Teppei Syokudo - Improving Store Performance: Data

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Data Exploration

In the very initial stages of the project, the problem is analysed and by looking at the available data and understanding the various aspects of the data. The main aim of this step is to ensure the following:

1. Maximize insight into a data set
2. Uncover underlying structure
3. Extract important variables
4. Detect outliers and anomalies
5. Test underlying assumptions
6. Develop parsimonious models
7. Determine optimal factor settings. (“e-Handbook of Statistical Methods”, 2016)

Through these steps, ultimately the problem is determined and a solution model is developed with analysis methods identified and worked towards; necessary data preparation is also determined.

Data Preparation

After preliminary examination of the data, the team has identified that necessary data preparation is required specifically in the following areas:

1. Sales and labour data is currently stored separately; a joining of the data is required before the performance of staff can be analysed.
2. Analysing the productivity of staff requires sales data to be in an hourly format, where the current data only stores the staff start and end work timings – new variables have to be created to indicate if a staff is working on a particular day in a particular hour.
3. Before providing useful analysis that can ascertain the product portfolio mix, sales data has to be broken down to each order. This data is currently stored in a POS system and the team is in the midst of investigating if the data can be retrieved and used meaningfully.

Data Analysis Methods

Evaluation of Existing KPIs

Correlation Analysis will be used to evaluate the effectiveness of existing KPIs. The team will then look at a particular KPI variable with sales (eg: drinks% and sales) to find out whether sales really is affected by that KPI.

Proposing New KPIs

The team will be using Clustering and Conjoint Analysis to identify the key variables that impact sales. Clustering sales data with labour data could help identify clusters with high sales, and the combination of variables leading to such sales values. Conjoint analysis would help identify the individual variables that are important for hitting sales values. In doing so, the team would be able to propose new KPIs based on those key variables.

Setting Numerical Targets

Through Clustering, the team will also be able to find out the right numerical targets to set for individual staff. For example, clustering of sales data and labour data could help determine who are the better salespersons who are able to make x # or $x of sales. This could be the numerical target that is set for the lower performing salespersons.

Product Portfolio Analysis

Last but not least, a Market Basket Analysis will be carried out to identify product pairings with high affinity. This will help Teppei to optimize its product portfolio to include only the most popular products. It will also identify suitable product pairings for cross-selling. Furthermore, it will also help to identify hidden trends that can spur new product development.

Deliverables

Following the analysis that are carried out, the deliverables at the end of the project is two-pronged: systems and recommendations.

Data Analysis Systems

A series of spreadsheets or systems that will allow staff to update the date as an input and retrieve the performance information as output. The system should be able to carry out necessary analysis seamlessly and allow variables such as KPIs to be edited. Secondly, as more data is accumulated, there is another system that takes in sales order information and provides the staff with the product affinity of the products sold in the restaurant.

Recommendations and Insights

Actionable insights and recommendations based on the initial conception of the systems will allow the team to provide actionable insights and recommendations that can be carried out and tested immediately to ascertain impact of analysis.

Sample Data

The following is a sample of the data that has been provided. At the time of writing, a Non-disclosure Agreement (NDA) is pending and has not been signed, and the data provided is obfuscated. Teppei has provided us with a list of data that we can use including the following:

• Sales data: average spending, number of customers etc.
• Product information: the class (drinks, rice, snacks etc.), supply quantity and COGS (cost of goods sold)
• Daily Sales & Product Report: report of sales figures on a daily basis
• Labour data: time in, time out, total working hours per month, number of leaves taken etc.

Out of all the information provided, we believe there are 2 main sources of data that will be analysed:

Sales Data

The following shows the sales data on a particular day, with the amount of sales and customers per hour.

The data will be cleaned to a table that has the following format:

Labor Data

The following shows labour data that states the time each staff starts and ends work on a particular day.

The data will be cleaned and formatted to the following:

The variables column for each staff represents whether a staff is at work at a particular date and time. In this example, Staff A is present at work on the 1st Jun 2015 at 11AM.

Once the above data has been cleaned to their respective format, we can then combine the data into a joined table which will allow us to carry out the Clustering and Conjoint Analysis.

Sales Order Data

At the time of writing, the team does not have access to the sales order data, but believes that upon cleaning, the format of the data will be as such:

Similar to the labour data, the Boolean variable for each Products represent if a particular product is bought in that product order. In this case, order #12 contains 1 order of product B and 1 order of product C.

Using the above information, we can carry out market basket analysis to find out the product affinity of each product to every other product, providing insights and customer purchasing behaviour to management.