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.

Data mining

Data mining is the process of sorting through large amounts of data and picking out relevant information. It is usually used by business intelligence organizations, and financial analysts, but is increasingly being used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods.

It has been described as "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data" and "the science of extracting useful information from large data sets or databases." Data mining in relation to enterprise resource planning is the statistical and logical analysis of large sets of transaction data, looking for patterns that can aid decision making.

Background
Traditionally, business analysts have performed the task of extracting useful information from recorded data, but the increasing volume of data in modern business and science calls for computer-based approaches. As data sets have grown in size and complexity, there has been a shift away from direct hands-on data analysis toward indirect, automatic data analysis using more complex and sophisticated tools.

The modern technologies of computers, networks, and sensors have made data collection and organization much easier. However, the captured data needs to be converted into information and knowledge to become useful. Data mining is the entire process of applying computer-based methodology, including new techniques for knowledge discovery, to data.

Customer value proposition

In the field of marketing, a customer value proposition consists of the sum total of benefits which a vendor promises that a customer will receive in return for the customer's associated payment (or other value-transfer). A customer relationship management software helps company to make customer value proposition.

In simple words: value proposition = what the customer gets for what the customer pays.

Accordingly, a customer can evaluate a company's value-proposition on two broad dimensions with multiple subsets:

• relative performance: what the customer gets from the vendor relative to a competitor's offering;
• price: which consists of the payment the customer makes to acquire the product or service; plus the access cost

The vendor-company's marketing and sales efforts offer a customer value proposition; the vendor-company's delivery and customer-service processes then fulfill that value-proposition. Agents web world needs to do a lot effort to have proper value-proposition.

Value-proposition as marketing tool
A value-proposition can assist in a firm's marketing strategy, and may guide a business to target a particular market segment. For example: "Firm Any Co. can provide benefits a, b, and c because of competencies x, y, and z."
Whether for a product, service or a company as a whole, this formulation can allow a firm to see if its competencies align with the segment that it plans to target.

Role of Technology in Customer Service

Customer Service we are talking here. Customer relationship management software is very much important for any business useing insurance software or agency management software to run their business. Today, we are going to talk about Role of Technology in customer service.

Technology has made available a wide range of customer service tools. They include support websites, the ability to have live chats with technical staff, databases tracking individual customer preferences, pattern of buying, payment methods etc., and tailoring products and service responses based on these advanced data. Specialist software that is designed for the tracking of service levels and for helping recognize areas for improvement are often integrated into other enterprise operational software tools such as ERP software.

Many companies have started to use new channels to capture customer feedback. With record number of people now communicating through mobile phone and sending texts, many argue that the next wave of customer feedback will primarily be captured through channels familiar to most consumers, such as mobile email and SMS. This will enable companies to track the opinions of their customers much more easily and gain valuable insight into how to improve service quality and enhance the customer experience.

ref: Insurnace Software, Insurance CRM, wikipedia

Applied psychology in customer service

Agency Management Software always includes CRM software. Insurance software with agency management system, insurance CRM, insurance sfa helps insurance industry people to manage their customer service in better way. We are talking here CRM software functions. We talked Sales Force management software, customer service, Implementations of customer service in our past post. Today we are going to talk about Applied psychology in customer service.

There are different levels of knowing your customers. Often, customer service relies on demographics or customer data collection. Yet, customer and customer dynamics as a group are affected through modalities of experience. Hence it is important to know your customers and to the culture that you want to create. This is where psychology enters into the realm of customer service.

According to Arthur F. Carmazzi, founder of Directive Communication, how person processes information will have a bearing on how he or she reacts in a given situation. Carmazzi says that there are four brain colors: green, red, blue, and purple. Knowing the brain color of a customer will help you understand his/her expectations of service and deliver accordingly. For instance, a red brain customer in a bank will value order and a systematic approach to enlisting him in a wealth management program.

ref: agency managment software, Insurance Software, wikipedia

Competitive advantage

A company may attempt to differentiate itself from its competition through the provision of better customer service. The consistent delivery of superior service requires the careful design and execution of a whole system of activities that includes people, technology, and processes; although, the rewards will include improved revenue from customers that are impressed with the service provided. Companies in insurance industry need to have insurance crm, insurancefa software with agency management system to deliver better service to customers.

Some companies do better than expected. In the 1980's, a customer called LL Bean and was surprised that he was greeted by name. The representative explained that AT&T provided Caller-ID service to all companies with toll-free lines (ten years before any phone company offered Caller ID to retail customers), and that Bean's computer had brought up the customer's record on her computer screen. She knew where he lived and what he had recently bought. If he wanted something new, she even knew the size and color to suggest. They also remembered the credit card number that had been used, although they could not be certain it was still valid.

In some cases, a company will have two interfaces: during "normal business hours" in the vendor's time zone, the caller will reach the Customer-Service Department, which can take new orders, trace recent orders, and solve problems; a person calling outside those hours will instead reach a fulfillment house, often in another state or country, and able only to take new orders. In most cases, fulfillment centers don't even have catalogs for the many companies they represent. If a problem arises, the answer is "Call between 8 a.m. and 5 p.m. Monday through Friday, Eastern Standard Time."



ref: agency management system, crm-sfa software, wikipedia

Implementation of Customer Service

We talked Sales Force Automation, Lead Capcture, Lead Distribution in our past post. CRM Software Functions we are talking here. Agency Management System (AMS) include Customer Relationship Management.

Customer service may be provided by a person (e.g., sales and service representative), or by automated means called self-service. Examples of self service are Internet sites.
Customer service is normally an integral part of a company’s customer value proposition.

Implementation of customer service
Customer service may be employed to generate such competitive advantage as a particular service proposition can be harder to copy for competitors.

The implementation of a particular customer service proposition must consider several elements of the organization

ref: agency management system, insurance crm, insurance software, wikipedia

Customer Service - CRM

CRM Software Functions we are talking here. Agency Management System ( AMS ) include Customer Relatinship Management. We talked Sales Force Automation, Lead Capcture, Lead Distribution in our past post.


Today we are going to talk Customer Service.

Customer service (also known as Client Service) is the provision of service to customers before, during and after a purchase.

According to Turban et al, 2002 “Customer service is a series of activities designed to enhance the level of customer satisfaction – that is, the feeling that a product or service has met the customer expectation”

Its importance varies by product, industry and customer. As an example, an expert customer might require less pre-purchase service (i.e., advice) than a novice. In many cases, customer service is more important if the purchase relates to a “service” as opposed to a “product".

Customer service may be provided by a person (e.g., sales and service representative), or by automated means called self-service. Examples of self service are Internet sites.



ref: Agency Management System, wikipedia