A large retail bank had a problem. Its customer service division was concentrating so much on operational efficiencies, such as shorter hold times, quicker speed to answer and more calls per agent, that it was failing to effectively capture and share the customer interaction information necessary to help measure and make progress against strategic customer objectives.
It had become obsessed with those efficiencies to the detriment of capturing and sharing interaction data.
The problem was that nobody had ever taken the time to quantify the impact that the absence of a more coordinated approach to measurement and learning was having on financial performance.
Recognizing the need for such an approach was a major step in the bank’s successful realignment, organizing its business in such a way that all its departments worked together, having a shared investment and a shared goal.
The key triggering event was the discovery by senior marketing executives that customer service, which had the tools, data access and skills to independently create its own outbound solicitations, had begun to implement its own ad-hoc telemarketing programs to compensate for what it saw as the shortfall in direct mail programs, without reference to their marketing colleagues and to strategic customer objectives.
This example illustrates one of the biggest challenges in creating a customer measurement “ecosystem” to effectively orchestrate cross-functional efforts, namely that the associated information needs, metric definitions and uses vary so much across functions and levels.
Figure 1 shows how complex this can be.
Once executives at the retail bank recognized that the lack of effective customer measurement meant uncoordinated activity, lost opportunity and lower EBITDA (earnings before interest, taxes, depreciation and amortization), measurement and learning immediately took on a different character and a much higher profile. Now managers could collectively unite on the downside of not effectively cooperating around, and operationalizing, the measurement and closed-loop learning process.
As a result, the company created a new function within marketing that would play a key liaison role with customer service, keeping the customer service apprised of marketing’s tactical plans and engaging the two departments in continuous dialogue about measurement, customer intelligence and the opportunities presented by customer interactions. The dialogue helped answer key strategic questions, validating or discrediting historic assumptions and “gut-feel” about the drivers and motivations behind observed customer behavior.
The creation of that new role bore fruit in a number of ways. It certainly addressed the need for partnership in measurement and learning, over time turning that capability to the benefit of customer service, which became much more agile at prioritizing and shaping customer interactions based on a detailed and quantified understanding of what was working and what wasn’t. Now there is a shared investment and stake in an objective measurement and learning process that includes both qualitative and quantitative analysis of the data initially captured by customer service but now seamlessly shared with marketing.
You can best achieve the alignment illustrated in this example by adopting the kind of framework shown in Figure 2, which approaches the measurement challenge from a customer engagement standpoint. The value of this kind of framework is that it lays the foundation for measures at all of the levels and functions involved in creating and executing customer strategy and measuring resulting performance.
Once you have laid out the vision in this way, the next step is to populate the framework with the key categories of metrics that will be needed to track performance. Those categories are shown and explained in Figure 3.
Filling out this framework can be a fairly involved process, but it provides the necessary backbone and discipline for defining the right measurement approaches at all levels and stages of customer engagement.
The principles upon which the most successful customer measurement frameworks are built take account of, and directly address, these challenges by ensuring the following:
- Continuity: Metrics must be part of a continuum, based on a unified and common framework.
Alignment: Metrics must support the achievement of common goals and help to track progress against strategic objectives.
Relevance: Metrics must enable and link directly to key decisions; there’s no “measurement for measurement’s sake”
Consistency: Definitions must be continually examined and diligently documented. Everybody has his or her own definition of “ROI,” for example.
These principles and frameworks can greatly help your organization manage and measure the effect of your customer investments. With them, you can identify, and link, critical measures that can allow you to assess progress step by step, at differing organization levels and across different functions. They also provide a sound basis for linking metrics together into a strong chain that can easily aggregate and translate into the kinds of measures that are most relevant for key decision-makers across all functions and levels.