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Customer Science and the Search for New Value

Innovation thrives when people have ideas.  Ideas are really just a question that begs to be answered, and in customer science that’s a hypothesis to be tested.

If ideas are just questions that need an answer, then it stands to reason that the faster you answer the questions, the more opportunity there is for innovation. Not all ideas lead to value… but some do.  The more questions you can pose, the higher the chance of finding ones that create valuable initiatives.  However, the opposite is also true, if people can’t get answers quickly, they eventually stop asking for them. 

This is where the magic of Customer Science proves itself.

Customer Science gives you the ability to find and analyse data in very short cycles, allowing you to answer almost any question quickly increasing your chances of finding a good idea that can be transformed into value.  

We can spend precious time brainstorming good (or bad) ideas, but without testing them, they are just concepts without any evidence to prove that they would work. So, the magic of Customer Science is to ask is not “what’s your idea?” but “how have you tested it?” or “how do you intend to test it?”

If you don’t ask these questions, ideas will be born with arguments in meeting rooms and the biggest voice wins, not the best idea.  The magic is to drive a culture in which ideas are supported with facts, not feelings.

So how does customer science test ideas?

With a targeted analysis of data we can support or kill an idea. Fact based answers allow decisions to be made and action to be taken with confidence and in this way, new ideas become valuable innovation. Sometimes this does lead to yet another set of questions that need to be answered with new data and in this process, the customer science journey to innovation is started.

Decision latency and the cost of answering questions slowly.

The longer you take to respond to innovative ideas, the less value you create.

In essence, there are three types of latency:

  1. Capture latency – How long, it takes to collect data to analyse a question
  2. Analysis latency – How long does it take to create information and insight from this data.
  3. Decision latency – How long to act on this insight – really two parts, deciding and then acting on the decision.

As shown in the diagram above, by compressing the time required to deliver information, we substantially increase the value of decisions.  When you can ask questions (or pose a hypothesis) and get answers in short time frames, it creates a culture of thinking and experimentation. 

Right now, people with innovative ideas need data that is not easily accessible.  These people need a more agile process where they can build ad-hoc analysis in short iterative sprints to create fast insight that encourages feedback.  Customer Science reduces this decision latency by collecting data, analysing it, and presenting insight in the shortest time possible.  To do this we have adapted concepts from Agile and the scientific method.

The basic idea is simple, to do the least amount of work, and get the most amount of information.

The diagram below outlines one of the Customer Science methods where we pose a hypothesis that is used to define scope and guide our data discovery for analysis. 

From the fresh insight, new actions can be agreed and experiments (or processes) created.  Action is then taken and measured for review, to quickly decide if an impact has been made, or not. 

If the action does not live up to the hypothesis, the initiative can be shut down quickly and at very little cost. 

If the action uncovers value it can be expanded and expanded as a new customer initiative with clear understanding of the ROI.

Here are the six steps:

  • Hypothesis proposed. Define the question. Experiments needs to have a clear question (hypothesis). This question is at the heart of the experiment.  The sole purpose of the experiment is to prove or disprove this question (and if it can’t then it has no purpose) A good experiment will tell you something, even if it’s something negative. If you already know the outcome, it is not an experiment, it’s a KPI.
  • Define the scope and investigate data. What data do you have to answer the question and where is located?  List your assumptions. What kind of assumptions do you have? What are the things you are unsure about or don’t know? List them all. Identify the most critical assumptions. We have lots of assumptions on any idea or solution, but it would be difficult to test them all at once. Focus on testing just the critical ones.
  • Analyse and uncover insight.  Use this insight to help design the experiments.
  • Design and run the experiment. Keep it simple. Design your experiment so that you can start tomorrow. The idea is to collect as much as information with as little effort as possible. Forget surveys and market research.
  • Action taken and measured. Collect data. Record everything: data you collect and record will guide you further.
  • Review results and decide on next steps. Assess the impact of the experiment against its goals. What did you learn? What do you need to change? Change your idea/solution based on what you learned. Do you need to repeat your experiment? Do you need a new experiment? Will you move forward with the solution or do you need more data? Decide how you are going to move on.

These agile customer science projects are carried out using an ‘Agile’ time boxed approach to ensure an outcome is delivered within the approved time and cost constraints. It is not intended to build a complete production ready solution, but to instead prove that there is valuable insight to be gained by running and evaluating targeted experiments. These learnings will be used to more accurately estimate the effort required to deliver bigger, customer-ready solutions.

How do we get started?

Step one

Run an ideation workshop. This workshop is used to surface up as many ideas as possible and dig deeper into each one to understand the value it could create and how we can understand them better.  We then look into what data we have available and the complexity involved in using it to test the idea.

Once we have list of candidate ideas the next step is to rank them based on value created vs complexity in testing them.

Step two

This gives us a shortlist. We then take the idea at the top of the list and scope it up for the first test.  It is designed just like a science experiment where we pose a hypothesis and go about proving or disproving its value.

Step three

Review the results to either launch an initiative, a business plan, or shut it down and pivot to another idea.  At this point you have a fact based decision to either keep moving forward or kill the idea.  The goal is to succeed fast or fail fast.  Both are good outcomes.

Step four

Rinse and repeat

If you’re interested in learning more, send me an email at [email protected]

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