Product Definition and Market Impact
With company's data warehouses now filled with terabytes of historic data it is crucial to adopt tools that would allow marketers to better understand their customers and build adapted strategies for a true one to one relationship management. The increasing demand to allow end users to access, analyze, and deliver information in an organization has amplified the need for the business intelligence (BI) market and its technology. Understanding customer behavior is important to adjusting business strategies, increasing revenues, and identifying new opportunities. Analytics are therefore today's hot button, helping businesses to increase their overall enterprise application return on investment (ROI).
the CRM arena, analytics are provided in two major flavors:
— which focuses on the historical customer patterns
Prescriptive analytics — which tends to predict
the customer's future behavior
of the traditional CRM packaged software providers like Siebel,
SAP, PeopleSoft, MS CRM and
E.piphany offer analytics modules that mostly fall into the
descriptive category. Predictive analytics have long been the focus of vendors
like SPSS, I-Impact, SAS Institute,
Business Objects SA, and Cognos Inc
Intelligence suites are even establishing a much broader market by crossing
into the domain of ERP providers that offer solutions for financial management.
In fact they are heavily investing in the emerging area of business performance
management (BPM). BPM analytics could play a significant role in CRM suites
for improving business process performance. However, data mining technology,
which focuses on identifying interesting patterns and developing predictive
models from data, has the greatest potential for enabling businesses to leverage
data resources for strategic business success.
applications are more and more leveraging faster processing speeds, more complex
algorithms, and existing data mining infrastructures to help enterprises navigate
through their unknown market. The analytics software market has a wide variety
of technologies, such as analytic servers and tools for query and reporting,
online analytic processing (OLAP), and data mining. These technologies have
traditionally been distinct and not integrated, which has resulted in segregation
of user communities unable to share information. There is now an awakening to
the potential as more predictive analytics capabilities are appearing embedded
with packaged CRM, ERP and SCM applications, where they're accessible to a broad
set of users who previously relied on spreadsheets, gut instinct, or a separate
world of BI software has many different tools. Some of them follow:
query and reporting tools: These are designed specifically to support
ad hoc data access and report- building by even the most novice users.
Analytical processing (OLAP) tools: Computerbased techniques used
to analyze trends and perform business analysis using multidimensional views
of business data.
Transaction Processing (OLTP) platforms: Computer systems designed
and built for conducting data analytics.
Analytical Processing Platforms (OLPP): Developed in recognition of
the limitations of conducting data analytics on Online Transaction Processing
(OLTP) platforms, OLPP provides a complete development and delivery
environment for predictive analytics not satisfied today by OLAP/OLTP processes.
OLPP enables users to develop a predictive understanding of the future behavior
and trends of customers, vendors, products, accounts, inventory, supply, and
transactions through support of ease of usage of traditional and cutting edge
scientific predictive modeling techniques.
mining software: The process of efficient discovery of nonobvious valuable
patterns from a large collection of data. It uses technologies such as neural
networks, rule induction, and clustering to discover relationships in data and
tools: Short for extract, transform, load; three database functions
that are combined into one tool to pull data out of one database and place it
into another database. Extract — the process of reading data
from a database. Transform — the process of converting the
extracted data from its previous form into the form it needs to be in so that
it can be placed into another database. Transformation occurs by using rules
or lookup tables or by combining the data with other data. Load
— the process of writing the data into the target database.
data-mart/warehouse: Is a blend of technologies aimed at the effective
integration of operational databases into an environment that enables the strategic
use of data. These technologies include relational and multidimensional database
management systems, client/server architecture, metadata modeling and repositories,
graphical user interfaces, and much more. Executive information systems (EIS):
provide high-level views of an organization by aggregating data from various
sources from within the organization and also external sources. Ad hoc enquiries
provide performance data and trend analysis for top-level management. Ease of
use is an important feature so that enquiries can be made without a detailed
knowledge of the underlying data structures. Graphical interfaces (GUI) make
it possible to request reports and queries without resorting to programming.
Trends Expanding the User Community
BI software implementations focused on internal projects that provide analysts
and managers access to business intelligence tools. Today the trend is changing
as a wider business community requires access to customer insights. Analysis
and reporting are no longer restricted to the aforementioned groups within organizations.
Any employee can, and should, benefit from BI software.
for most companies today, the use of data mining models within campaign management
is a manual, time-intensive process. When a marketer wants to run a campaign
based on model scores, he or she has to call a modeler (usually a statistician)
to have a model run against a database so that a score file can be created.
The marketer then has to solicit the help of an IT staffer to merge the scores
with the marketing database. This disjointed process is fraught with problems
and errors and can take weeks. Often, by the times the models are integrated
with the database, either the models are outdated or the campaign opportunity
solution is the tight integration of data mining and campaign management technologies.
Under this scenario, marketers can invoke statistical models from within the
campaign management application, score customer segments on the fly, and quickly
create campaigns targeted to customer segments offering the greatest potential.
Campaign management and data mining, when closely integrated, are potent tools.
applications are the perfect vehicle for transmitting the required knowledge
to different strata of business community. New alliances are therefore predictable.
More and more operational CRM analytic vendors align with more advanced, prediction-oriented
analytics companies. Marketing automation and analytics are definitely the hotspots
for the CRM market this year.
Objects recently announced its integration with Salesforce.com to provide the
CRM customers with advanced reporting and analysis solution. PeopleSoft released
its Predictive Analytics Solution. Siebel continues its tight integration with
SPSS and SAS.
personalization of sales channels can be described as, what product / services
/ prices customers must be offered based on interaction feedback and the prediction
of their behavior pattern. The amount of data that needs to be analyzed to make
this accurate and appealing to the customer is complex and high volume. Data
mining can provide the necessary analytic means to uncover significant rules
or patterns. For the dissected and configured data to be useful there has to
be some action taken to give it some value. That action could be campaign management,
email, direct mail management, or a myriad of other actions.
of today's CRM applications have included features that provide automated customer
oriented actions. It is now critical to many CRM vendors to complete the process
by adding an analytic engine to their CRM offering. This should homogenize all
the CRM functions under the same business model and allow a broader scope of
users have access to the data they need when they need.