Reflection – Learning Launch

Challenge:

The challenge was to launch and scale a business portfolio in a space which had no clear  adjacency to our current business line. This was to diversify revenue streams from the  current business, which had little scope of innovation and growth and, as per the BCG  growth/share matrix1, was indeed a cash cow.

Post ideation and evaluation of multiple alternatives, we decided to venture into creating a  platform for Hyperautomation of business systems. The key market that we decided to  target was our existing customer base in both the government and commercial space,  primarily in the business workflow heavy areas since this was the area we knew in depth.

Selection:

Since we were venturing out into a new area, the first thing that needed to be done was to  test key customer requirements and expectations. We also needed to test the defensibility  of our basic assumptions. Since we figured out that there might be a need to tweak our  assumptions or, indeed, pivot further, we wanted a method to quickly engage with the  customer base and test out our basic assumptions before scaling or discarding. Thus, a  learning launch was the best approach for our scenario.

Application:

In order to start the learning launch process, a set of hypotheses was created. These  hypotheses reflected the ideas and use-cases that we wanted to bring to the customers,  using our portfolio of services and tools. Each of these ideas was against a specific need of  our customer base and how we intended to meet these specific needs. These also  articulated how each of these ideas compared and stacked against alternatives.

Following the creation of hypotheses, we wanted to figure out from some of our deepest  engagements and customers if these assumptions were true against the use cases that we  had developed. Then we formed multi-functional teams to engage with tightly mapped  stakeholders in our customer base to figure out if these ideas and use cases actually gelled  with the customer and helped solve some of their pressing real world needs and problems.

Multi-functional teams helped in the process, since some of these use cases related to  finance and accounts areas whereas some of the other use cases related to areas related to  customer success. Therefore, professionals from finance, human resources, marketing and  technology groups came together for these excercises. These being very diverse areas under  a broad-based business workflow domain, therefore multi-functional teams helped in

1 BCG Growth/Share Matrix - https://www.hall.biz/wp-contenthome.html

extracting value from the discussions with the stakeholders in the various customer  organizations.

Insight:

Following the exercise, we learned which of our ideas and use cases were actually solving  the pressing needs of the customer set and meeting up to our initial assumptions. We also  learned which of these ideas were contrary to our initial assumptions and failed to meet the  requirement of either solving a need or being better than the alternatives. While the former  ideas and use cases were selected for continued development and scaling up later on, the  later were discarded or changed to align to the messages from the market.

Beyond the core finding as above, we also learned how to rearrange question sets, how to  organize multi-functional teams and how to primarily create market aligned hypotheses in  order to make the next iteration faster and more conducive to customer testing.

Indeed, we used entrepreneurial learn-as-you-go process to make failures cheap both  financially and personally. We ensured that our next iteration was faster and also resulted in  a larger success ratio.

Approach:

For a brand-new product or service launch, which does not have any, or at most scant,  adjacency to our current business a learning launch is indeed the best approach. This is  typically when we are trying to pivot or launch a diversified business line.

The alternative is a big bang approach, where a completely matured business line, service or  product is launched. Firstly, this requires immense amount of investment. This also requires  deep and accurate market research and a bang-on level of assumptions and insights. Even  then, such a big-bang launch for a diversified or pivoted business line is fraught with  immense risk and I would suggest learning launch typically since resources are always finite.


Reflection – Learning Launch Challenge : The challenge was to launch and scale a business portfolio in a space which had no clear adjacency to our current business line . This was to diversify revenue streams from the current business , which had little scope of innovation and growth and , as per the BCG growth / share matrix1 , was indeed a cash cow . Post ideation and evaluation of multiple alternatives , we decided to venture into creating a platform for Hyperautomation of business systems . The key market that we decided to target was our existing customer base in both the government and commercial space , primarily in the business workflow heavy areas since this was the area we knew in depth . Selection : Since we were venturing out into a new area , the first thing that needed to be done was to test key customer requirements and expectations . We also needed to test the defensibility of our basic assumptions . Since we figured out that there might be a need to tweak our assumptions or , indeed , pivot further , we wanted a method to quickly engage with the customer base and test out our basic assumptions before scaling or discarding . Thus , a learning launch was the best approach for our scenario . Application : In order to start the learning launch process , a set of hypotheses was created . These hypotheses reflected the ideas and use - cases that we wanted to bring to the customers , using our portfolio of services and tools . Each of these ideas was against a specific need of our customer base and how we intended to meet these specific needs . These also articulated how each of these ideas compared and stacked against alternatives . Following the creation of hypotheses , we wanted to figure out from some of our deepest engagements and customers if these assumptions were true against the use cases that we had developed . Then we formed multi - functional teams to engage with tightly mapped stakeholders in our customer base to figure out if these ideas and use cases actually gelled with the customer and helped solve some of their pressing real world needs and problems . Multi - functional teams helped in the process , since some of these use cases related to finance and accounts areas whereas some of the other use cases related to areas related to customer success . Therefore , professionals from finance , human resources , marketing and technology groups came together for these excercises . These being very diverse areas under a broad - based business workflow domain , therefore multi - functional teams helped in 1 BCG Growth / Share Matrix - https://www.hall.biz/wp-contenthome.html extracting value from the discussions with the stakeholders in the various customer organizations . Insight : Following the exercise , we learned which of our ideas and use cases were actually solving the pressing needs of the customer set and meeting up to our initial assumptions . We also learned which of these ideas were contrary to our initial assumptions and failed to meet the requirement of either solving a need or being better than the alternatives . While the former ideas and use cases were selected for continued development and scaling up later on , the later were discarded or changed to align to the messages from the market . Beyond the core finding as above , we also learned how to rearrange question sets , how to organize multi - functional teams and how to primarily create market aligned hypotheses in order to make the next iteration faster and more conducive to customer testing . Indeed , we used entrepreneurial learn - as - you - go process to make failures cheap both financially and personally . We ensured that our next iteration was faster and also resulted in a larger success ratio . Approach : For a brand - new product or service launch , which does not have any , or at most scant , adjacency to our current business a learning launch is indeed the best approach . This is typically when we are trying to pivot or launch a diversified business line . The alternative is a big bang approach , where a completely matured business line , service or product is launched . Firstly , this requires immense amount of investment . This also requires deep and accurate market research and a bang - on level of assumptions and insights . Even then , such a big - bang launch for a diversified or pivoted business line is fraught with immense risk and I would suggest learning launch typically since resources are always finite .