The simple brainstorming tool that surfaces better CX insightsby
Insight advantage comes from defining what information is valuable, then finding it. One way to break the grip of current answerability is brainstorming questions using the following matrix, which surfaces the key questions and stakeholders that need to be considered.
I keep six honest serving-men,
(They taught me all I knew);
Their names are What and Why and When
And How and Where and Who.
In order to gain an insight advantage, organisations require a proactive approach to identifying the data required. That means identifying the questions to be asked – those with answers that deliver most value – not just defaulting to asking only the questions that can be answered with existing data sets.
Existing data can answer some important questions but its limitations constrain insight optimisation. Advantage comes from defining what information is valuable (both in absolute and relative terms), then foraging beneath the surface of ready accessibility to find it.
One way to break the grip of current answerability is brainstorming questions using a matrix with Rudyard Kipling’s six honest serving men, as described above (with one major addition), as one dimension with all the different stakeholders that a business has as the other.
Being a writer, Kipling was not concerned with how much - how much something would cost to make or how much revenue it would generate. (Nor would he have been concerned with subsidiary quantifiers such as how long – time being the other dimension that needs to be forecast and tracked.) But in the commercial world such quantifying questions are critical and need to be added to his other questions.
Adding the stakeholder perspective
On the stakeholder axis there would be customers, employees, shareholders, partners, suppliers and society (to include government and regulatory bodies as well as environmental and community bodies). All businesses, whether consciously or not, engage in a value exchange with each different stakeholder group. With each one there needs to be both value creation and value extraction – both ‘what’s in it for you’ and ‘what’s in it for us’. Making this explicit is a powerful way to formulate strategy.
Figure 1 below describes how value can be created and extracted for each group. Thinking about these in business-specific terms helps define the questions to be asked.
Figure 1 – Value Offered and Sought by Stakeholder Group
In addition to the stakeholders outlined, there is an additional group for whom the serving men questions are equally important – competitors, both current and potential – a negative stakeholder.
Understanding who might become competitors in the future, what new offerings existing competitors plan to introduce, why customers buy from them are just a few of the many competition-related questions that would yield valuable answers. Adding competitors completes the matrix - a blank version of which is shown in Figure 2 below.
Figure 2 – Question Prompts by Stakeholder
With this matrix as the starting point and the potential sources of value (Figure 1) as a prompt, working through each square generates a list of questions. After which, these can be prioritised using value of the answer as a first screen and return on investment required (to ensure collection costs are factored into the process) as a second.
Why ’Why?’ should be pre-eminent
Are some questions more important than others? Definitely. Can this process be aided by identifying whether some types of questions less important than others? For example, can what be ranked above who, when above where, how above how much? Maybe, but such an approach risks key questions being missed.
Of particular concern is the idea that in a Big Data world why is no longer important. As described in my earlier article, the idea that identifying correlation was all that mattered (and that causation could be ignored and the scientific method with it) was first mooted in 2008.
One of biggest critics has been Nate Silver – the forecaster who achieved prominence due to the accuracy of his US Presidential election predictions and author of the best-selling The Signal and the Noise. Silver defines meaningful relationships as "those that speak to causality rather than correlation and testify how the world really works".
If we don’t seek to understand how the world really works, then our ability to posit and test new relationships and thereby learn is limited. And when learning is limited, so is innovation and continuous improvement - indeed any approach to enhancing performance. As Silver states "The numbers have no way of speaking for themselves. We speak for them. We imbue them with meaning. Data-driven predictions can succeed – and they can fail. It is when we deny our role in the process that the odds of failure rise".
Silver’s words were written before the latest fears about how artificial intelligence and software robots will take over white collar jobs. But they are even more relevant in that context, highlighting how analysts can add value to computer-generated outputs by providing meaning, adding nuanced assessments and employing imagination to generate ideas.
These capabilities are what differentiate human intelligence from artificial intelligence. When we stop asking why, analysts will be replaced by robots. If we continue to seek to understand causality, we can enjoy a complementary existence.
Perhaps the last word on this should go to Kipling who definitely understood the relative importance of why to his other honest serving men, in particular to its role in human learning. His poem, I Keep Six Honest Serving-men…’ continues:
I send them over land and sea,
I send them east and west;
But after they have worked for me,
I give them all a rest.
I let them rest from nine till five,
For I am busy then,
As well as breakfast, lunch, and tea,
For they are hungry men.
But different folk have different views;
I know a person small—
She keeps ten million serving-men,
Who get no rest at all!
She sends'em abroad on her own affairs,
From the second she opens her eyes—
One million Hows, two million Wheres,
And seven million Whys!