The future of analytics & AI in retail: the difference between descriptive, predictive & prescriptive analytics

Ever wondered why some companies are more agile and capable of leveraging customer data to navigate uncertain marketing landscapes? 

The secret is in today’s hottest and trendiest topic in the tech world: Predictive AI models.

Picture this: while your competitors scramble to react after customers have already jumped ship, you’re being proactive about identifying customers who are most likely to churn. 

While they struggle to find a way to make up for lost revenue, you’re seeing more and more profitability coming from at-risk customers who you managed to retain. 

But here’s the real magic: when unexpected changes or market conditions throw their predictions off course, an AI-powered company will use the models as a compass, quickly adjusting their marketing and sales strategies to stay on track. 

Do you want to harness the power of AI to foresee customer behavior, anticipate market changes, and strategically pivot your approach?

Today we’re looking at descriptive, predictive & prescriptive analytics and how they can unleash the potential of data-driven decision-making and unlock a new era of marketing and sales prowess. 

The future is bright, and AI is leading the charge – let’s dive in!

The Role of Analytics and AI in Shaping the Future of Retail

People in different cities, such as Rome or Milan, have specific wine preferences, varying by season and local producers. 

Even if you zero down and strive to reach people with your wine products, complementing the enchanting ambiance of a warm evening in Rome, preferences will still differ, even within neighborhoods in Rome. 

And this is just the beginning. 

Humans are becoming more complex than ever. Any possible product is within their reach, and their choices are infinite – how do you deal with this unparalleled level of market sophistication?

The current frontier for analytics and AI is optimization through personalization and localization. 

With their help, retailers will be able to navigate this complexity and extract more value from pricing, promotions, and assortment decisions in every store’s aisle and every website page.

Over the next three to five years, leading retailers will revolutionize the online retail playbook.

The norm will be approaching access to consumers with customized recommendations and offers based on their purchase history, location, online searches, time of day, social media activity, and even their physical aisle location.

Each shopper may receive unique discount coupons weekly, and analytics tools will employ artificial intelligence to continuously improve the algorithm based on the impact of these offers.

As retailers clean and regularly update their customer data, they will unlock increasingly powerful insights. 

Rather than needing to make constant assortment changes in every store or season, prescriptive analytics will empower retailers to focus on a few hundred options alone. 

This highly targeted approach will impact their most valuable customers and vendors while allowing for quick adjustments in online stores.

Commercial and category management teams will collaborate more closely with operations, store management, and IT departments. 

They will also work with vendors as strategic partners to achieve shared goals of serving customer needs by developing mutually beneficial category strategies.

The new tools not only empower retailers in improving pricing and assortment but also provide valuable insights on:

• store location selection
• optimizing store layouts to cater to local shoppers
• personalizing the online shopping experience for each customer, 
• and enhancing advertising, purchasing, inventory management, and shipping efficiency

Today, only a handful of retailers are exploring the challenges and implications of monetizing these insights.

We’re talking about the best-in-class pioneers, trailblazers, and mavericks of this Arena.

Tomorrow? 

The Era of Big Data is quickly wrapping around retail – online, offline, or a hybrid system. Whether you embrace or fight it, the destination will be the same. 

Yet, your survival during this journey depends on your agility and openness to accept this shift, adapt, and join the players who already take advantage of AI in analytics.

The Power of Predictive Analytics: Forecasting Customer Behavior and Market Trends

But why would you fight it? Why would you deny yourself a head start in growing your business? If you had a tool allowing you to excel or progress more quickly than competitors -would you really ignore it?

Customer Data volumes are snowballing quicker than your ability to analyze this data.

Additionally, there is a shortage of data science talent in the market. 

Competition grows by the minute as consumers learn to compare products and prices across a broader range of brands – including online retailers and specialty stores. 

Predictive analytics are helping level the playing field for all retailers – particularly benefiting hybrid companies. 

These tools provide timely, customized, and detailed recommendations that category managers can combine with their real-world experience and business knowledge to generate excitement around their products. 

Analytics enables you to automate more of the data science tasks that were previously performed manually, expanding your planning and management capabilities. 

Predictive Analytics and Customer Behavior

Imagine what would have happened if Henry Ford’s son had never recognized the company’s need to hire new talent. We would have never witnessed and enjoyed the Mustang.

Truth be told, predicting and adapting to market trends has always been essential for business success. 

But we couldn’t always rely on almost real-time information extracted from customers’ behaviors.

Predictive analytics is pivotal in unraveling the complexities of customer behavior and market trends. 

Analyzing vast volumes of historical data is similar to scouring through consumers’ minds and deciphering their preferences and purchasing habits. Maybe even predicting their future actions. 

These insights are instrumental in developing:

• targeted marketing campaigns
• personalized product recommendations
• unique customer experiences

Evidently, AI-powered analytics will never fully replace the human mind. Each brings unique information and abilities that complement one another.

In fact, research shows that, in complex activities like retailing, the synergy between humans and machines is far more potent than working in isolation. 

It’s only a matter of combining the two sides’ strengths to skyrocket your performance in the long term.

Exploring Advanced Analytics Techniques: Machine Learning and AI in Retail

The power of predictive analytics in forecasting customer behavior and market trends cannot be overstated.

Yet, you may want to play the Devil’s Advocate.

Or you’re fascinated by the subject and what to explore it further.

We prepared a series of ideas on how ML and AI can revolutionize the Retail Industry.

Prediction Models Transforming Strategy

Let’s explore how prediction models are revolutionizing the way companies strategize. 

Take, for example, a global trading firm involved in the sourcing and distributing of commodity bulk chemicals. In early 2019, they started using AI-based prediction models to understand the buying processes of their clients. 

The process revealed that quality-related factors were crucial for getting picked over the competitors. 

Armed with this insight, they adjusted their approach accordingly.

However, by May 2020, the firm realized its AI-model predictions needed to be revised. 

Upon closer analysis, they found that delivery-related terms had become better predictors of being short-listed by clients. 

Responding swiftly, they successfully switched their engagement model globally. 

AI predictions allowed the firm’s leaders to quickly adapt their marketing and sales strategies to align with market shifts – rather than relying on macroeconomic data or quarterly revenue reports.

In this example, the company set specific goals for the AI model, focusing on improving overall business outcomes, not just providing accurate predictions. 

By leveraging historical data, AI models give companies a deeper understanding of the relationships between their actions and market or customer responses.

Understanding the Role of Feedback Loops

Traditional marketing and sales needed the concept of feedback loops.

AI prediction models excel at capturing trends at a granular level, such as individual transactions. 

These models’ field-level insights empower companies to update and fine-tune their marketing and sales strategies swiftly.

I.e., AI prediction models enable you to bridge the gap between strategy and execution.

Transforming the Segmentation Process

The focus on feedback loops is reshaping the practice of segmentation as well. 

Traditionally, segmentation involves identifying groups of customers with everyday needs and characteristics to tailor products, identify target customers, and design engagement strategies. 

However, AI-based prediction models are taking you to the next level. 

To fully grasp the concept of this advancement, think of pay-to-play games. 

The trial version is fun to play around with while waiting for the bus. But you’re going to need the full version to beat the Boss.

It’s the same with traditional vs. AI-powered customer segmentation.

For example, an AI model may determine which customers are better served by telesales teams. Or which customers are more likely to respond positively to specific price promotions. 

You can utilize AI model predictions to allocate appropriate marketing and sales resources to seize each demand opportunity effectively.

With the exceptional targeting capabilities of predictive models, it becomes easier to align organizational capabilities with customer preferences. 

This is especially valuable in rapidly changing environments where market conditions and customer behavior evolve faster than organizational capabilities can adapt.

Overall, prediction models powered by AI are revolutionizing business strategy.

They enable companies to make data-driven decisions, close the strategy-execution gap, optimize marketing and sales efforts, and uncover valuable insights for success in an ever-evolving marketplace.

Yet, not all prediction models are created equal. 

Let’s move on analyze how different ways to analyze data impact how you do business.

Enhancing Customer Experience: How Descriptive, Predictive, and Prescriptive Analytics Drive Personalization

There are three different ways to analyze data, each involving machines in various roles. 

Descriptive: Understanding the Past

Descriptive analytics, also known as “business intelligence,” helps managers understand past events by looking at It relies on specific and objective historical data patterns.

It uses tools like dashboards to show performance information, which helps managers make decisions based on previous events.

Descriptive analytics focuses on using past data to guide future actions. 

However, because, as humans, we can’t process large amounts of detailed data, data people often rely on summarized information, which can lead to making too broad decisions or having to guess how past trends continue in the future.

Descriptive analytics primarily relies on internal transaction data, which is readily available and inexpensive, as opposed to external data, such as customer information or market surveys, which is more expensive and time-consuming to obtain and analyze. 

Managers often combine this data with their own experience and intuition. 

Yet, this approach depends on individual managers’ ability to avoid biases and not cherry-pick data supporting their views.

Considering all these fallbacks, does descriptive analytics help retailers? 

Yes. 

An analysis of past purchase patterns and interactions reveals products customers love, content they find relevant, and even leaking points in the customer journey. 

For example, a clothing retailer may use descriptive analytics to find customer segments that predominantly purchase casual wear during summer. 

With this information, the retailer can create targeted marketing campaigns, promotions, and personalized recommendations for summer casual wear for those segments.

Predictive: Hypothesising on What Might Happen

Predictive analytics uses machines to predict likely outcomes based on different variables, helping managers choose the best course of action. 

For example, predictive analytics can forecast wins and losses, estimate how customers respond to marketing actions, or group customers into specific segments.

Similar to descriptive analytics, predictive analytics has limitations. 

For example, predicting the future or even individual variables with certainty can be challenging. Factors such as competition, supplier performance, and even the weather can alter results, so there isn’t 100% precision. 

Additionally, predictive models can only handle a limited number of variables, so they may only capture some factors influencing a decision. 

Making more detailed predictions requires collecting more detailed data, which can be challenging for businesses.

Lastly, while well-designed predictive models can lead to better financial results and business performance, building these models can be expensive and complex.

Predictive analytics enables retailers to anticipate customer needs and preferences, encouraging proactive decision-making. 

For instance, an online retailer may employ predictive analytics to forecast which products will likely be popular during the upcoming holiday season. 

Using the findings, he can then adjust the inventory accordingly, ensuring that popular products are readily available.

Prescriptive: Guiding Decisions

Prescriptive analytics involves machines making decisions based on managers’ goals. It uses large amounts of data to analyze market conditions and run experiments. The devices then provide specific guidance to managers, focusing on outcomes, risks, and costs.

Prescriptive decisions depend on market predictions for expected revenues and consider uncertainty and potential costs. 

Unlike predictive analytics, which only focuses on predicting outcomes, prescriptive analytics considers the level of uncertainty and adjusts decisions accordingly. 

For example, a retailer with low inventory and logistics costs might aggressively replenish their stock when they expect increased demand. However, a more conservative strategy might be better if logistics costs are high and the market is uncertain.

Well-designed prescriptive models can lead to better financial results and business performance. However, they require specific software, hardware, and expertise to set up.

Prescriptive analytics combines historical data with advanced algorithms to guide decision-making processes.

For example, a grocery retailer may utilize prescriptive analytics to optimize its pricing strategy for perishable items. 

By analyzing factors such as demand patterns, inventory levels, and competitor prices, the retailer can determine the optimal pricing strategy for maximizing revenue while minimizing waste. 

This might mean a unique discount close to the expiration date, product bundle offers, or a special campaign targeting people who love those items.

Case Studies: Two Real-World Examples of Successful Implementation of Descriptive, Predictive, and Prescriptive Analytics in Retail

A serious trap in any data-related research field is the sheer fascination you fall into when unearthing obscure insights. 

We call it analysis paralysis – overthinking or overanalyzing a situation to the point where you make no decision.

Let’s look at practical initiatives based on your analytics results and how they facilitate long-term business growth.

Marketing: Understanding Customers and Innovating Products

You’ll find predictive analytics at work on every significant eComm, helping retailers set the best product prices and sending personalized offers to prospects. 

Even offline retailers use analytics to optimize their pricing strategies and target specific audiences.

Predictive models aren’t just useful for pricing, though. 

They also help organizations develop new services and products based on customer behavior. 

In a recent report, 46% of businesses mentioned using analytics to create innovative products and generate additional revenue streams. 

The retail and healthcare sectors are leading the way in this area, with 58% and 57% utilizing analytics for product development. 

Marketing is currently one of the primary applications of predictive analytics in enterprises. 

This is partly because many marketing tools and systems already have advanced analytics capabilities, often leveraging artificial intelligence. 

In fact, predictive analytics has quietly become a part of many organizations, driving marketing strategies without them even realizing it.

Key takeaway: predictive analytics will transform your marketing practices, enabling you to understand your customers better and innovate products and services. 

Whether setting the correct prices, sending personalized offers, or gaining insights for product development, predictive analytics is becoming an indispensable tool for staying ahead in today’s competitive market landscape.

Supply Chain Management: Ensuring Efficient Operations

Predictive analytics also play a crucial role in operations, logistics, and supply chain management. 

Just think about it – how often did you roll your eyes and leave a site frustrated because your favorite product was out of stock? Couldn’t they think they’d need to stock up on sunscreen at the beginning of the summer season?!

Managing the supply chain means making informed purchasing decisions. 

• How much to buy? 
• When to buy?
• From where should they source the products?

Buying too much can result in tied-up funds which could be better utilized elsewhere.

Buying too little exposes you to the risk of losing business, customers, and revenue opportunities.

Statistical models that accurately predict future events take the edge off, giving you clear guidelines for all these issues.

Key Takeaway: Regarding supply chain, predictive analytics will help you make informed purchasing decisions and ensure efficient operations. With the ability to predict orders in real-time, you can optimize their supply chain processes and improve overall efficiency.

Wrap Up

As a final thought, let’s consider how humans can combine their creativity and imagination with AI’s automation capabilities. 

Humans excel when data is limited, and their intuition is crucial in unfamiliar contexts. ML & AI shine when making decisions, even if the data is sparse or the tasks are repetitive. 

They mainly thrive in environments flooded with vast amounts of rich data.

It’s essential to recognize that machines have their limitations too. 

They may struggle to generate relevant outcomes when faced with insufficient data, highly ambiguous situations, or conflicting objectives that hinder data interpretation. 

Essentially, it’s about finding the right balance between human intuition and ML capabilities. 

By harnessing the power of both, you can confidently tackle complex problems, leveraging data-driven insights to drive success.

Good luck!

Frequently Asked Questions about Descriptive, Predictive, and Prescriptive Analytics