retail case study python

Python for Retail Analytics

• data-driven organization
• python programming

Why use Python in retail analytics? This article will explain the benefits of using Python for your retail analyses and the importance of data-driven retail strategies.

Data is a precious asset for any type of business. For instance, we all know there is a huge jump in sales before Christmas. However, we sometimes need to go beyond the obvious to make the most out of our data. We need to go below the surface and dig to extract insights because data often encapsulates information that’s difficult for the human eye to notice.

Sometimes a trend or something related to the underlying data distribution is what we need. This hidden information provides insights that help improve the business from many different perspectives. The more we know about the business, the better and more accurate decisions we make. Thus, the outcome of data-driven retail strategies is usually much better than those that do not take the data into account. But what does this have to do with Python?

Python Means Business

Python is a programming language that is mostly associated with data science . It’s almost always the first choice when it comes to processing and analyzing data . As a data scientist working in the retail analytics domain, I can assure you that Python is the go-to programming language for a handful of reasons. Many factors contribute to making Python stand out from other programming languages. Refer to the article Why Python Is the Perfect First Programming Language for Beginners for more details.

Python’s user-friendliness and easy-to-learn syntax are a serious advantage because most people working in retail are not software developers. Even non-technical business professionals can learn Python thanks to its intuitive syntax. This is also the main reason why Python is the number one programming language for beginners. Our Learn Programming with Python track consists of 5 interactive Python courses designed for complete beginners. It contains over 400 coding challenges to help you improve your Python skills quickly. Once you cover the basics, it's not an exhaustive process to master Python .

Python’s other key advantage is its big and active community. This makes it even easier to learn Python – new and seasoned programmers alike can quickly find answers to most questions from the community. It can be painful and demotivating to spend hours looking for an answer. We don’t have such issues with Python because it is a mature programming language; most of the potential issues are well-known and most questions have already been answered by the community.

Python is also highly advantageous in terms of the third-party libraries developed by the community. Libraries like pandas, scikit-learn, and Matplotlib expedite and simplify data tasks that otherwise would take us a long time to complete.

Now that we’ve explained why Python is so popular in general, let’s talk about Python’s role in retail analyses. To better understand the importance of Python for retail analytics, let’s first elaborate on the importance of data in the retail industry and how Python helps businesses grow .

How Python Helps with Data for the Retail Industry

Data is of crucial importance in the retail industry. It has the potential to improve every part of the business. Data-driven retail strategies should be planned and implemented as a complete product, taking into account every aspect of the business.

The initial part of the retail operation starts with purchasing products. If we buy excess amounts of product, we might be wasting money on inventory. It costs money to keep the inventory in the warehouse. On the other hand, we might lose some sales if we don’t have enough inventory. So, it’s best to keep inventory at an optimum level. The only way to achieve this is to predict sales amounts accurately, which is also known as demand forecasting. The more accurate our forecast is, the better we manage inventory.

In retail, we deal with time-series data – a sequence of observations ordered by time. For instance, the daily sales quantities of a product in a supermarket is an example of time-series data. There are many different strategies used for time-series predictions. It can be as simple as calculating a moving average or building a highly complex tree-based model using the XGBoost algorithm . Whichever strategy we decide to use, there is a Python-based tool or library for implementing it.

Retail industry analytics cover pricing strategies as well. A typical large retail store contains thousands of different products, so determining optimal prices is a big challenge. High prices mean high profit margins. But if we lose sales because of high prices, revenue will decrease and we might actually be losing money. Data comes to the rescue to solve this two-way problem.

The use of Python in retail does not end when we sell the product. We use Python for retail data analysis to see how our strategy is performing. We may build dashboards with Python to present the solution, performance, and results to other stakeholders or management teams.

Retail industry analytics is also interested in customer behavior analysis. We segment customers into clusters (i.e. groups) based on their shopping behavior; these groups are used to develop personalized deals. Customer segmentation and personalized marketing strategies enhance the customer shopping experience. Businesses also try to understand customer loyalty and predict customer churn . If managers know which customers are likely to leave, they can take necessary actions to keep these customers. All these operations are done using machine learning algorithms; Python has several machine learning libraries that are free to use.

Retail Analytics Use Cases with Python

I have been in the retail analytics domain for over three years. I’m currently working at a consulting firm that provides data-driven solutions and services to many different types of retailers. I have frequently experienced how using Python simplifies and expedites data-driven tasks. The business operations and dynamics change depending on the type of retailer, but the importance of data remains the same.

When we talk about data-driven retail strategies, we’re not talking about a small spreadsheet we can handle with Excel or a single SQL table with a few thousand rows. We usually work with large datasets that require distributed computing for processing and analyzing. Typically, retail analytics datasets have millions of rows. Two years of historical sales data for a large retailer can easily reach a few billion rows . The more data we have, the more accurate and robust our data-based decisions are.

When we get to such large amounts of data, traditional tools often can’t handle the load. So we use tools that support distributed computing. These tools almost always come with Python support – or even better, are Python-native. Thus, having Python in your skill set will always be an advantage.

One of the most preferred tools for large-scale data processing is Spark, an analytics engine that spreads both data and computations over clusters to handle large-scale data more efficiently. If you know Python, you can easily use Spark through PySpark . It is a Python library that serves as an interface to use Spark. PySpark combines the simplicity of Python syntax with the efficiency of Spark.

We live in the era of Cloud computing. Most of my company’s customers host their data on the cloud. As a company that has clients all around the world, we must be able to work with all Cloud providers. Thankfully, Python libraries make this task quite easy. For instance, boto3 is a Python library that provides an object-oriented API to access Amazon Web Services.

Python and Retail Data Analysis

Time series forecasting is not an easy task. In many cases, an in-depth exploratory analysis is required to understand the data and create informative features. Retail data analysis is not only done as a preparation for forecasting but also to understand the dynamics of the business. In fact, every organization needs a data analyst to extract informative insights from the data.

After the data analysis generates features, a machine learning model is trained with them to predict the demand. For exploratory data analysis, we use Python libraries like pandas, PySpark, Polars, Matplotlib, and Seaborn. In the modeling part, we have different options like scikit-learn, Prophet, and XGBoost.

By using Python in our data-based products and services, we’ve managed to help retailers reduce their costs and increase their profitability by a significant margin.

Other Benefits of Using Python in Retail

Let’s also talk about some indirect benefits of using Python for retail analytics. First of all, Python is a mature and well-known programming language with applications in a variety of industries. If you take a look at the history of Python , you’ll see that it’s been around for a long time.

In a software tool, long-term stability is of great importance and should never be ignored. When we write a code base or create a product, we invest time, energy, and money in it. We need to make sure it will be reliable in the future. When we use Python, we don’t need to worry about going out of date, at least for the near future.

On the other side of the table are skills. There is obviously no shortage of developers with Python skills. We can easily find people with Python knowledge. Even if we can’t, we can teach our employees Python in a short time because it’s an easy-to-learn language with an intuitive syntax.

Last but not least, the Python community is very active and constantly developing new tools or improving the existing ones. This is important because technology changes at a rapid pace. Keeping up with the new technology is only possible with such an active community of users and developers.

Ready to Use Python in Retail Analytics?

Data is at the heart of retail analytics; the success of a product or service depends on how well its team processes and analyzes the data. Python is a well-established and highly performant tool in the data science ecosystem. Businesses that want to leverage the power of data for profitability and long-term benefits should adopt Python into their workflows.

If you are working or planning to work with retail analytics, we offer the Python for Data Science track that consists of 5 interactive Python courses . It’s designed for complete beginners with no background in IT; even if you have no Python experience, you can still learn a lot from this track.

Thanks for reading and happy learning!

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Case study : Applying Data Science tools and techniques to eCommerce

The participants greatly appreciated that the trainers were both knowledgeable and approachable so that everyone felt at ease to ask any questions they had. We would happily recommend Cambridge Spark as a training provider in the Data Science space.

Alexandra Tcheng, Data Scientist at Carrefour

In this case study, we aim to address:

1)   How Data Science is currently applied within the Retail (eCommerce) industry

2)   How Cambridge Spark worked with Carrefour to deliver a bespoke Data Science with Python training course, with the aim of developing their team’s understanding of some of the core Data Science tools and techniques, and proficiency in Python

The bespoke course that we ran for Carrefour was the   Data Science in Production with Python.   Get in touch to see how it could benefit your organisation.

Introduction

It’s no doubt that eCommerce has been an uprising giant in the retail space, across multiple verticals. 

In 2021 alone, retail e-commerce sales amounted to approximately 4.9 trillion U.S. dollars worldwide . This figure is forecast to grow by 50% over the next four years, reaching about 7.4 trillion dollars by 2025. As technology and consumers' tastes have evolved over the years, the user experience (UX) of consumers has played a pivotal role in staying relevant and competitive in the market space. One of the drivers behind this is   Data Science .

Masses of cross-platform data

The nature of eCommerce sees customers going through multiple touch-points; from clicking on an advertisement, to clicking through various products of interest, right through to making a purchase and leaving a product review. 

The data from these integrated platforms mean that eCommerce retailers sit on a goldmine of data, ready to be mined and provide actionable insight to decision-makers— helping retailers to reduce their bottomline through the effective and strategic targeting of new and existing customers. 

Figure one:   decision-making process consumers go through when purchasing online

Data amassed from the various platforms can help build a picture around the retailer's consumer personas, their purchasing habits, and which mediums are effective at moving them along the stages of the decision-making process and be successfully acquired as a customer — as well as how long this process generally takes.

Equally, this data is particularly valuable for providing insight into which prompts and steps can be taken to prevent customer churn and basket abandonment.

Data science in retail creates a commercially-minded collaborative environment to discover and realise new opportunities.

-   Information Age 

Applications of Data Science to eCommerce businesses

Data Science serves to help eCommerce retailers with two strategic objectives:

1.  The acquisition of customers

2.   The retention of customers

Figure two:   Data Science in practice within eCommerce retailers

Applications of Data Science in eCommerce

Predicting customer churn.

Customer churn - also known as attrition - occurs when customers stop doing business with a retailer, or when a subscriber cancels their subscription. A   predictive churn model   can help identify which of your customers will stop engaging with you and why.

Examples of churn can also include: 

• Closure of an account
• Non-renewal of a contract or service agreement
• The decision to shop at another store
• Use of an alternative service provider

Segmenting and highlighting your star customers

Depending on a variety of attributes and behaviours, retailers can highlight which consumers require more nurturing and bespoke pricing strategies to prompt them to checkout their items using customer segmentation models.

For example, retailers could apply a simplistic   RFM scoring model , taking into account the days since the consumers' last purchase (recency), the total number of purchases (frequency) and total money they've spent (monetary value) to classify them. From there, retailers can strategically interact with consumers and suggest offers or prices which typically convert those with similar profiles to them.

Retailers can also use   customer lifetime value (CLV)     modeling to understand which channels their most valuable customers are coming from, what types of engagement are associated with them, and then incentivise these behaviours and invest more into channels which deliver the best return on investment (ROI). 

Driving sales with intelligent product recommendations

eCommerce retailers are able to   apply different algorithms   and techniques to enable their   recommender   systems   to generate recommendations.

The most popular ones are:

• Association rules (recommendations based on their presence alongside other products)
• Collaborative filtering (recommendations based on customer profiles, ratings and reviews)
• Content-based filtering (where product recommendations are based on an analysis of products and finding similarities with other active users)
• Hybrid filtering (a select mix of the above)

Extracting useful information from customer reviews

Retailers are able to use Natural Language Processing (NLP) techniques to   scrape their customers' reviews   and extract useful information about why they're leaving reviews are positive or negative - enabling them to prioritise any feature or product updates, in order to maximise satisfaction/user experience.

Forecasting demand

Retailers are able to run time series machine learning models, allowing them to:

• Optimise staffing levels to ensure they have the optimum amount of resource to fulfil orders
• Manage inventory more effectively
• Forecast future product demand for their current product offering and new product launches

Optimising prices

Applying Machine Learning in eCommerce stores is an effective approach to   determining the optimum price   for each product and service, especially when compared to the traditional method of pushing a product out with a set price, only to apply aggressive markdowns to products not performing as well due to high price points. 

Price optimisation machine learning models are able to take into account:

• Competition
• Company objectives
• The weather/season

These factors ensure that the initial, best, discounted and promotional prices that are determined by the machine learning models are optimal.

Looking for Data Science training for your team? 

Get in touch now

Case study: Carrefour

a)  Predict the churn of customers Carrefour were looking for training to develop their Data Science team's knowledge of the advanced Data Science tools and techniques they're able to use, in order for them to:

b)   Enhance their customers' shopping experience by enabling greater personalisation of suggested products and communications.

To approach the skill gaps presented to us, we were able to implement a bespoke Data Science, outlined below:  

• Machine Learning - Ensemble models
• Technical   - Writing production-ready code
• Object-Oriented Programming - Classes, method, state - Inheritance - Anti-patterns - Single Responsibility Principle - Coupling and Cohesion - Encapsulation
• Functional Programming constructs in Python - Filter, map, reduce, zip, izip, partial - List and Dict comprehensions - Mutable and immutable objects
• Modules and Packages - Packages, sub-packages, modules, __init__.py - Project structure, imports - Installable packages, setup.py, command-line entrypoints
• Unit Testing in Python - Unittest, assertions, set up - Testing best practices
• Code Quality and Tools - Pylint, pep8, mccabe - Writing Pythonic code

Training outcomes

Alexandra Tcheng, Data Scientist at Carrefour, said:

“We really enjoyed the first day, where we explored decision trees and the ways we could use them.”    “We especially liked the fact that the training focused on industry best practices whilst building upon modularised training. We found the hands-on exercises helpful as they enabled us to see how learnings could be applied practically on real projects.”   “The participants greatly appreciated that the trainers were both knowledgable and approachable so that everyone felt at ease to ask any questions they had. We would happily recommend Cambridge Spark as a training provider in the Data Science space.“

Interested in training for your teams?

Whether you're looking to train 5 people or 100 people, we have a variety of scalable training solutions to help you address a wide spectrum of training needs within the fields of Data Science, Artificial Intelligence, or Software Engineering.

About Carrefour

Over the past 40 years, the Carrefour group has grown to become one of the world’s leading distribution groups. The world’s second-largest retailer and the largest in Europe, the group currently operates four main grocery store formats: hypermarkets, supermarkets, cash & carry and convenience stores. The Carrefour group currently has over   9,500   stores, either company-operated or franchises.

Get in touch with a Cambridge Spark