By Retail Doctor Group, in association with Ebeltoft Group

Big Data and AI and its Benefits for Retail 

Artificial Intelligence (AI) and Data Analytics have revolutionised the retail industry, providing deep insights into not only customer behaviour, but also internal processes, supply chain management, and staffing challenges too. The 5th Industrial Revolution (5IR) and its resulting technological advances enable retailers to: 

  • create wholly-personalised shopping experiences. 
  • streamline their supply chain operations. 
  • implement dynamic pricing strategies to stay competitive.  

Nearly half (44%) of all Australian retailers plan to invest in AI and automation in 2024. 27% of retail leaders acknowledge the critical role of AI and data analytics in personalising their customer’s shopping experiences, and the adoption of these technological tools in retail is expected to grow exponentially. 

Moreover, the potential for AI in retail is unlimited. Generative AI is projected to contribute between $45 billion and $115 billion annually to the Australian economy by 2030 – if adoption continues to accelerate at its current pace.  

Given the AI-driven efficiencies in pricing, inventory management, and customer experience personalisation, Retail Doctor Group predicts this trend will likely grow at a rate unsurpassed by any other trend to date. 

Here, we take a look at the data, what it means for retailers, how it’s already being implemented and used successfully, and where the opportunities lie – even for micro retailers. 


  • ​​The Global Retail Landscape
  • ​The Australian Context: Embracing the Future
  • ​Mitigating Misinformation in the Australian Retail Sector
  • ​What is Big Data in Retail?
    • ​The 6 Vs That Characterise Big Data
  • ​Best Use Cases for Data Analytics in Retail
    • ​Applications for Data Analytics in Retail
      • ​Customer Experience
      • Business Operation
      • ​Risk Management and Fraud Detection
  • ​Ethical Considerations in the Use of Data and AI-Driven Systems
  • ​Staffing Considerations in the Use of Data and AI-Driven Systems
  • ​Storage Considerations in the Use of Data and AI-Driven Systems
  • ​Strategies for Australian Retailers
  • How to Implement AI and Data Analysis in Retail
  • Conclusion: The Future of Retail is Data-Driven​

The average person generates around 2 MB of data per second. That’s about 16 GB of data by just one person every day.  

Back in 2020, the retail industry generated 40 terabytes of data every hour, according to collaborative research by Microsoft and Thinque. To put that into perspective, it’s 40,000 GB of new insights into customers’ purchasing behaviour generated by the data these customers create while shopping per hour! 

Of course, those numbers are from back when lockdown first started. Today, Walmart’s Data Café processes more than 2.5 petabytes (2.5 million GB) of data every hour. This is their analytics hub, and it helps the retailer make real-time inventory decisions, while also assisting with operational efficiencies for every one of its stores globally. 

Research by Forbes indicates that 2.5 quintillion bytes of data is generated around the world every day. Nearly 90% percent of all available data in the world was generated in the past two years, and according to research by McKinsey, this is expected to double in size every two years. 

The problem is… only around 40% of a company’s data is actually analysed. With so much valuable insight going to waste, retailers are losing out on  increasing their operating margins by as much as 60%! 

The Global Retail Landscape

There’s been a monumental shift in retail over the past decade or so, evolving beyond anyone’s wildest dreams for this era. This is largely due to the pandemic and its face-to-face limitations, but the shift was always driven by evolving customer needs, which have significantly contributed to the rise of e-commerce giants like Amazon, triggering expectations for personalised and seamless shopping experiences in every other retailer customers interact with.

Nowadays, of course, Amazon leads the race in the search for an AI solution that satisfies those needs. Their systems provide advanced and highly-personalised purchasing recommendations that rely heavily on analysing and using the customer data they collect. This includes browsing history, customer reviews, and purchasing trends.

Because of this, traditional retailers have had no choice but to adapt to this new, online, environment – or risk becoming obsolete. This competitive environment has been the key driver behind the widespread adoption of AI and data analytics in the retail sector.

The Australian Context: Embracing the Future

The good news is, Australia’s retail industry is well-aligned to these global trends. While the pace of adoption may differ compared to other developed economies, the potential of AI and data analytics in business success cannot be overstated enough.

According to a 2024 study by PricewaterhouseCoopers, 70% of Australian CEOs believe AI has the potential to significantly improve their businesses. This sentiment is further echoed by the Australian Bureau of Statistics, which reports that the use of big data analytics in Australian businesses has grown steadily over the past decade.

Embracing AI in retail is not just about levelling the playing field with digital disruptors;

 It also offers an opportunity for retailers to leverage their strengths

 and actually get the upper hand over rivals.

 ~ Professor Anton van den Hengel, Australian Institute for Machine Learning, University of Adelaide

In their defense of a lack of traction in adopting AI and data analytics, 78% of Australian CEOs are somewhat more concerned about cybersecurity risks than their global counterparts (64%). Another worrying factor that 63% of Australian CEOs surveyed have to contend with is the growing spread of misinformation on the Internet – the very place where AI, big data, and future sales are to be found.

Mitigating Misinformation in the Australian Retail Sector

The spread of misinformation on the Internet holds significant implications for the adoption and use of AI and data analytics in the Australian retail sector. This includes:

  • damage to retailers’ reputations.
  • loss of consumer trust.
  • potential financial losses.

Misinformation can take various forms, such as false claims about product quality or safety, misleading advertising, or fake online reviews. The widespread use of social media and online platforms in Australia has made it easier for misinformation to spread rapidly, specifically through what has come to be known as influencer fraud.

Influencer fraud involves using fake social media influencers to promote products. These fake influencers are created using AI-generated images and personas, but still go on to amass large followings because their content genuinely appears to be real.

Another form of misinformation involves using AI algorithms to generate fake product reviews for certain items. These reviews are designed to boost consumer trust in certain brands’ products, but violate consumer protection laws.

Spreading false claims about the safety and quality of a brand or retailer’s fresh produce is a dirty competitor trick to generate mistrust in one (trusted) product or service to influence a growth in trust in a less-trusted product/service. More good news is that retailers in Australia seem much more aware of the significant backlash, loss of consumer trust, and legal ramifications these fraudulent actions would cause than their global competitors.

The use of AI and data analytics clearly have the potential to identify and target susceptible consumer groups. AI algorithms amplify the reach of misinformation and promote misleading content through targeted advertising or personalisation algorithms.

Being aware of how AI and data analytics can be misused in Australian retail is actually a good thing. However, AI and data analytics can also be powerful tools in combating misinformation, identifying patterns and sources of misinformation, while also tracking its spread and impact.

But before all of this can happen, it’s best to know a little more about what we mean when we talk about data (sometimes referred to as Big Data) and AI in the retail sense.


Big data refers to large, diverse sets of information from a variety of sources

 that grow at ever-increasing rates.

 ~ Investopedia

What is Big Data in Retail?

Big data is an often-used term these days, though few people really know what it’s about. In a technical sense, it’s the data collected by technology from customers’ interaction with devices that themselves interact in some way with the Internet of Things (IoT). This data contains larger, more complex data sets that primarily collect their information from new data sources.

This data can be classified into three distinct types:

  • Structured: financial records and transactions.
  • Semistructured: emails and web server logs.
  • Unstructured: documents, text and multimedia files.

If we look at this from a shopper’s journey point of view, we recognise the data and how it fits in with that journey. For instance, our shopper reads a blog online and views a video about a product (unstructured data). She then adds that product to her cart, triggering the need for use of her private data to deliver the item. This then triggers an email sequence (semistructured data) and leads the customer to a portal where payment handlers are needed to finalise the transaction (structured data).

Obviously, this is a simplified explanation and does not offer all the nuances of a shopper’s journey from a data point of view, but you get the idea: their journey starts in an unstructured way (as they make their initial decisions) and passes through semistructured systems to become structured data.

The data generated in this shopping journey is therefore what retailers need to analyse to tailor personalised experiences for their customers.

The 6 Vs That Characterise Big Data

We now know that big data combines structured, semistructured, and unstructured data from a customer’s interaction with a specific brand or retailer in their shopping journey on the Internet. The resulting data has a number of characteristics, often referred to as the V’s of data:

  1. Volume: the amount and size of the data being managed.
  2. Variety: relates specifically to the three types of data (unstructured, semistructured, and structured), but may also include raw data.
  3. Velocity: generated at incredible speeds, data should be updated on a real- or near-real-time basis, rather than traditional daily, weekly or monthly updates. Managing data velocity is critical to data analysis as the information further expands into AI and machine learning. The analytical processes here can automatically find the data patterns to generate those much-needed insights.
  4. Veracity: inaccurate data produces analysis results that are full of errors. Veracity is the process of verifying accuracy in data sets, which need to be fixed through data cleansing processes.
  5. Value: the most important “V” of the six from a retailer’s point of view, and can be likened to the concept of ROI in marketing.
  6. Variability: sets of big data may be formatted differently or have multiple meanings when received from other data sources. It is wise to remain aware of these complications when processing data.

If you look around, you’ll find other “Vs” to characterise your big data; but these six are the most important of them in a retail sense.

Best Use Cases for Data Analytics in Retail

Now that we have a better idea of what it means when we talk about data, analysing that data, and how it gets used in a retail sense, let’s take a look at how the data is collected and where it’s best put to use.

AI systems log and collect the raw data from various customer touch points, such as:

  • physical stores.
  • online platforms.
  • shopping carts.
  • loyalty programs.
  • social media interactions.

The vast amounts of data generated can then be analysed to give retailers valuable insights into customer behaviours, their preferences, and their buying patterns. Collectively, this data shows retailers how to tailor their offerings, optimise their operations, streamline their sales process, and ultimately, enhance customer satisfaction.

Take Starbucks, for example. Their loyalty program and mobile app collects customer data that helps them provide their customers with personalised offers and suggested recommendations, thereby increasing customer engagement. This has had a significantly positive impact on their bottom line – and their popularity! – resulting in higher sales per customer visit and a knock-on effect to their loyalty program, boosting sign-ups.

Applications for Data Analytics in Retail

The applications of AI and data analytics in retail are diverse and can be broadly structured into three key areas:

  1. Customer experience.
  2. Business operations.
  3. Risk management and fraud detection.

1. Customer Experience

Personalising the customer experience means recommending products based on the customer’s past purchases and browsing behaviour. Retailers can also offer targeted promotions, using various channels to deliver personalised content.

Another way that the customer experience can be enhanced is by using AI-powered chatbots to assist customers with product inquiries, order tracking, and basic troubleshooting. While this does free up human resources for more complex tasks, retailers will need to make sure that the chatbots are well-trained to avoid irrelevant instructions and sending customers on wild goose chases.

Sentiment in the customer experience is critical. Retailers must analyse customer reviews and social media conversations to identify trends and pain points in their customers’ journeys. This will help retailers gain better insight into customer sentiment towards products, services, and brand perception.

2. Business Operations

Demand forecasting uses AI algorithms to predict future demand for specific products, enabling retailers to optimise inventory management, minimise stock shortages, and avoid overstocking. AI can also be used to analyse historical inventory data and identify problem-patterns so their supply chain can be optimised, delivery times can be improved, and costs can be reduced.

For instance, Target has a reputation for targeting expectant mothers in their offers, using predictive analytics to identify mother-to-be needs, based on their changed shopping behaviours and purchasing decisions. Walmart also uses predictive analytics to reduce overstocking and out-of-stock situations, thereby forecasting demand.

Dynamic AI-powered pricing strategies should also be implemented. These will help retailers automate the process of adjusting prices, automatically taking factors like competition, customer behaviour, and demand into account.

3. Risk Management and Fraud Detection

AI algorithms can help retailers with fraud detection, identifying and preventing fraudulent transactions, protecting retailers from financial losses. In addition, retailers should routinely be performing risk assessments by analysing customer data to determine their creditworthiness. This will help retailers manage the financial risks associated with online transactions.

To prevent loss in-store, retailers can use AI-powered video analytics that track and identify suspicious activity in stores. This will minimise the risk of potential theft.

The Australian Government is committed to making Australia a global leader in responsible and inclusive AI.

 For Australians to realise the immense potential of AI we need to be able to trust it is safe, secure, and reliable.

 ~ AI Ethics Framework, Australian Department of Industry, Science, and Resources

Ethical Considerations in the Use of Data and AI-Driven Systems

Chief amongst the Australian government’s ethical considerations is the desire for businesses (and governments) to implement principles and practices that draw on the highest-possible ethical standards when using AI and big data. This includes the design, development, and implementation of AI. By doing so, retailers can benefit from:

  • growing public trust in products and brands.
  • higher consumer loyalty and buy-in to AI-enabled services.
  • increased transparency that eliminates bias, deception, discrimination, unfair manipulation, and unjustified surveillance.
  • proper data governance and management.
  • appropriate data and AI system security measures that identify potential security vulnerabilities.

You can read more about Australia’s 8 AI Ethics Principles here.

Staffing Considerations in the Use of Data and AI-Driven Systems

Finding the right people with the right skills is going to be challenging at first. The fierce competitiveness of these much-needed skills – combined with the lack of formal training solutions in this expanding field – may make useful data extraction a frustrating part of the adoption process. There are, quite literally, just a handful of people on the market who can read this data and make sense of it.

The need for ongoing training and development should also not be overlooked. Employees will need to keep up with evolving technologies and analytical techniques, but retailers (especially micro enterprises) will do well to take up some training themselves in an effort to:

  • understand the technology and its implications for business success.
  • curb dishonest practices that may see employees selling off data.
  • learn more about what their customers truly want.

Storage Considerations in the Use of Data and AI-Driven Systems

Big data is commonly stored in a data lake, a centralised data repository. Here, large amounts of data are stored, processed, and secured in either raw, structured, semistructured, or unstructured formats, which can all be processed without size limits.

However, processing data of this magnitude in a cost-effective way is a challenge – not just for retailers, but in general, too. For this reason, cloud storage and managed big-data-as-a-service (BDASS) companies have grown in popularity as a solution to the cost question. Not only that, but the right cloud provider will also allow you to scale up long enough to complete big data analytics projects, and then scale back down again.

This is an ideal solution for micro retailers, for instance, allowing them to inflate the service as required, yet deflate back down to a cost-effective and everyday level of service when done. In this way, the retailer will only pay for the storage and compute time it needs on an adhoc basis, while the data is still collected and ready to be used when called for.

In Australia, BDASS partners like Pernix, Teradata, SAP, SAS Institute, IBM, Salesforce, QlikTech, and even Microsoft are well-known for delivering world-class cloud-based services.

Strategies for Australian Retailers

As you may have guessed from reading this post, guiding micro retailers to the forefront of technology is an RDG goal. The adoption of AI and data analytics might appear daunting because of resource limitations in smaller businesses. However, with the right approach – and the right partner – even micro retailers can leverage these technologies to gain a competitive edge.

Start small but think big – and scale strategically. The biggest concern should be prioritising the quality of data collected:

  • Partner with data scientists, AI specialists, and industry consultants – service providers who can ensure the accuracy and reliability of the data used for AI and analytics.
  • Make an investment in data cleansing and governance practices that are transparent, ethical, and relevant.
  • Involve all stakeholders from the start, including them in the decisions that impact your long-term business goals.
  • To build trust, communicate with your customers in a transparent and open way about what data you collect and why it’s collected.
  • Don’t forget the human touch. Human expertise and creativity remain crucial to maintaining a balance between the power of technology and the irreplaceable value of human interactions in customer journeys.

Above all, encourage a data-driven decision-making environment that values collaboration, holds the customers’ interests at the heart of your efforts, and fosters a culture that cares about ethical personalisation methods.

How to Implement AI and Data Analysis in Retail

Here’s a practical implementation plan that’s actionable enough for even micro retailers to implement:

Step 1: Identify key priorities and specific areas where AI and data analytics can address your biggest business challenges, such as optimising marketing campaigns, improving customer service, or managing inventory more efficiently.

Step 2: Utilise readily-available free or low-cost data analytics tools and platforms that cater to smaller businesses. Many cloud-based solutions offer user-friendly interfaces and pre-built AI functionalities that require minimal technical expertise.

Step 3: Focus on your customer data collected through your existing systems, such as point-of-sale terminals (structured data) and loyalty programs, which will help you gain better insights into your customers’ behaviours.

Step 4: Partner with service providers who offer pre-packaged analytics solutions that are tailored to your specific business needs.

Step 5: Invest in employee training on the basics of data interpretation and utilisation to foster a data-driven approach within the organisation. Check that whoever you partner with offers training so you can empower your team.

Conclusion: The Future of Retail is Data-Driven

The adoption of AI and data analytics is not a passing fad; it represents a fundamental shift in the retail industry. As technology continues to evolve and data becomes even more readily available, these tools will become essential for all retailers, regardless of size or location, to compete effectively and thrive in the digital age.

Follow the examples set by the case studies we’ve featured. You don’t need to roll out your AI and data analytics plan on a scale as large as theirs, but you will have to start somewhere. Let their success guide you to find the plan that’s right for you.

But most of all, don’t be a part of the 60% that does not consider the data important enough to analyse or use. It’s literally handed to you on a plate, 2 MB per second. What are you waiting for?

If you’re not sure how your brand can adjust to meet this evolving trend, collaborate and learn from a partner with over 20 years’ of consulting expertise in retail.

Contact the Retail Doctor Group, a retail advisory and consulting practice that builds retail channels and increases the performance of retail and FMCG businesses through our customised & transformative ‘Business Fitness™’ methodologies.

Since 2005 we have partnered with our clients to build powerful, award-winning, sustainable, and “fit” implemented retail. Ensuring our clients consistently achieve above benchmarks, build sales and margin results. We stay with our clients to ensure success.

As the Australian elected member of International Retail Experts, Ebeltoft Group, we have more than 20 years of experience as retailers and consultants in all retail channels, segments and regions. Today, members of the Ebeltoft network advise 80 of the 100 largest retail companies in the world.

Want to know more about the Future of Retail and prepare your retail strategies? Schedule an appointment with our Insights division by e-mailing us at or calling 02 9460 2882.