Example Reflection – Learning Launch Tool

Challenge & Selection The Learning Launch Tool is a powerful tool that I used together with my team to design and  implement a web-based customer awareness tool in the context of life insurance business. Life  insurance – and especially the disability insurance segment – is a difficult field in the sense that  customers often do not self-drivenly want to deal with this topics. Furthermore, the target  customer groups vary from professional beginners over young-married to mid-agers.  Consequently, we did not know exactly what kind of approach we should use to pick up the  customer needs at the right place. The range of hypothesis and assumptions was large and the  option space was broad and heterogeneous.

To tackle these challenges we decided to choose an iterative prototyping approach which uses the  Learning Launch techniques of validating ideas and assumptions to hit customer needs.

Application First of all, we set up an interdisciplinary team where we put together business experts like sales  experts, product managers, marketing experts as well as IT experts. We also tried to vary the ages  and years of experiences of the team members to garantuee a variety of hypothesis and reduce  the risk of confirmation biases within the group.

Our ideation phase to create hypothesis statements was based basically on design thinking tools:  We defined so-called „personas“ - i.e. archetypes representing the most important customer  groups - and sketched the „as-is“ customer journey to stimulate the creative process taking into  account the broad range of customers and their pain points. For stating our key assumptions on  the customer needs, we derived from our „as is“ journey the „should be“ customer journey  identifying the gaps and, therefore, the potential value creation. As we wanted to implement a  customer awareness tool we focused on the first phase of the customer journey.

To prioritize our key assumptions, we consulted market research results on customer needs and  our own t-NPS results. „t-NPS“ stands for customer touchpoint-based net promoter scores we  evaluated over time. The quantity of data was of a significant order of magnitude. The NPS score  allowed us to focus on the most painful customer touchpoints. We also take into account some  financial aspects in the prioritization process by conducting a business leakage analysis to identify  the most rewarding measures. Some of our key assumptions were for example „The customer does not like to share personal information on his health and constitution with an anonymous interview  partner.“ or „Purchasing a disability insurance product relies on trust.“.

To test our key assumptions we designed an iterative prototyping approach with prototype  releases on a regular basis. Every iteration consisted of a certain set of features as known from the  common agile approaches e.g. scrum sprints. For example, our first prototype was a simple „click  dummy“ raising some basic features for the awareness tool. After every iteration cycle we  conducted customer acceptance tests and interviews to confirm or disconfirm our feature  implementations representing our assumptions. Depending on the results we a) further developed and refined our feature set and/or b) pivoted our scope by changing the feature set and/or c)  dismissed some features.

Finally after several iteration cycles, we created a simple, but insightful and customer-centric click-

by-click awareness tool which is still online on our web page.

Insight & Approach From my point of view, the strength of the Learning Launch Toolset is based on the following two  aspects: Firstly, we were forced to continuously questioning our hypotheses by means of „hard  facts“ (quantitative data, customer feedback, etc.) and a clear and transparent process. Secondly,  this approach teached us to discard tons of ideas. Especially in the field of unknown customer  behaviors, this is a really strong approach to create reliable ideas and to mitigate the risk of failure.

But of course, we faced some challenges: Providing suitable data and un-biased statistics is  demanding! Since (quantitative and qualitative) data is key for identifying the „must be trues“, you  have to spend a lot of effort in supply these data. For example, the evaluation of the NPS scores is  costly since they depend on customer interviews – each of them taking at least a couple of  minutes. Next time, I would propably try to reduce the number of customer groups in advance to  reduce the uncertainty of our assumptions.

