Geographic segmentation is beneficial for a large-scale campaign execution when the product to be promoted is largely understood and needed by a wide and diverse group of consumers. Segmentation of this type generally focuses on locating a center point, for example a branch, and radiating from that center point in terms of miles, census tract, ZIP Code, or a predetermined radius. This approach is also beneficial when the socioeconomic status of the individuals within the geographic segment is similar. The negative aspect of geographic segmentation is the assumption that everyone with the geographic footprint is identical, displaying the same predictors of behavior. An exception to this rule are the banks which use geography as part of their SEO/SEM strategy and highly target based on radius plus the appropriate demographics. This strategy can focus on the collection of email addresses in exchange for email offers where the direct response can be tied specifically to the marketing channel. In a non-bank case study, Granite Springs, NY’s Traditions 118 restaurant earned a $75 return for every $1 invested by consistently executing this strategy26.
Demographic and socioeconomic division is perhaps the most widely used form of marketing segmentation; however, this methodology focuses on the descriptive nature of an individual as opposed to making a prediction with regard to desire to purchase a specific product. One of the largest benefits of demographic segmentation is its simplicity and cost. It can easily be explained to frontline staff and tends to be relatively inexpensive to acquire. Typical appended elements include income, family size, occupation, and homeownership status. Internal elements include age, presence and balance of product, service indicators, branch assignment, and tenure. The downside of this methodology is the assumption that everyone within the same demographic behaves identically. In other words, there is little understanding of customer differences if one views demographics only. Recognizing that demographic and socioeconomic segmentation play an important role in purchase patterns of financial assets27, one must also recognize the importance of psychographic attributes to increase a firm’s knowledge of the consumer.
Psychographics build a mental model of consumer buying patterns in the context of the consumer’s life cycle. Typically these models combine demographics, product behavior, and financial assets data to create clusters of consumers who demonstrate like-behavior and socio-economic status28,29. Psychographics are an attitudinal layer variable. This form of segmentation allows the marketer to gain more insight into the desires of the consumer beyond just simple facts such as age or income. In other words, psychographic data defines why consumers do what they do. This is important particularly from a financial marketing perspective as the use of psychographics, combined with demographic data, provide the bank with the ability to develop the appropriate products and marketing strategies to gain consumer trust27. If a bank is to adapt psychographics as part of its overall segmentation strategy, it must be aware that elements such as lifestyle, interest, attitudes, and personality are fluid over time and may not be practical for a small- to medium-size institution that lacks the in-house analytical power to constantly monitor and validate psychographic model assumptions.
Banking is a mature market. In large part, there are only two ways to significantly increase market share. One, acquire a competitor, or, two, take business from one’s competitors through aggressive marketing strategies. From a marketing perspective, the use of behavioral data is a superior tool both in its ability to increase sales as well as its ability to do so at far less cost than broad-based communication approaches. No longer can a financial institution simply target by socioeconomic factors, demographics, or psychographics; it must use behavioral cues in order to differentiate between consumers who would seemingly be classified in like-segments30. Practically speaking, behavioral segmentation for banks focuses on tactical analysis of credit data, propensity models, and data gleaned from ACH, online banking, or credit card transactions. Armed with this knowledge, a financial institution can create specific marketing communications based on recent transaction data. Most common examples of this type of data include real-time analysis of credit applications at other financial institutions, ACH or online bill pay transactions wherein payments for loans are made from the home institution’s product to another financial institution, and/or large retail purchases indicating opportunities for short-term credit products. The nature of behavioral segmentation provides the opportunity for real-time communication across a wide range of marketing channels including direct mail, email, point-of-sale devices, and mobile channels as well as personal contact at the branch or call center level. The downside of using behavioral data as a marketing driver is that it does require detailed, in-depth data sets, models and market testing. Additionally, the importance of action when using behavioral data is critical. In order for the use of behavioral data (and its considerable expense) to make financial sense, a bank must act immediately on the triggers that are produced with this type of analysis. This means near real-time marketing reaction to behavior events that will drive product purchase.
Once the optimal segmentation strategy has been agreed to, and the expectations of performance have been defined, the bank marketer must turn attention to the next most critical piece in the communication equation: the message.
24. Kim T, Hoon-Young L. External validity of market segmentation methods. European Journal of Marketing. 2011;45(1/2):153-69.
25. Robinson J. The Economics of Imperfect Competition. 1st ed. New York, NY: Macmillan; 1933.
26. Compton J. Savoring geotargeting’s effects. DM News. 2015 June 2015:34.
27. Shalini Kalra Sahi, Dhameja N, Arora AP. Predictors of preference for financial investment products using CART analysis. Journal of Indian Business Research. 2012;4(1):61-86.
28. Targeting by financial behavior and wealth
29. The end of demographics: How marketers are going deeper with personal data [Internet].; June 30, 2011. Available from: http://mashable.com/2011/06/30/psychographics-marketing/.
30. Mojsiewicz M, Batóg B, Wawrzyniak K. Application of Factor Analysis in Behavioral Segmentation on the Base of Semiometric Scale. Folia Oeconomica Stetinensia. 2008;7(1):23, N/A.[/fusion_text]