DAEHAR
Daehar Cosmetics (name is representational) is a multinational cosmetic and beauty product company that has been in business for more than 110 years. The company has a strong presence in the global market, with its products sold in over 150 countries.
Skills
Date & Duration:
/Mar'2023 /4weeks
/Sales Dashboard
/Data Visualization
/Predictive Analysis
My Role
As the designer tasked with weaving the experience for multiple users across different geographic locations, I started with some workshops and questionnaires to get a better understanding of the user needs. Once I had the user needs defined, I worked closely with the client product owners and the development team to understand more about the brand guidelines and limitations in terms of technology in order to better align my solution.
Project Summary
Daehar has developed a scenario-based sales prediction model that will help sales managers forecast sales and come up with better marketing strategies to help achieve Daehar's targets.
The model includes two high-level use cases:
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In the first use case, sales managers can manipulate certain parameters and see how the overall sales change. This will help them understand the impact of different factors, such as price, conversion rate, and glance views, on overall sales.
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In the second use case, sales managers can input their target order revenue values into the model to see what each parameter should be in order to achieve that number. This will help them develop a sales strategy that is specifically designed and backed by data to meet their target.
The model is based on historical sales data and data from other sources. It uses a variety of statistical techniques to predict sales.
Challenges
As a brand with multiple franchises and a wide range of products, it was important that users could get the correct insights from the dashboard, regardless of the level of detail they were viewing. This was challenging, as there were many different scenarios that users could choose from, and the insights they would need would also depend on the timeframe they were interested in.
At the lowest levels, the amount of data could be overwhelming. It was therefore important to understand, distill, and select the most important bits of information for the dashboard. I would like to thank the product owner and data analysts for their guidance in this process.
The filters and selection of the granularity of the data had to be well defined so that the sales managers could accurately focus on the franchise or product that they are responsible for. Further, to enable the users to tailor the forecast to their exact needs, features like bulk edit and row level edits had to be provided for the scenario-level data.
Solution
As a brand with multiple franchises and a wide range of products, it was important that users could get the correct insights from the dashboard, regardless of the level of detail they were viewing. This was challenging, as there were many different scenarios that users could choose from, and the insights they would need would also depend on the timeframe they were interested in.
At the lowest levels, the amount of data could be overwhelming. It was therefore important to understand, distill, and select the most important bits of information for the dashboard. I would like to thank the product owner and data analysts for their guidance in this process.
The filters and selection of the granularity of the data had to be well defined so that the sales managers could accurately focus on the franchise or product that they are responsible for. Further, to enable the users to tailor the forecast to their exact needs, features like bulk edit and row level edits had to be provided for the scenario-level data.