CRM Unit - 5

Unit- V

Database Marketing – Prospect database – Data warehouse and Data Mining – analysis of customer relationship technologies – Best practices in marketing technology in Indian scenario.

What is database marketing?

Database marketing is a form of direct marketing that uses databases of customers to generate targeted lists for direct marketing communications(See also Direct Marketing). Such databases include customers’ names and addresses, phone numbers, e-mails, purchase histories, information requests, and any other data that can be legally and accurately collected Information for these databases might be obtained through application forms for free products, credit applications, contest entry forms, product warranty cards, and subscriptions to product newsletters.

Using our opening example, a database at a technology store might well be able to produce a list of customers who had purchased similar products and might be interested in a new promotion. These databases, once built, allow businesses to identify and contact customers with a relevant marketing communication.

Features

 The salient features of database marketing include:

ü      Extending help to a company to reach its target audience;

ü      Stimulating the customer demand; and

ü      Recording and maintaining an electronic database of the customer, and all commercial contacts, so that the business firm could improve their future contacts and devise a more realistic marketing strategy.

 Characteristics of Database Marketing 

§  Each actual or potential customer is identified as a record on the marketing database.

§  Each customer record contains information (used to identify the likely purchases of particular products and how they should be approached) on:

§  Identification and access (eg. Name, address, telephone No)

§  Customer needs & characteristics (demographic and psychographic information about customers, the industry type and decision making unit information for the industrial customers)

§  Campaign Communications (whether the customer has been exposed to particular marketing communication campaigns)

§  Customers past responses to communications done as a part of the campaigns

§  Past transactions of customers (with the company and possibly with the competitors).

§  This enables the firm to decide on how to respond to the customer needs.

§  The database is used to record the responses of the customer to the firm’s initiatives. (e.g. marketing communications or sales campaigns).

§  The information is also made available to the company’s marketing policy makers which enables them to decide:

§  The target markets or segments appropriate for each product or service.

§  The marketing mix (price, marketing communications, distributions channel, etc) appropriate for each product in each target market.

§  This step is vital in relationship marketing.

§  The marketing campaigns are devised in such a manner to provide the most relevant information that the company is seeking.

Advantages of Database Marketing

             The one-on-one marketing, which directs the customized offerings to individual customers, has provided an additional thrust to database marketing.  It has employed the database to capture the interactions between a firm and its customers at each point of time and utilizes the data analysis to search for patterns in these interactions.  These patterns provide the most attractive potential customers besides providing clues in customizing the products, pricing and promotions of a product.  When utilized in the proper manner, the database marketing could provide insights into the customer’s buying behavior across the product categories, so that the companies could devise their programmes and plans to the “whole customer”, then the customer seen only through the narrow view of their own products and brands.

 Disadvantages of Database Marketing 

§  The cost incurred in setting up the software and hardware requirements has made the database marketing expensive in its establishment.

§  The database often demands new skills and organizations from new analytical and decision-making skills in sales and marketing to a revamped information system organization that could support the entirely new class of users.

§  The database marketing depends on the data quality.  While the observational data is powerful, the corrupted observational data could be ‘powerful misleading’.  The quality also depends on the quality of analysis and the extent to which the databases are linked.

§  Till now, the database marketing has been primarily used as a tactical tool.

Prospect Database

Prospects are non-customers with profiles similar to those of existing customers. The prospect database should include as much information about prospects as the customer database does about customers. For obvious reasons, however, the prospect database does not contain any transaction history data. Marketer can use a prospect database to design marketing campaign to target prospects with the intent of acquiring them as new customers.

Data Warehouse

Development of a data warehouse includes development of systems to extract data from operating systems plus installation of a warehouse database system that provides managers flexible access to the data.

The term data warehousing generally refers to the combination of many different databases across an entire enterprise. Contrast with data mart.

Objective of Data Warehouse

·         Efficient distribution of information via the WEB

·         Minimize technical involvement by enabling users to generate and maintain their own reports.

·         Create a user-friendly reporting environment

·         Provide easy access to data from different sources

·         Lay the foundation and develop plans for full data warehouse development and implementation.

Characteristics and functioning of Data Warehousing

        A common way of introducing data warehousing is to refer to the characteristics of a data warehouse.

·         Subject Oriented

·         Integrated

·         Nonvolatile

·         Time Variant

Subject Oriented:

        Data warehouses are designed to help you analyze data. For example, to learn more about your company's sales data, you can build a warehouse that concentrates on sales. Using this warehouse, you can answer questions like "Who was our best customer for this item last year?" This ability to define a data warehouse by subject matter, sales in this case, makes the data warehouse subject oriented.

Integrated:
        Integration is closely related to subject orientation. Data warehouses must put data from disparate sources into a consistent format. They must resolve such problems as naming conflicts and inconsistencies among units of measure. When they achieve this, they are said to be integrated.

Nonvolatile:
        Nonvolatile means that, once entered into the warehouse, data should not change. This is logical because the purpose of a warehouse is to enable you to analyze what has occurred.

TimeVariant:
        In order to discover trends in business, analysts need large amounts of data. This is very much in contrast to online transaction processing (OLTP) systems, where performance requirements demand that historical data be moved to an archive. A data warehouse's focus on change over time is what is meant by the term time variant.
Typically, data flows from one or more online transaction processing (OLTP) databases into a data warehouse on a monthly, weekly, or daily basis. The data is normally processed in a staging file before being added to the data warehouse. Data warehouses commonly range in size from tens of gigabytes to a few terabytes. Usually, the vast majority of the data is stored in a few very large fact tables.

Data Mining

Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery. Data mining is ready for application in the business community because it is supported by three technologies that are now sufficiently mature:

• Massive data collection
• Powerful multiprocessor computers
• Data mining algorithms

The Scope of Data Mining

Data mining derives its name from the similarities between searching for valuable business information in a large database — for example, finding linked products in gigabytes of store scanner data — and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find exactly where the value resides. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by providing these capabilities:

• Automated prediction of trends and behaviors. Data mining automates the process of finding predictive information in large databases. Questions that traditionally required extensive hands-on analysis can now be answered directly from the data — quickly. A typical example of a predictive problem is targeted marketing. Data mining uses data on past promotional mailings to identify the targets most likely to maximize return on investment in future mailings. Other predictive problems include forecasting bankruptcy and other forms of default, and identifying segments of a population likely to respond similarly to given events.

• Automated discovery of previously unknown patterns. Data mining tools sweep through databases and identify previously hidden patterns in one step. An example of pattern discovery is the analysis of retail sales data to identify seemingly unrelated products that are often purchased together. Other pattern discovery problems include detecting fraudulent credit card transactions and identifying anomalous data that could represent data entry keying errors.

Data mining techniques can yield the benefits of automation on existing software and hardware platforms, and can be implemented on new systems as existing platforms are upgraded and new products developed. When data mining tools are implemented on high performance parallel processing systems, they can analyze massive databases in minutes.

Analysis of Customer relationship Technologies

CRM (customer relationship management) analytics comprises all programming that analyzes data about an enterprise's customers and presents it so that better and quicker business decisions can be made. CRM analytics can be considered a form of online analytical processing (OLAP) and may employ data mining. As Web sites have added a new and often faster way to interact with customers, the opportunity and the need to turn data collected about customers into useful information has become generally apparent. As a result, a number of software companies have developed products that do customer data analysis.

Benefits of CRM Analytics

CRM analytics can provide

1. Customer segmentation groupings (for example, at its simplest, dividing customers into those most and least likely to repurchase a product);

2. Profitability analysis (which customers lead to the most profit over time);

3. Personalization (the ability to market to individual customers based on the data collected about them);

4. Event monitoring (for example, when a customer reaches a certain dollar volume of purchases);

5. What-if scenarios (how likely is a customer or customer category that bought one product to buy a similar one)

6. Predictive modelling (for example, comparing various product development plans in terms of likely future success given the customer knowledge base).

 Data collection and analysis are viewed as a continuing and iterative process and ideally over time business decisions are refined based on feedback from earlier analysis and consequent decisions.

Benefits of CRM analytics are said to lead not only to better and more productive customer relations in terms of sales and service but also to improvement in supply chain management (lower inventory and speedier delivery) and thus lower costs and more competitive pricing.

Best Practices in Marketing Technology in Indian Scenario

• Top Performers are four times more likely than Everyone Else to adopt marketing automation tools.
• We are now entering the age of relationship marketing, and it’s proving to be challenging because the legacy / disconnected marketing infrastructure we relied on decades ago was never designed to support relationship marketing.
• Conversion events are a measure of how effective your marketing and sales efforts are at driving top line growth. Top Performers have an affinity for measuring and optimizing individual interactions inside the buying and sales cycle and they make optimizing the path to revenue scalable and manageable using lead scoring within marketing automation tools. 
• The minute two or more channels (even the web, landing page, or email) play a role in driving prospect or customers to purchase, a centralized multi-channel platform with lead scoring capabilities will be essential to optimizing the path to revenue.

The 7 Essential Best Practices for Integrating Marketing Technology with CRM are:

1. Use lead scoring to prioritize leads and increase CRM adoption. 
2. Allow reps to push sales-ready leads that are not read to buy back to nurture marketing campaigns.
3. Use prospect behavior to route leads to appropriate campaigns or even sales contact.
4. Automate the next best action for reps from within CRM.
5. Close the loop on measurement by using marketing and sales data in reporting and analytics.
6. Implement a bi-directional flow of data between marketing and sales technologies. Giving marketing and sales visibility into how a contact actually engaged with the brand.
7. Nurturing should account for all marketing and sales channels a prospect chooses to engage in, which demands a multi-channel platform and the ability to route leads accordingly.