Achieving Excellence in Customer Management
|During its brief history, the science and technology surrounding Customer Management has acquired something of a mixed reputation, but recent research from Henley Management College may shed light on why some projects have failed while other succeeded.
Customer management (CM) is at a critical point in its development. It is emerging from a management fad and unfulfilled promises into, potentially a management tool of great strategic importance. Whether or not it achieves its potential depends on turning this promise into a practical procedure, with clear steps and actionable methods. Of course the journey to success is always a challenge and one that can be made much smoother by learning from the experiences of others. The Henley Centre for Customer Management has carried out extensive research amongst best practice firms and from that research can reveal six key lessons, which will help you on your journey.
Lesson 1: CM is not for everyone
Lesson 2: Not everyone can do CM
Sadly failure is common for many CM initiatives. The reason: a certain set of preconditions need to be in place before they can work. Research uncovered 15 of these success factors, grouped loosely under the headings of marketing strategy, IT strategy and company culture. The effects of these preconditions are additive, so that weaknesses in some are compensated for by strengths in others. Important factors include things like well-defined segments, strong cultures and market-oriented IT systems.
Lesson 3: It's best fit, not best practice, that matters
The idea of CM best practice was emphatically debunked in the research. The ideal form of CM depended on the nature of the market and the capabilities of the company. Companies that tried to copy the best practice of other firms in different contexts tended to fail. Treat the idea of a single 'best practice' with caution and, instead, look for the type of CM that is a `best fit' with the particular context of your company's market and internal situation.
Lesson 4: Justify your investment, not someone else's
Justifying investment in customer management is never easy. Marketing departments that depended solely on financial arguments could never make the case for CM because so much of the benefit is hard to quantify. Their more savvy rivals used three kinds of arguments: financial (it will save X and make Y); strategic (competitive threats and market changes) and linked decisions (other investments and strategies connected to CM). Every company's finance director was amenable to a different blend of arguments, as if a different key was needed to open each firm's capital investment coffers (see figure).
Lesson 5: Manage the team over time
Successful CM requires exceptionally effective cross-functional working. However expecting everyone from board member to customer service assistant to be in on every meeting is clearly unworkable. We discovered that the critical variable is the project life cycle stage. Effective project management deployed very senior but narrow teams at the start, gradually broadening them and introducing less senior membership as the project advanced. The critical features of this approach included a 'brain storming pool' as the project went from definition to the building phase, and the careful channelling of the team's effort by project leaders, to avoid the whole task going off down a blind alley.
Lesson 6: It's insight that matters, not just data
Firms that extracted real customer insight were those that had synthesised both hard, digitally held data and soft, qualitative knowledge. They grasped that data was only a means to an end – the understanding of customer motivations. Data that revealed this (eg, conference attending, re-print requesting, appointment cancelling), combined with hard data (eg, financial and market research), highlighted previously unexpected segments based on needs and motivations.
In practice, this meant more effort was put into integrating data from different sources (eg, sales team reports and market research data), rather than heavyweight crunching of easily available but less useful operational data, such as ordering patterns.