Capabilities > Business Intelligence > Customer Analytics

Customer Success Story

Comprehensive customer analytics target high-leverage opportunities for cross selling

Situation: Tepid industry market conditions force company to look for new revenue generating opportunities

Approach: Leverage current customer relationships by cross-selling along geography and service lines

Solution: Structured analytic solution coupled with web-based client information system provide insight intelligence on customer behaviors and relationships

Cusotmer Analytics


One of the most strategic assets of any organization is its customer relationships. These relationships have been fostered, often, over a number of years and many organizations rely heavily on these trusted relationships. However, customers and relationships, are intangible assets and are thus under-analyzed and underutilized. Customer relationships are often dispersed and distributed throughout various foci in an organization. Hence, there is not adequate thought and governance regarding active management of these valuable relationships. There are certain aspects of these relationships that need to be addressed centrally and holistically. Most organizations that do analyze customer relationships, look at these associations individually instead of as a whole. Holistically looking at the entire customer portfolio has some interesting ramifications regarding decision-making especially when prioritizing resources.


Customer portfolio analysis" notwithstanding, it is obvious that many organizations rarely leverage their current customer relationships adequately. One of the main reasons is the lack of reliable information about customers (even though many transactional CRM systems have collected massive amounts of customer data). This is driven by a general lack of quantifiable customer information and the difficulty in arriving at measurable metrics regarding customer relationships. These predicaments, however, can be addressed by using a structured methodology to collect and assess organization-wide customer data. Confida's phased approach to customer analytics provides the fundamentals needed to evaluate an organization's customer portfolio.


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Customer data is often dispersed throughout various sources in an organization. Some sources like transaction system repositories are more formalized while others like Excel spreadsheets are less administered. To complicate matters, much customer data is not recorded and captured, because it is difficult and time-consuming do so. Additionally, some employees (e.g., salespersons) are averse to sharing valuable customer data with the company. For these reasons and others, sourcing customer data from various disparate sources is a formidable challenge.

In order to consolidate the data obtained from various sources, one must completely understand the assorted data elements and databases involved. This phase of the process is more daunting than it seems because often data owners are unwilling or unable to share adequate information. Complete understanding of the data and the underlying structures is similar to solving a puzzle without having all the puzzle elements readily available.


Data validation and data integrity are always the most time-consuming elements of the process. Often times, fifty percent or more of a project's time is dedicated to this phase of the effort. Projects led by other consultants often bestow a cursory wave of the hand at data validation and integrity efforts. This is due to the fact that data validation and integrity are detailed and time-consuming efforts. At Confida, however, we believe that decision-making is only as good as the data that feeds it (and we thus spend much of out time with this part of the process).

A distinction needs to be made between data validation and data integrity. Data validation refers to the process of ensuring that the consolidated data warehouse contains the data that the user has intended. Data integrity, on the other hand, ensures that the data that the user has provided is correct. In many cases, the process involves running the data through various filters, from simple alphanumeric filters to ensure data consistency to complex logical filters to ensure data efficacy. This process involves multiple passes and iterations; although some of the filters are completely automated, many tasks require a great deal of time-consuming human intervention.


The first four steps of the process result in a working data warehouse containing all the relevant customer data. This repository is a valuable test-bed for running sample analyses and rudimentary investigation. The initial part of the fifth phase involves the creation of simple analyses which serve to reinforce the validity of the data warehouse. Gradually, more complex multivariate analysis can be performed and this can be followed by conducting complicated individual customer and customer portfolio analyses. Feedback from these analyses will eventually feed into a set of featured analyses that will become the organizational standard.

The final step of the process is not always implemented (although in many ways it can be the most important part of the process). This involves structuring the standard analyses into easy-to-understand reports that are always available to decision-makers. Confida uses sophisticated information mapping techniques and a web-based interface to design intuitive reports that answer sophisticated customer questions. This system provides an organizational memory that provides decision makers with an understanding of individual customer relationships and portfolio-view of its customers.


A system like Confida's Customer Management Portal (CMP) provides a rich panoply of information to help organization identify customer retention issues, recognize cross-selling opportunities and categorize the most efficient and effective allocation of customer-facing resources. Forward-looking questions such as which relationships are on the decline, where is the organization having success with its customers and which relationships are proving to be the most profitable are straightforward to answer with CMP. Having access to rigorously cleaned data and sophisticated reports provides key decision-makers with the ability to make real-time decisions regarding individual customer relationships and perhaps more importantly regarding a structured portfolio-view of an organization's customers.