Great analysts tell great stories . Stories, after all, make results user friendly, more conducive to decision making, and more persuasive.
The analyses will in turn enrich the initial stories and lead to deeper insights.
What is important here is that the storyline, told before the analyses, enables an authentic human element to surface that would be more difficult to extract from the data alone.
We can look at the stories to see how a customer journeys takes place and the decisions that are made on route. The more people that follow the same route the more we can look at the in market behaviour of others that potentially travel the same journey.
The end is not the journey and many journeys can lead to the same end so how reasonable is it to assume that people travelling the same journey will arrive at the same destination? What path did our customer take to become our customer?
In other words, we were interested not just in what customers were buying, but in the mechanics of how they make their purchases and how this may make them loyal. After the analysis, the true story of a customer’s path to loyalty is in fact revealed.
Where do these stories come from? They may come either from the experience of an expert in the sector or brand or from qualitative research using observation or in-depth interviews.
In order for a story to truly enable analytics, the story development process needs to be rigorous. We use the framework of grounded theory to ensure that the data and overarching storyline inform each other and are coherent with each other. The idea is for the analyst to navigate back and forth between the data and the developing story to ensure a good balance between the creative narrative and the analytics that reveal the facts and details of the story.
The enabling storyline should not be too restrictive; it needs to support the development of the plot and characters as they emerge from the analysis, but without bias. Conversely, the storyline can suggest specific questions to be asked of the data for a more in depth analysis. Data can be manipulated to make any suggestion and interpretation.
In a world that’s flooded with data, it becomes harder to use the data; there’s too much of it to make sense of unless you come to the data with an insight or hypothesis to test. Building stories provides a good framework in which to do that.
Data Scientists and Data Analysts are the forensic scientists of Big Data and having a hypothesis can help them piece together the information either confirming or disproving the hypothesis and moving forward to a new story or a more complete story.
The story of the customer journey is hidden within the noise of data but by understanding your existing customers journeys this information can be used to create a better understanding of the journey of potential customers and how you can influence their story.
What is your customer journey and what stories will be revealed?