The Future of CRM

Updated: December 09, 2010

Command, control, communication and intelligence (C3I)

Future CRM applications must be capable of supporting a real time interactive command, control, communication and intelligence (C3I) center that orchestrates a true enterprise wide customer centric approach.

C3I systems have been used for centuries by the military to orchestrate all the resources that are applied in an area. Generals rely on C3I systems for timely information from diverse commands and rapid coordinated decision implementation from a centralized command post. C3I systems are an important decision support tool that leaders use to evaluate and coordinate large volumes of information and decisions, they do not replace leaders.

In today's business enterprises we have virtually no enterprise wide C3I systems that integrate, control and support individual departmental objectives. Customer centric strategies require or rely upon a combination of resources from across the enterprise. We have SVP's, EVP's and CEO's who have that responsibility, however, they have not been empowered with the C3I tools that they need to accomplish this gargantuan task in real time across vast organizations with perhaps millions of individual decisions made each day that affect their customers.

In today's enterprises we need to begin with the question of what is our most important asset and decisions that need to be made? Hopefully it will not take long for everyone to agree that customers are the most important asset. Decisions that impact customers and their relationships with the enterprises are the most important decisions that a company can make. Everything begins with, ends with and flows from the enterprise's interactions with its customers. Enterprises should craft a long term strategic goal of adopting the best possible tools for interacting with their customers and create the best possible database of customer information and predictive analytics. Within a C3I systems strategy, customer information and predictive analysis should be the basis for all interactions with customers. Centralized information and predictions should be used to control every discipline in a company that affects each customer.

I propose that after customers the most important thing for enterprises to optimize is pricing - assuming that strategic profits are the long term goal of the enterprise. Once we know our customers we need to implement the customer centric dynamic pricing and/or availability models that optimize their lifetime value to the enterprise based on how enterprises optimally allocate their resources. Today most enterprises just place most customers into "segments" where their tactical value is used to determine a price for them on a transactional basis. Worse yet, many companies think that the "points" accumulated in a "loyalty program" are sufficient and do not preferential pricing and availability to high value lifetime customers compared to unknown buyers. Why are we doing this today? Perhaps we cannot implement true customer centric strategies because we cannot predict customer's lifetime lifecycles and values and translate that into dynamic pricing and availability models.

Enterprise C3I must optimize lifetime profitability for each customer while it also optimizes prices and availability. This will require the integration of systems across the enterprise and the creation of a new type of CRM database, predictive analytics and interface. This is the biggest and highest value opportunity for any enterprise that can identify customers.

This new predictive customer lifetime lifecycles database will become the information source and catalyst for all one to one marketing, pricing and customer relationship optimization / loyalty programs. The predictive analytics will tell enterprises what offers and communications to send, what products a customer will want next and when they are likely to want them, and what offers have worked and not worked for similar customers before. These benefits will be derived from building lifetime lifecycle predictions for every customer along all their dimensions. This concept is patent pending with all rights reserved and will covered in more detail in future articles.

Steps to Achieve These Goals

First, enterprises need to change their perspectives and look at all of their actions from the holistic perspective of each individual customer, and all the things that affect each individual customer, and not restrict their perspective to the perspective of each area in a company that affects customers. The focus should not be from one part of the enterprise outwards. We need to move from an outward micro based perspective, based on the perspective of each individual area of an enterprise looking out at customers, to an inward macro perspective that assimilates what the customer experiences from the enterprise based on the affects of all the areas of an enterprise that impact them as an individual customer. We should begin to use our tools to assimilate what the customer is experiencing from the enterprise across all the stimuli that affect the customer. This will assure that all interactions with customers from disciplines like loyalty programs, marketing, distribution, availability and pricing are coordinated around and optimize the same enterprise wide vision and lifetime value of a customer. If we truly want to optimize our customer's values we need to be able to see ourselves from their perspective and from their shoes.

Second, we need to create very detailed predictive behavioral models of the predicted lifetime value and preferences of each individual customer along all the dimensions of their past and predicted future lifetime behavior. A model that predicts the next action or the next few actions for a future decision that is a combination of separate dimensional influences is a good place to start. The next step, to truly understand our customers and increase our ability to derive predictive analytics, is to build models that track each customer's individual dimensions separately. The model must predict their entire lifetime for all individual dimensional life cycles that each customer has. The model should then dynamically combine the needed constantly updated dimensions of each customer to predict future customer preferences or actions across a myriad of customer and enterprise decision points that are impacted by combinations of these dimensions. We need to track the smallest level of individual detail so we can understand all the possible nuances in their past and future combinations and utilize this nano understanding in future predictions and decisions. (Note: - the information in this paragraph is patent pending in the US & internationally ALL RIGHTS RESERVED. This information should give users a strategic and competitive advantage).

Enterprise wide customer centric strategies and interactions across the whole enterprise should be based on these predicted lifetime strategic values and expected preferences of each customer. Today many times each of these disciplines acts within its own silo and just focuses on the tactical or immediate value of the customer for one transaction and how that benefits their one discipline or department. Each individual customer needs to also be tracked against the predictions of their future behavior and any variations must be realized immediately and the correct actions must be immediately taken by the enterprise. This is the foundation for building a one to one relationship which is the basis for one to one marketing.

This individual customer tracking and response model should become the center of a C3I customer centric one to one marketing approach. This one to one marketing approach defines the enterprise's marketing strategy for known customers. This new automated tracking and one to one response approach will require very tight integration with advanced marketing automation software, web analytics, database mining and analytical tools and optimization software. Most importantly it will require minds that have an understanding of all of these capabilities to set up and train people how to manage these processes.

There are striking similarities between where enterprises need to go and where military and intelligence communities have gone with C3I. Enterprises should see the emergence of a "command staff" that operates this new C3I customer centric decision support system. Their task will be to assure that the mandates of the SVP's, EVP's and C level executives are routinely implemented in all daily operations as well as in future decisions. The deeper details of how this customer centric one to one marketing should function will be addressed in later articles. For now, let's get the predictive individual customer analytics models foundation agreed upon and built.

Third, our attitudes must change to acknowledge that we are in new "flat earth" where technology has created a more level playing surface between enterprises and customers. Today's customer can be driving through the middle of nowhere and surf the web to find the best price for any item in any city, along with many independent customer reviews, and then order that item in a few minutes. We can no longer deal with customers as poorly informed pockets of separated demand. The customer should no longer be considered the target of a department. The customer should be interacted with as the lifetime partner of the entire enterprise. Without the customer there would be no enterprise. As customers get savvier, and realize more of the traditional tactics that they have been "subjected to," enterprises will need to rethink their relationships with and approaches to customers. The new CRM will play a central role in this new relationship. Perhaps this new perspective and process for dealing with these new more enlightened and empowered customers should be called Customer Relationship Optimization (CRO) instead of CRM?

How did the current the interactions between customers and enterprises develop the way that they have? Our processes have become hostages to old ideas of how to apply analytics and optimization using information technology. We have developed silos of increasingly powerful capabilities and enterprises and have not invested enough in the macro system structure needed to balance and orchestrate these silos of analytics. We have turbocharged engines now, yet traction control and power steering have not been applied…yet. Luckily the system integration and control applications that we need are not that difficult to build and implementing them will greatly magnify, focus and integrate the capabilities that we have already built. The simple addition of cruise control can make a long journey far more manageable. Yesterday enterprises sought vendors for each solution that they needed. Tomorrow enterprises need to seek system integrators who can combine capabilities across the enterprise into functioning C3I integrated customer centric systems.

Enterprise Optimization

This same approach should be applied to the supply side of the Enterprise and all of its vendors. Customers and vendors can be combined and called "entities" and perhaps then CRM should be called Entity Optimization (EO). Likewise, Revenue Management and pricing should be combined with Supply Chain Management and Logistics to create Profit Optimization (PO). PO should seek to optimize and balance both supply and demand simultaneously through one to on efforts at the level of each entity on both curves. This is Nano Entity Economics.

  1. Future CRO or EO applications must adhere to and facilitate this change. EO must become the domain of a new type of leader. Gathering and processing information must be replaced with cutting edge predictive analytics, controlled tests and chains of interconnected models that support a wide array of systems - all focused on identifying and optimizing the lifetime value of each customer or entity over their entire lifetime for the entire enterprise across all of their interactions with the enterprise.

How to Implement and Integrate C3I and Entity Optimization:

The following is a summary of what this new approach entails and what needs to change to enable and accelerate these new directions.

In order to become real-time C3I masters of many very vital enterprise resources, we must:

  1. Encompass ALL POSSIBLE information about a customer (internal and external, behavioral and demographic) from all available sources into one database.
  2. The data must be updated in real time with every new observation and interaction with the entity. The database must be the only enterprise source of entity data that directs entity interactions and must be used by all areas of the company. Everyone must share the same data and view of customers and vendors/suppliers.
  3. Build and support predictive models that leverage that data. The models should predict the entity's future behavior and value to the enterprise across all the individual dimensions of their behavior using their entire lifecycles for each individual dimension, not just based upon a few future points of contact. The models must predict what a customer will want, and when in their lifecycles they will want it, across all the different dimensions of the customer's behavior and existence. These models should be used for customer centric dynamic pricing that utilizes the lifetime value of the entity and not just their past value. These models must be kept updated after every new data point from an entity is gathered and added to the database. Also when data from other entities shows that there is change in their lifecycles, that may help predict one entity's actions, the predictions should be updated across all entities that have similar expected behavior patterns for that dimension.
  4. This new predictive and constantly updated information must be available in usable summarized formats 24/7/364 to all parts of the enterprise. It cannot be "calculated on the fly" when it is needed.
  5. The enterprise needs to be able to have a tool that will enable to it select a customer, or a product or a date and have the system pull together all information from across the enterprise so the user gets a macro view of how that enterprise is impacting that customer, date or product from their perspective. This new knowledge should direct the enterprise's relationship with the customer, and therefore, all the actions of the enterprise.
  6. Plan, track and coordinate all of the enterprise's interactions that can impact that customer using the EO data and models. EO should coordinate these efforts to assure entity's do not get too many messages, or the wrong messages, and turn off the enterprise. The goal is to assure that all enterprise actions comply with what the customer wants, and optimizes the enterprise's relationship with the customer based on the predictive analytics.
  7. Use control groups and testing of actions to determine the best actions to take at any point in any dimensional lifecycle. Store those actions and automate their application at the right points in other entities future lifecycles.
  8. Those who run the C3I must have the responsibility of assuring that from each individual customer's perspective all the actions of the enterprise are coordinated and optimize their lifetime lifecycles and loyalty.
  9. These same principals must be applied to segments of customers. Customers who we know, but do not have enough data to create individual customer profiles along all the dimensions of their behavior.
  10. Utilize this approach to narrow the size of segments so that all their members have a much closer affinity across all of their dimensions of behavior. Segments should have more in common than one or two dimensions for just their next few immediate actions or decisions.
  11. Entities can belong to numerous segments as well as their own segment of one.

Knowing our customers or entities

All customers are not "known." Before we can gather enough information about an individual customer to identify and interact with them as an individual, they will belong to a "segment of customers." EO's goals should be to determine the optimal ways in terms of speed, depth of understanding and relationship building to turn a member of a segment into a predictable individual customer. An individual customer is someone who the enterprise had enough data about to statistically track and predict as an individual. The cost of acquiring new customers is tremendous compared to the cost of treating an existing customer the right way and striving and doing whatever is necessary to keep them loyal and growing.

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