Why have so many CRM initiatives fallen short?

Despite the ubiquity of operational CRM, analytics and BI, and other technologies most firms are unable to profitably provide the customer experience they envision. The information is there, but it’s not being exploited in an optimal manner.

“Despite having a wealth of transaction data, few organizations
have developed capabilities to aggregate, analyze, and use customer data to
generate real business value,” wrote Jeanne Harris and Thomas Davenport for the
Accenture Institute for Strategic Change. “Though the business world is
witnessing an explosion of interest and investment in CRM
software and other analytic technologies, many fail to exploit those
technologies effectively. As a result, customer satisfaction ratings remain
largely unchanged.”

A fundamental cause of this situation is the separation of  strategy formulation, and implementation across multiple business units and functions.  One department might achieve success in its area of
responsibility, yet undermine overall customer satisfaction and increase churn.
Similarly, sales success in one area could actually erode profit per user, if
sales efforts are bringing in customers that are quite costly to serve.

Large enterprises are not renowned for free flow of knowledge and action across organizational boundaries. In fact, obstacles at several layers typically inhibit or even prevent the type of sharing and collaboration required.

First, there are technology barriers that impede the simple transfer of data. Siloed systems and inconsistent data definitions make it difficult or impossible to assemble a cohesive picture of customers across products and touch points. Even if such data-sharing is possible, there are usually semantic barriers as well. Every function or department has its own language, tools, metrics and perspectives. Looking further, there are process barriers — no established ways to transform that knowledge into effective action, especially if it requires mobilizing resources from multiple business units.

An effective decision intelligence platform dissolves those knowledge barriers at all three levels:

Technology enablers — the capacity for transferring knowledge across internal boundaries

The underlying “intelligence architecture” enables information to flow across previously disconnected databases and systems. It includes a rich suite of analytical capabilities, so new ideas can be tested and refined in virtual environments rather than in concrete ones.

 Adaptive competencies — common semantics for surfacing, testing, deploying and assessing ideas

There is consistency at strategic and tactical levels. Forecasts, programs, analytic models, and touch points are synchronized, using a common language and aligning with shared financial objectives.

Iterative process — continuously identifying financial opportunities, taking action, and learning from the outcomes

The organization that adopts this process and culture will have transformed itself from an operational/reactive entity into a wellspring of constant, proactive renewal.  It will mobilize resources around specific financial opportunities rather than functional or product groups, and it will foster activities that truly optimize the deployment of customer management assets.

The interests of the actors involved must be transformed to effectively share and assess knowledge . The holistic decision intelligence process aligns interests by creating common financial objectives and an operational framework across the  team that co-exists with their respective departmental-level environments. Therein lies the challenge: using decision intelligence technology to enhance overall control outcomes by blurring functional control boundries.