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Analytical Customer Relationship Management (CRM)
As online companies continue to add new and often faster ways of interacting with
customers, the opportunity and the need to turn data about customers into useful
information has become a necessity. As a result, there are a number of software tools
that have been created to analyze customer data. As one of the most dynamic customer
management tools, we at Straight Marketing utilize Analytical CRM, as it addresses the
analysis of customer data for a host of different purposes. In general it is used to design
and execute targeted marketing campaigns that optimize marketing effectiveness.
Analytical CRM takes into account product and service decision-making as well as pricing
and new product development. Try to imagine how efficient your business would be if
you knew what customers wanted and could quickly act on those needs.
The Benefits of Analytical CRM
Analytical CRM provides customer segmentation. To illustrate, it divides customers into
those that may or may not utilize your services again, or gives you information that helps
in the process of your service. Not only does it determine profitability or which
customers generally lead to the most profit over time, it also provides the ability to
market specifically to individual customers based on the data collected. As a predictive
]modeling tool, Analytical CRM can help your company compare future successes based

2. on the customer knowledge database. Over time, the analysis helps with business
decisions founded on earlier analysis. When it comes to sales, marketing and service, the
benefits of CRM analytics are many and usually lead to better and more productive
customer relations. Rather than struggling to find out what makes your customer tick,
with CRM Analytics you know which people are going to be your best customers and how
to find your prospects, quickly and efficiently.
Understand Your Customers At a Higher Level
As a growing company, the information you can acquire through Analytical CRM is
priceless. As a marketing strategy, it delivers proven results across multi-departments.
Rather than wasting time trying to figure out things about your customer, CRM analytics
helps you find out where they are, who is your most profitable customer and what are
their behaviors. Once evaluating or identifying potential clients, you can deliver far more
effective results. Investing your time and money into Analytical CRM will more than
support your important business decisions; it will play a vital role in the success of your
organization.
The results of a CRM strategy are far reaching and can either be implemented in basic
software or in a more complex software installation depending on your company’s
requirements. We at Straight Marketing implement various CRM software packages that
address a client’s needs whether it is for processes, sales or services. If you are ready to
grow your business, contact us today to speak with our marketing experts. We are here
to help you by implementing the most powerful online marketing solutions. Feel free to

3. contact us at 800-713-9884 or e-mail us at info@straightmarketing.com with any
questions or inquiries and we will respond promptly.
What is OLAP?
OLAP (or Online Analytical Processing) has been growing in popularity due to the increase in data volumes and the recognition of the business
value of analytics. Until the mid-nineties, performing OLAP analysis was an extremely costly process mainly restricted to larger organizations.
OLAP allows business users to slice and dice data at will. Normally data in an organization is distributed in multiple data sources and
are incompatible with each other. A retail example: Point-of-sales data and sales made via call-center or the Web are stored in
different location and formats. It would a time consuming process for an executive to obtain OLAP reports such as - What are the
most popular products purchased by customers between the ages 15 to 30?
Part of the OLAP implementation process involves extracting data from the various data repositories and making them compatible.
Making data compatible involves ensuring that the meaning of the data in one repository matches all other repositories. An example of
incompatible data: Customer ages can be stored as birth date for purchases made over the web and stored as age categories (i.e.
between 15 and 30) for in store sales.
It is not always necessary to create a data warehouse for OLAP analysis. Data stored by operational systems, such as point-of-sales, are
in types of databases called OLTPs. OLTP, Online Transaction Process, databases do not have any difference from a structural
perspective from any other databases. The main difference, and only, difference is the way in which data is stored.
Examples of OLTPs can include ERP, CRM, SCM, Point-of-Sale applications, Call Center.

4. OLTPs are designed for optimal transaction speed. When a consumer makes a purchase online, they expect the
transactions to occur instantaneously. With a database design, call data modeling, optimized for transactions the record
'Consumer name, Address, Telephone, Order Number, Order Name, Price, Payment Method' is created quickly on the
database and the results can be recalled by managers equally quickly if needed.
OLAPs have a different mandate from OLTPs. OLAPs are designed to give an overview analysis of what happened. Hence
the data storage (i.e. data modeling) has to be set up differently. The most common method is called the star design.
Figure 2. Star Data Model for OLAP
The central table in an OLAP start data model is called the fact table. The surrounding tables are called the dimensions.
Using the above data model, it is possible to build reports that answer questions such as:

5. The supervisor that gave the most discounts.
The quantity shipped on a particular date, month, year or quarter.
In which zip code did product A sell the most.
To obtain answers, such as the ones above, from a data model OLAP cubes are created. OLAP cubes are not strictly
cuboids - it is the name given to the process of linking data from the different dimensions. The cubes can be developed
along business units such as sales or marketing. Or a giant cube can be formed with all the dimensions.