Reflection Beescoop Logistics (beta programme) – Learning Launch Tool

Challenge

For a small logistics company called Beescoop who’s operation is dependent on freelance drivers  with the target audience being individuals requiring delivery of goods and/or relocating from one  place to another we had a challenge of matching drivers to customers. Not being in control of the  driver’s actions or personal schedules meant one driver could be assigned a job while personally  having a different job which led to delays and dissatisfied customers. Because a job was already taken  on-delay measures for reassigning drivers were not put in place as well as means of communication  throughout our platform.

With communication being between the client and the driver, personally we had no records to look  back on and therefore were unaware of the stress our customers were facing and how we could help  them. This also meant we had no data to perform thorough research on to improve our customer  service and meet new customer demands as well as come up with innovative ideas.

Selection

We chose to use the Learning Launch Tool. With the learning launch tool, we can test out new features  and tools with our customers and get feedback from them so we can decide on whether or not it is  worth the effort to scale or not. In-order to accomplish this, we made use of the ethnographic tool as  well. We are dealing with customers who are going through different experiences and with different  needs and requirements. Before we can test any feature, we must learn what it is our customers are  really experiencing.

We also made use of the Mind Mapping tool, our customers are providing use with data, we need to  gather that data, find patterns and insights from it so we can decide which features to put in on  Learning Launch sprints (small releases to test features).

By making use of the Learning Launch Tool, we can roll out small features (i.e. messaging feature,  community, etc.) and get results on how they make use of it and whether we can integrate into our  next release for all the customers. For example, during one of the beta programme, we added an inbox  feature where a customer can directly contact a driver within the platform. This beta test ran for two  weeks, and all our customers and drivers were making full use of it and therefore we have scaled and  customers are making full use of it.

Application

We made use of Learning Launch by initiating a beta programme and sent invitations to our clients for  them to sign up. Here, they can give out suggestions as to what it is, they would like changed, get likes  and reaction from other customers as well as communicate or comment on the requested changes.  This provides us with a base to initiate our “What is” research and a community for our customers to  engage.

By making use of Mind mapping, we can extract the patterns on the data gathered from the beta  programme and feedback from customers (such as the request for drivers to be changed during delay).  From then, we generate the possible best solution (like drivers near me or available driver list select)  to incorporate the requests brought forward while in the same time creating criteria customers were  not aware of before (innovate).

Once we have decided on the design criteria, we then come up with possible design solutions. From  the solutions gathered, we categories them in our “What Wow Zone” to perfectly identify those that  align with our current capabilities, business model and address what customers want (in the case of  driver delay, we decided to allow a user to select a new available driver, driver near me is our of  current budget as it requires mapping and contracts).

Solutions which satisfy the conditions are then categorized as our “wow solutions” and are prioritized  and are put in line for the next beta release for customers to try out and give feedback.

Insight & Approach

By initiating the beta programme and thus making full use of the Learning Launch Tool, I have noticed  that there are features which I personally might think are great and fantastic only to find out they are  not as great for the customers (confirmation bias).

Our customers can now, on their own find problems and provide solutions. For example, when some  are giving suggestion, some are pointing our errors while others reply to correct other customers.

By combining this with Mind Mapping, we are looking to making the beta programme community  portal public to registered users. That we’ll allow all our customers to look up solutions, help other  customers and provide feedback. We have noted that customers requesting collection/delivery are in  a hurry and we have therefore decided to integrate the portal into the App itself for fast easy access.

To enhance our use of the Learning Launch tool, we place to do a pre-beta release. Here we do not  release an actual feature but rather a simulation of the feature, where users will see how the feature  will work. This help reduces the strain on the development as well as the number of small features to  release and at the same time save costs on resources.


Reflection Beescoop Logistics ( beta programme ) – Learning Launch Tool Challenge For a small logistics company called Beescoop who ’s operation is dependent on freelance drivers with the target audience being individuals requiring delivery of goods and/or relocating from one place to another we had a challenge of matching drivers to customers . Not being in control of the driver ’s actions or personal schedules meant one driver could be assigned a job while personally having a different job which led to delays and dissatisfied customers . Because a job was already taken on - delay measures for reassigning drivers were not put in place as well as means of communication throughout our platform . With communication being between the client and the driver , personally we had no records to look back on and therefore were unaware of the stress our customers were facing and how we could help them . This also meant we had no data to perform thorough research on to improve our customer service and meet new customer demands as well as come up with innovative ideas . Selection We chose to use the Learning Launch Tool . With the learning launch tool , we can test out new features and tools with our customers and get feedback from them so we can decide on whether or not it is worth the effort to scale or not . In - order to accomplish this , we made use of the ethnographic tool as well . We are dealing with customers who are going through different experiences and with different needs and requirements . Before we can test any feature , we must learn what it is our customers are really experiencing . We also made use of the Mind Mapping tool , our customers are providing use with data , we need to gather that data , find patterns and insights from it so we can decide which features to put in on Learning Launch sprints ( small releases to test features ) . By making use of the Learning Launch Tool , we can roll out small features ( i.e. messaging feature , community , etc . ) and get results on how they make use of it and whether we can integrate into our next release for all the customers . For example , during one of the beta programme , we added an inbox feature where a customer can directly contact a driver within the platform . This beta test ran for two weeks , and all our customers and drivers were making full use of it and therefore we have scaled and customers are making full use of it . Application We made use of Learning Launch by initiating a beta programme and sent invitations to our clients for them to sign up . Here , they can give out suggestions as to what it is , they would like changed , get likes and reaction from other customers as well as communicate or comment on the requested changes . This provides us with a base to initiate our “ What is ” research and a community for our customers to engage . By making use of Mind mapping , we can extract the patterns on the data gathered from the beta programme and feedback from customers ( such as the request for drivers to be changed during delay ) . From then , we generate the possible best solution ( like drivers near me or available driver list select ) to incorporate the requests brought forward while in the same time creating criteria customers were not aware of before ( innovate ) . Once we have decided on the design criteria , we then come up with possible design solutions . From the solutions gathered , we categories them in our “ What Wow Zone ” to perfectly identify those that align with our current capabilities , business model and address what customers want ( in the case of driver delay , we decided to allow a user to select a new available driver , driver near me is our of current budget as it requires mapping and contracts ) . Solutions which satisfy the conditions are then categorized as our “ wow solutions ” and are prioritized and are put in line for the next beta release for customers to try out and give feedback . Insight & Approach By initiating the beta programme and thus making full use of the Learning Launch Tool , I have noticed that there are features which I personally might think are great and fantastic only to find out they are not as great for the customers ( confirmation bias ) . Our customers can now , on their own find problems and provide solutions . For example , when some are giving suggestion , some are pointing our errors while others reply to correct other customers . By combining this with Mind Mapping , we are looking to making the beta programme community portal public to registered users . That we ’ll allow all our customers to look up solutions , help other customers and provide feedback . We have noted that customers requesting collection / delivery are in a hurry and we have therefore decided to integrate the portal into the App itself for fast easy access . To enhance our use of the Learning Launch tool , we place to do a pre - beta release . Here we do not release an actual feature but rather a simulation of the feature , where users will see how the feature will work . This help reduces the strain on the development as well as the number of small features to release and at the same time save costs on resources .