The Business Case

Have you ever wondered if retailers actually make more money from price discounts? Or what is the impact of promotions in Amazon on the sales in Walmart? SAP gives you a unique opportunity to find out by analyzing their sales data.

 

 

The Research Problem

The task for the SAP case will be to identify the volume uplift drivers, measure the promotional effectiveness and measure the cannibalization effect from main competitors. The goal is to analyze the impact of price reduction and promotions on volume of sales by also taking into account competitor’s prices.

 

Download the dataset for the case here…

See the discussion for this case in the Data.Chat here…

 

 

The SAP expert for the Datathon

Agamemnon Baltagiannis, Industry Expert, Principal Data Scientist – Team Leader, Global Data Science Hub @ SAP

Agamemnon is the leader of SAP Global Data Science Hub located in Athens, part of SAP Customer Innovation & Enterprise Platform global team. He has extended experience in Data Science, Machine Learning, Digital Transformation and his main goal is driving customer growth through Innovation and Data Science digital prototyping.

He holds a PhD in Applied Mathematics and Mechanics, an MSc in Electronics and Telecommunications and a BSc in Physics. He carried to his role on SAP Global Data Science team a wealth of customer facing and applied data science experience. He has worked as a Senior Advisor for a Big4 consulting firm in Advisory Center for Enterprise Intelligence, an R&D Senior Data Scientist for a market research company in Analytics Center of Excellence, a Research Data Scientist for a startup company, an R&D Software Engineer for a European Telco vendor and a researcher for Universities and other companies in Consumer Packaged Goods (CPG), Manufacturing, Banking and Telecommunications industries.

Expected Output and Paper

Produce insights and present them in a meaningful way that will help a potential customer make decisions on price strategy or future promotions.

Article instructions

The main focal point for presenting the results from the Datathon from each team, is the written article. It would be considered by the jury and it would show how well the team has done the job.

Considering the short amount of time and resources in the world of Big Data Analysis it is essential to follow a time-tested and many-project-tested methodology CRISP-DM. You could read more at http://www.sv-europe.com/crisp-dm-methodology/
The organizing team has tried to do the most work on phases “1. Business Understanding” “2. Data Understanding”, while it is expected that the teams would focus more on phases 3, 4 and 5 (“Data Preparation”, “Modeling” and “Evaluation”), so that the best solutions should have the best results in phase  5. Evaluation.
Phase “6. Deployment” mostly stays in the hand of the case-study providing companies as we aim at continuation of the process after the event. So stay tuned and follow the updates on the website of the event.

1. Business Understanding
This initial phase focuses on understanding the project objectives and requirements from a business perspective, and then converting this knowledge into a data mining problem definition, and a preliminary plan designed to achieve the objectives. A decision model, especially one built using the Decision Model and Notation standard can be used.

2. Data Understanding
The data understanding phase starts with an initial data collection and proceeds with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information.

3. Data Preparation
The data preparation phase covers all activities to construct the final dataset (data that will be fed into the modeling tool(s)) from the initial raw data. Data preparation tasks are likely to be performed multiple times, and not in any prescribed order. Tasks include table, record, and attribute selection as well as transformation and cleaning of data for modeling tools.

4. Modeling
In this phase, various modeling techniques are selected and applied, and their parameters are calibrated to optimal values. Typically, there are several techniques for the same data mining problem type. Some techniques have specific requirements on the form of data. Therefore, stepping back to the data preparation phase is often needed.

5. Evaluation
At this stage in the project you have built a model (or models) that appears to have high quality, from a data analysis perspective. Before proceeding to final deployment of the model, it is important to more thoroughly evaluate the model, and review the steps executed to construct the model, to be certain it properly achieves the business objectives. A key objective is to determine if there is some important business issue that has not been sufficiently considered. At the end of this phase, a decision on the use of the data mining results should be reached.

6. Deployment
Creation of the model is generally not the end of the project. Even if the purpose of the model is to increase knowledge of the data, the knowledge gained will need to be organized and presented in a way that is useful to the customer. Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data scoring (e.g. segment allocation) or data mining process. In many cases it will be the customer, not the data analyst, who will carry out the deployment steps. Even if the analyst deploys the model it is important for the customer to understand up front the actions which will need to be carried out in order to actually make use of the created models.