Shortly after getting promoted to vice president of Westboro Technologies, Joe Corporate receives a personalized e-mail from his bank offering him a new car loan. Tempted by thoughts of a new BMW, Joe clicks on the car-loan URL and logs on to MegaMerger Bank's site using his banking password. Web- personalization software serves up a car-loan application pre-populated with his account information and special rates for valued customers. The five-year rate sounds good to Joe, but, wondering if they would extend the term to six years, he decides to click on the intriguing "Call MegaMerger officer" button.

Unbeknownst to Joe, MegaMerger's analytics engine has tagged him as a platinum customer for 13 consecutive months. This classification, which is reserved for the 11 percent of the bank's customers who generate about 87 percent of their profits, entitles him to specific privileges including special rates and the quickest service possible. Joe's click triggers workflow that bumps his request to the top of the queue for the bank's pending business rules which, in turn, routes his call to the top of the bank's customer-telephony integration (CTI) queue. The CTI software pulls his account record from the customer relationship management (CRM) front-office software; and a few seconds later, Joe's data populates the screen of a wealth management officer whose voice sounds through Joe's PC speakers, "Hi, Mr. Corporate, thanks for visiting our newly improved Web site. How can I help you today with your car-loan needs?"

Joe, clearly impressed, asks for a six-year loan at five-year rates. The officer glances at a pop-up window that authorizes her to offer discounts within a certain range based on the customer's profitability rating. Joe's request is quickly approved. "Click the 'refresh' button now," she says. Seconds later, Joe is viewing his newly discounted rates on the Web site as he receives an e-mail offering auto insurance from MegaMerger's insurance division.

It doesn't get any better than this, Joe thinks to himself as he signs his online application and becomes a happier and more profitable customer. He never even felt compelled to shop around.

MegaMerger Bank is way ahead of the curve. Today's $48 billion demand-driven e-business landscape, as exciting as it is, is straining companies' abilities to satisfy customers' demands, precisely when those same customers are more empowered than ever to slip away and test-drive the next vendor that satisfies a search-engine query. Whereas businesses formerly defined their competitive advantage in terms of how efficiently they could produce and distribute a product, today they must apply that same management rigor to something intangible: understanding and then delighting their customers. Additionally, they must achieve this goal consistently across all of their customer touchpoints, including call centers, field sales, partner sales, e-mail and the Web.

A new industry initiative, called demand-chain management, has emerged to optimize companies' multi-channel strategies for interacting with customers, assessing their needs and providing timely solutions that satisfy their needs – especially the needs of their best, most profitable customers.

Little did Joe Corporate know that that the e-mail he received from his bank was triggered by a business rule that automatically sends car-loan offers to platinum customers three weeks after they receive a pay raise of 10 percent or more. Nor could he realize that a less profitable, "silver" customer might receive the same e-mail but would be routed – more slowly – to a telemarketing person instead of a bank executive. All Joe knows is that the bank made it very easy to say yes. In fact, the whole series of events leading to Joe's acceptance of the negotiated loan terms took less than two minutes, thanks to Mega-Merger's recent efforts to refine all their business processes in the spirit of demand-chain management.

Demand-chain management provides a framework for continually gathering important data, storing it in one central environment, analyzing it and acting on it in ways that ensure profitability. In its best form, demand-chain management even anticipates customers' needs well before they express them.

In today's highly competitive marketplace, companies owe it to themselves to orchestrate their business model more scientifically than ever before. Too often the ingredients for a business's success remain locked away in multiple legacy systems that no one has ever bothered to integrate, let alone analyze. Traditional CRM systems, for example, can integrate the operational activities of sales, marketing and customer service departments; but they cannot study the implications of their customer data and proactively tailor their offerings accordingly.

As Yankee Group's Chris Selland recently said, "It's not so much that the market needs new products, but that it needs holistic products." CRM databases are replete with important data; so it should come as no surprise that the most advanced CRM systems are being extended to incorporate all of the necessary ingredients for demand-chain management: planning, integrating, analyzing and executing a business model that puts customers in the front seat.

Following is a simple overview of the four steps required for a successful demand-chain management implementation.


A business plan should search for answers to some of the most fundamental questions about your business, such as:

  • What are this company's strengths?
  • What do we not know about our business?
  • What do we want to measure?

Every department in your company should participate in this exercise, not only to stimulate interesting, diverse perspectives, but also to ensure that everyone is committed to the project and will actually use the solution they help design. Even at this early stage, businesses are well advised to partner with a demand- chain management expert that has a track record in implementing vertical solutions for your given industry.

Integrating Data

Integrating all sources of customer data into one central repository is the first step toward truly knowing your customers. Traditionally, different areas of a company have relied on standalone databases that reveal their own limited, department-specific view of a customer's identity and allow them to do little more than manage a finite set of operational tasks. We see evidence of this every time a business call is cut short with, "I'm sorry, but I don't have that information on my computer; I'll have to transfer you to a different department."

Only by consolidating all corporate databases' partial definitions of a customer (or product, or any entity) into one data warehouse – a demand-chain management (DCM) warehouse – can you come to appreciate the complex, interrelated facets that make up a customer.

Integration requires a three-step process:

  1. Extract customer data from all disparate customer databases. (Using a wizard-driven interface simplifies the process.) Typical candidates are databases used for customer support, sales records, marketing, e-mail management, Web portals and transaction history, including e-commerce. Also worth considering are external demographic and psychographic data (data relating to customers' habits and preferences), Internet service provider (ISP) data and e-mail profiles.
  2. Consolidate the data in one DCM warehouse.
  3. Cleanse the data by eliminating redundancies and inconsistencies.

Consolidating databases is not a trivial task, but the rewards are well worth it. It allows you to:

  • Accurately categorize and rate your customers, products and services, and act on that information.
  • Provide customer-facing employees with a single, comprehensive view of every customer at every touchpoint.
  • Provide customers with a single, comprehensive view of the company and what it has to offer.

Customers look at you as one company, and they expect you to handle their interactions effectively regardless of which touchpoints they choose to use. Meeting this demand is especially challenging on the Web, where customers expect 24x7 access to a contextually rich, personalized self-service account with simple tools for viewing their entire profile including any past interactions and transactions.

Analyzing Data

Once you populate a DCM warehouse, you can start building a model that describes the multiple characteristics of each customer – or product, service, etc. – and rates those characteristics' relative importance. As Larry Goldman, director of Braun Consulting, recently said, "CRM analytics effectively get the customer into the boardroom."

For example, whereas some banks may judge their customers only in terms of their average balance, a bank such as MegaMerger could segment its most profitable customers based on an algorithm driven by the following variables:

  • Average balance
  • Salary
  • Number of bank services used by customer
  • Whether customer direct-deposits paychecks
  • Profitability of each service used (as measured by multiple variables)
  • Ratio of customer's use of different channels (store, call center, Web, e-mail)
  • Cost to support customer (as measured by multiple variables)
  • Time period
  • Anticipated lifetime value

Online analytical processing (OLAP) products are particularly well-suited for such multivariable analyses. Unlike conventional reporting tools, which are limited to two or three variables, OLAP products can build models composed of up to 16 variables. The best OLAP solutions can analyze vast data sets efficiently and in real time, allowing users to "slice and dice" different sets of variables at will in search of telltale patterns, trends and correlations that help steer your business in new directions.
Typical OLAP outputs include analyses of sales and marketing, customer retention, customer segmentation, predictive customer demand, forecasts, just-in-time inventory, quality tracking, profitability, manpower and pricing.

Given the complexity of customer relationships – be it with customers, partners and your own staff – it's no surprise that the market for "analytical CRM," the application of OLAP technology to CRM data, is expected to grow from $1.4 billion in 1999 to $4.1 billion in 2004, representing almost half of the overall CRM revenue.1

Analytical CRM solutions can help answer questions such as:

  • Which variables govern how profitable a customer is?
  • Which have been our most productive campaigns, partners, products and territories?
  • What percentage of our e-commerce sales are merely shifting revenue from one channel to another, as opposed to generating new revenue?

Executing New Business Models

Companies should refine their business processes and organizational structures based on the improved customer understanding gained through analysis. The goal should be to optimize all customer touchpoints and focus all energies on profitable customers and products.

Demand-chain management strives to optimize every "chain" in a business's supply and demand chains, from product design and marketing to order entry and just-in-time inventory. Each chain is inextricably linked with the next one and is constantly providing new data to be interpreted and acted upon. Only with such real-time adjustments can a business remain relevant in an economy that shifts at the speed of the Internet.

Each department should define its own optimization project that is coordinated with the rest of the "links" in the chain, as shown in Figure 1. The key in all cases is to capitalize on the insight gained from analyzing all aspects of your business.

Process to be Optimized Examples
Product optimization
  • Set up alerts that automatically notify a factory of defects when they reach a certain threshold.
  • Double capacity of distribution during months of November and December to accommodate anticipated sales from current marketing campaign.
  • Shut down production facility in California and expand Arizona facility based on profitability ratings.
  • Provide additional personalization services on Web site.
Market optimization
  • Conduct promotional analyses: Mine DCM warehouse for correlations found among customers who bought given product; target prospects exhibiting same behavior patterns.
  • Time the introduction of a new product differently for each channel.
  • Market product ABC only through the Web based on past performance.
Sales optimization
  • Ensure that all customer-facing staff can access the entire profile of every customer.
  • Hire new salespeople to attend to newly identified customer segmentations.
  • Reassign 15 percent of telemarketing personnel to Web initiatives to accommodate Web traffic growth patterns.
  • Set up alerts for lack of demand or excess of demand based on real-time analyses. Stop enhancing unprofitable products.
  • Develop partnerships to help build vertical solutions for different industries.
  • Employ cross-selling and up-selling strategies based on correlations found among installed base’s past behavior.
  • Set up alerts to warn account executives of predicted defections based on correlations found among customers who already defected.
  • Send e-mail warnings to salespeople when their customers log urgent service inquiries including SLA non-compliance.
  • Spend most time calling high-profit customers.

Service optimization

  • Devise new routing methods for categories of inquiries that statistically have been taking too long to resolve.
  • Define new business rules that route top-priority (most profitable) customers to top-tier service personnel.
  • Implement pop-up windows that authorize service representatives to offer special services, coupons, etc., based on customer’s profitability rating or in reaction to recent problem reported by customer.
  • Dynamic pricing: Proactively switch customers’ phone plans to more economical plans that match their usage patterns.

Figure 1: Process Optimization Examples

Do It Again

Every new process set in motion should be periodically measured against the anticipated results in terms of revenue, profitability and customer loyalty, so that you can regularly adjust the process as needed. Such an analyze-execute- analyze-execute circular pattern can repeat indefinitely within the context of a bigger closed loop that measures a given department which, in turn, exists within a business-wide closed loop that rolls all success metrics into one consolidated view. Only this way can businesses continually optimize their business models without resorting to any guesswork.

In the past, businesses could dictate how customers would do business with them. They would optimize one primary distribution channel and force customers to adapt to it. If you wanted a book, you had to visit a book store during their business hours. If you wanted to buy a shirt from a catalog store, you had to call their toll-free number and maybe even be put on hold.

Things have changed. The Web, more than anything else, has precipitated a shift in culture by delivering a global, around-the- clock self-service marketplace to customers' fingertips. Never before have customers found it so easy to learn about a given industry, study different competitors and ultimately dismiss your value proposition without even talking to anyone in your company.

Existing customers can go away – silently – a year before anyone even realizes they're gone. If anything should be optimized, it's customer retention. It costs a business about five times as much to acquire a new customer than to keep an existing one.

Joe Corporate might have looked elsewhere for a loan if his bank had not e-mailed him an offer. But what business knows Joe Corporate better than MegaMerger does? People at the bank owe it to themselves to keep Joe happy and profitable!

The methods for incorporating demand-chain management may vary from industry to industry, but the fundamentals are always the same. It requires an obsession with knowing your market, knowing your customers and fine-tuning your multichannel strategy accordingly. Demand-chain management systematically delivers on its promise to ensure profitability while optimizing the lifetime value customers receive from their relationship with your company. The alternatives leave too much to chance.

1. Datamonitor's "Analytical CRM: Releasing the Value" Report, 1999.

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