Introduction:

At Mass Analytics Marketing Mix Modeling (MMM) projects are conducted following a predefined process based on the well known CRISPDM process.

This sixphase process starts with setting the foundations of the project by accurately and thoroughly understanding the business requirements discovering the available data. After that comes the crucial data preparation phase where the data is processed and transformed into the required format. This phase is also culminated by a data check milestone to confirm the accuracy of the data with the different stakeholders.

After building the model and validating it the project outcomes are reported to the client. In this final and important step the project outcomes are delivered to share insights and actionable recommendations. The model is consequently deployed to help optimize future marketing budgets and improve KPIs

The Marketing Analytics summer internships will focus on the Data Preparation and Project Delivery steps. Two very important phases in the CRISPDM process that require essential skills that should be acquired and harnessed by any successful data analyst wishing to work on modelling at a later stage.

Key attributes / Main competencies:

• Engineering or Business School
• If Business preferably Major Marketing or Finance& Minor in Analytics
• If Engineer Major in Data Science Applied Mathematics and Modeling
• Microsoft Office skills required
• Experience with common data science toolkits (R Python etc) is a plus
• Experience with common data visualization tools (Power BI etc) is a plus
• Fast learner Enthusiastic and autonomous
• Technical & Business oriented
• Analytical and problemsolving skills
• Tech & data Savvy knowledge
• Fluent in written and spoken English

Period: (2 3 months)

Common Responsibilities:

• Understand MMM process and outputs
• Work closely with Marketing Science Team
• Provide user guide documentations
• Build a comprehensive presentation about the internship outcomes.

Topic 1

2. Data Preparation

In todays business environment data is being created and collected at an increasing pace. Consequently organizations cannot expect manual data preparation processes to scale. Indeed the success of most Data Analytics projects including MMM is highly correlated with the success of the data preparation phase.

At MASS Analytics the Marketing Science team uses various data preparation tools to efficiently and accurately process the data. This data is usually complex with multiple formats and coming from various sources. These tools are designed to accelerate and facilitate the data prep phase through the creation of automated data processing pipelines.

2. Role:

• Understand the context of MMM projects and their data preparation steps
• Input Data Reproduce Data preparation pipelines & Explore
• Report bugs if any and suggest new features and improvements for the used tools
• Create new Data preparation pipelines to improve efficiency and reinforce automation.

Topic 2

1. Description: Reporting Results & Insights

Every MMM project ends with results & recommendations. This phase consists in communicating the project outcomes and findings to our end clients. In general it takes the form of static presentations (e.g. PowerPoint) and/or dynamic dashboards (e.g. PowerBI). This phase is very crucial because we deliver the project results to help optimize marketing budgets and improve KPIs. Reporting results successfully demonstrates the Data Analysts ability to address the stakeholders business questions and to provide the expected insights and recommendations.

2. Topic related Role:

Understand projects contexts and the modeling prediction and optimization results.

Gather projects specific information.

Build dynamic dashboards based on previous project results.

Use those results to conduct an analysis by industry segment.

Provide new results visualization ideas.