CUSTOMER RETENTION

An integrated Strategy of 4 C’s
During the past two decades, customer satisfaction management has emerged as a strategic imperative for most of the firms. In the 1980’s achieving higher satisfaction ratings became a goal in itself. Only during the 1990’s was there a wide spread realization that customer satisfaction ratings were only means to strategic ends, i.e. customer retention. Today the emphasis is shifting. Beyond designing strategies to attract new customer and create transaction with them, companies are going out to retain current customers and build lasting relationship with them. We can take example of instant life insurance rates company who need to have best relation with their customers, so that they can have term life insurance business from them again and again.

Why Customer Retention
Customer retention helps a firm to increase its profitability and understand consumer preferences and needs. An organization cannot work in an ivory tower. It should gin customer’s support to continue operating and this support is gained from loyal customers. The changing demographics, the slow growth of economy more sophisticated competition, overcapacity in many industries mean fewer customers to go around. Attracting new customers has turned to be very difficult. Hence, marketers have to change the tendency of finding new customers at the cost of the old ones, but also have to realize that cutting down of defections in halves would double the average company’s growth rate.

Recommendation

We have discussed on so many factors related to business like marketing, management, product management etc…. Today we are going to discuss on customer satisfaction which is most important factor of any small business and organization. We will talk some features related to customer satisfaction which can help us to provide more satisfaction to out customer.

Expectation and experience were positively and significantly distributed for economic and aesthetic values. In the case of economy, this might be due to the fact that the consumers mentally accepted the price of the product, while buying itself. Aesthetic value was also similarly observed during the purchase.

Service and durability were the values for which the difference between expectation and experience were found to be negative and this state might lead to an overall dissatisfaction. These areas, thus. Must be given immediate attention by the manufactures and attempts must be made to convert the negative disconfirmation state to positive disconfirmation to have satisfaction.

Managerial Implication
This study helps to Identify the gap. By taking appropriate action, customers can be satisfied. Customer satisfaction alone can retain the customers and ultimately, this acts as the base for new customers.

Analysis

To identify the value dimensions of television, factor analysis was carried out. The eight factors having Eigen values greater than 1.0 were extracted. These eight factors represented by 65.8 per cent of the variance of the attributes considered for the study.

The factors identified were ‘technology, aesthetic value, durability, service, physical characteristics, status, economic value and functional value.

The priority of values identified by the respondents was studied using Friedman’s two-way ANOVA. The test confirmed the existence of significant differences among their perception levels of values at 1 per cent level. Further, non-parametric multiple comparison test was also done to identify the order of priority among the perceived values.

Expectation and experience levels of values derived by reducing the attribute using factor analysis were measured using 10 point scale. I was talking with owner of blinds store who are selling vertical blinds and roman shades online, he says that factor analysis helps them to find out the trend and interest of their clients in their blinds product.

From the paired‘t’ test economic and aesthetic values, durability, and service were found to be significantly distributed. Further, aesthetic and economic values were found to be positive. It implied that the experience levels for these values were distributed in the higher side than the expectation. But, in the case of durability and service the t-values were found to be negative which implied the experience of these values were negative but significant. Negative sign indicates that the experience of these values was found to be less than the expected level and the differences were also significant.

Methodology

To study the above specified objectives, first attempt was made to identify the values attached to the product television. Pre-test two was conducted with a sample size of thirty to standardize the questionnaire contains a list of 28 attributes relating to the product, television. The respondents were asked to indicate the level of importance attached to each attribute.

The second part of the questionnaire was intended to measure the expectation level of values identified by the pre-test I and the third part relating to the experience level of these values.

Corn-bach alpha was used to measure the reliability of the questionnaire. The alpha value reflected the high degree of internal consistency. With certain deletion of listed values the questionnaire was finalized for further study.

Sampling Design
The sample for the study was drawn from Annamalainagar by taking a random sample of 100 respondents, 79 were found to be fit for further analysis.

Tools for Analysis
The generated data had been duly analysed using the statistical tools viz., factor analysis, rank correction test for agreement, ANOVA and ‘t’-test.

Customer Satisfaction-1

It is necessary to satisfy the expectation levels of different values and identify whether there is any gap between expectation and experience of such values. If needed action plan to fill the gap is to be taken to satisfy the customers. Satisfaction of a customer is the base of retaining the customers and helps to acquire new customers. Customer data management and data updating which leads towards customer satisfaction is most important part of customer relationship management software.

Expectation (E1) and Experience (E2) value
To retain the customers, it is highly important to verify how far these perceived values are satisfied. By nature, customers tend to compare the actual experience with their expectation based on the benefits they derive, while using the product. If the expectation is higher than the experience (E1 > E2) it is said to be negative disconfirmation, if the expectation is equal to the experience it is confirmation (E1 = E2); and if the expectation is less than the experience, it is said to be positive disconfirmation (E1 < E2). Negative disconfirmation leads the dissatisfaction to satisfaction.

Objectives
The main objectives of the study are:
1. To measure the level of expectations of values
2. To measure the experience level of the values
3. To identify whether there is any significant difference between the values expected and experienced.

Customer Satisfaction

We have discussed on so many factors related to business like marketing, management, product management etc…. Today we are going to discuss on customer satisfaction which is most important factor of any small business and organization. We will talk some features related to customer satisfaction which can help us to provide more satisfaction to out customer.

The Base for Customer Acquisition and Retention
A product is a bundle of values. The values may be comfort, safety, pride, economy etc. Each product may offer a combination of some values. The values priority may vary from individual to individual. Even for an individual the values priority may vary from product to product. Further, priority of values varies based on the situation.

Application
It is necessary to satisfy the expectation levels of different values and identify whether there is any gap between expectation and experience of such values. If needed action plan to fill the gap is to be taken to satisfy the customers. Satisfaction of a customer is the base of retaining the customers and helps to acquire new customers.

Advances in Database Marketing-1

Because of the complexities of B2B marketing and the intricacies of corporate operations, the demands placed on any marketing organization to formulate the business process by which such a sophisticated series of procedures may be brought into existence are significant. It is often for this reason that large marketing organizations engage the use of an expert in marketing process strategy and information technology (IT), or a marketing IT process strategist. We can take example of Term Life Insurance Rates providing business and vertical blinds, roller shades and natural wood blinds Company who are doing businesses B2C. For them database marketing is much important and cost effective too. Although more technical in nature than often marketers require, a system integrator (SI) can also play an equivalent role to the marketing IT process strategist, particularly at the time that new technology tools need to be configured and rolled out.
New advances in cloud computing and marketing's penchant for both outsourcing services to third-party agencies and avoiding involvement in the creation of complex technological tools has provided a fertile soil for Software as a Service (SaaS) providers to centralize the marketing database under a hosting service model that incorporates functions from CRM Software, content management and business intelligence under one offering to automate the marketing process.

Advances in Database Marketing

While the idea of storing customer data in electronic formats to use them for database-marketing purposes has been around for decades, the computer systems available today make it possible to gain a comprehensive history of client behavior on-screen while the business is transacting with each individual, producing thus real-time business intelligence for the company. This ability enables what is called one-to-one marketing or personalization.

Today's Customer Relationship Management (CRM) systems use the stored data not only for direct marketing purposes but to manage the complete relationship with individual customer contacts and to develop more customized product and service offerings. However, a combination of CRM, content management and business intelligence tools are making delivery of personalized information a reality.

Marketers trained in the use of these tools are able to carry out customer nurturing, which is a tactic that attempts to communicate with each individual in an organization at the right time, using the right information to meet that client's need to progress through the process of identifying a problem, learning options available to resolve it, selecting the right solution, and making the purchasing decision.

Database Marketing – 5

Laws and regulations
As database marketing has grown, it has come under increased scrutiny from privacy advocates and government regulators. For instance, the European Commission has established a set of data protection rules that determine what uses can be made of customer data and how consumers can influence what data are retained. In the United States, there are a variety of state and federal laws, for example, for online blinds, vertical blinds and roman shades company, who doing business online, the Fair Credit Reporting Act, or FCRA, (which regulates the gathering and use of credit data), for term life insurance industry the Health Insurance Portability and Accountability Act (HIPAA) (which regulates the gathering and use of consumer health data), and various programs that enable consumers to suppress their telephones numbers from telemarketing.

Evolution
While the idea of storing customer data in electronic formats in order to use them for database-marketing purposes has been around for decades the computer systems available today make it possible to have the complete history of a client on-screen the moment he or she calls. Today's Customer Relationship Management systems use the stored data not only for direct marketing purposes but to manage the complete relationship with this particular customer and to further develop the range of products and services offered.

Database Marketing – 4

As apart of our talk on database marketing today we are going to discuss on analytical and modeling which is also important factor of the database marketing. We have already discussed on many important factors like consumer data, business data, and source of data for database marketing.

Analytics and modeling
Companies with large databases of customer information risk being "data rich and information poor." As a result, a considerable amount of attention is paid to the analysis of data. For instance, companies often segment their customers based on the analysis of differences in behavior, needs, or attitudes of their customers. A common method of behavioral segmentation is RFM, in which customers are placed into subsegments based on the recency, frequency, and monetary value of past purchases. Van den Poel (2003) gives an overview of the predictive performance of a large class of variables typically used in database-marketing modeling.

They may also develop predictive models, which forecast the propensity of customers to behave in certain ways. For instance, marketers may build a model that rank orders customers on their likelihood to respond to a promotion. Commonly employed statistical techniques for such models include logistic regression and neural networks.

Database Marketing – 3

Business data
For many business-to-business (B2B) company marketers, the number of customers and prospects will be smaller than that of comparable business-to-consumer (B2C) companies. Also, their relationships with customers will often rely on intermediaries, such as salespeople, agents, and dealers and the number of transactions per customer may be small. In B2c, business is having direct relation with customer. For example, an online blinds store who are selling roller shades and woven wood shades products.

They don’t have any intermediaries. Business is selling directly to customer. As a result, business-to-business marketers may not have as much data at their disposal. One other complication is that they may have many contacts for a single organization, and determining which contact to communicate with through direct marketing may be difficult. On the other hand the database of business-to-business marketers often include data on the business activity of the respective client that can be used to segment markets, e.g. special software packages for transport companies, for lawyers etc. Customers in Business-to-business environments often tend to be loyal since they need after-sales-service for their products and appreciate information on product upgrades and service offerings.

Sources of customer data often come from the sales force employed by the company and from the service engineers. Increasingly, online interactions with customers are providing b-to-b marketers with a lower cost source of customer information.

For prospect data, businesses can purchase data from compilers of business data, as well as gather information from their direct sales efforts, on-line sites, and specialty publications.

Database Marketing - 2

Database marketing has flourished in sectors, such as financial services, telecommunications, and retail, all of which have the ability to generate significant amounts transaction data for millions of customers. Database marketing applications can be divided logically between those marketing programs that reach existing customers and those that are aimed at prospective customers.

Consumer data
In general, database marketers seek to have as much data available about customers and prospects as possible. For marketing to existing customers, more sophisticated marketers often build elaborate databases of customer information. These may include a variety of data, including name and address, history of shopping and purchases, demographics, and the history of past communications to and from customers. For larger companies with millions of customers, such data warehouses can often be multiple terabytes in size.

Marketing to prospects relies extensively on third-party sources of data. In most developed countries, there are a number of providers of such data. Such data is usually restricted to name, address, and telephone, along with demographics, some supplied by consumers, and others inferred by the data compiler. Companies may also acquire prospect data directly through the use of sweepstakes, contests, on-line registrations, and other lead generation activities.

Database Marketing - 1

Direct and database marketing organizations, on the other hand, argue that a targeted letter or e-mail to a customer, who wants to be contacted about offerings that may interest the customer, benefits both the customer and the marketer. As a part of our talk on database marketing now we will talk on some important factors of the database marketing like source of data, customer data, business data etc….. Any business needs to have customer database and it always helps to use these customer databases for marketing. A blinds company with special products like roller shades and woven wood blinds require keeping customer database and using such database again and again to get more and more business from those customers.

Sources of data
Although organizations of any size can employ database marketing, it is particularly well-suited to companies with large numbers of customers. This is because a large population provides greater opportunity to find segments of customers or prospects that can be communicated with in a customized manner. In smaller (and more homogeneous) databases, it will be difficult to justify on economic terms the investment required to differentiate messages. CRM software with sales force automation helps manages marketing and maintain database too.

As a result, database marketing has flourished in sectors, such as financial services, telecommunications, and retail, all of which have the ability to generate significant amounts transaction data for millions of customers. Database marketing applications can be divided logically between those marketing programs that reach existing customers and those that are aimed at prospective customers.

Database marketing

Database marketing is a form of direct marketing using databases of customers or potential customers to generate personalized communications in order to promote a product or service for marketing purposes. The method of communication can be any addressable medium, as in direct marketing.

The distinction between direct and database marketing stems primarily from the attention paid to the analysis of data. Database marketing emphasizes the use of statistical techniques to develop models of customer behavior, which are then used to select customers for communications. As a consequence, database marketers also tend to be heavy users of data warehouses, because having a greater amount of data about customers increases the likelihood that a more accurate model can be built. The "database" is usually name, address, and transaction history details from internal sales or delivery systems, or a bought-in compiled "list" from another organization, which has captured that information from its customers. Typical sources of compiled lists are charity donation forms, application forms for any free product or contest, product warranty cards, subscription forms, and credit application forms. The communications generated by database marketing may be described as junk mail or spam, if it is unwanted by the addressee.

Privacy concerns-2

The danger occurs when the summarized data paints an untrue picture of things. This can lead to a company taking improper actions which could be detrimental to the prosperity of the company. The threat to an individual’s privacy comes into play when the data, once compiled, causes the data miner to be able to identify specific individuals, especially when originally the data was anonymous. Aggregating data from multiple sources allows profiles of individuals to be created . In order for the information derived from the data that is mined to be meaningful one must assume that the data which is in the repository is accurate and complete. In addition, one must assume that the analysis was done in a way that would produce a reliable result. A common saying is “garbage in garbage out” meaning if the data that is input into your repository is of poor quality, your analysis, or output, will also be of poor quality .

The steps that may be taken in order to protect your customers, from whom you are collecting data, and your company are to specify the purpose of the data collection and any data mining projects, how the data will be used, who will be able to mine the data and use it, the security surrounding access to the data, and in addition, to provide a way for individuals to update data which was collected from them. This also assists in ensuring the data is accurate. One may additionally modify the data so that it is anonymous so that individuals may not be readily identified.

blinds, roller shades, woven wood shades

Privacy concerns-1

There are also privacy and human rights concerns associated with data mining, specifically regarding the source of the data analyzed. Data mining provides information that may be difficult to obtain otherwise. When the data collected involves individual people, there are many questions concerning privacy, legality, and ethics. In particular, data mining government or commercial data sets for national security or law enforcement purposes, such as in the Total Information Awareness Program, has raised privacy concerns.

The following facts have increased the urgency and difficulty regarding data mining and protecting the privacy of the individuals about whom the data was collected: the decreased cost of data mining tools and the prevalence of those tools, an increase in the amount of data being collected and stored, an increase in the use of data aggregation, and the use of data warehouses as the stores for the data from several sources.

“Data mining by itself is ethically neutral”. There are several ethical issues which are raised by the topic of data mining: “the suitability and validity of the methods used in any given data mining application, the degree to which confidentiality and privacy obligations are respected, and the overall aims of a given data mining application”.

One must take into consideration the reliability of the source of the data which is being mined, the reason that the data was collected originally, and any aggregation that has taken place A danger which is inherent to data mining projects is the possibility of erroneous information resulting from data aggregation. Data aggregation is when the data which has been mined, possibly from various sources, has been put together so that it can be analyzed.

Algorithms

There are various data mining algorithms which can be used to build the mining model. But choosing the right algorithm for the right business task is critical. Different algorithms can be used to do the same business tasks but each algorithm produces different results.
The various types of algorithms are as follows:
1. Classification algorithm predicts one or more discrete variables, based on the other attributes in the dataset. eg: Microsoft Decision Trees Algorithm.

2. Regression algorithm predicts one or more continuous variables, such as profit or loss, based on other attributes in the dataset. eg: Microsoft Time Series Algorithm.

3. Segmentation algorithm divides data into groups, or clusters, of items that have similar properties. eg: Microsoft Clustering Algorithm.

4. Association algorithm finds correlations between different attributes in a dataset. The most common application of this kind of algorithm is for creating association rules, which can be used in a market basket analysis. eg: Microsoft Association Algorithm.

5. Sequence analysis algorithm summarizes frequent sequences or episodes in data, such as a Web path flow. eg: Microsoft Sequence Clustering Algorithm.

A data mining application can adopt different algorithms for different functions, for example we can use segmentation algorithms for exploring data and regression algorithms for prediction functionalities.

roman shades, vertical blinds, window blinds

Search for Window Treatments-1

Yesterday I and my family visited one mall. It is very big and well known shopping mall in our area. I was there to buy something unique and different for my house. I saw so many antique things which are useful to decorate the house like wall picture, flower vas etc…. Actually I was interested in window treatments items. Fortunately I found one store and they have many types of blinds on their display. For the first time I have seen these much collections of blinds at one place. The way they have displayed their product range was so attractive.

They are having window blind & shade products. That includes wood blinds, faux wood blinds, mini blinds, and vertical blinds. Their window shades includes roman shades, cellular shades, pleated shades, roller shades, and woven wood bamboo shades. I was interested in vertical blinds for my drawing room. Salesman their explain me vertical blinds are very popular and appealing for sliding glass doors and larger windows. He told me to select between cord and chain operation and wand controlled blinds. My kids are already grown so, I don’t have any threat to my kids safety. We decided to go with chain instead of cord.

Than he told us to select from a variety of wood, fabric patterns, and basic vinyl on our verticals so that our blinds are coordinated with the rest of our decorations. He told us that for more unique look, sliding panel shades offer both shade and safety with their baton draw design. These large panels will add ambiance to any formal room in your home! So, at last I decided to go with vertical blinds for my drawing room.

Data Mining -2

Forecasting, or predictive modeling provides predictions of future events and may be transparent and readable in some approaches (e.g., rule-based systems) and opaque in others such as neural networks. Moreover, some data-mining systems such as neural networks are inherently geared towards prediction and pattern recognition, rather than knowledge discovery.

Metadata, or data about a given data set, are often expressed in a condensed data-minable format, or one that facilitates the practice of data mining. Common examples include executive summaries and scientific abstracts.

Data mining relies on the use of real world data. This data is extremely vulnerable to collinearity precisely because data from the real world may have unknown interrelations. An unavoidable weakness of data mining is that the critical data that may expose any relationship might have never been observed. Alternative approaches using an experiment-based approach such as Choice Modelling for human-generated data may be used. Inherent correlations are either controlled for or removed altogether through the construction of an experimental design.

Recently, there were some efforts to define a standard for data mining, for example the CRISP-DM standard for analysis processes or the Java Data-Mining Standard. Independent of these standardization efforts, freely available open-source software systems like RapidMiner and Weka have become an informal standard for defining data-mining processes.

Data Mining -1

We are talking data mining in our last post and we were talking background of data mining. We continue our talk on data mining in this post.


Data mining identifies trends within data that go beyond simple analysis. Through the use of sophisticated algorithms, non-statistician users have the opportunity to identify key attributes of business processes and target opportunities. However, abdicating control of this process from the statistician to the machine may result in false-positives or no useful results at all.


Although data mining is a relatively new term, the technology is not. For many years, businesses have used powerful computers to sift through volumes of data such as supermarket scanner data to produce market research reports (although reporting is not always considered to be data mining). Continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy and usefulness of data analysis.


The term data mining is often used to apply to the two separate processes of knowledge discovery and prediction. Knowledge discovery provides explicit information that has a readable form and can be understood by a user.