What happens when data science meets retail?

Is DS the same thing as AI? Well, let’s see.

Data science stands at the crossroads of artificial intelligence (AI) and statistics. So, not all AI is DS, but there’s a huge overlap.

AI = DS, when it’s used to gain insights. Therefore, all predictive analytics and recommendation systems are data science.

But much of AI stuff isn’t DS. For example, speech recognition, natural language processing (NLP), robotics, etc.

The same way, DS isn’t always about AI. For instance, you can do some basic data science even in MS Excel.

Let’s sum this up:

Generating cat images is not data science, but generating a sales report is.

No wonder 70 percent of the companies said they focus on growing revenue by leveraging AI and related technology.

Let’s remember Walmart, one of the largest retailers, which is undergoing a digital transformation.

In general, it helps businesses personalize marketing campaigns, improve customer experience, and drive sales as a result.

Companies that excel at personalization generate 40 percent more revenue from those activities than average players.

The most common examples of successful data science use are Amazon and Netflix.

Netflix analyzes user preferences and habits globally. This allows the service to suggest interesting content for viewers to watch.

As for Amazon, you can easily search for and add products to your cart, with the platform also providing recommendations for similar items.

Price optimization

Finding a price point that works for both customers and the retailer is crucial. Pricing decisions are based on a variety of factors:

• Demand;
• Manufacturing costs;
• The average customer’s purchasing power;
• Customer behavior;
• Competitors’ promotions;
• Market trends;
• Other.

What’s more, setting the right prices is driven by precision, attention to detail, and agility, all of which digital pricing transformations (using digital tools and techniques to redesign pricing strategies) facilitate, according to McKinsey.

“Done well, technology-enabled pricing transformations can enhance pricing to generate 2-7 percentage points of sustained margin improvement with initial benefits in as little as 3-6 months.”

Therefore, data science takes price optimization to a whole new level, allowing companies to analyze customer reactions to promotions in real-time, including sales, discounts, and advertising campaigns.

Using regression analysis and predictive modeling, retail businesses can test multiple pricing to forecast demand at different price points for a specific product. It helps them figure out the best rates for different client segments and situations.

Also, dynamic pricing techniques allow retail businesses to modify prices in reaction to shifts in competition, demand, or other variables.

As a result, they optimize sales and profitability while staying competitive in the market.

Common examples that use data science for price optimization are various airlines (e.g., Delta, American Airlines).

They use dynamic pricing algorithms based on factors like demand, time of booking, and historical data.

It enables them to maximize revenue by dynamically adjusting ticket prices in response to market tendencies and customer behavior.

Another great example is Kroger’s mobile app, which uses data science to send customers personalized item recommendations, including prices, for additional insights.

Inventory management

In order to stock up appropriately, retailers try to predict how much of a certain item customers would wish to buy during a specific time frame.

Sales trends are analyzed to identify patterns and develop plans to optimize product delivery and inventory management.

Machine learning (ML) algorithms and data analysis platforms enable retailers to forecast demand and optimize inventory levels, reducing stockouts and overstocking.

What’s more, companies also use demand forecasting to help them store goods in case of emergencies.

For instance, Zara, a global fast-fashion leader, uses data science to optimize inventory management.

By monitoring real-time sales data and customer feedback, Zara adapts production strategies, restocking popular items and minimizing excess inventory for less popular ones.

One more good example: Ralph Lauren and Michael Kors used machine learning to sell fewer garments via markdown prices and get the most of their inventories.

Fraud detection

In the first half of 2024, UK businesses were greatly impacted by an increase in retail fraud.

The analysis of data from NFIB reveals a 545 percent (!) increase in reported cases compared to the same period in 2023.

The financial impact has been severe, with losses soaring to 5.4 million pounds.

According to the global report, organizations lose 5% of revenue to fraud each year.
So, it’s highly important to prevent those losses and also build customer trust. Data science assists well in these goals, too.

One of the most common frauds are return fraud and credit card fraud.

Return fraud is an online scam type: a person buys an item from a retail store with the intent to return it immediately. They can also use duplicate receipts to get money back.

Credit card fraud is a type of identity theft. It involves unauthorized access to another person’s credit card info in order to charge purchases or withdraw funds from the account.

To detect suspicious activities, DS uses vast amounts of data to analyze operations, user activity, and transactions in real time.

By implementing advanced algorithms and ML techniques, it can quickly identify patterns and anomalies that may indicate fraudulent behavior.

This proactive approach allows organizations to take immediate action to prevent financial losses and protect their reputation.

Moreover, it increases transparency in internal processes, reducing the likelihood of employees participating in fraud.

Supply chain management

Supply chain management is a significant part of the retail operations. It includes demand forecasting, inventory management, route optimization, risk management, and supplier administration.

We’ve already discussed how data science ensures effective demand forecasting and inventory management above.

Let’s see how it helps with optimizing routes, managing risks, and suppliers.

Route optimization

Data science can optimize logistics routes and schedules by considering variables like shipping information, traffic patterns, delivery windows, and meteorological conditions.

As a result, this approach reduces carbon emissions, speeds up deliveries, and lowers transportation costs for retail businesses.

For example, DHL Supply Chain uses data-driven decision-making for route optimization, which enhances their overall productivity and improves decision-making processes.

Risk management

Risk management is crucial for identifying potential hazards in the supply chain and developing backup plans.

According to the Mckinsey report, 83% of respondents experienced at least some raw material shortages over the past year. 90% say that they want to further increase resilience, and almost three-quarters expect to increase the budget allocated to resilience-related actions.

Data science can help identify potential hazards like delays in delivery or commodity flow disturbances, which results in risk reduction.

Also, it ensures business continuity, minimizes output and income loss by streamlining quality control procedures and reducing downtime.

Supplier administration

Data science aids in assessing supplier performance and identifying areas for improvement in supply chain management.

By analyzing data like delivery times, quality indicators, and pricing, data scientists can identify well-performing suppliers.

This enables businesses to enhance supply chain procedures, negotiate better contracts, and improve overall efficiency.

Other use cases

We’ve discussed the most common uses of data science in the retail industry. Naturally, there are more to them.

Some retailers use DS to assist in talent acquisition, merchandising, and choosing a new store’s location.

For example, Starbucks uses data science techniques to select a location for launching a new affiliate based on traffic, demographics, and customer behavior.

Other DS applications include customer lifetime value prediction (CLV), social media trend forecasting, warranty analytics, and so on.

Ultimately, data science in retail can provide valuable insights that help businesses make informed decisions and stay competitive in the market.

Increased sales & revenue

Increased sales and revenue are achieved through targeted marketing strategies and personalized customer experiences.

Additionally, data science helps retailers optimize inventory management and pricing strategies to maximize profitability.

Improved customer satisfaction

Efficient company management, proper stocking of items, and adequate staff can enhance customer satisfaction by ensuring timely and accurate service.

What’s more, better personalization and loyalty programs contribute to customer retention.

Reduced costs

Retail costs can also be reduced thanks to data science. DS has allowed companies to better understand consumer behavior and make more informed pricing decisions.

As a result, customers can now enjoy lower prices on a wider range of products.

Better decision making

By offering specific, fact-based insights, data science revolutionizes decision-making procedures, which leads to more strategic and useful results.

Competitive advantage

The more customers retailers have, the better.

Yet, it is not only quantity that matters, but also quality. Loyal customers drive repeat purchases.

Therefore, data science provides a competitive advantage by boosting customer retention and innovating operational processes.

Privacy policy

Develop and enforce clear privacy policies, which outline data collection, processing, storage, and sharing within the organization.

Also, provide employee training on best practices, security protocols, and compliance requirements to foster a data security culture.

These are the first steps you should take to avoid data leaks. For more details, check out this article.

Lack of skilled professionals

Successful data science projects require data analysts and scientists to have problem-solving capabilities and a deep understanding of ML and AI algorithms.

Yet, companies face a talent shortage in data science, struggling to find the right team with in-depth knowledge and domain expertise.

This is because plenty of applicants are underqualified. They simply don’t have relevant data backgrounds. No STEM degree, just a 3-month boot camp certification.

So, they often know the basics, but not much more.

But data science is a real science. To be good at it, strong statistical and math knowledge is needed.

To hire top professionals, reaching out to DS companies with technical expertise and business understanding is a viable option.

Choose a company you can trust and do some background research on the data scientists that will work with you.

Implementation costs

DS implementation costs include infrastructure expenses, frequent software and hardware upgrades, and managing project scope, which can lead to budget overruns.

Yet, this can be easily tackled with the right approach.

To reduce costs and ensure efficient project management, establish clear objectives, consider cloud services, and implement agile methodologies.

It’s important to encourage team collaboration, monitor metrics regularly, and invest in continuous learning to optimize resource allocation.

Also, most data science projects only take 2-3 months to complete, so they’re not too expensive. You don’t always need a big team, even 2 specialists can be enough if they know your industry.

Finally, not every company needs data science. But if you do, the investments return fast.