Data Science Automation in Retail [Use Cases, Advantages, and Disadvantages]

Data science is the study of data to obtain valuable insights to make informed business decisions. The entire data science project cycle involves many steps, such as data ingestion, cleaning, model building, data visualization, etc. These processes can be time-consuming and require the data scientist or data analyst to wait until one process is completed before moving to the next step. Data science automation can minimize this lost time by handling these processes without the need for human intervention.

This article discusses data science automation, its use cases, advantages, and disadvantages. We also discuss how Revelate provides data fulfillment solutions to automate data ingestion or transfer using different data science tools.

What is Data Science Automation?

Automation refers to automating repetitive tasks using certain tools and techniques. This helps complete routine and complex tasks quite quickly. Using suitable tools and methods, data science automation automates data collection, data processing, model building, and other data science tasks.

Data Science Automation is quite popular in the retail and eCommerce sectors. They use different data science tools and techniques and techniques to automate various tasks rather than having to perform them manually.

Understanding Automation in the Data Science Life Cycle

The life cycle events in a data science project consist of the following tasks:

1. Data collection
2. Data cleansing
3. Exploratory data analysis
4. Model building
5. Model evaluation
6. Model deployment and maintenance

These tasks can be time-consuming and repetitive to perform manually. To increase efficiency, repetitive tasks that don’t need human intelligence can be automated. Tasks such as data collection and processing can be automated, reducing data scientists’ workload to a large extent.

The different steps that can be automated in a data science project are discussed in more detail below.

Data Collection

High-quality data is the foundation of any data science project. Adequate time and resources must be dedicated to ensuring data quality. If data is not carefully analyzed once collected, it can lead to unreliable results. Data collection is a rigorous process, and automating it could save a lot of time for data scientists in the long run. Whether the data is from surveys or internal operations, automation can greatly benefit the data science project.

• Companies often collect data from surveys, interviews, consumer complaints, reviews, and social media. Collecting data from all these data streams and storing them in the right format requires a lot of effort and time.
• Companies can use survey tools like JotForm, Zonka Feedback, Magpi, and Fulcrum to collect the survey, reviews, and complaints data in the right format. All these tools help automate the data collection process from user-generated content.
• Companies also collect data from internal operations such as logs and event data like user clicks and transactions. Automation tools like Apache Kafka can stream and store event data at a specific location.
• Companies might also need to move data from one location to another. This requires utmost care because business data is often sensitive, and transferring it through the internet can lead to data breaches. In such cases, data science platforms like Revelate can provide specific data science tools to move data from one location to another with proper data security.

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Data Cleansing

Data cleansing is one of a data scientist’s most time-consuming and difficult tasks. The data cleansing step includes formatting, error correction, and data preparation as the necessary steps. Data scientists do these steps manually using software like Microsoft Excel and programming languages like Python, Ruby, and SQL. However, spotting the errors and correcting them when done manually takes a lot of time and is quite repetitive for data scientists. Automating the data cleansing process lessens the burden for data scientists, so they can focus on tasks that need to be done manually.

• Most data generated from internal events or website clicks are stored in JSON format. This data cannot be analyzed directly and needs to be converted into a tabular format. Data scientists can use programming languages such as pandas or PySpark in Python to create data science tools that can repeatedly convert the incoming JSON data into the tabular format without requiring manual intervention.
• For processing data generated from surveys, reviews, and complaints, data science tools can be used to automate tasks such as grammar correction and classification of reviews and complaints using sentiment analysis.
• Data cleansing automation tools such as OpenRefine, Trifacta, WinPure, and TIBCO Clarity can also process data instead of creating tools from scratch.

Data Exploration

Data exploration is used to find any underlying patterns in the dataset that can help generate useful insights. In this step, the data is carefully analyzed, which helps get an overall sense of what’s happening within the data that we are dealing with.

• Data analysts employ statistical tools to find patterns in data to better understand the data’s nature. They manually load the dataset into tools such as SPSS and SAS. Then, they use it to calculate different metrics to analyze and obtain useful information from the data.
• Instead of manual operations, data scientists and data analysts can use certain data science tools and libraries that automate the data exploration process. This makes it easier for data scientists to understand the data with little effort.
• Some data science automation tools and libraries for performing exploratory data analysis are DataPrep in Python, Autoviz, which provides automated visualization of data; speedML, which has a specific module dedicated to data exploration; and others. These data science automation tools use graphs and the distribution of data to understand and compare the data in the datasets. Moreover, they can also handle missing values and other anomalies in the data very easily.

Model Building and Use of Automated Machine Learning (AutoML) Modeling

A data science project involves iterative, rigorous, and time-consuming modeling tasks. After data cleaning, Data Scientists tend to apply different machine learning models to the data set. It includes changing algorithms, hyperparameters, or even the sample dataset used for training. Keeping account of all the iterations of the modeling process to find the best machine learning models is a tedious task.

Automated Machine Learning (AutoML) Modelling is a process of automating the model selection and training tasks in a data science project. AutoML is a collection of tools and libraries that automates the model selection and training process. To be precise, it automates machine learning models’ selection, composition, and parameterization.

• AutoML is an open-source library that simplifies each machine-learning process, from processing a raw dataset to implementing an effective machine-learning model. Therefore, AutoML provides tools that help us identify the best machine-learning model for a given dataset.
• Some of Python’s most popular AutoML libraries are LightAutoML, AutoKeras, and Auto-sklearn. These libraries not only help in the data preparation and model that works best for a dataset, but it also learns from the models that did well on related datasets and automatically generate a group of top-performing models as a part of the optimization process.
• various no-code AutoML tools such as MakeML, CreateML, Google AutoML, DataRobot, and MonkeyLearn help data analysts automate modeling tasks in a data science project, even if they don’t know how to code.

Model Evaluation

Once a machine learning model is created, we must evaluate its accuracy and efficiency. Model evaluation employs several evaluation techniques to understand a machine learning model’s performance, strengths, and weaknesses.

If the model is working fine with its accuracy up to the mark, it is considered to be fit for the use case for which it has been created. Otherwise not. We can automate the model evaluation process using the AutoML tools for model building.

Data Science Automation in Retail and E-Commerce

Data science automation can be very useful in the retail sector. Next, we discuss some use cases of retail data science automation.

Customer Segmentation

Customer Segmentation is used to group customers based on their behavior and the likelihood of specific actions, events, or circumstances that will occur in the future. Using customer segmentation, a retail chain or an e-commerce platform can predict each customer’s probability to do any action, including a purchase, a repeat purchase, etc.

• When done manually, customer segmentation can be done only based on attributes of the customers that are directly visible to the data scientists, such as location, age, gender, etc. This process takes time and often leads to errors.
• Companies can customize the customer segmentation task using different classification and clustering algorithms in machine learning. These algorithms dig deep into the patterns in the data and can group customers using more nuanced information about their buying behavior.
• CRM tools like Google Analytics, Baremetrics, Piwik Pro, and Optimove facilitate automation in customer segmentation to a large extent. These tools and advanced machine learning models can be used for customer segmentation. They assist businesses in locating, organizing and utilizing the data required to develop useful consumer segments and help in data science automation.

Personalized Marketing

Customer segmentation identifies the customers in groups based on their behavior. However, personalized marketing focuses on providing customers with products and services that cater to individual customers’ preferences. E-commerce websites can target customers based on their tastes, behaviors, and areas of interest.

• Generally, personalized marketing is facilitated by collecting customer data and employing certain technologies and intelligent algorithms to identify each customer’s behavior. The data scientists use metrics such as the number of clicks by a user, time spent on the site, past purchases, views, and the customer’s wishlist to generate personalized recommendations.
• Companies can automate personalized marketing tasks using OptinMonster, SendinBlue, Drip, and HubSpot. These tools allow data collection and the creation of personalized recommendations for customers in an automated manner. In the backend, these personalized marketing platforms also use data science automation tools. However, companies don’t have to worry about how things work at the backend; they only need to focus on the results.

Price Optimization

Prices play a crucial role in the e-commerce industry. Due to ease of access, customers can easily  compare prices of products and services and then decide which they would like to purchase. E-commerce companies must ensure that their pricing is tempting and affordable enough for customers to purchase their products. At the same time, these companies still need to generate a profit, which typically means that a delicate balancing act must be performed at all times.

• For price optimization, data scientists consider several factors, including customer buying behavior, competitor pricing for the same product, price flexibility, customer location, and more. Data scientists collect all the information on the prices and the customers. After this, they use machine learning models to predict the maximum price a customer would be willing to pay for a product or service.
• If a company doesn’t have the resources or time to implement price optimization algorithms, they can use retail data science tools such as Vendavo, Prisync, Competera, and others. These tools can help companies generate profits and predictable sales by providing best-in-class pricing services.

AI Chatbots for Customer Support

Businesses often need to cater to customer requests and complaints to fulfill their expectations. These complaints and requests are often handled by customer executives who talk to the customer behind the chat application. Manual handling of the entire process leads to longer customer wait times and long working hours for customer executives. Companies can use AI chatbots to facilitate customer requests and complaints using chats.

• An AI chatbot is an automated computer program that mimics human communication using text messages, voice chats, or sometimes both. These chatbots are very effective in automating repetitive communications. The chatbot is fed common questions that customers ask, along with standardized answers. This helps free up valuable time for customer service representatives to focus on customers with more complex questions and support queries.
• AI chatbots are great for businesses as they do not have human limitations. They can easily connect with customers at any place and at any time. Moreover, they can serve a large number of customers at the same time. They can also engage with customers to help them stay on the website longer. These chatbots can also be integrated with social media platforms to increase user engagement. Some popular chatbots are Amazon Lex and Azure Bot Service.
• OpenAI, Google, and other companies have recently introduced large language models (LLMs) such as ChatGPT and Bard. These LLMs answer questions and prompts just like a human. E-commerce companies can train these LLMs for their use case and automate more complex customer service tasks using chatbots.

Inventory Management

Every business that sells products must maintain an inventory to meet demand. This applies to the e-commerce industry as well.

• Manual inventory management is complex and time-consuming. Automation reduces errors, and better inventory management and demand forecasting can be realized.
• The entire process of inventory management can be automated. Retailers can use QR codes on the products to track sales and existing inventory. This also generates structured inventory data to be analyzed for better inventory management. Data scientists can use time-series forecasting and cost analysis to maximize profits through better inventory management.

Warranty Analysis

Every customer who buys a valuable item expects it to have a definite lifetime. Due to this, companies often offer a warranty to assure the customer about the product quality and its lifetime of use.

Suppose the warranty period is too short compared to the typical usable lifetime of the product. In that case, customers will be dissatisfied if their product fails and they cannot receive support to fix it. On the other hand, if the warranty period is too long, an organization can lose money on repairs of older products.  . Therefore, it is crucial to establish a perfect warranty period that is long enough to provide a good product life but short enough to prevent unnecessary expenses.

Data science automation can help identify trends in product issues, the number of customers who return products, questionable or fraudulent customer behavior, average product lifetime, etc. These metrics can help decide the warranty period of the products in a manner that maximizes customer satisfaction and reduces costs for the company.

Location of New Stores

Retail chains can use data science automation to find the best possible locations for new brick-and-mortar stores. Normally, stakeholders manually decide the location of retail outlets by studying population data and competitors’ presence. This process can lead to errors due to prejudices, confirmation bias, or cultural bias.

The data science tools specifically designed for retail chains can use metrics like demography, per capita income, population, competitor presence, etc., to help identify locations for opening new outlets. These algorithms do not pose the risk of any bias and can help identify areas with the most sales and growth opportunities.

Fraud & Security

“In every seed of good, there is always a piece of bad.” While most consumers and retailers on a retail platform will do business with good etiquette, there might be retailers or consumers that engage in fraudulent activities. Data science tools can identify and freeze a user’s account if it finds any irregular transactions or buying behavior. Machine learning algorithms can also identify questionable behavior patterns, such as repeatedly purchasing and returning items, purchasing the same item in large quantities, etc.

Advantages of Data Science Automation

1. Time-saving: Data science automation can help companies save a lot of developer time. Data scientists can automate repetitive and time-consuming data cleaning and transformation tasks. This will allow them to focus on more important tasks like data exploration and data model building.
2. Consistency: Automated data science tools can provide a consistent approach to data analysis, ensuring that the same methods are used for each analysis. This reduces the likelihood of human errors and increases the accuracy and reliability of the results.
3. Scalability: Companies can use data science automation tools to process large volumes of data in a short amount of time. Automation makes it easier to analyze big data and conduct complex analyses that would be impossible to do manually.
4. Improved productivity: By automating data science tasks, data scientists can increase productivity and complete more work in less time. This can lead to faster and more accurate insights, improving decision-making.
5. Cost-effective: Data science automation tools can be more cost-effective than hiring additional staff to perform the same tasks. Additionally, using automated tools can help reduce the risk of errors and improve the accuracy of results, which can be cost-effective in the long run.

Limitations of Data Science Automation

While automation can greatly increase the efficiency of data science projects, there are also several limitations to data science automation. Some of these limitations are as follows:

1. Lack of creativity: Automation tools are great for repetitive and structured tasks. However, they are not very good at generating creative and innovative solutions. Companies must employ good data scientists to provide the creativity required to solve complex and novel business problems.
2. Limited data context: Automation tools are often limited in understanding the context of the data they are working with. This can lead to incorrect conclusions or misleading insights.
3. Bias in automation: Like any tool, automation tools are not immune to bias. If the data used to train these tools is biased, the results produced by these tools will also be biased. For instance, if the segmentation algorithm is trained on biased data with less accuracy, it will keep giving inaccurate results until the model is retrained.
4. Need for human interpretation: Even with automation, there is still a need for human interpretation of the results. Automated tools can provide insights, but it takes a data scientist to understand the full context and implications of the insights.

How Revelate Empowers Data Science Automation as a Data Fulfillment Platform?

If you want to start using automation in your business, you likely need to transfer your data from one place to another. Here, it is important to understand that data transfer can pose great challenges to data security and Privacy. In this situation, Companies like Revelate manage the complex procedures involved in moving data and ensure that companies have the right authorizations to do so safely.

Unlock Your Data's Potential with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started

Conclusion

The article discusses automation in data science, focusing on the retail and e-commerce industries. It provides an overview of the automated processes involved in data science. It highlights the areas that can be automated to improve efficiencies, such as data collection, cleansing, exploration, and machine learning. The article then showcases how these automation techniques can be applied to various e-commerce use cases, from predictive customer segmentation to fraud and security.

If you want to learn more about data science automation or transfer your data securely from one place to another, you should try Revelate. Book a free trial to learn more.