During the month of July 2020, I did a a short self-paced Virtual Internship through Forage called 'Data Analytics Consulting Virtual Internship' under KPMG Australia. It contained three modules or tasks. These modules took me through an entire process of analysing data and interpreting it for improvement in marketing strategies of an organisation. Given below, is a little more detail about each module/task.
Sprocket Central Pty Ltd is a (hypothetical) medium size bikes & cycling accessories organisation. They basically require help with its customer and transactions data. The organisation has provided a large dataset relating to its customers, to effectively analyse it to help optimise its marketing strategy. In this task we had to start the preliminary data exploration and identify ways to improve the quality of the given data. The client asked the team to assess the quality of their data, as well as make recommendations on ways to clean the underlying data and mitigate the issues.
I was assigned to take a look at the datasets we had received and draft an email to them identifying the data quality issues and how this may impact our analysis going forward. I had to ensure that it is ready for our analysis in phase two. I was asked to take notes of any assumptions or issues we need to go back to the client on. As well as recommendations going forward to mitigate current data quality concerns. I evaluated the data set focusing on the data quality dimensions: Accuracy, Completeness, Consistency, being up to date, Relevancy, Validity and Uniqueness.
A new list of 1000 potential customers with their demographics and attributes was given. However, these customers did not have prior transaction history with the organisation. If correctly analysed, the data would reveal useful customer insights which could help optimise resource allocation for targeted marketing. Hence, improve performance by focusing on high value customers.
Through this task, our goal was to boost business by analysing Sprocket Central Pty Ltd's existing customer dataset (And datasets from the Australian Bureau of Statistics) to determine customer trends and behaviour. Using the existing three datasets (Customer demographic, customer address and transactions), we had to recommend which of these 1000 new customers should be targeted to drive the most value for the organisation. In building this recommendation, we were asked to start with a PowerPoint presentation which outlined the approach which we were going to take. The task included ensuring that the PowerPoint presentation included a detailed approach for the strategy behind completing the analysis including activities – i.e. understanding the data distributions, feature engineering, data transformations, modelling, results interpretation and reporting.
After building the model we need to present our results back to the client. Visualisations such as interactive dashboards often help us highlight key findings and convey our ideas in a more succinct manner. Hence, we needed to support our results with the use of visualisations.
We were asked to develop a dashboard that could be presented to the client. In the dashboard, we had to display the data summary and results of the analysis using our creativity in layout and presentation.
We had to keep in mind the important business context when presenting the findings:
- What are the trends in the underlying data?
- Which customer segment has the highest customer value?
- What do you propose should be Sprocket Central Pty Ltd ’s marketing and growth strategy?
- What additional external datasets may be useful to obtain greater insights into customer preferences and propensity to purchase the products?
Specifically, the presentation should specify who should be targeted out of the new 1000 customer list as well as the broader market segment to reach out to. I used Tablau for the dashboard creation for this module.