To comply with my non-disclosure agreement, I have omitted and obfuscated confidential information in this case study. All information in this case study is my own and does not necessarily reflect the views of the original organization.
Over a period of 4 months, I was responsible for planning, estimation, and delivery of design.I was involved in selecting research methodologies and conducting stakeholder workshops.I was working closely with the team of highly experienced visual designers and front-end developers for maintaining the quality of implementation.
Imagine you are a company with innovative technology. You have been using this technology as service and your team is growing multi-fold every few months. You are working with fortune 500 clients and the demand for work is increasing day by day.
You have assigned talented "data scientists" to each of these clients. They are trained to analyze the marketing data and use the tools and technology to create an actionable marketing plan. They can help clients track their existing campaigns against multiple variables including weather, festival or state elections.
But you can not keep on adding more experts/data scientists to work on more and more clients. The service-based model is not scalable
What were we trying to achieve?
For every engagement with the client, data-scientists spend days gathering all the relevant marketing/campaign data from the client. Then they take weeks on-boarding, unifying and making data customizable and meaningful. The data-scientists deploys this data into their analytics engine and processes further for insights. Based on that they create the marketing strategy and plan based on clients requirement.
Although the process seems straightforward it was time-consuming. There was a lot of back and forth between the client and data-scientists in order to come up with a concrete plan.