data was collected via personal interview with individual respondents and a sample of around 52,000 each year was achieved. Around 4,200 respondents over 14 years of age are interviewed each month (over 48 weeks) from all states and territories in Australia. The method for the analysis was inspired by Kennedy and Ehrenberg (2001a), who devised a simple yet effective method for determining differences in profiles of customers for competitive brands. Each brand s customer profile is simply compared to the average brand to look for large deviations, which would be expected if brands were successfully targeting different segments of consumers. Deviations of more than five percentage points are highlighted. Although a large sample is used in this study, the analysis has been conducted with the top ten financial services brands by penetration to limit the likelihood of smaller brands biasing results due to sampling error. Results and Discussion The first segmentation variable to be explored is gender. Table 1, below presents the proportions of males and female customers for each financial services institution, complemented in the right hand columns with the deviations from average brand. Table 1: Gender profile of Australian banking customers (2009 and 2011) Proportion by gender Deviation by gender Proportion by gender Deviation by gender Male Female Male Female Male Female Male Female Commonwealth Bank ANZ Bank Westpac National Australia Bank St George Bendigo Bank Suncorp Metway BankWest ING Bank of Queensland Average Table 1, above presents the gender profile for each of the top ten financial services brands in Australia. Columns two and three (for 2009); and six and seven (for 2011) present the proportion of men and women who are customers of each of the top ten Australian banks by market share, along with an average across all brands in the last row. Columns four and five 4

5 (for 2009) and the last two columns (for 2011) present the deviations from average brands. The results suggest no notable difference in customer profile for any of the ten brands on gender in either year. Table 2, below presents the deviation from average brand for age group for the ten brands. Table 2: Age profile of Australian banking customers (2009 and 2011) Deviation from average brand for age group Deviation from average brand for age group Commonwealth Bank ANZ Bank Westpac National Australia Bank St George Bendigo Bank Suncorp Metway BankWest ING Bank of Queensland In line with the method used by Kennedy and Ehrenberg (2001a) deviations equal to or greater than five percentage points are highlighted in bold and italics. Unlike the findings for gender, the age profiles presented in Table 2, above, suggest a few deviations from average. For example, ING appears, in both years, to have fewer older customers and more in the age bracket than the average financial institution, while Bendigo Bank gets a slightly older profile of customer. The Commonwealth bank has a slightly higher proportion of younger adults than the average brand. Table 3, below, presents the results for income. Table 3: Income profile of Australian banking customers (2009 and 2011) Deviation from average brand for age group Deviation from average brand for age group < $15k $15k to <$20k $20k to <$25k $25k to <$30k $30k to <$40k $40k to <$50k Total >$50k Total >$100k Under $15k $15k to <$20k $20k to <$25k $25k to <$30k $30k to <$40k $40k to <$50k Total >$50k Total >$100k Commonwealth Bank ANZ Bank Westpac National Australia Bank St George Bendigo Bank Suncorp Metway BankWest ING Bank of Queensland Table 3, above, highlights the deviations from average financial services brand across a number of income ranges. The results, for both 2009 and 2011, suggest that the Commonwealth Bank and Bendigo Bank get a slightly less wealthy profile of customer than the average brand, while ING stands out as getting a profile of customer that is slightly wealthier. This is also reflected with socio-economic groups as shown in Table 4 below. 5

6 Table 4: Socio-economic profile of Australian banking customers (2009 and 2011) AB C D E FG AB C D E FG Commonwealth Bank ANZ Bank Westpac National Australia Bank St George Bendigo Bank Suncorp Metway BankWest ING Bank of Queensland With the socio-economic groups Bendigo Bank has, over both periods, less AB s and more FG s than average whereas ING has more AB s, consistent with the income demographics presented in Table 3. It is interesting to note that Suncorp Metway has increased its share of AB s between the two periods. Discussion The results suggest little evidence for deviations from average for the gender variable, and a few minor deviations for age group. The deviations that were found would hardly be sufficient to suggest that any brand has successfully targeted any particular segment. Whilst it may appear that in 2011 both ING and Suncorp Metway are targeting the higher income/ab segments it is important to note that in absolute numbers each of the top 4 banks has more customers in those segments than ING and Suncorp Metway combined. Given the large sample size of over 50,000, and method used in this study, the results suggest compelling evidence that researchers are unlikely to find sustainable evidence that different financial services brands serve different segments of the market in Australia. The results of this study suggest that any bank s customer is a typical bank customer. Given the number of previous conceptual studies that outline the importance of segmentation, targeting and positioning and the previous case studies highlighted that specifically indicate there are segments in the financial services market, the results of this study may be surprising. However, when one considers empirical studies conducted across industries such as financial services (e.g. Hammond et al., 1996; Kennedy & Ehrenberg, 2001a) retail (Kennedy & Ehrenberg, 2001a) and media (Barwise & Ehrenberg, 1988; Nelson-Field et al., 2011; Nelson-Field & Riebe, 2010; Winchester & Lees, 2013), the results are consistent with earlier research. Conclusion Segmentation and targeting are portrayed as key strategic activities for marketers (M. McDonald & Dunbar, 1998; Yankelovich, 1964), however the results of this study suggest that there may be some industries in which either targeting has not been successfully implemented or segments simply do not exist. Given that individual banking brands do not appear to attract unique customer segments future research should consider the success of particular banking products in attracting specific customers using a wider range of segmentation variables. In addition, further research is required in a range of other industries across other countries to establish whether this empirical finding is generalisable across industries. 6

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