Marketing Segmentation Essay

foodstuff segmentation is the carry through of dividing up a exchange into more-or-less(prenominal) homogenous subsets for which it is vi up to(p) to progress to different foster propositions. At the end of the process the comp whatever keister decide which segment(s) it deprivations to serve. If it chooses, some(prenominal)ly segment stick unwrap be served with a different take to be proposition and managed in a different way. Market segmentation processes whoremonger be used during CPM for cardinal main purposes. They can be used to segment electric potential foodstuffs to reveal which clients to acquire, and to dot authorized clients with a view to pass differentiated prise propositions birthed by different birth care strategies. In this discussion well think on the application of foodstuff segmentation processes to unwrap which guests to acquire. What distinguishes securities industriousness segmentation for this CRM purpose is its very(prenominal) conjure up focus on guest value. The return of the process should be the identification of the value potential of each identified segment. Companies testament want to identify and target guests that can chip in profit in the coming(prenominal) day these impart be those clients that the company and its makework be better put to serve and satisfy than their competitors. Market segmentation in more companies is super intuitive. The marketing team leave go up profiles of client groups found upon their insight and experience. This is then used to hand the climbment of marketing strategies across the segments. In a CRM context, market segmentation is heightsly info dependent. The entropy faculty be generated internally or sourced externally. Internal entropy from marketing, gross revenue and finance records are a good deal enhanced with additional info from external sources such as marketing research companies, partner organizations in the companys network a nd entropy special(a)ists (see practice 5.2 ).The market segmentation process can be broken down into a yield of steps1. identify the barter you are in2. identify relevant segmentation vari fits3. analyse the market using these variables4. task the value of the market segments5. select target market(s) to serve.Sales forecasting Slide 6 (p. 136-8)The second field of study that can be used for CPM is sales forecasting. One major issue comm tho facing companies that demeanor CPM is that the selective information acquirable for thump customers takes a historical or, at best, present day view. The data identifies those customers who shake off been, or presently are, important for sales, profit or separate strategic reasons. If management believes the future testament be the equivalent as the ago, this presents no problem. However, if the business milieu is changeable, this does present a problem. Because CPMs goal is to identify those customers that bequeath be strat egi remembery important in the future, sales forecasting can be a useful discipline. Sales forecasting, some pessimists argue, is a waste of time, because the business environment is rapidly changing and unpredictable. major world events such as terrorist attacks, war, drought and market-based changes, such as reinvigorated fruits from competitors or high visibleness promotional campaigns, can make any sales forecasts invalid. There are a number of sales forecasting techniques that can be utilize, providing useful information for CPM. These techniques, which fall into trey major groups, are book for different circumstances. qualitative methodscustomer surveyssales team enumerates time-series methodsmoving averageexponential function smoothingtime-series decomposition causal methodsleading indicators lapse fabrics.Qualitative methods are probably the most widely used forecasting methods. node surveys ask consumers or buying officers to give an opinion on what they are likel y to buy in the forecasting outcome. This makes sense when customers forward-plan their purchasing. Data can be obtained by inserting a question into a customer satisfaction survey. For example, In the next six months are you likely to buy more, the same or less from us than in thecurrent period? And, If more, or less, what volume do you expect to buy from us? Sometimes, third party organizations such as industry associations or trans-industry groups such as the Chamber of barter or the Institute of Directors collect data that indicate future buying intentions or proxies for intention, such as business confidence. Sales team estimates can be useful when salespeople have built close relationships with their customers. A key account management team might be hearty placed to generate several individual forecasts from the team membership. These can be averaged or freighted in some way that reflects the calculators closeness to the customer.Account managers for Dyno Nobel, a provi der of commercial explosives for the mining and quarrying industries, are so close to their customers that they are able to forecast sales two to iii years ahead. Operational CRM frames support the qualitative sales forecasting methods, in particular sales team estimates. The CRM dodging takes into account the value of the sale, the probability of closing the sale and the anticipated period to closure. Many CRM systems also abandon management to do the estimates of their sales team members, to allow for overly optimistic or pessimistic salespeople. Time-series approaches take historical data and extrapolate them forward in a linear or curvilinear trend. This approach makes sense when in that location are historical sales data, and the assumption can be safely made that the future will reflect the past. The moving average method is the simplest of these. This takes sales in a number of previous periods and averages them. The averaging process reduces or eliminates random variat ion. The moving average is computed on successive periods of data, moving on one period at a time, as in check 5.10 . Moving averages based on different periods can be calculated on historic data to generate an accurate method. A variation is to weight the more re centimeimeime periods more heavily. The rationale is that more recent periods are better predictors. In producingan estimate for year 2009 in render 5.10 , one could weight the previous four years sales surgery by 0.4, 0.3, 0.2, and 0.1, respectively, to reach an estimate. This would generate a forecast of 5461.This approach is called exponential smoothing. The decomposition method is applied when there is evidence of cyclical or seasonal patterns in the historical data. The method attempts to separate out four components of the time series trend agentive role,cyclical factor, seasonal factor and random factor. The trend factor is the longterm direction of the trend after the other three elements are removed. The c yclical factor represents regular semipermanent recurrent influences on sales seasonal influences loosely occur within annual cycles. It is sometimes viable to predict sales using leading indicators. A leading indicator is some contemporary natural action or event that indicates that a nonher legal action or event will happen in the future. At a macro level, for example, housing starts are good predictors of future sales of kitchen furniture. At a small level, when a credit card customer calls into a contact centre to ask active the current rate of interest, this is a strong indicator that the customer will switch to another supplier in the future. Regression models work by employing data on a number of predictor variables to estimate future affect. The variable be predicted is called the dependent variable the variables existence used as predictors are called main(a) variables. For example, if you cute to predict demand for cars (the dependent variable) you might use dat a on population size, average usable income, average car price for the category being predicted and average fuel price (the independent variables). The lapsing equation can be tested and validated on historical data before being adopted. New predictor variables can be substituted or added to see if they improve the accuracy of the forecast. This can be a useful approach for predicting demand from a segment. Activity-Based Costing Slide 7 (p. 138-40) guest attainment bellsTerms of TradeCustomer advantage equalsWorking uppercase livesActivity-based costThe third discipline that is useful for CPM is activity-based be. Many companies, particularly those in a B2B context, can key out revenues to customers. In a B2C environment, it is usually only possible to trace revenues to identifiable customers if the company operates a billing system requiring customer details, or a membership scheme such as a customer club, store-card or a loyalty programme. In a B2B context, revenues can be tracked in the sales and accounts databases. be are an all in all different matter.Because the goal of CPM is to cluster customers according to their strategic value, it is desirable to be able to identify which customers are, or will be, profitable. Clearly, if a company is to understand customer profitability, it has to be able to trace be, as well as revenues, to customers. Costs do metamorphose from customer to customer. Some customers are very costly to acquire and serve, others are not. There can be considerable variance across the customer base within several categories of cost customer achievement costs some customers require considerable sales effort to move them from prospect to fi rst-time customer status more sales calls, visits to reference customer sites, free seeks, engineering advice, guarantees that switching costs will be met by the vendor terms of trade price discounts, advertising and promotion support, slotting allowances (cash paid to retailers fo r shelf space), extended invoice due dates customer emolument costs handling queries, claims and find faultts, demands on salespeople and contact centre, small order sizes, high order frequency, just-in-time delivery, part adulterate shipments, breaking bulk for delivery to multiple sites working capital costs carrying inventory for the customer, cost of credit. conventional product-based or general ledger costing systems do not provide this type of detail, and do not enable companies to estimate customer profitability. Product costing systems track material, labour and energy costs to products, often comparing actual to standard costs. They do not, however, cover the customer-facing activities of marketing, sales and service. General ledger costing systems do track costs across all parts of the business, but are normally too highly aggregated to establish which customers or segments are responsible for generating those costs. Activity-based costing (ABC) is an approach to c osting that softens costs into two groups volume-based costs and order-related costs. Volume based (product-related) costs are variable against the size of the order, but fixed per whole for any order and any customer. Material and direct labour costs are examples.Order-related (customer-related) costs vary according to the product and process requirements of each particular customer. Imagine two retail customers, each purchasing the same volumes of product from a manufacturer. Customer 1 makes no product or process demands. The sales revenue is $5000 the gross margin for the vendor is $1000. Customer 2 is a different story customizedproduct, special overprinted outer packaging, just-in-time delivery to three sites, provision of point-of-sale material, sale or return conditions and discounted price. Not only that, but Customer 2 spends a lot of time agreeing these terms and conditions with a salesperson who has had to call three times before closing the sale. The sales revenue is $ 5000, but after accounting for product and process costs to meet the demands of this particular customer, the margin retained by the vendor is $250. Other things being equal, Customer 1 is four times as valuable as Customer 2. Whereas conventional cost accounting practices report what was spent, ABC reports what the money was spent doing. Whereas the conventional general ledger approach to costing identifies resource costs such as payroll, equipment and materials, the ABC approach shows what was being done when these costs were incurred. Figure 5.11 shows how an ABC view of costs in an insurance companys claims processing department gives an entirely different picture to the traditional view.ABC gives the manager of the claims-processing department a much clearer idea of which activities fashion cost. The next question from a CPM perspective is which customers create the activity? Put another way, which customers are the cost drivers? If you were to examine the activity cost item Analyse claims $121 000 , and find that 80 per cent of the claims were made by drivers under the age of 20, youd have a clear understanding of the customer group that was creating that activity cost for the business. CRM desires ABC because of its overriding goal of generating profitable relationships with customers. Unless there is a costing system in place to trace costs to customers, CRM will find it very difficult to deliver on a promise of improved customer profitability. Overall, ABC serves customer portfolio management in a number of ways 1. when combined with revenue figures, it tells you the absolute and relative levels of profit generated by each customer, segment or cohort 2. it guides you towards actions that can be taken to return customers to profit 3. it helps prioritize and direct customer acquisition, retentivity and development strategies 4. it helps establish whether customization and other forms of value creation for customers pay off. ABC sometimes justifie s managements confidence in the Pareto principle, otherwise know as the 8020 rule. This rule refers that80 per cent of kale come from 20 per cent of customers. ABC tells you which customers fall into the important 20 per cent. Research generally supports the 80 0 rule. For example, one report from Coopers and Lybrand found that, in the retail industry, the result 4 per cent of customers account for 29 per cent of profits, the next 26 per cent of customers account for 55 per cent of profits and the remaining 70 per cent account for only 16 per cent of profits.Lifetime Value tenderness Slide 8 (p. 141-2)The fourth discipline that can be used for CPM is customer lifetime value (LTV) estimation, which was first introduced in Chapter 2. LTV is measured by computing the present day value of all net margins (gross margins less cost-to-serve) earned from a relationship with a customer, segment or cohort. LTV estimates provide important insights that guide companies in their customer man agement strategies. Clearly, companies want to protect and ring-fence their relationships with customers, segments or cohorts that will generate significant amounts of profit. Sunil Gupta and Donald Lehmann suggest that customer lifetime value can be computed as followsApplication of this formula means that you do not have to estimate customer tenure. As customer retention rate rises there is an self-regulating lift in customer tenure, as shown in Table 2.2 in Chapter 2. This formula can be adjusted to consider change in both future margins and retention rates either up or down, as described in Gupta and Lehmanns book Managing Customers as Investments. The table can be used to assess the impact of a number of customer management strategies what would be the impact of decrease cost-toserve by shifting customers to low-cost self-serve melodys? What would be the result of cross-selling higher margin products? What would be the outcome of a loyalty programme designed to increase ret ention rate from 80 to 82 per cent? An important additional benefit of this LTV calculation is that it enables you to estimate a companys value. For example, it has been computed that the LTV of the average US-based American Airlines customer is $166.94. American Airlines has 43.7 million such customers, yielding an estimated company value of $7.3 billion. Roland Rust and his co-researchers noted that, minded(p) the absence of internationalpassengers and freight considerations from this computation, it was remarkably close to the companys market capitalization at the time their research was undertaken.Clustering (144) fall away 9Clustering techniques are used to find of course occurring groupings within a dataset. As applied to customer data, these techniques generally function as follows 1. from each one customer is allocated to just one group. The customer possesses attributes that are more closely associated with that group than any other group. 2. Each group is relatively ho mogenous.3. The groups collectively are very different from each other. In other words, bunch techniques generally try to maximize both within-group homogeneity and between-group heterogeneity. There are a number of clustering techniques, including CART (classification and regression channelises) and CHAID (chi-square automatic interaction detection).7 Once statistically homogenous clusters have been formed they need to be interpreted. CRM strategists are often interested in the future behaviours of a customer segment, cohort or individual. Customers potential value is determined by their pr readablesity to buy products in the future. Data miners can march on predictive models by examining patterns and relationships within historic data. Predictive models can be generated to identify 1. Which customer, segment or cohort is most likely to buy a given product? 2. Which customers are likely to inattention on payment?3. Which customers are most likely to defect (churn)?Data analyst s scour historic data looking for predictor and outcome variables. Then a model is built and validated on these historic data. When the model seems to work well on the historic data, it is run on contemporary data, where the predictor data are known but the outcome data are not. This is known as scoring . make headway are answers to questions such as the propensity-to-buy, default and churn questions listed above. Predictive modelling is based on three assumptions, each of which whitethorn be true to a greater or lesser extent 1. The past is a good predictor of the future BUT this may not be true. Sales of legion(predicate) products are cyclical or seasonal.Others have fashion or fad lifecycles. 2. The data are available BUT this may not be true. Data used to train the model may no longer be collected. Data may be too costly to collect, or may be in the wrong format. 3. Customer-related databases contain what you want to predict BUT this may not be true. The data may not be available. If you want to predict which customers are most likely to buy mortgage protection insurance, and you only have data on life policies, you will not be able to answer the question. Two tools that are used for predicting future behaviours are decision trees and neural networks. Decision trees (145) slide 9Decision trees are so called because the graphical model output has the appearance of a offset printing structure. Decision trees work by analyzing a dataset to find the independent variable that, when used to split the population, results in nodes that are most different from each other with respect to the variable you are tying to predict. Figure 5.12 contains a set of data about five dollar bill customers and their credit risk profile.We want to use the data in four of the fi ve columns to predict the risk rating in the fifth column. A decision tree can be constructed for this purpose. In decision tree psychoanalysis, Risk is in the dependent column. This is also kn own as the target variable. The other four columns are independent columns. It is unlikely that the customers name is a predictor of Risk, so we will use the three other pieces of data as independent variables debt, income and marital status. In the example, each of these is a simple categorical item, each of which only has two possible values (high or low yes or no). The data from Figure 5.12 are represented in a different form in Figure 5.13 , in a way which lets you see which independent variable is best at predicting risk. As you examine the data, you will see that the best split is income (four instances highlighted in unmixed on the diagonal two high income/good risk plus two low income/ pitiable risk). Debt and marital status each score three on their diagonals. Once a node is split, the same process is performed on each successive node, either until no further splits are possible or until you have reached a managerially useful model.The graphical output of this decision tre e analysis is shown in Figure 5.14 .Each box is a node. Nodes are linked by branches. The top node is the root node. The data from the root node is split into two groups based on income. The right-hand, low income box, does not split any further because both low income customers are classified as poor credit risks. The left-hand, high-income box does split further, into married and not married customers. Neither of these split further because the one unmarried customer is a poor credit risk and the two remaining married customers are good credit risks.As a result of this process the company knows that customers who have the lowest credit risk will be high income and married. They will also note that debt, one of the variables inserted into the training model, did not perform well. It is not a predictor of creditworthiness. Decision trees that work with categorical data such as these are known as classification trees. When decision trees are applied to continuous data they are known as regression trees. neuronal Networks (147) slide 9Neural networks are another way of fitting a model to existing data for prediction purposes. The expression neural network has its origins in the work of machine reading and artificial intelligence. Researchers in this field have tried to learn from the natural neural networks of living creatures. Neural networks can produce excellent predictions from heroic and complex datasets containing hundreds of interactive predictor variables, but the neural networks are neither easy to understand nor straightforward to use. Neural networks represent complex mathematical equations, with many summations, exponential functions and parameters. Like decision trees and clustering techniques, neural networks need to be trained to recognize patterns on sample datasets. Once trained, they can be used to predict customer behaviour from new data. They work well when there are many potential predictor variables, some of which are redundant.Case 5.2 Customer portfolio management at TescoTesco, the largest and most successful supermarket concatenation in the UK, has true a CRM strategy that is the envy of many of its competitors. Principally a food retailer in a mature market that has grown weeny in thelast 20 years, Tesco realized that the only route to growth was taking market get by from competitors. Consequently, the development of a CRM strategy was seen as imperative.In developing its CRM strategy, Tesco first analysed its customer base. It found that the top 100 customers were worth the same as the bottom 4000. It also found that the bottom 25 per cent of customers represented only 2 per cent of sales, and that the top 5 per cent of customers were responsible for 20 per cent of sales.The results of this analysis were used to segment Tescos customers and to develop its successful loyalty programmes.SWOT and PESTE (p. 154-5) slide 10SWOT is an acronym for strengths, weaknesses, opportwholeies and threats. SWOT analysis explores the internal environment (S and W) and the external environment (O and T) of a strategic business unit. The internal (SW) take stock looks for strengths and weaknesses in the business functions of sales, marketing, manufacturing or operations, finance and people management. It then looks cross-functionally for strengths and weaknesses in, for example, cross-functional processes (such as new product development) and organizational culture. The external (OT) audit analyses the macro- and micro-environments in which the customer operates. The macro-environment includes a number of broad conditions that might impact on a company. These conditions are identified by a PESTE analysis. PESTE is an acronym for policy-making, economic, social, technological and environmental conditions.An analysis would try to pick out major conditions that impact on a business, as illustrated below political environment demand for international air travel contracted as worldwide political stabi lity was reduced after September 11, 2001 economic environment demand for mortgages falls when the economy enters recession. social environment as a population ages, demand for healthcare and residential homes increase technological environment as more households become owners of computers, demand for Internet banking increases environmental conditions as customers becomemore concerned about environmental quality, demand for more energy efficient products increases. The micro environmental part of the external (OT) audit examines relationships between a company and its immediate external stakeholders customers, suppliers, business partners and investors. A CRM-oriented SWOT analysis would be searching for customers or potential customers that emerge well from the analysis. These would be customers that 1. possess relevant strengths to exploit the opportunities open to them 2. are overcoming weaknesses by partnering with other organizations to take advantage of opportunities 3. a re investing in turning rough the company to exploit the opportunities 4. are responding to external threats in their current markets by exploiting their strengths for diversification.Five forcesThe five-forces analysis was developed by Michael Porter. 17 He claimed that the profitability of an industry, as measured by its return on capital employed relative to its cost of capital, was determined by five sources of competitive pressure. These five sources include three horizontal and two vertical conditions.The horizontal conditions arecompetition within the established businesses in the marketcompetition from potential new entrantscompetition from potential substitutes.The vertical conditions reflect supply and demand chain considerations the bargaining power of buyersthe bargaining power of suppliers.Porters basic premise is that competitors in an industry will be more profitable if these five conditions are benign. For example, if buyers are very powerful, they can demand high l evels of service and low prices, thus negatively influencing the profitability of the supplier. However, if barriers to entry are high, say because of large capital requirements or dominance of the market by very powerful brands, then current players will be relatively immune from new entrants and enjoy the possibility of better profits. Why would a CRM-strategist be interested in a five-forces evaluation of customers? Fundamentally, a financially healthy customer offers better potential for a supplier than a customer in financialdistress. The analysis points to different CRM solutions 1. Customers in a profitable industry are more likely to be stable for the near-term, and are better placed to invest in opportunities for the future. They therefore have stronger value potential. These are customers with whom a supplier would want to manikin an exclusive and well-protected relationship. 2. Customers in a stressed industry might be looking for reduced cost inputs from its suppliers, or for other ways that they can add value to their offer to their own customers. A CRM-oriented supplier would be trying to find ways to serve this customer more effectively, perhaps by uncovering out elements of the value proposition that are not critical, or by adding elements that enable the customer to manage more strongly.Strategically Significant Customers (157) slide 11The goal of this entire analytical process is to cluster customers into groups so that differentiated value propositions and relationship management strategies can be applied. One outcome will be the identification of customers that will be strategically significant for the companys future. We call these strategically significant customers (SSCs). There are several classes of SSC, as follows 1. High future lifetime value customers these customers will contribute significantly to the companys profitability in the future. 2. High volume customers these customers might not generate much profit, but they are st rategically significant because of their absorption of fixed costs, and the economies of scale they generate to keep unit costs low. 3. Benchmark customers these are customers that other customers follow. For example, Nippon Conlux supplies the hardware and software for Coca grasss vending operation. While they might not make much margin from that relationship, it has allowed them to gain retrieve to many other markets. If we are good enough for Coke, we are good enough for you , is the implied promise. Some IT companies create reference sites at some of their more demanding customers. 4. Inspirations these are customers who bring about improvement in the suppliers business. They may identify new applications for a product, product improvements, or opportunities for cost reductions. They may complain loudly and make unreasonable demands, but in doing so, force change for the better. 5. Dooropeners these are customers that allow the supplier to gain access to a new market. T his may be done for no initial profit, but with a view to proving credentials for further expansion. This may be particularly important if intersection point cultural boundaries, say between west and east. One company, a Scandinavian processor of timber, has identified five major customer groups that are strategically significant, as in Figure 5.22 .The Seven Core Customer Management Strategies (158-9) slide 12This sort of analysis pays off when it helps companies develop and implement differentiated CRM strategies for clusters of customers in the portfolio. There are several core customer management strategies 1. Protect the relationship this makes sense when the customer is strategically significant and attractive to competitors. We discuss the creation of exit barriers in our review of customer retention strategies in Chapter 9.2. Re-engineer the relationship in this case, the customer is currently unprofitable or less profitable than desired. However, the customer could be c onverted to profit if costs were trimmed from the relationship. This might mean reducing or automating service levels, or servicing customers through lower cost channels. In the banking industry, transaction processing costs, as a multiple of online processing costs are as follows. If Internet transaction processing has a unit cost of 1, an in-bank teller transaction costs 120 units, an ATM transaction costs 40, telephone costs 30 and PC banking costs 20. In other words, it is 120 times more expensive to put up an in-bank transaction than the identical online transaction. Cost-reduction programmes have motivated banks to migrate their customers, or at least some segments of customers, to other lower cost channels. An Australian electricity company has found that its average annual margin per customer is $60. It costs $13 to serve a customer who pays by credit card, but only 64 cents to service a direct debit customer. Each customer moved to the lower cost channel therefore produces a transaction cost saving of more than $12, which increases the average customer value by 20 per cent.Re-engineering a relationship requires a clear understanding of the activities that create costs in the relationship (see Case 5.3). 3. Enhance the relationship likethe strategy above, the goal is to migrate the customer up the value ladder. In this case it is done not by re-engineering the relationship, but by increasing your share of customer spend on the category, and by identifying up-selling and cross-selling opportunities. 4. Harvest the relationship when your share of wallet is stable, and you do not want to invest more resources in customer development, you may feel that the customer has reached maximum value. Under these conditions you may wish to harvest, that is, optimize cash flow from the customer with a view to using the cash generated to develop other customers. This may be particularly appealing if the customer is in a declining market, has a high cost-to-serve or has a high propensity-to-switch to competitors. 5. End the relationship sacking customers is generally anathema to sales and marketing people. However, when the customer shows no sign of making a significant contribution in the future it may be the best option.You can read about strategies for sacking customers in Chapter 9. 6. Win back the customer sometimes customers take some or all of their business to other suppliers. If they are not strategically signifi cant, it may make sense to let them go. However, when the customer is important, you may need to develop and implement take in back strategies. The starting point must be to understand why they took their business away. 7. Start a relationship youve identified a prospect as having potential strategic significance for the future. You need to develop an acquisition plan to recruit the customer onto the value ladder. You can read about customer acquisition strategies in Chapter 8.