Exploring the retail business together with NIMPOS
Data in today's society has become a commodity to be traded with. Using tools from the fields of statistics and machine learning, collected raw data can be crafted into gems of valuable information. Information that when presented wisely can generate insights on how to enhance and accelerate future business.
Combine has set out on a journey of adventures, exploring different industries with the following question in mind. How can we utilize the competence at Combine to help our customers “Enter the next level”?
In this blog post, we are exploring the retail business together with NIMPOS.
NIMPOS is a Swedish based company who offer a revolutionary simple and safe point of sale system suitable for both large and small companies thanks to its scalability. A full description of NIMPOS and their products can be found here. Having access to more and more data from transactions, NIMPOS is asking Combine for guidance on how to utilize the stored transaction data to help their customers enhance their business.
Combine develop and maintain a free and open source data analysis tool called Sympathy for Data. Sympathy is a software platform for data analysis and is built upon Python. It hides the complexity of handling large amounts of data in the data analysis process, which enables the user to focus on what is really important.
The first step in any data analysis task is getting access to the data. After creating a VPN connection, the data from NIMPOS database is easily imported into Sympathy by utilizing its powerful import routines.
Some of the data we got access to:
- Reference ID (one per transaction)
- Article ID
- Article Name
- Transaction Date
- Quantity (number of sold articles)
- Article Price
Now, with the data imported the powerful data processing capabilities of Sympathy is at our hands. The data is first preprocessed to filter out missing and unreasonable data, after which the analysis can start.
A few analyses have been implemented:
- Predicting the increase in the number of customers.
- Expected number of sold articles together with confidence bounds.
- Customer intensity variation
- For each day of the week
- Hour-by-hour for each weekday
An overview of the flow is presented in the figure.
Sadly we do not have any information to connect an individual transaction to unique customers, and no other customer features are available, e.g. age or sex, and this narrows down the possible analyses.
This post is the first in a series, where we have laid the ground for upcoming posts. We introduced the reader to the problem, some of the data, the tools, and a few analyses implemented.
In one of the upcoming posts, we will showcase the possibilities of connecting the strengths of Sympathy for Data, for processing and analyzing data, together with the interactive reporting made possible by Sympathy web services.
Stay tuned and don’t miss out on future posts. In the meantime, I suggest you read earlier posts or download Sympathy for Data and start playing around with some example flows. You won’t regret it!