All organizations are not human proof
Insight maturity model
Every organization aims to be customer centric. Many understand that reaching real customer centricity requires at least three elements to be addressed: understanding data and human, transforming how the organization thinks and operates, and finally creating an experience. It is all clear on paper. Yet many struggle even with their first steps: insights and data.
We have identified four stages of maturity, that explain why some organizations make better use of their data and insights, hence speed up their transformation and experiences. The model is based on our observations on many organizations.
First stage: Denial
Companies on their first stage of insight maturity have a tendency to think that everything is feasible. Yet, they are driven by “not relevant” attitude. These companies do not have shared language to talk about customers – and overall, their culture is weak on acknowledging that human beings come with different motives and needs. When the first stage companies are utilizing partners for insights, they merely look for help with conducting surveys or getting their CRM functionable.
Second stage: Ignition
With the second stage the competence begins to grow. Companies begin to acknowledge for example techical limitations, but not their own limitations of insightfulness. Typically, the main driver is on a manager level, leading to a situation where human insights and data are not yet valued as a strategic asset. However, basic building blocks are often complete: customer motives are well known, and collected data is easily available for everyone
Third stage: Acceleration
Fast competence growth tends to create structures, sometimes silos. Moreover, the third stage often comes with a polarization. Remarkable investments on data collection and data science capabilities make in-house activities fast-forward. Partners are seen as a resource rather than a skill or quality jump.
On this stage, companies’ capabilities of utilizing deep human understanding increases rapidly. They are familiar with customer emotions and hidden needs, furthermore, are able to see them as market potential and turn them into strategic decisions. Despite of the fast competence growth, third stage companies are most sunk into silos. Data science and human science live in different blocks. They are both praised, but not connected.
Fourth stage: Discovery
Data and insights on the fourth stage are driven by business top-decision makers. They have a real willingness to stand out from competitors with insightfulness. In-house skills are strong, but partners appear again in the picture: they are expected to discover new and open views to alternative worlds. The last stage of this model seems difficult to reach as it requires a high level of in-house competence, and yet a humble attitude towards own limitations. It takes until the ultimatum to really acknowledge what one doesn’t yet understand.
On which stage are you? Or do you recognize a new ladder? Contact us or join the conversation!