Optimizing Sales Campaign Effectiveness - Essay Example

ECRM Question Using Marketing Analytics how would you optimize sales campaign effectiveness using demographics and psychographics? Introduction Consumer demographics and psychographics play a crucial role in determining the nature and scope of demand for any given product or services. Marketing analytics involves the measurement of the consumer demographic and psychographic data so that the organizations get a clear idea of who buys their products and why. This knowledge in turn provides the basis of forecasting demand as well as making changes to marketing strategies or even to the product design or appearance. Thus, marketing analytics provides the crucial data on which strategic decisions are made by organizations. The following paragraphs will describe the scope and meaning of the terms consumer demographics and psychographics and then explains how marketing analytics is used to optimize sales campaigns for targeting the customers. Consumer Demographics and Marketing Analytics Consumer demographics consist of data related to factors like the gender, age groups, marital status, income levels, cultural and social background, regional backgrounds, political or religious affiliations and educational background (Park and Srinivasa, 1994). It is understood that these factors help in shaping the customer’s preferences and likes and also shape his overall attitudes towards products or services as well as the actual consumer behavior. Fig 1: Demographic profile impacts on the consumer behaviour Source: Author For example, the age group of the customer will determine what lifecycle stage he is in – if the customer is in his 30s then it is likely that he would be starting on his career and would have a household with spouse and kids. This lifestyle stage would involve the customer in purchases related to house maintenance, groceries or health or medical insurance. Similarly, a teenager would prefer to purchase music while a woman with kids would buy baby food. Demographic data therefore provides a great amount of insight into the likely preferences and needs of potential customers. It is therefore essential from the marketing point of view as it provides a basis of forecasting demands by the companies. By collecting accurate demographic data, the organizations can get a clear picture of prospective customers for their own specific products and services (Brewer, 2005). For example, an organization that manufactures ready to make processed foods would select a target demographic profile for its potential customers as educated married couples with both spouses working and having a substantial income. This profile is chosen because it is expected that people who are married and have kids and are also working, would be hard pressed for time and would therefore prefer processed foods over cooking lengthy meals on their own after spending a tiring day at school. However, the organizations also realize that such couples need to have fair amount of education so that they are not biased against using the processed products, and are able to appreciate the nutritional and time saving value provided by such foods. Additionally, as the processed foods costs higher than home prepared food, it is required that the potential customers have higher income. Thus it can be seen from the above that using the demographic data, consumer behaviour can be adequately predicted. Consumer behaviour itself consists of a range of actions and modes of operations for the customers and its measurement too involves using marketing analytics techniques. For example, customers may buy a product, the may spend time browsing among the products or they may make a specific product request or complaint or return the product. When the consumer spends some time on browsing among the brands of a generic product, the data about what brands the customer was deliberating on and what brand he finally selected and why, could provide a useful pointer towards the effectiveness of the sales campaign or towards the brand image. Similarly, consumer behaviour could also be studied by observing customer’s complaints or requests. Marketing analysis enables the collection of data on demographics and consumer behaviour respectively via a variety of different means (Philip, 1999). The demographic data is largely collected at various points of contacts with the customer like invoicing and billing, maintenance and after sales service, and complaints and requests at the call centers. An easy approach to collecting demographic data is that to get the customer’s feedback on surveys which can be distributed in stores or through online means. The demographic data is collected using a nominal scale that makes use of codes to signify information (for example, in case of gender data, 1 can be used for male and 2 for female) or interval scale (for example, for income levels, 1 can be used for lower-middle income group, 2 for middle income group and 3 for higher income group). The behavioural data is collected largely on the basis of the invoices and other points of contacts like customer complaints and customer care departments that receive requests and complaints from the customers. However, another way of obtaining data on customer behaviour is through observation of the customers in the store, on the website or through survey of the customers (Evans, Foxall and Jamal, 2009). The customers can be directly asked about what they need and what they intend to buy in the store or the site or through surveys distributed to internet users or to general public in the region. Also, the online behaviour like the total amount spent or the breakdown of the amount spent and the time of the week and the day at which a purchase is made – are correlated with demographic data to arrive at a more holistic profile of the customer (East, Wright and Vanhuele, 2008). Once the demographic data is available with the organization, it can conduct an analysis on the profiles of their products’ potential buyers. This profile, in addition to the consumer behaviour data, is useful in informing the organizations about the marketing plan and strategies that can be used for boosting sales. On the basis of the trends in demographics, organizations can be prepared with the appropriate plan of action for advertising and promoting their products. For example, if the demographic trends show that that there is an increase in the number of older couples living alone and that such couples prefer to do their household shopping during the weekdays and in early part of the day (Chiagouris and Kahle, 1997). For such customers, the retailers can develop a promotional plan of giving discounts for conducting shopping during the later part of the day or they can provide for home delivery for them for an extra charge. Thus, it can be seen that having the knowledge about who purchases what and why provides a powerful tool in the hands of the marketers to capitalize on their sales revenues. By understanding the customers, their needs and their limitations, marketers are able to tailor their promotional and sales programs in a cost effective and targeted manner. Consumer Psychographics and Marketing Analytics In addition to the demographics, another factor - consumer psychographics, that plays an important role in the development of the overall customer profile and in determining the customers’ attitudes and behaviours towards various products. This factor is more personal as it gauges the customers’ mindset and decision making process as well as determines the attitudes, feelings and thoughts that the customer may have towards a product or service. The consumer psychographics too determine the consumer behaviour, but the process is mediated by the development of attitudes that get translated into appropriate behaviour when conducive environment is met (Evans, Foxall and Jamal, 2009). Consumer psychographics data is used by marketing analytics to understand the nature of the consumers – things like if the database of their consumers are risk takers or risk avoiders, are the customers more attracted to visual messages or to written texts, do they like to feel challenged or feel safe, if they are dominating or get influenced by others, what values they have, what causes they favour etc. The personality profile of the customers provides the marketers with the information with which a tailor made plan of promotion can be developed (Russel, 2006). This is especially true in the case of marketing and promotion communication. The advertising messages need to be appealing and interesting to the customers and what interest or appeals to the customers is gauged using marketing analytics on the personality data of the customers. Fig 2: Psychographic profile impacts on the consumer attitudes and behaviour Source: Author For example, in the case of a website that makes subscribers for dating, the personality type data is extremely useful for the marketers in profiling their customers and in contacting them with suitable communications. The breakdown of the personality type of the total number of people who visit the site would show the webmaster who are the people (if they are complaint or dominant, looking for serious relationship or not etc.) who are more interested in online dating and subscribing to the site. This psychographic data, when correlated with the data mining data like the amount of time different personality type spend on different pages, frequency of visits etc., help the marketers in better serving the customers. Also, marketing communications can be made to appeal to the customers – for example, in the case of people who are more visual, images can be used liberally, while , for text-oriented individuals, more textual messaging can be used. As in the case of demographic data collection, the psychographic data is also collected in various ways. One way is to use the marketing analytics tools to assess the amount of time a customer spends on the various sections of the website, the type of products that he browses or the final purchases that he makes. However, a more direct method of collecting psychographic data is using psychological survey instruments to assess the personality and values of the survey. These instruments can be made available to the customers at the checkout point or the customers can be deliberately contacted with the request to participate in the survey. For marketers that do not have a website, the psychographic data is available through industry reports and market research reports that they can make use of. Summary Thus it can be seen from the above discussion that marketing analytics tools and techniques enable the marketers in obtaining the consumer demographics and consumer psychographics data and in converting the acquired data into relevant knowledge about the customers. This knowledge then provides the marketers with specific strategies to boos their marketing communications and sales. Question 2: If you wanted to personalize a website by displaying products or services that reflect the user’s interest, what data mining techniques would you use? Explain the reasons for your choice. ? Introduction Data mining is employed to detect patterns and trends from the available data and these trends can be analysed to make predictions about the future consumer behaviour on the site (Blattberg, Kim and Neslin, 2009). Data mining is a powerful technique to gauge the needs of the customers and it provides the marketers with the tools to serve the customers in a fairly customized manner (Brown, 2007). The most popular and traditional data mining technique is based on RFM or the Recency, Frequency and Monetary Value. The logic behind this techniques is to make a prediction on the basis of the user’s past behavour – like if he had made a purchase recently, or if he shows consistent purchase behaviour over time and also the amount he may have spent in the past. These factors enable the marketers to make a fairly accurate guess about what he may do in the future (Chen et al, 2009). However, there are several more sophisticated and complex algorithms as discussed later in this answer. Though the background data is collected using various modes of data collection and provides the relevant demographic and psychographic details of the customers, the data mining techniques help in identifying the trends in consumer behaviours and predicting what different groups of customers may be interested in (Brewer, 2005). Additionally, onsite navigation data provides a list of the sequence of actions that the visitor performs on the site and thus provide insights about his likes, preferences or things on the site that hold the customers’ attention. This information about onsite customer behaviour in conjunction with the insights gained from the background demographic and other personal data form the basis of personalizing the website for the customers so that products or services that they are interested in reading about or purchasing are shown to them (Brewer, 2005). The above is achieved using suitable data mining techniques. There are several techniques of data mining that can be employed to enable the customers to view the products and services that are of interest to them and that they are likely to purchase. However, the suitability of the data mining technique that is used is assessed in terms of how it enables the collected data on consumer behavour and demographics to be analysed effectively and to be integrated with the existing data so that the system keeps regenerating itself. Data mining effectiveness is also dependent upon the authenticity, reliability and credibility of the data collection methods and on the integrity of the collected data itself. A data mining model can at best be used for analyzing the raw data that has been already collected and using the analysis information for generating rules or algorithms with which the web content can be generated in a personalized manner (Eisenberg and Eisenberg, 2006). Web Usage Data Mining There are several data mining techniques that are employed for web personalization like content filtering, collaborative filtering, rule based data mining, sequential pattern discovery, probabilistic modeling web usage data mining among others. It is however, recommended that the web usage data mining technique be employed as the personalization of the website is to be undertaken in real time and dynamic manner, and web usage mining is able to provide the flexibility with which data can be pooled from different resources and analysed to detect patterns and make predictions. Web usage data mining is a program of discovering and analyzing patterns automatically from the click-streams and associated data that are collected as a result of the user interaction with the website (Fayyad, Piatetsky-Shapiro and Smyth, 1996). Web usage mining enables the capturing of the patterns and trends of the customers when they interact with the website and these are presented in the form of lists and glossary of pages, links or resources that the users of matching demographic profiles access frequently. This knowledge is of great use to the webmasters who can get a better understanding of how the customers like to navigate the website, where they face difficulties, what makes them spend more time on a particular page or what makes them leave the page or the site quickly. This in turn can be used for making the website more attractive, readable, interesting and engaging for the customers. It also provides great insights about placement of content or advertisements on the website (Kosala and Blockeel, 2000). As such, web usage mining provides potent insights about making the website better and generating more traffic, in addition to providing an automatic personalization for the users. In order to be able to give personalization or to make recommendations to the user, the system should be able to make a prediction about what the user may be interested in, and this prediction is made possible on the basis of various factors. These factors include the visitor’s current and past browsing pattern, the data from the other visitors’ past interactions, the demographic profile of the current user and the patterns of behaviour detected for other users of the same demographic profile etc (Mobasher, Cooley, Srivastava, 2000). These data are collected in a dynamic manner while the users are browsing or interacting with the website and it may also be proactively collected by surveying the users or making them answer intermittent questions on the website. In either case, the data mining technique should be able to develop a prediction algorithm, on the basis of which a pattern of interaction with the website could be developed and the users can be recommended with the next steps that they would like to follow. Web usage data mining is most appropriate tool for making a dynamic predictive algorithms that make recommendations to the customers online (Mulvenna, Anand, and Büchner, 2000). This is because, in most of the other models of data mining, the user profile that is developed is based on a single criteria about the user or on the rating of a product by users, while in the case of web-usage data mining a more comprehensive and dynamic user profile is developed which results in more flexible recommendations. For example, in the case of content based data mining, the prediction or the recommendation is made solely on the demographic match of the user with other users and their pattern of activity on the website. Also, in the case of collaborative filtering data mining model, the rating given to a particular product by other customers forms the basis of the recommendation of the new product to the current user. In contrast, the web usage mining approach, a diverse number of techniques and algorithm are used to collect and collate data and to develop an understanding of the patter before making the recommendations. Additionally, web usage mining also provides empowerment to the webmasters as only the website owners or the webmasters are able to develop the transactional logs about the amount of time spent, pages viewed, or clicks made etc. This is in contrast to the content based or structure based techniques that enable the data to be accessed by the public in general (Nissenbaum, 1997). These transactional logs show the website owner the real-time data on the actual user bahaviour on the site, and thus provide more insight about web personalization and customization for the user. Also, web usage mining enables the provision of more targeted on site search by customizing the search and omitting results that that the customer is most likely to ignore. This adds another degree of personalization and presents the products or resource that are most suitable for the customers even when they have a preconceived notion of what they are searching for. Web Usage hosting is therefore a useful way of providing added service and support to the customer and for boosting the end objectives (time spend, purchases made etc.) for the website. Summary Web personalization or presenting the customer with the navigational tools, resources or products that he or she may be most interested in, leads to better interaction of the consumer with the website. It also brings benefits of cost savings or revenue generation via purchases or ads etc. Web personalization is enabled using data mining techniques that have evolved over time to become more complex and dynamic and at the same time to provide better results of personalization. Web Usage data mining technique is one of the most suitable techniques for personalization of the website as it is based on a diverse methods of data collection and analysis algorithms. Web usage data mining brings forth the activity of the customer on the site (measured through logs and clicks) and matches it with the demographic or other details (Mulvenna, Anand, and Büchner, 2000). This leads to the development of a pattern of liking or preferences, and based on this the customer can be presented with the most suitable pages or links to proceed. Web usage hosting is unique as it gives the power to the webmaster to monitor user behaviour and to take advantage of the pattern and present those pages that are more likely to generate revenue. Another advantage is that the transaction data is held privately with the webmaster and the data mining results in better customer satisfaction (Nissenbaum, 1997). Question 4: As an e-business manager you have to formulate a business strategy using permission marketing principles and customer information acquisition principles. The strategic intent is to become e-business enabled. What would you include in your CRM strategy and what benefits would be delivered? Introduction E-enabled businesses are businesses which conduct operations using electronic space and the Internet. In majority of the cases, organizations tend to be partially e-enabled and continue to conduct their transactions via the physical means as well (Chaffey, 2003). For example, in the case of banks, the services are largely e-enabled as the customers can conduct majority of their transactions over the Internet website of the bank. However, the banks still maintain their branches and head offices and encourage the customers to conduct some part of their transactions – like changing personal details like address – through the branch or the head-office. However, there are certain business models, that largely are developed for making use of the internet, and that enable the performance of almost all the processes online. These include online magazines, search sites, or on line retailers like the Amazon. In the case of businesses which have online presence, it is essential for them to be able to gather sufficient data from the customers so that their demographic and psychographic profiles can be developed and the customers may be provided with a better overall experience on the website (Godin, 1999). The e-business strategy is therefore determined by what is the end result is that is desired by the organization. Organizations may be looking for promotion and advertising of their products from their websites, or they may have the objective of providing information about the organization or its products and retail stores. Additionally, the objective could be to encourage the customers to buy from the website and to provide their feedback and suggestions on the products and services. In all the above cases, it is desirable that the organization should be clear about what it needs and how it wants to proceed in order to achieve its objective – hence the relevance of a e-business strategy. As an e-business manager, it is important to develop an e-strategy for making the relevant and desirable products and services available online and for encouraging the customers and potential customers to spend time on the site browsing as well as making purchases (Denzin and Lincoln, 2000). For attaining a fully e-enabled business, all the above listed objectives – from making the information available and promoting and advertising the products and the company, to encouraging online purchases – are essential (Keller, 2000). A business that is e-enabled therefore conducts all its processes and operations online though it may indulge in other modes of advertising and marketing. However, in order to become e-enabled, the organization should also have the tools and techniques as well as a strategy to collect and collate consumer demographic and psychographic data. This data in turn enables the e-marketer to develop a specific strategy to target customers and to help them navigate the site in a manner that increases the likelihood of purchase behaviour. E-business strategy therefore requires using permission marketing principles and customer information acquisition principles to help in the collection of relevant data from the customers. Data collection is an integral part of CRM strategy and specific CRM policies can be initiated to facilitate further collection of data (Aaker, 1997) as explained in the following sections. Permission Marketing Principles Permission marketing is a concept that is based on the premise that customers that have already given their consent or who have agreed to receive messages from an organization or website, are more likely to take the communications more seriously and pay more attention to the communication content. This can be made as a CRM strategy to get the customers to become favorably inclined towards receiving the company’s communications as well as acquire a positive attitude towards the products and services (Nielsen, 2002). This is in contrast to the intrusive marketing, where the ads or the promotional communications are hurled at the customers or potential customers randomly and the customers tend to respond by closing their minds to such communications. There are several principles of permission marketing that need to be adhered to so that the customers do not feel interrupted or their privacy invaded by the marketers. One principle of e-permission marketing is to let the customers make request for more information on the product or service listed on the site. This is achieved by providing a tab or a link on the website that registers the ‘more information needed’ requests and collects the email or other mode of contact from the customer. This way, the site is able to collect data on the customers’ preferences and the customer can also be navigated to similar products or services that he made a request for. In addition to the ‘request’ option, e-permission marketing is also facilitated by providing the customers with the choice of subscribing to specific feed or to newsletters about the company or the site. This opt-in provision also requests additional data from the customer like listing the reason why he or she is interested in getting more information, his demographic details or any other details that the marketers can use in developing more targeted newsletters or communications. Another principle of e-permission marketing that is essential is that the opt-in provision should also come with a voluntary opt-out option for the customers so that they can disengage themselves as and when they need (Rhodes, 2009). This ensures that the customers feel empowered vis a vis the website and hence they think that the communications that they receive are reaching them because they opted for it. Additionally, in order to increase the opt-ins and as a part of its CRM strategy, the website can offer discounts or freebees that encourage the customers to enroll in the opt-ins as well as continue to accept and read the communications. By providing incentives, the website can also encourage the customers to fill in detailed forms that capture data on their demographic characteristics as well as on their psychological make-up (Brown, 2007). By using the above e-permission marketing principles, the database on the customers can be maintained and more targeted approach taken to developing and delivering marketing communications. In addition, there e-permission marketing enables the collection of more and more data from the customer and in understanding their behaviour on the site, which in turn helps in the development of a better and easily navigable website. Customer Information Acquisition Principles Information acquisition is the process of extracting the relevant information from a variety of sources and it entails not only locating the sources, but also extracting, compiling and collating the information that is relevant and usable for making decisions. The acquisition of information from the customers needs to be undertaken in a manner that the collected data is credible, reliable and relevant and that it is also complete and holistic so that statistical and interpretive assessments can be made on its basis. The important aspect of customer information acquisition is to know at what points the data needs to be collected and how it needs to be used (Arnold, 2007). For example, in the case of a website selling a variety of products, the information can be obtained using weblogs and time logs about what customers are searching for and how much time they spend on each page or section. This information can be further fortified by actively using customer feedback, by providing the customers with navigation options and by asking the customers to give quick answers about their choices. This data collected over the period of time enables the marketer to understand on-site customer behaviour and when it is combined with the demographic and psychographic data of the customers, it provides useful insights about developing the overall e-business strategy (Blatterberg, Kim, and Neslin, 2009). Summary The data that is acquired using the e-permission principles and customer information acquisition principles provides is used for formatting the website to facilitate better navigation and purchase, and to provide for better marketing communications and advertising options. References Aaker, J. L. (1997). Dimensions of Brand Personality. Journal of Marketing Research 34 (3), 347-356 Arnold, J. 2007. Email Marketing For Dummies. NY: For Dummies August, J., (1991). Joint Application Design. New York: Yourdon Press Computing Series, Prentice Hall. Blatterberg, R. C., Kim, B. D. and Neslin, S. A. ( 2009). Database Marketing: Analyzing and Managing Customers (International Series in Quantitative Marketing. NY: Springer. Brewer, C. (2005). Zoom in on your customers: The latest Customer-Intelligence tools leverage both human smarts and processing power. (Web Marketing).: An article from: Computer User Computer User, 19(3), 28(2). Brown, B.C. (2007). The Complete Guide to E-mail Marketing: How to Create Successful, Spam-free Campaigns to Reach Your Target Audience and Increase Sales. FL: Bruce C. Brown (Author) › Visit Amazons Bruce C. Brown Page Find all the books, read about the author, and more. See search results for this author Are you an author? Learn about Author Central Atlantic Publishing Company. Chaffey, D. (2003). Total E-mail Marketing. Oxford: Butterworth Heinemann. Cheng, Y. L., Kuo, M, Wu, S. and Tang, K. (2009). Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data. Electronic Commerce Research and Applications 8 (5), 241-251 Denzin, N.K. and Lincoln, Y.S. (2000). Handbook of Qualitative Research. London: Sage Publications. East, R., Wright, M. and Vanhuele, M. (2008). Consumer Behaviour: Applications in Marketing. NY: Sage Publications Ltd Eisenberg B. and Eisenberg J., (2006) "Waiting for Your Cat to Bark?", Thomas Nelson, Nashville Evans, M. M., Foxall, G. and Jamal, A. (2009). Consumer Behaviour. NY: Wiley Fayyad, U.M., Piatetsky-Shapiro, G. and Smyth, P. (1996). From Data Mining to Knowledge Discovery: An Overview, in Advances, in Advances in Knowledge Discovery and Data Mining. Cambridge, Massachusetts: AAAI Press / The MIT Press. Godin, S. (1999). Permission Marketing. Retrieved from www.permission.com. Retrieved on 5 October 2010 Kosala R. and Blockeel, H. (2000). Web Mining Research: A Survey. ACM SIGKDD, 2(1) 1-15. Keller, K. L. 2000. Conceptualizing, Measuring, and Managing Customer-Based Brand Equity. The Journal of Marketing, 57(1), 1-22 Chiagouris, L. and Kahle, L. R. (1997). Values, Lifestyles, and Psychographics (Advertising and Consumer Psychology). NY: Psychology Press Mobasher, B., Cooley, R., Srivastava, J. (2000). Automatic personalization based on web usage mining. Communications of the ACM, 43(8), 142-151. Mulvenna, M.D., Anand, S.S. and Büchner, A.G. (2000). Personalization on the net using web mining. Communications of the ACM, 43(8), 123-125. Nissenbaum, H. (1997). Toward an approach to privacy in public: challenges of information technology. Ethics & Behavior, 7(3), 207-220. Nielsen, J. (2002). Request Marketing. Retrieved from www.useit.com/alertbox/20001015.html Retrieved on 5 October 2010 Park, C. S. and Srinivasa, V. (1994) A Survey-Based Method for Measuring and Understanding Brand Equity and Its Extendibility Journal of Marketing Research, 31(2), 271-288. Philip, S. Y. (1999). Data Mining and Personalization Technologies. International Conference on Database Systems for Advanced Applications (DASFAA 99). Retrieved from http://maya.cs.depaul.edu/~mobasher/papers/aw07-mobasher.pdf Retrieved on 5 October 2010 Rhodes, J. S. (2009). Opt In Email List Building: How to Build and Run a Successful Opt In List. NY: CreateSpace Russel, C. (2006). Best Customers: Demographics of Consumer Demand. NY: New Strategist Publications Cheryl Russell (Author) › Visit Amazons Cheryl Russell Page Find all the books, read about the author, and more. See search results for this author Are you an author? Learn about Author Central Sterne, J. (2002). Web Metrics: Proven Methods for Measuring Web Site success. New York, NY: John Wiley & Sons, Inc. Read More