Data Mining in Customer Acquisition Essay Example
Managing the customer lifecycle is the last activities in primary stage of CRM value chain. This activities involves customer acquisition, customer retention and customer development. However, why the companies needs to focus to customer acquisition when the cost involved in customer acquisition is high rather than customer retention? We know that even the most customer-centric company like amazon.
com can't assure its customer to be royalty to them forever. The existing customer may switch to competitors, they don't need that products/services anymore, or even die.So, there is time where the customer are losing its value and need to be replaced by acquiring new customer. Acquiring new customer does not always means get a totally new customer from the competitors or new to the company. New customer can be an existing customer that is n...
ew to the category when they found a new category of solution for their existing need. Like Amazon's customer, currently their customer may only buy book from Amazon.
But when amazon offer new product called Kindle which is an e-book reader, the current customer may switch to buying Kindle instead of book.When a company acquiring new customers, it is not simply targeting all the new customer with all the promotion tools and methods. The first thing that the company do is to estimate the customer lifetime value to find whether it worth to acquire. Finding the prospects customer to target on is important since not all customer segments are giving the same value to the company.
After having prospect customer in hand, the company may needs to choose the appropriate approaches to get to the customer.Data mining can often help in finding
the customer that gives the highest value to the company and using appropriate promotional tools to get to the customer. As the amount of data increases, the process of choosing relevant demographic would be troublesome without data mining techniques. Data mining helps the company in the following ways: 1.
To differentiate and value customers and distribution partners 2. To determine the likelihood that a customer will purchase a specific ordering 3. To offer the right product to the right customer via the right channels. . To measure sales activity and performance overtime.
Differentiate and value customer The ability to identify prospects that will ultimately bring value to the organization relies heavily upon clustering, undirected form of segmentation. This is an Exploratory Data Analysis (EDA) technique used to dissect a heterogeneous collection of data into manageable number of homogeneous subgroups. This would group customers that posses similar characteristics and clearly describe why one group is different from another.Once the customer has been grouped, the next step is to calculate the customer lifetime value (CLV).
CLV is a measurement of what a customer is projected to be worth over a lifetime. It is the potential contribution of the customer to the company over period of time. Projecting CLV helps the company in benchmarking for how much it would or should be willing to invest to acquire a customer. In calculating the Customer Lifetime Value, the company needs to refer to the customer purchase history to find out the retention rate in a period of time and value generated in its lifetime.With the forecast of acquisition cost per each customer, the company would get the potential revenue that
it will earn in future.
Says the promotion cost is RM1 million to 100,000 selected customer and the company get 5. 5% responds from the customer. Each of the responded customer would generate sales at RM69. 95 for their product. So, 5.
5% * 100,000 is equal to 5,500 responded customer. Thus, 5,500 * RM69. 95 = RM384,725 is generated from the promotion. In short term, the company may loss RM612,272 but each of these customer, due to the anticipated CLV, will generate RM440,000 in profit and gain RM2. million in revenue.
Determine the Likelihood that a customer will purchase a specific ordering Data mining can also be used to determine the likelihood of a customer that will purchase a specific ordering. Given the current customer base for product x, who will most likely to become purchasers of product x? This question can be answered by using data mining in conjunction with customer database that include internal data about a customer's relationship and interactions, and derived data that identify the customer's value to the company and similarities or differences from other customers are applied.Each customer is designated a score that rank orders customer propensity to purchase relative to another. using this purchase propensity data within the customer acquisition process creates a more effective sales result. Offer the Right Product to the Right Customer Via the Right Channel After knowing the target customer that will generate the highest lifetime value to company, determine the best way to access those customer is the next step.
Combining the derived information about agent's or broker's strengths and value to the company guides the effort of all-important linkage to the
most effective distribution.The right agents/brokers are not those who have large list of current customers who have not purchased yet. They are those who have proven they can sell the types of coverage to those prospects that have been identified as the most likely to purchase. Data mining helps the company to make it easy to access this information and distribute leads with minimal work effort. Measure Sales Activity and Performance Overtime Measure sales activity and performance can be done by assessing the operational activity and success associated with customer acquisition.This activity includes day-to-day analysis of prospects in the sales pipeline at a different points and can supply the company with important information about the effectiveness of their distribution partners.
Another way to measure performance is through intelligence information that can be derived from sales activity. This intelligence supplies management with aggregated information that is used to drive future strategies related to the sales and customer acquisition function. ConclusionNowadays, data mining is very well-known to optimize the customer acquisition efforts. By combining the internal and derived data, it can improve hit ratio and decrease cost in customer acquisition. Data mining contribute greatly in identifying opportunities and weaknesses in the business that on the surface may not obvious.
Thus, the result in deploying data mining tools in customer acquisition are getting the right customer with the minimum cost to generate the maximum revenue since it is targeting the valuable prospect customer with the appropriate channels. References:Sudaran, J 2008, 'Managing the Customer Lifecycle: Customer Acquisition', Lecture note distributed in the topic HBM271, Swinburne University, Sarawak on 13 October 2008. Kudyba, Stephan 2008, Managing Data Mining, Idea Group
Inc. retrieved 20 October 2008, from http://books. google.
com. my/books? id=5-Ofjvv3q7AC=PA92=PA92=data+mining+in+customer+acquisition =bl=5Buv2HVqL6=fB_dt63Hvrni_-ar764iP6Dspjo=en=X=book_result=8=result#PPA92,M1 Berson A, Smith S & Thearling K 2008, Customer Acquisition and Data Mining, retrieved 20 October 2008, from http://www. thearling. com/text/chapter10/chapter10.
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