Learning Launch Reflection

Challenge & Selection

I am Sales Analytics Coordinator for zolie (National Institute  of Coffee) of a Beauty Direct Selling company  that have almost 415k representatives (sellers) in our cluster. Data analytics are very important for  build consistent insights to make certain decisions about product launches or incentives programs  to improve their profits and company itself. These 415k representatives are organized in different  zones or regions in each country depending also of different selling behaviors that segmented the  representatives in different ways. Changing our work methods to an agile method, we star  working different multi skills teams to have different knowledge resources working in the same  “table”. We focused in work in projects than lasts between 2 and 4 weeks (depending in the size of  the project and the impact it will have).

Application

We usually put together work teams that not necessarily already exist. This means that work in  agile teams made of people of different functional areas of the company that have direct relation  with the project itself. We consider that projects tests have to be shorts, good and cheap. That’s  why these don’t last less than 2 weeks (they have to bring real impact and solutions) but less than  4 weeks (shorts and cheap). Any person of each team bring insight about his knowledge area  before discussing any action. After that we build the business case understanding which  hypothesis we are going to prove (or test) and how we are going to measure if that hypothesis had  the results we expected or not. After launching we present the business case to our sponsor (or  financial planning maybe) to make the skeptical questions to reinforce the project.

Insight & Approach

To have quick learning and continuous improvement, we first launch the project (or program) in a  controlled zone or region to measure and reduce possible impacts. We identify which will be our  Group of Control (Representatives of similar characteristics to the ones we are talking to) to have  fast and simple comparison of the results of the project.

Ones we realize that the project brings the result we expected, we scale this project to the rest of  the region or the country or either the cluster.

If project does not bring good results, we register what doesn’t result and iterate with some  changes after analyzing what we think we have to change with the learnings we take

