Quantium has had a data partnership with a large supermarket brand for the last few years who provide transactional and customer data. You are an analyst within the Quantium analytics team and are responsible for delivering highly valued data analytics and insights to help the business make strategic decisions.
Supermarkets will regularly change their store layouts, product selections, prices and promotions. This is to satisfy their customer’s changing needs and preferences, keep up with the increasing competition in the market or to capitalise on new opportunities. The Quantium analytics team are engaged in these processes to evaluate and analyse the performance of change and recommend whether it has been successful.
You are part of Quantium’s retail analytics team and have been approached by your client, the Category Manager for Chips, who wants to better understand the types of customers who purchase Chips and their purchasing behaviour within the region. The insights from your analysis will feed into the supermarket’s strategic plan for the chip category in the next half year.
You have received the following email from your manager, Zilinka.
'Hi,
Welcome again to the team, we love having new graduates join us!
I just wanted to send a quick follow up from our conversation earlier with a few pointers around the key areas of this task to make sure we set you up for success.
Below I have outlined your main tasks along with what we should be looking for in the data for each.
Examine transaction data – look for inconsistencies, missing data across the data set, outliers, correctly identified category items, numeric data across all tables. If you determine any anomalies make the necessary changes in the dataset and save it. Having clean data will help when it comes to your analysis.
Examine customer data – check for similar issues in the customer data, look for nulls and when you are happy merge the transaction and customer data together so it’s ready for the analysis ensuring you save your files along the way.
Data analysis and customer segments – in your analysis make sure you define the metrics – look at total sales, drivers of sales, where the highest sales are coming from etc. Explore the data, create charts and graphs as well as noting any interesting trends and/or insights you find. These will all form part of our report to Julia.
Deep dive into customer segments – define your recommendation from your insights, determine which segments we should be targeting, if packet sizes are relative and form an overall conclusion based on your analysis.
Make sure you save your analysis in the CSV files and your visualisations – we will need them for our report. If you could work on this analysis and send me your initial findings by end of next week that would be great.
Looking forward to reviewing your work.
Thanks,
Zilinka'
We need to present a strategic recommendation to Julia that is supported by data which she can then use for the upcoming category review however to do so we need to analyse the data to understand the current purchasing trends and behaviours. The client is particularly interested in customer segments and their chip purchasing behaviour. Consider what metrics would help describe the customers’ purchasing behaviour.
You are part of Quantium’s retail analytics team and have been approached by your client, the Category Manager for Chips, has asked us to test the impact of the new trial layouts with a data driven recommendation to whether or not the trial layout should be rolled out to all their stores.
You have received the following email from Zilinka.
'Hi,
Thanks for your feedback earlier, I’m glad you find my follow up emails helpful in ensuring your on the right track.
For this part of the project we will be examining the performance in trial vs control stores to provide a recommendation for each location based on our insight. Below are some of the areas I want you to focus on, of course if you discover any other interesting insights feel free to include them in your findings.
Select control stores – explore the data and define metrics for your control store selection – think about what would make them a control store. Look at the drivers and make sure you visualise these in a graph to better determine if they are suited. For this piece it may even be worth creating a function to help you.
Assessment of the trial – this one should give you some interesting insights into each of the stores, check each trial store individually in comparison with the control store to get a clear view of its overall performance. We want to know if the trial stores were successful or not.
Collate findings – summarise your findings for each store and provide an recommendation that we can share with Julia outlining the impact on sales during the trial period.
Remember when working with a client visualisations are key to helping them understand the data. Be sure to save all your visualisations so we can use them later in our report. We are presenting to our client in 3 weeks so if you could submit your analysis by mid next week that will give us great amount of time to discuss findings and pull together the report.
Keep up the good work!
Thanks,
Zilinka'
Julia has asked us to evaluate the performance of a store trial which was performed in stores 77, 86 and 88.
Create a measure to compare different control stores to each of the trial stores to do this write a function to reduce having to re-do the analysis for each trial store. Consider using Pearson correlations or a metric such as a magnitude distance e.g. 1- (Observed distance – minimum distance)/(Maximum distance – minimum distance) as a measure.
Once you have selected your control stores, compare each trial and control pair during the trial period. You want to test if total sales are significantly different in the trial period and if so, check if the driver of change is more purchasing customers or more purchases per customers etc.
Task 3 is targeted specifically at building your ability to recognise commercial, actionable insights from your analysis and displaying it in a clear and concise way for your client, with minimal jargon. At Quantium, our analyst graduates sometimes work as what we like to call “hybrids” (a mix of analyst and consultant duties) so developing your presentation skills early is a huge win!
As both technical tasks 1 and 2 were open ended in terms of insights, this model answer will focus on the layout and the order of your inclusions, including where to include graphs, taglines, written insights and recommendations.
As part of Quantium’s retail analytics team, you have been conducting a range of analysis on transaction and purchase behaviour data to provide key recommendations to your client, the Category Manager of chips, who is putting together their strategic plan.
With our project coming to an end its time for us to send a report to Julia, based on our analytics from the previous tasks. We want to provide her with insights and recommendations that she can use when developing the strategic plan for the next half year.
As best practice at Quantium, we like to use the “Pyramid Principles” framework when putting together a report for our clients.
For this report, we need to include data visualisations, key callouts, insights as well as recommendations and/or next steps.
