Emotion Lines Flatline Credibility
Service blueprints are an excellent method for aligning a team on the sequence of moments in a service experience and identifying breakdowns, inefficiencies, and opportunities to remove friction for customers and front stage team members. As service designers, we expect leaders to use our maps for alignment, and our KPIs to inform expensive organizational decisions, but when we arbitrarily plot points on a service map we invite the question, “What else is arbitrary?”
Among all the pieces of data that inform the creation of a blueprint, emotion is most frequently the victim of arbitrary representation. Usually represented as a line (see example imagery), emotion is an appealing potential source of empathy by showing backstage workers just how the service is impacting customers as people.
While emotions are an easy way to visualize identify where problems exist– they’re the hardest to source reliable data from. Imagine you’re co-creating a blueprint for an auto mechanic service. We could assume the emotional state of customers (frustrated, anxious, upset, etc.) at various moments in the delivery cycle, but the mechanic is concentrating on fixing the car or ordering a part, not assessing the customer’s emotional state. That means that we don’t have reliable data. When we arbitrarily plot those emotional points on what should be a systematic measurement, we obscure the value, and economic impact of our work after a measurement (map) has been made.
Here is a great example of a terrible service map. This map is entertainment masquerading as information.
Service mapping exists to enable better business decisions. There are many other ways to express the emotions people feel across the moments of a service experience, like using simple iconography. In my mapping I use “+” for positive, “-” for negative, and “/” for moments that are neither positive or negative, but are causing friction. The important differentiator between iconography and emotion lines is granularity: we can reliably assign positive or negative emotions coarsely, but often don’t have the data to assign “frustration” or “impatience” accurately. By operating at the right level of granularity we identify breakdowns and successes, but don’t force the map to carry too much interpretive weight.
Behavioral economics tells us that most human beings have a “loss aversion” — a prospective loss carries twice as much weight as a prospective gain. This means that without evidence and verifiable projections it will always be easier to make incremental changes and put off large changes. How might evaluating the potential outcomes of key decision points in the service through the lens of loss aversion impact our understanding? By choosing valid measurements and accountable numbers, we create a sound basis for meaningful Return on Investment (ROI) for the business and Return on Experience (ROE) for the customer.
Mapping services are an excellent way to show the service system and create a shared reality that fosters clarity when making important decisions, but emotion lines do not position our service mapping to be actionable. Let’s stay focused on documenting the current realities of how a business can modify its services to serve people better and how we can create a measurable strategic advantage in the short and long term.
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