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DSC Tech Library
This section of our technical library presents information and documentation relating to CRM Solutions and Customer relationship management software and products. Providing customer service is vital to maintaining successful business relationships. Accurate and timely information provided in a professional manner is the key to any business and service operation.
Telemation, our CRM software application, was built on this foundation. But the flexibility to change is just as important in this dynamic business environment.
Telemation call center software was designed with this concept from the very beginning.
That is why so many call center managers, with unique and changing requirements, have chosen and continue to use Telemation CRM software as their solution.
Our Telemation CRM solution is ideally suited for call center service bureaus.
Gaining Insight to Customer Behavior Through The Use of Time Series Data
By David Long
Source: Equifax Knowledge Engineering
CRM2day.com
Today, organizations across all industries are establishing and deploying CRM strategies with the objective of reinventing the way that they acquire, develop, and retain customers. The success of these strategies is measured by the organization's ability to create profitable growth through establishing broader and longer-term customer relationships in a hyper-competitive market that is driven by increasingly empowered consumers. Time Series Data helps identify the customer behaviors that motivate your customers and allows you to dynamically tailor personalized marketing offerings.
This proliferation of choices has created a shift in the traditional buyer-seller relationship where consumers are increasingly taking control of the buying experience. As a result, consumers are demanding that organizations fully understand their individual needs and desires and expect the organization to tailor their offerings with the appropriate product, message, and channel. Due to this dramatic shift of control, it is imperative that organizations have a more thorough understanding of their customers. Successful CRM strategies link this customer knowledge with the optimal marketing message to result in a more profitable and long-term customer relationship.
Essential to these strategies is the development and deployment of dynamic segmentation systems that allow companies to more effectively target new customers and develop and retain existing relationships. While most organizations have developed segmentation systems within each line of business, few have successfully worked across the barriers created by disconnected product silos and linked together these "product-centric" customer views.
Those organizations that effectively break down these internal barriers have the ability to create a more holistic view of the customer and are able to significantly enhance their ability to understand the value of each customer relationship across the enterprise. This enterprise-wide knowledge of the customer serves as an integral enabler that allows organizations to develop marketing strategies that optimize product offerings and channels to achieve deeper customer loyalty and improved bottom line profits.
The most comprehensive view of the customer must also take into account that customer's relationships with other companies. While the creation of an enterprise-wide customer profile is typically limited to the specific experiences within the organization, a more overall understanding of the customer can be gained by incorporating external sources of data within the customer segments. An understanding of the customers' relationships outside of the organization will provide valuable insight into their motivation in originating, maintaining and ceasing relationships. Through this fact-based understanding of the customer, organizations can tailor their strategies to make their relationship more valuable to the customer.
Background
Historically, most companies have utilized internal master file and transaction level data to create homogenous customer segments to execute cross-sell, up-sell and customer retention strategies. In order to understand the unique consumer characteristics of each segment, many companies have augmented their internal data bases with static views of external data from such sources as the consumer credit file and various demographic and lifestyle databases.
These additional data sources have added supplementary views of the consumer that have enabled organizations to gain a better understanding of both prospective and existing customers. Using this data, probability models and advanced segmentation systems that allow organizations to effectively assess such behaviors as – response, revenue, risk and profitability – have been designed to drive the execution of marketing programs targeting customer acquisition, development and retention. While effective, these models and segmentation systems have been developed at a relatively broad level in that the underlying external data that drives them are typically compiled at a static view, or single-point-in-time. Several examples of these data attributes include:
- Number of open credit card accounts
- Number of open retail accounts
- Credit line utilization on credit card accounts
- Credit line utilization on retail accounts
- Presence of a mortgage
- Household income
- Home ownership/value
- Presence of children
- Automobile ownership
While these models and segmentation systems have continuously proven to accurately delineate future activity, they tend to profile consumers in terms of characteristics and not behaviors, therefore making it very difficult to link those profiles to specific marketing offers.
The Next Generation of Customer Insight
Market saturation and intense competition for a limited universe of profitable customers has spawned the need for unparalleled customer insight and knowledge. As a result, segmentation systems that were once producing very satisfactory results are proving to be increasingly ineffective. Therefore, it is imperative that organizations begin to understand their prospective and existing customers in terms of their behavioral motivators (e.g. needs and desires) rather than their performance probabilities.
This critical insight and knowledge can only be attained by the utilization of trended, or time series data. It is only through understanding the customer in terms of where they are in the consumer lifecycle that the organization can anticipate the consumer's next need, and react accordingly. The use of time series data, coupled with sophisticated data mining and analytical techniques, can provide a much deeper understanding of customer-buying behavior. This new and evolving view of the consumer serves as the foundation for fact-based customer relationship management and is enabling organizations to make more intelligent marketing decisions by tailoring the product, message and channel that will ultimately lead to a more profitable, long-term relationship.
Value of Time Series Credit Data
The key to the value of time series credit data is its ability to combine multiple month's of data into behavioral trends that provide a level of insight that goes well beyond anything currently available. This insight results in a philosophical and logical migration from viewing the consumer as a single profile, to one that leverages the reality that consumer actions are actually compilations of individual relationships, which can only be understood with a longitudinal view.
This consumer would typically have been described in terms of the total outstanding balances, maximum outstanding balance, and number of financial relationships as of the April timeframe. By viewing the consumer behavior with each relationship, the consumer can now be understood in terms of propensity to revolve, tendency to consolidate debt, level of usage in each relationship, and relationship loyalty.
Time Series Data Identifies Customer Behaviors
The underlying power of time series data is in the ability to identify the motivating behavior of the customer. This new level of data enables organizations to gain valuable insight as it facilitates the migration of the customer view from a static profile to a mosaic of purchasing behavior. As a result, these dynamic views of customer activity are the basis from which innovative and powerful data points are designed. Utilizing advanced data mining and analytical techniques, these attributes drive robust segmentation systems that track consumer behavior across the various stages of their individual financial lifecycle. Several examples of those behaviors are as follows,
- Propensity to Revolve
- Propensity to Move
- rice Sensitivity
- Product Loyalty
- Propensity to Consume
Unlike those derived from static data, these descriptive attributes continuously evolve as the customer's behavior changes over time and describe why a consumer is profitable or unprofitable. This insight into a consumer's behavior provides organizations with valuable knowledge and allows them to dynamically tailor their market offerings to the customer and migrate from a traditional product-based marketing strategy to one that is driven by the customer's motivations and needs.
Utilizing external data sources, organizations have the ability to view comprehensive customer behavior encompassing multiple relationships across multiple points in time. Therefore for the first time, organizations are in a position to anticipate the customer need based on a robust pattern of behavior. For example,
- Does the customer have a history of establishing relationships with new organizations?
- How quickly is the customer establishing relationships with other organizations?
- What is the frequency with which the customer shifts his purchasing activity across several organizations?
- Is your "wallet share" with the customer improving or declining?
- If the customer is purchasing more of your product is it the result of improving "wallet share" or as a result of increasing consumption?
- If the customer is purchasing less of your product is it the result of declining "wallet share" or as a result of decreasing consumption?
A comprehensive understanding of these very important aspects of the customer's behavior will allow the organization to anticipate the future needs and desires of its customers and market to them accordingly. This valuable insight will also enable organizations to link shifting customer behavior with major life events (e.g., additional income, purchase of a new car, purchase of a new home).
Conclusion
The success of Customer Relationship Management is dependent upon the organization's ability to truly understand the individual customer and market to them with the optimal product, message and channel. An essential component of this one-to-one marketing strategy is the creation and deployment of a robust customer segmentation system. Through the use of powerful time series data and sophisticated data mining and statistical techniques, organizations can gain valuable insight into the motivation behind customer behaviors.
This knowledge will change the way organizations assess customer relationships and will revolutionize traditional marketing strategies from a product-driven process to one that is customer-driven. This shift will allow organizations to create products based on the customer's needs and desires and develop more profitable and longer-term customer relationships.
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