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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).

In the CRM arena, analytics are provided in two major flavors:

  • Descriptive analytics — which focuses on the historical customer patterns
  • Prescriptive analytics — which tends to predict the customer's future behavior

Many 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

Business 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.

Analytical 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 analytics department.


The world of BI software has many different tools. Some of them follow:

End-user query and reporting tools: These are designed specifically to support ad hoc data access and report- building by even the most novice users.

Online Analytical processing (OLAP) tools: Computerbased techniques used to analyze trends and perform business analysis using multidimensional views of business data.

Online Transaction Processing (OLTP) platforms: Computer systems designed and built for conducting data analytics.

Online 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.

Data 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 make predictions.

ETL 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.

Packaged 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

Traditionally 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.

Unfortunately, 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 has passed.

The 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.

CRM 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.

Business Objects recently announced its integration with 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.

User Recommendation

True 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.

Most 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.

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