Inexpensive Marketing Tools for SMEs
Today small and medium-sized enterprises (SMEs) play a key role in the economy and are considered the engines of global economic growth. In today’s environment of mature economies, stagnant markets and fierce competition, consumers are increasingly informed and demanding personalized treatment and products and services that meet their needs. In this context, SMEs can remain in the market, and maintain a competitive advantage, if they are able to respond to customers’ needs in a timely manner. That is possible if supported by the appropriate information systems and information technologies. Actually, many SMEs are far from accessing all the available data, because they have neither the knowledge nor financial capacity to acquire tools that allow you to extract knowledge from your internal and external databases. However, is possible by combining a database that provides behavioral information from your prospects and combining that data with the spatial information of those customers. This joint allows a comprehensive analysis that is possible through the use of segmentations techniques, which supports marketing campaigns in an effective way, promoting visibility in the market, and allowing acquiring or maintaining a strategic positioning, using inexpensive tools.
KeywordsCompetitive advantage Database marketing GeoMarketing RFM model Costumer segmentation
Small and medium sized enterprises can gain a competitive advantage and create sustainable business by adopting information technologies (IT) and information systems. Sales and marketing will only be successful if supported by good databases (DB). IT properly harnessed, can be competitive advantage of a company over its competitors. Companies need to develop competitive advantages based on an adequate use of IT, which are an essential element of success in today’s competitive global market. This is fundamental for SMEs, whose survival depends on the adequate use of IT.
Marketing decisions, which aim to define the best strategic plans to address the market, choosing the best advertising campaign, select the segment and the type of product to offer, should and must result from an analysis of information and data available. We can say that the basic principles of marketing are applicable to large and small businesses. Marketing in small businesses can be categorized as: culture (analysis of consumer needs); strategy (development to enhance actual and potential market position); and tactics (analysis of the 4Ps - Product, Price, Place, Promotion - to influence the performance and growth) . Some authors have referred the inability of SMEs to make strategic marketing decisions [2, 3], because of the way an owner\manager acts, they make most decisions on their own, and respond to current opportunities, so decision making occurs according to personal and business priorities at any given time.
At the moment it is fundamental for any company to know in detail its action market. However, this is only possible by combining the database\Database Marketing (DBM), that provides behavioral information from your prospects and combining that data with the spatial information of those customers. This joint allows a much more comprehensive analysis and is made possible through the segmentation techniques.
The competitiveness resulting from globalization and also the evolution of information technology has scarified the market view, since the extent of competition is no longer limited by the geographical space of a city, state or country, but rather has competitors for the whole world to a click away. To survive in the global market, with an increasingly and aggressive competitiveness, focusing on the customer is becoming a key factor for SME’s, and customer retention is very important because of their limited resources.
This paper is organized as follows: after this introductory part is presented the literature review, based in Database Marketing and customers segmentation using the RFM model and GeoMarketing (GM). In the 3th section, is combining the DBM that provides behavioral information from prospects; with GM, providing data with the spatial information of customers; in order to provide a comprehensive analysis through the use of segmentations techniques, supporting marketing campaigns in an effective way, promoting visibility in the market, and allowing acquiring or maintaining a strategic positioning, using inexpensive tools. In the last section, we draw some conclusions.
Over the last decades, organizations have increasingly come to trust on technology to support communication and information processing in almost all areas of their operations. Marketers and others related to marketing function have been seeking the best way to introduce such information and communication technology successfully into their business.
The contemporary marketing practices framework  and the resulting empirical research findings highlight the information and communication technology challenges for marketers during the 1990s and until today .
Organizations should to develop their strategies in order to gain competitive advantage over their competitors. Competitive advantage can be understood as seeking unique opportunities that will give the company a strong competitive position. The markets become more competitive and many companies understand and recognize the importance of retaining current customers. The benefits associated with customer loyalty are widely recognized within business. Singh  suggest that customer loyalty is rapidly becoming, “the marketplace currency of the twenty-first century”. This is a commonly held view in the academic field, which supports the need for strategist’s businessmen and marketers to adopt a customer-centric vision [7, 8].
2.1 Database Marketing
The Database Marketing (DBM) is an essential part of marketing in most organizations. The basis of the DBM is that at least part of the communication of organizations with their customers is direct . We can say that the DBM is normally covered by classical statistical inference, which may fail when data are complex, multidimensional, and incomplete.
Nowadays database marketing approach is differentiated by the fact that much more data is maintained in databases, and that the data are used in more sophisticated ways.
There are various definitions of DBM, with different approaches or perspectives, showing some improvement over the concepts . In marketing perspective, the DBM is an interactive approach to marketing communications, which uses addressable communication media, such as telephone, mail and Internet , or DBM is a strategy that is based on the premise that not all customers or prospects are equal, and the collection, maintenance and analysis of detailed information about customers and prospects, marketers can modify their marketing strategies.
In a simple way, the DBM involves gathering information about past, current and potential customers, to build a database that improve the marketing effort. The information includes: demographics, what the consumer likes and dislikes, tastes, purchasing behavior and lifestyle .
With the advancement of information technology, in terms of processing speed, and in terms of storage space, the flow of data in organizations has grown exponentially, suggesting different approaches to the DBM. Generally, it is the art of using the data collected, to create new ideas to make money , or add other costumer information in a database (lifestyle, transaction history, and others), and use these information as the basis for customer loyalty programs to facilitate contacts and to enable future marketing planning, . The DBM can be set to collect, store and use the maximum of useful knowledge about customers and prospects, to their benefit and profit.
DBM is a marketing tool oriented to databases, increasingly the focus of the strategies of the organizations . All definitions have in common a main idea, the DBM is the process that uses the data stored in database marketing, in order to extract relevant information to support marketing decisions and activities by understanding customers, which will satisfy their needs and anticipate their desires.
2.2 RFM Model
There are several direct marketing response models using consumer data, among them, one of the classic models, known as RFM model, this model identify customer behavior , determining the probability of consumers responding to a direct marketing promotion based on the recency of the last purchase, the frequency of purchases over the past years, and the monetary value of a customer’s purchase history , and it’s a good model to SME’s.
Peter Hardie et al., presented a stochastic model to estimate the customer life time value, using as explanatory the RFM variables. Colombo introduced a simple stochastic model based on RFM to respond to what customers should a organization focus to make a product offering . Both studies have in common the same motivational principle: customer behavioral measures are key indicators to predict future behavior of these .
The RFM model is a model used to analyze and predict customer behavior . RFM model is the most frequently adopted segmentation technique, focuses on the three behavioral variables of recency, frequency, and monetary value, and is used to segment customers using information related to recency, frequency, and monetary value (which are combined into a three digit RFM code, covering five equal quintiles). Recency represents the time period since the last purchase; frequency is the number of purchases in a given period of time; while monetary is the amount of money spent in this time period . These variables can be used to segmenting customer’s behavior from databases. These three variables are considered powerful predictors of future behavior and form the basis of database marketing. Recency enables the prediction of future value, while frequency and monetary value enable the estimation of the current value. The combination of these three dimensions (RFM) allows combined analysis of current and future customer value. The higher the RFM score, more probable it is for a customer to respond to a marketing action.
Companies can maximize the return of campaigns and minimize marketing costs if they know to which customers should send promotions. These customers can therefore be considered of greater value to the company because their past behavior indicates a positive intention to maintain these relationships.
The RFM model, allows the quantification of customer behavior through the development of a quantitative framework and allocating customers, of behavioral patterns. It is through this attribution of behavioral patterns and subsequent grouping segments. It is possible to perform an economic feasibility analysis at the level of future promotional investments . Companies use RFM analysis to determine whether and how to invest in their direct marketing customers . The RFM model only works with DB\DBM of existing customers. The basis for its operation will be the purchase history of each customer. The analysis of each variable of the RFM method is done separately. In the case of recency, consumers who have bought recently are more probability to respond to a new offer than someone who has bought long time ago. Frequency in different types of business may use other frequencies, as we can use the average number of purchases per year, the number of calls made per month in the case of a telephone operator. The frequency represents the number of iterations between the customer and the company in a given period. There are two possible approaches: the exclusion of all clients who began to be outside the period and the weighted average period of time that is as customer. For the case of monetary value, the database before the data are separated into quintiles must be ordered by the total value of purchases per year, per month or more depending on the timeline indicated for the business. Then ordered quintiles from 5 (spend more) to 1 (spend less).
Thereafter, variables are grouped together and used to segment DBM in RFM cells. If we choose for each RFM attribute, 5 classes, with the numbers 1 to 5, we obtain 125 different classifications. Thus the 555 customer is a very recent customer, very frequent and with a high volume of purchases, whereas a customer 111 is little recent, infrequent and with low volume of purchases. There is a possibility of hierarchizing the classifications, following the valuation that is common to give the integers.
The higher the RFM score, the more profitable the customer is to the business both now and in future. High RFM customers and probably are most likely to continue to purchase and visit, and to respond to marketing promotions. Low RFM score customers, are the least likely to respond to promotions. No doubt, those high RFM customers represent future business potential, because the customers are willing and interested in doing business with you. With RFM we can decide who to promote to and predict the response rate and increase customer loyalty and profitability.
The RFM model revealed interesting customer segments that could be targeted using appropriately designed marketing campaigns. We believe that model will provide significant business value. This model is known and appreciated for its simplicity, since it can be used without requires specialized statistical software, and also their results are easily understood by users.
GeoMarketing is a marketing approach that comes from joining the disciplines of marketing with geography. GM brings a new dimension, the spatial dimension, to the study and analysis of the socioeconomic, behavioral, demographic and statistical phenomena of a given market, phenomena that have always been present in studies and marketing strategies .
GM is the application of Geographic Information Systems (GIS) to the field of business. In general terms, it is the integration of geographical concepts into the marketing environment, such as areas of operation, distribution and location of sales points. GM is a branch of geography coupled with concepts of marketplace, based on the processing of geographic information that allows the concatenation, organization and manipulation of data referring to users and research from a geographic point of view .
GM combines several variables, such as social information (such as age, sex and level of education), economic information (such as wage level and market potential), geography and plants). This combination allows a better understanding of the reality of the market and companies can take full advantage of this situation as several layers of customer information in a given geographical area allow access to a greater knowledge of their current customers and potential. Thus, it is possible to verify the influence of a certain location on the competitors, consumption activities and on the variables of the marketing-mix. Geography has the function of giving the coordinates to the marketing, coordinates that allow a company to know better its market, namely who buys and where it buys, how often it does, among others. Thus, as each market segment is identified and delimited on the map, it is also identified which sites that have the greatest potential of consumption are the service or product in question.
The applicability and importance of GM as a business tool are relevant. In fact, it has an enormous breadth of application and is an important component of the marketing strategy of companies in different sectors (food, transport, banking, insurance, hotels, among others).
To truly be used as a methodology to support decision making, GM must incorporate into spatial analysis several qualitative variables derived from the understanding of human geography.
This technique allows a more accurate understanding of the connections between consumers and physical space, whose interaction provides innumerable innovative marketing possibilities for companies .
GM is therefore a tool that is extremely useful for managers, helping them to make decisions and making the identification of opportunities and threats to their business more efficient. In addition, it is possible to state that GM techniques contribute to a better allocation of available resources; preventing issues such as the lack of demand, poor public acceptance or excessive competition; and to optimize organizational results, since it is able to identify the ideal environment for business development and, in the case of franchises, target regions with a coverage gap, which ends up representing a cost for a network of stores.
3 Combining DBM and GM
The basis for successful marketing strategy is the identification of specific customer groups that have homogeneous characteristics in detail. In order to identify these segments, a very large number of information is necessary to know the particularities of each group and to be able to satisfy their needs. The targeting process is long and complex because it requires the confirmation that the segments exist, the determination of their characteristics and location; from this information it is possible to devise ways of allocating each customer in the correct segment.
The knowledge about consumers retained in DBM, plus the GM information on where and how these customers are graphically placed in the market, is critical to implementing a marketing approach. There is a growing need for a more accurate understanding of the market, which manifests itself through increasing and specific segmentations. This segmentation is due to the gradual fragmentation of the population and the need to define a differentiated strategy for each segment .
By combining DBM that provides behavioral information from your prospects and combining GM with data from the spatial information of those customers, it´s possible to perform a comprehensive analysis and is possible through the use of segmentations techniques; which supports marketing campaigns in an effective way, promoting visibility in the market, and allowing acquiring or maintaining a strategic positioning.
Although the range of applications from GIS to business issues is quite broad, the structure of the market-leading programs makes them particularly well suited for DBM. They integrate three types of files: DB\DBM, geographic files and point files. The geographic files contain the geographical entities (areas, lines, points) defined by their coordinates (latitude, longitude), and serve to produce the maps themselves, forming the most critical and expensive part of the system. Point files are a hybrid of the first two. Point files include information associated with point locations that are not durable geographic entities. The classic case would be a customer database (DB\DBM). These, once properly geocoded from the costumer address, postal code or other attribute, can be placed on the map. In addition, the data available are associated with location and can be manipulated taking this information into account. The interaction of these three large blocks allows the assembly of maps, the application of colors, patterns and symbols (to represent different types of data simultaneously) and the performance of several aggregation, disaggregation and statistical calculations.
The typical end product can be an analysis of the potential market, segmentation, location of customer bases and prospects, or branch location. It can also be the planning and projection of responses to campaigns or the projection of market trends, any of the numerous studies where location is important.
Currently there some open source tools available to SMEs, such as QGIS  and GVSIG . QGIS is currently the reference in open source GIS software, customizable with a multitude of plugins and with the power of the OpenGEO suite. GVSIG is also very complete. With these two desktop GIS it´s possible perform analysis of geographical coverage, market penetration, demand potential, among others, like in the most common applications of GM. But if we need more arsenal of spatial analysis, we can go through Spatial Datamining and test its excellent available tools for commercial territories, regionalization, spatial clustering, and spatial interpolation. Another example is Geoda , also has excellent resources for space econometrics, spatial regression, and integration with statistical software. These tools also allow SME´s to spatially analyze the data about clients stored in the DBM. Even is possible have free online geocoding resources, to assign geographic codes to customers.
Nowadays, to survive in the global market, with an increasingly and aggressive competitiveness, focusing on the customer is becoming a key factor for SME’s, and customer retention is increasingly important, conditioned by SME’s limited resources.
By joining the concepts of geography and marketing, we have GeoMarketing, which allows study the relations between the territory or space and the strategies and policies of Marketing. Being the territory the space where the company, its customers, suppliers and distribution points are located.
GIS integrate DBM, geographic files and point files. Combining the customer segmented extract from DBM using RFM model, with GM geographic files, its possible return the geocoded from costumer address, postal code or other attribute, can be placed on the map. In addition, the data available are associated with location and can be manipulated taking this information into account. The interaction of these files (DMB, geographic files and point files) allows the assembly of maps, the application of colors, patterns and symbols (to represent different types of data simultaneously) and the performance of several aggregation, disaggregation and statistical calculations. The typical result can be an analysis of the potential market, segmentation, location of customer bases and prospects, or branch location. It can also be the planning and projection of responses to campaigns or the projection of market trends, any of the numerous studies where location is important.
This work provides a comprehensive review of a combining application of DBM with GM, using open source tools, that helps marketers visualize and quickly identify important customer segments, and to develop the marketing effective strategy; allowing acquiring or maintaining a strategic positioning; being a good and innovative strategy for SMEs.
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