What is retail analytics? The AI-driven guide for 2025

Cost-of-living pressures and ever-increasing competition are making one thing clear to retailers in Australia and New Zealand: customer loyalty has rarely been more essential to their bottom line.

We’ve gathered global insights and trends from more than 8,350 shoppers and 1,700 retail industry decision-makers and compiled them in our latest Connected Shoppers Report .

What we’ve learned is that customer loyalty is getting harder to earn, with 74% of shoppers reporting they’ve switched brands over the last year. Rising customer expectations, along with the convenience of digital marketplaces, mean consumer devotion is more strained than ever. As a result, the modern retailer needs to work harder than ever to deliver the experiences customers expect.

But there’s hope for getting a handle on things: Retail analytics gives brands the tools to predict behaviour, personalise experiences, and thrive in a loyalty fractured marketplace. In this guide, we’ll explain how it all works, how to get started, and how AI can be the key to unlocking growth in 2025.

Retail analytics is the process of collecting and analysing data from various sources, such as CRM platforms, loyalty programs and point of sale (POS) systems, to develop a clearer picture of customer habits, uncover trends, and generate insights.

The goal is to support smarter decision-making and help retailers engage with and meet the needs of customers in real time. For instance, by combining online browsing behaviour with in-store purchase metrics, a retailer could use the findings to:

• Optimise inventory levels, avoiding understocking or stockouts
• Tweak product pricing to better reflect market conditions
• Display items on store shelves that customers often buy together
• Offer personalised marketing and offers to frequent shoppers
• Forecast future sales trends and plan supply-chain and marketing strategies

Currently, only 17% of store associates feel they have access to a unified view of customer data. Retailers consider difficulty accessing business insights one of their top five customer experience challenges.

Retail analytics, particularly when combined with AI, offers a way of bringing this data together and using it to anticipate demand and deliver experiences that foster true brand loyalty.

At its core, retail analytics follows a fairly simple but well-defined flow:

Data collection → Data analysis → Insights → Decisions

Let’s explore each of these components in more detail.

Successful analytics begins with great data. Businesses need to start by collecting information from across their business ecosystem, as well as the broader market, including:

• Ecommerce platforms
• CRM records
• In-store analytics
• Point-of-sale systems
• Loyalty programs
• Inflation metrics
• Seasonal trends
• Competitor pricing and promotions

Ultimately, the goal here is to gather every piece of available information and unify it in one easily visible place so it’s ready for analysis. Tools like Data Cloud can help with this by bringing all of a retailer’s data together into a single, connected view.

Once the retailer unifies its data, it’s time to process the data and analyse it for insights. Previously, this was a time-consuming manual task. But now, artificial intelligence and machine learning (ML) can analyse data and detect patterns in real time at a scale and speed previously impossible.

These tools can identify trends, forecast demand, segment customers, and make predictions by exploring millions of records simultaneously. For instance, the right AI model could constantly analyse demand to deliver proactive alerts of potential stockouts, or it could predict which customer segments would be most likely to churn and recommend offers to keep them on board.

Next, the business needs to gather all of the AI insights so they’re easy to put into action. This typically means visualising information to make it more accessible for stakeholders and teams.

An analytics platform like Tableau can help with this by transforming AI insights into visualised dashboards. This creates a central hub for employees to find the information they need to make decisions confidently and deliver outstanding experiences to customers.

All that’s left to do is to transform insights into action. Retailers can now use the information they’ve collected to adjust pricing, personalise promotions, optimise inventory placement or speed up service. All of this drives better customer experiences and ultimately boosts sales and loyalty over time.

There are four different approaches to retail analytics that serve distinct business needs and help answer specific questions about past, present, and future performance. Let’s take a look:

Descriptive analytics uses historical data to answer the fundamental questions of “what happened”, “where”, and “when”. It helps businesses uncover patterns and spot trends in previous quarters, laying the foundation for diagnostic, predictive, and prescriptive methods.

For instance, a retailer might use descriptive analytics to track best-selling products by season. It could then use this information to compare performance across stores, informing merchandising and inventory decisions for the upcoming season.

An analytics solution like CRM analytics can support this process by analysing and aggregating data at scale (and speed). This makes it easier for businesses to uncover patterns that would otherwise go unnoticed, supporting deeper insights and predictions.

Diagnostic analytics takes things a step further. It answers the question “Why did this happen?” by investigating the reasons behind outcomes. This gives context to performance and helps businesses plan ahead and avoid past mistakes.

As one example, descriptive analytics might reveal a sudden drop in sales in March. Diagnostic analytics could then determine why this occurred, whether a competitor offered a promotion, there were delays in the supply chain, local weather conditions kept shoppers at home, or some other factor was responsible.

Again, data is key here. Businesses need to use AI and ML to cross-reference multiple data sources and pinpoint the underlying factors driving trends. From there, platforms like Agentforce plus Tableau can deliver insights directly to key decision makers, setting the stage for deeper predictive and prescriptive analytics.

Predictive analytics uses the historical “what”, “why”, and “when” to predict what will happen in the future. This lets retailers use the information they’ve gathered to plan ahead, optimise their operations and fix issues before they arise.

For example, a retailer could use predictive analytics to forecast Black Friday sales based on past performance, as well as external factors like inflation, helping them ensure proper stocking and staffing on key dates. This type of analytics is also common in “what-if” analyses, such as when predicting how adjusting prices by 10% before a holiday sale would impact profitability.

AI solutions can elevate this type of analytics by running thousands of simulations and generating reports in seconds, something that would be impossible manually. This gives businesses the confidence to make data-driven retail decisions and act faster on consumer trends.

Prescriptive analytics uses all of the information gathered through previous methods to answer the question “What should we do next?”

This is the crown jewel of AI, automation, and big data. It not only predicts outcomes, but it also recommends the next-best strategy to achieve the desired result. In essence, it turns insights into action.

For instance, an agentic AI solution like Agentforce can analyse business data to draw insights and use this to suggest real-world strategies, such as:

• The best time for seasonal product markdowns to clear stock and maximise profits
• High-value customers who would be the best candidates for personalised offers
• Custom discounts that service agents can send to customers while on call
• Opportunities for upselling and cross-selling based on product affinity patterns
• Optimal inventory and staffing based on demand trends

All of this reduces guesswork and makes decision-making more automatic, freeing up time for teams to deliver exceptional experiences to customers.

We’ve touched briefly on the different ways retail analytics can support your business goals in 2025, but let’s take a moment to narrow down both customer-facing and operational benefits.

• Personalised customer experiences - Retail analytics gives businesses the tools to tailor interactions and promotions to individual customers. This lets businesses create better experiences, driving customer engagement and loyalty.
• Inventory management - AI-driven retail analytics forecasts demand, letting retailers anticipate trends and adjust inventory proactively. Doing so avoids expensive overstocks and stockouts, increasing efficiency while keeping customers satisfied.
• Efficiency - Ninety-four per cent of retail customer service professionals say AI saves time . Retailers can use analytics to optimise supply chains, fine-tune training flows, and speed up day-to-day tasks, improving efficiency across the entire organisation.
• Smarter pricing - By analysing market conditions, competitors, historical sales, and customer behaviour, retailers can adjust prices in real time, staying competitive while maximising profitability.
• Fraud prevention - AI analytics can also analyse transaction patterns to detect anomalies and potential fraud signals. This lets businesses be proactive and respond to problems quickly, minimising risk and potential revenue losses.
• In-store productivity - Eighty-one per cent of retailers say inefficient processes and technology drain store associate productivity. AI analytics flips this narrative, giving employees both the time and resources to work smarter and deliver better experiences at every stage of the customer journey.
• Decision-making - By allowing businesses to identify trends and respond to market shifts in real time, retail analytics removes the guesswork from decision-making, turning intuition into actionable insights.

The key to unlocking these benefits is to leverage AI and ML to their full potential. Eighty-five per cent of those we surveyed agree that AI advancements are transforming retail. Let’s take a look at how they’re making an impact and how this tech can help you meet your goals for increased engagement and loyalty.

According to our latest Connected Shoppers Report , leveraging AI is the number one opportunity available in the retail industry in 2025.

And retailers are already making headway. Currently, 76% are increasing their AI investments . Retailers that have already embedded AI into their operations are reaping the benefits, with 93% of retail customer service representatives reporting that it reduces costs and 94% saying it saves time on day-to-day operations.

In short, the opportunities to deliver hyper-personalised experiences and gain insights into every aspect of retail operations mean the shift from business intelligence to AI forecasting has moved from a nice-to-have to a full-blown priority.

As an example of how this looks in practice, Retail Cloud can unite all of your inventory, transaction and shopper data in one place, giving associates everything they need to complete every request and fulfil every order in one dashboard. From there, Agentforce can use this unified data layer to spot patterns, automate time-consuming tasks, surface insights instantly, and recommend the next-best action for associates in real time.

Previously, this was impossible to achieve without dozens of disparate systems and a team of experts to collate the information. But now, it’s an instant and ongoing process.

Related: Fisher & Paykel delivers luxury service at scale with Agentforce

So, what does the future hold? Let’s take a look at some of the ways retail analytics will continue to evolve in line with AI adoption and changing customer preferences.

• Unified commerce - Eighty-eight per cent of retailers say unified commerce is critical to success . In the coming years, unified data platforms will become the standard as businesses realise AI can only deliver its full potential when every customer, sales, and inventory touchpoint is connected.
• AI agents - Today, 43% of retailers are piloting AI agents, but 75% say they’ll be essential by 2026 . The future will see agents become the equivalent of a ‘digital team member’ that automates tasks and recommends actions in real time, moving from optional extras to the backbone of retail operations.
• Gen Z adoption - Right now, Gen Z shoppers are 10 times more likely than boomers to use AI for product discovery. As this generation’s spending power increases, retailers will be pushed to embrace AI-driven experiences if they want to stay relevant in the coming era of retail loyalty.
• Ethical analytics - With 73% of customers wanting to know when they’re engaging with AI , transparency and ethics will move from being pure compliance measures to competitive differentiators. The future of retail will reward businesses that can bake ethics and transparency into their analytics from the start.

Ultimately, these trends illustrate that the retailers that invest in AI analytics today will be the ones best positioned to succeed in tomorrow’s marketplace.

Lastly, let’s finish up with some quick tips for getting started with retail analytics in 2025.

Start by identifying what you want your analytics to achieve. Do you want to improve customer loyalty, forecast demand more accurately, optimise pricing, or improve staff efficiency? Having these objectives in mind will shape the data you prioritise and how you measure your success.

What tools do you currently have at your disposal, and how can you unify them?

Map out your current solutions to identify silos, and look for ways to link everything together. For instance, perhaps you can integrate your POS system with your CRM to connect customer interactions with sales data, or maybe you can sync your ecommerce platform with your inventory management system to give a clearer view of demand and stock levels.

You don’t have to unite everything here, but it’s good to start thinking about where you might encounter problems so that your future plans aren’t slowed down by fragmented data.

Rather than overhauling everything at once, start with one or two low-stakes pilot projects, such as sales forecasting ahead of peak periods or personalising loyalty offers for high-value customers. Aside from building confidence in the tools, showing value early can also help to secure buy-in from stakeholders and build momentum for organisation-wide adoption.

At this point, you’re ready to scale up your initiative with AI analytics software. Platforms like Retail Cloud and Commerce Cloud can unify your data across channels to build an AI-ready foundation that supports everything your predictive and prescriptive analytics efforts.

From there, Agentforce can help to analyse your unified data and deliver evidence-backed recommendations across every field of your retail business, supporting smarter decision-making while giving teams more time to focus on standout shopping experiences.

Retail analytics is the key to making informed decisions and turning data into actionable customer insights that drive value. This is especially true when you combine your efforts with AI that will automate the busywork for you.

Salesforce Retail Cloud, along with the full suite of Salesforce CRM tools, can unify your data, deliver predictive and prescriptive insights, and help your business work faster and smarter in our increasingly AI-driven landscape.

Retailers in Australia and New Zealand will be increasingly defined by their ability to act now, adapt and meet evolving expectations. Try Retail Cloud for free to see how Salesforce can help you turn data into immediate insights and deliver experiences that build loyalty and drive growth.