Pricing products effectively remains one of the most challenging aspects of retail management. It's a problem. You get a product you have never handled; how do you price the item for sale now? Getting it wrong can significantly impact profitability and competitiveness. Many people today have suggested that the new AI Chatbots can help, but none, as far as I know, have shown any proof of this. So, to address this problem, we conducted a comprehensive evaluation of six free AI chatbots to assess their effectiveness in pricing recommendations for Australian retailers.

Our Testing Methodology

We tested a scenario of a small newsagency in Keysborough, a typical Melbourne suburb located in a strip shopping centre. The test focused on pricing a specific product: a Pilot Frixion Ball Erasable Gel Pen pack containing three pens (black, blue, and red) with a fine 0.7mm tip using only free CHATbots.

Why only Test Free AI Tools for Stock Pricing?

We restricted the test to free AIs because most retailers are only now experimenting with AI chatbots. Few have purchased an AI Chatbot plan, whose costs now vary from about $25 to $200 plus GST (I have a customer who uses AI in our POS Software and runs a bill of up to $10 daily). Once you get into paid plans, there is a massive difference in what you get.

Free tools can offer an accessible starting point, but not everyone is equal for all tasks, as you will see here.

So, we limited our evaluation to these six popular free AI chatbots:

ChatGPT (OpenAI)

Claude (Anthropic)

DeepSeek

Google AI

Grok 3

Qwen

We wanted to test meta AI, which had announced a significant update, but unfortunately, it was not available when this report was written.

After multiple iterations to refine our approach, we developed a standardised prompt that described the retail location, business type, and product specifications. We then evaluated each chatbot's response based on nine key performance indicators. It all took a lot of time.

Evaluation Framework

Each AI tool was assessed across nine KPIs, with each scoring out of 10, giving us a maximum possible score of 90.

Quality of Information

Accuracy and relevance of data regarding the product, competitors, and market conditions

Usefulness

If the advice is impractical, what is the point of getting it?

Clarity

We are all busy people; we need something well laid out and comprehensible.

Actionability

We wanted clear, implementable recommendations

Accuracy

AI Chatbots do make errors and mistakes. We want correct information on costs, retail prices, and market trends

Adaptability

Not surprisingly, we found that to price appropriately in local retailing, the advice needs to be for a specific location and a store's customer demographics

Depth of Analysis

Besides price, we would like advice on various pricing strategy aspects

Creativity

It would be lovely to get information on innovative suggestions for marketing, e.g. bundling, promotions, or other sales strategies

Customer-Centric Approach

Not all customer segments react to pricing similarly, so we want to know how each responds. You rarely care if you get too much information.

Results Summary

The performance of each AI chatbot is summarised in the table below:

Notably, the top three performers delivered remarkably similar price recommendations, suggesting a similar practical use by a retailer on the top tools.

Detailed Analysis of Each AI Tool

1. Google AI (Score: 87/90)

Strengths:

• Provided accurate pricing recommendations based on local competitor analysis, correctly identifying Coles' price range of $9.50–$14
• Suggested appropriate margins of 35–50%, aligning with industry standards
• Delivered a logically structured report with clear reasoning

Areas for Improvement:

• Some sections contained overly technical markup calculations that would be challenging to understand.
• User interface could be more intuitive for retailers without technical expertise

Key Insight:

Google AI excels at tracking and analysing local competitor prices, making it highly effective for crafting a pricing strategy.

2. Qwen (Score: 83/90)

Strengths:

• Proposed innovative bundling strategies, such as pairing pens with notebooks at $12.99 to increase perceived value
• Included practical promotional messaging suggestions (e.g., "Save $1 vs Coles!")
• Presented information in an accessible, actionable format

Areas for Improvement:

• Assumed a wholesale cost of $6.50, which appeared to be unrealistically low based on market research

Key Insight:

Qwen's focus on bundling opportunities and targeted promotional strategies makes it particularly useful for retailers looking to maximise revenue through upselling techniques.

3. Grok 3 (Score: 81/90)

Strengths:

• Provided detailed customer segmentation analysis, correctly identifying pensioners as a key demographic in the Keysborough area
• Recommended a four-week price testing strategy to refine the pricing approach based on actual sales data

Areas for Improvement:

• Suggested profit margins were lower than industry standards
• Report contained unnecessary repetition

Key Insight:

Grok 3's demographic analysis capabilities make it particularly valuable for retailers to align pricing strategies with local customer profiles.

4. DeepSeek (Score: 65/90)

Strengths:

• Effectively highlighted product features (such as erasable ink) as unique selling points
• Suggested strategic product placement near complementary items to encourage cross-selling

Areas for Improvement:

• Recommended pricing ($12.99–$13.99) significantly exceeded competitor rates
• Technical terminology like "left-digit effect" was used without explanation. A left-digital effect charges a $10 item as $9.99. Did you know that? We did not know until we looked it up.

Key Insight:

DeepSeek appears better suited for premium or specialty product pricing than standard retail items with established market positioning.

5. ChatGPT (Score: 58/90)

Strengths:

• Provided a well-written, easily comprehensible report

Areas for Improvement:

• Wrong information, e.g. it inaccurately estimated competitor pricing ranges
• Did not check the local pricing of the product
• Lacked depth in analysis and failed to provide sufficiently actionable recommendations

Key Insight:

In our test, ChatGPT's generalist approach proved inadequate for the nuanced requirements of a retail pricing strategy in a shop in the Australian market.

6. Claude AI (Eliminated)

Claude AI was disqualified from the final evaluation due to its inability to access real-time data and lack of localisation features for the Australian market, rendering its recommendations useless.

Key Findings and Implications

If looking at an AI Chatbot, you need to look at:

Real-time competitor analysis

You will not be able to do a good job of pricing if you do not have local information; this led to a pricing recommendation by ChatGPT that was disconnected from market realities.

Value-added bundling recommendations

Does it offer ideas to sell the product

Demographic-specific insights

In most shops, there are several different customer demographics, and these when pricing needs to be considered.

Overly technical presentations

Some Chatbots made us feel that the complexity was excessive, making the report difficult even though we told them not to make it complex.

Conclusion and Recommendations

For Australian SMB retailers seeking to use AI tools for pricing, Google AI, Qwen and Grok 3 emerged as the best due to their accuracy in competitor tracking and logical approach to margin calculations. Any of these can do the job well. If I were pricing an item and wanted some advice, any of these three Chatbots would give me good advice. As there must be a winner, Google AI won.

If you are interested in looking into this technology, I would suggest:

1. Testing multiple free AI tools to identify which best aligns with your specific business needs
2. Please don't assume the chatbot knows your local market conditions; the more you tell it, the better. You are better off assuming nothing.
3. Check the AI report, as wrong information was sometimes supplied.
4. Gradually expand your AI Chatbot over time.

This research demonstrates that free AI chatbots can provide valuable pricing insights for Australian retailers, though their effectiveness varies significantly across tools. By selecting the appropriate AI assistant and providing relevant contextual information, retailers can enhance their pricing strategies without investing in expensive subscription services.

Have you tried any of these tools yourself doing this type of test? Please share your experiences in the comments below!

Written by:

Bernard Zimmermann is the founding director at POS Solutions, a leading point-of-sale system company with 45 years of industry experience. He consults to various organisations, from small businesses to large retailers and government institutions. Bernard is passionate about helping companies optimise their operations through innovative POS technology and enabling seamless customer experiences through effective software solutions.

As a retail consultant, I am excited about how AI can be used to support small—and medium-sized business (SMB) retailers.

We decided to address the problem of receiving too much information today. No one has time to wade through the mountains of reports we are getting, so I decided to test which free AI solution will deliver results for your shop.

We chose free because most SMB retailers are currently experimenting with AI, and as a result, many are utilising free AI solutions. There is no point in testing something few are using.

We extensively evaluated six leading free AI tools to answer this question, testing them against real-world retail reporting scenarios. We made and rated over 200 reports in total. What we discovered might surprise you, but the results certainly did surprise me. This analysis will be helpful and save you a lot of time.

What the test is addressing

There is a significant gap between the data and the time and knowledge needed to use it effectively. A modern POS system like ours generates hundreds of reports, which require considerable time to review to identify sales patterns, inventory levels, supplier performance, and financial statements. However, finding time to extract meaningful insights from these reports is another matter.

Yet the promise is that AI tools can do this and quickly process reports, identify trends, spot anomalies, and suggest actionable improvements.

The Free AI Landscape

Like so much in the world, not all AI tools are equal; some are better than others, and each one has account limitations.

We tested six popular tools to help you navigate these options:

ChatGPT (OpenAI)

Claude (Anthropic)

DeepSeek

Google AI

Grok 3

Qwen

Evaluation Criteria for AI Tool Performance

Now, we know that all of them are good, but there is always a case where even the best six runners in the world have one who is better, and that is what we wanted to find out: the best free AI for retailers.

Each tool was evaluated on its ability to handle our tests based on these criteria:

Information Accuracy

Accuracy formed the cornerstone of our evaluation. Without accurate information, even the most sophisticated analysis becomes worthless for making informed business decisions. We meticulously verified whether each AI tool could process retail data without introducing errors or misinterpreting figures. This involved cross-checking calculations against known values and assessing whether the tools maintained data integrity throughout the analysis. In retail, where margins are often tight, minor inventory valuation or sales forecasting inaccuracies can lead to costly mistakes.

Clarity of Presentation

Accurate information is only valuable if presented in an understandable format. We assessed each tool's ability to structure information logically with clear headings, appropriate visual elements, and a coherent flow that retail managers could easily navigate. We examined whether complex data was transformed into straightforward insights that wouldn't require a data science degree to interpret. A good report should communicate the key points to a retailer without requiring them to wade through jargon.

Actionable Insights

Data without direction offers limited value to retail businesses. We evaluated each tool's ability to convert raw information into practical recommendations that retailers could implement. I am very proud that our POS system provides our customers with tools they can utilise. I want the AI report to do the same. I want to know what specific opportunities were identified in my inventory optimisation, which products are underperforming, and what concrete actions I need to take from my supplier. Good tools should describe what is happening and what should be done next.

Business Relevance

We evaluated each tool's ability to focus on issues that matter most to Australian retailers rather than generic business statistics. Did the AI for example identify seasonal trends in an Australian retail cycles, did it highlight my supplier performance to my business. Information that is not relevant creates noise rather than value.

Consistency in Analysis

Consistency in reporting is crucial for tracking performance over time and making reliable comparisons. We examined whether each AI tool maintained a consistent approach to analysis in its report and whether its outputs provided a coherent narrative. We do not want contradictory findings. Retailers need to trust that the insights they receive follow logical patterns and don't send them in conflicting directions. Inconsistent analysis can lead to confused decision-making and undermine confidence in the technology itself.

This comprehensive evaluation framework enabled us to assess each AI tool beyond its surface capabilities, focusing instead on how effectively it would serve the practical needs of Australian retailers wanting to extract value from their business data.

Test 1: Long Trend Stock Report Analysis

The first test was designed to evaluate how the AI would perform if it were given a vast amount of data that retailers are receiving. If the AI cannot handle the data, it's of minor use to retailers. Retailers have lots of data today.

Now, understanding inventory performance is critical for any retailer. Seasonal trends, slow-moving items, and bestsellers all impact their bottom line, so we ran a comprehensive stock trend report spanning hundreds of pages. It's the kind of data most retailers can obtain but rarely find the time to analyse correctly. Our test data spanned 12 months and exceeded 300 pages in length.

Tool Performance

ChatGPT

Failed almost immediately, as it ran out of credits, rendering it essentially useless for comprehensive stock evaluation. Even before hitting its limits, it failed to provide actionable insights that would aid practical retail decisions. The reality is that a retailer, after running this report, would almost certainly want to rerun it to see whether anything different changes the outcome. I might have tested this year and last year, but here I get nothing. As such, we immediately dropped ChatGPT.

Claude

Initially performed better. It identified some fundamental product trends on the first run. Then it ran into credit limits. However, it did identify some fundamental product trends, but its inability to handle follow-up questions made it impractical for the iterative nature of retailers' needs. As such, we dropped it immediately.

DeepSeek

Attempted a different approach to the credit limit problem. It took only a tiny section (6%) of the information. While this allowed it to complete the task without running out of resources, it did not give much.

Google AI

The first problem was that Google required CSV files, while all the others accepted Excel format, which we preferred. However, it did identify fundamental product trends; however, we all felt it lacked the depth needed for effective inventory management. Its surface-level insights wouldn't provide much of a competitive advantage for retailers looking to optimise stock levels.

Grok 3

Boy, were we impressed with this AI. It took the entire report without issues. It then provided a detailed trend analysis that would help retailers make smarter decisions. For example, it identified some products specifically for BBQs and reported that they sold well during the summer. It also spotted anomalies that would be easy to miss in manual review, such as products that underperform only during specific weather conditions.

Qwen

It performed admirably by identifying anomalies and supplier diversity trends, though it didn't match Grok 3's depth. It correctly helped identify problematic stock items. Unfortunately, it offers fewer actionable recommendations for improvement than Grok 3.

ChatGPT failed

Test 2: Trial Balance Analysis

Accurate financial reporting is the backbone of retail success. The second test focused on a small compact trial balance dataset. What we wanted was an analysis that didn't require an accountant to understand.

Tool Performance

DeepSeek

It produced precise observations but struggled with depth when analysing discrepancies. Its summarised approach meant that nuanced financial issues, which could significantly impact a retail business, were overlooked.

Google AI

Here, we got straightforward summaries that aligned with our general ledger data but it lacked depth in identifying anomalies. We felt that although it was helpful for essential reconciliation, it wouldn't alert a retailer to subtle patterns.

Grok 3

Wow, it delivered a detailed financial summary with cross-referenced data for accuracy. It flagged discrepancies that required further investigation, allowing us to explore these issues. This capability could be invaluable for retailers without accounting expertise in maintaining the financial health of their business.

Qwen

It did a good job of highlighting significant balances and unexpected changes effectively. Again, it did not match Grok 3's level, but it did come up with much good stuff.

Test 3: Supplier Purchases Report

Managing supplier relationships is critical for maintaining healthy margins and consistent product availability. The third test examined a supplier purchases report to evaluate performance, track expenditures, and identify inefficiencies.

Tool Performance

DeepSeek

It did produce some quick overviews. It struggled with detailed metrics, such as cost per transaction or order accuracy. Its summarised approach meant it missed some critical inefficiencies; we did not think it was trivial, as these sorts of things directly impact margins.

Google AI

It did provide a structured summary but lacked in-depth spending analysis by category or supplier benchmarking. While helpful for basic understanding, we did not see key KPIs, such as identifying problematic vendors.

Grok 3

It did offer comprehensive supplier evaluations with detailed metrics. It identified inefficiencies. We thought it was suitable for managing dozens of suppliers. It was good, with its actionable tips.

Qwen

It did highlight anomalies as well as Grok 3 in the supplier but lacked actionable details.

Summary Performance 

When evaluating these tools specifically for retail applications, clear patterns emerged across all three test scenarios:

Practical Implementation for Your Retail Business

Understanding how these tools perform in controlled tests is helpful, but implementing them in your daily operations is where real value emerges. Here's a practical approach to leveraging AI for business improvement.

Start Small and Focused

Begin with a specific business challenge rather than trying to analyse everything at once. Consider identifying your slowest-moving stock items for clearance, evaluating which suppliers offer the best value for similar products, or analysing sales patterns to optimise staffing during peak hours. Starting with a focused approach allows you to see tangible benefits quickly while building your comfort with the technology.

Prepare Your Data

Export relevant reports from your POS system in a format your AI tool can process. Depending on what tool you use, you need CSV or Excel. I prefer Excel but its your call. Check first that your data is clean and good. If you feed the AI rubbish, you will get rubbish back.

Ask Specific Questions

Frame your queries in specific, actionable terms rather than general requests. Instead of asking the AI to "Analyse my stock," try something more targeted, such as "Which product categories show seasonal patterns, and when should I increase inventory for winter?" Similarly, rather than requesting the AI to "Check my finances," ask, "Are there any unusual expense patterns compared to last year, and which categories show the largest percentage increases?" Specific questions yield specific, actionable answers.

We found that general queries often provided incorrect answers, requiring multiple attempts to obtain a satisfactory response.

Implement Findings Systematically

Test your questions systematically and record the question that yields the answers you want. This systematic approach ensures that AI becomes a valuable part of your business improvement cycle rather than just an interesting experiment.

Focus on applying insights

The best analysis is useless if it is not applied. Each of your analysis sessions should end with clear action items to be implemented and tracked.

Recommendations for Australian Retailers

Based on comprehensive testing and practical retail experience, here are my specific recommendations for retailers looking to leverage free AI tools:

Use Grok 3 as your primary analysis tool. I am told it will soon be charged, but now it appears to be the best in the free AI market. We were impressed with its ability to handle complex questions and our interactive questions, which is excellent if, like me, you like following your natural curiosity.

"A good question in business does not lead to an end; a good question opens doors you never knew existed."

Consider using Qwen; it's excellent. We found it helpful as it gave a good second opinion. It can be especially valuable when making significant business decisions.

If you're currently using ChatGPT or Claude, be aware of their significant limitations for business analysis. Their credit restrictions make them impractical for the iterative analysis that delivers real value. You may find yourself frustrated when analysis suddenly stops.

Conclusion: The Future of Retail Intelligence

Retailers, rather than using intuition alone, can use the Free AI tools now available to gain insights.

Written by:

Bernard Zimmermann is the founding director at POS Solutions, a leading point-of-sale system company with 45 years of industry experience. He consults to various organisations, from small businesses to large retailers and government institutions. Bernard is passionate about helping companies optimise their operations through innovative POS technology and enabling seamless customer experiences through effective software solutions.

It is estimated that in five years, AI POS will drive 55% of Australian consumer spending by 2030. This projection shows how AI will revolutionise retail in our country. As a retail and point-of-sale (POS) systems expert, it is essential to explore this transformation and its implications for small brick-and-mortar Australian retailers.

So, we will create a new section in this blog, "AI Musings," to explore artificial intelligence's musing on modern retailing. This space will blend AI-generated insights (approximately 80%) with my thoughts (20%) to delve into the most significant technological revolution in retail today.

The State of Modern Retail in Australia

AI dominates discussions at every retail conference today, signalling its emergence as the new frontier in retail technology.

Adopting AI in retail is not just a prospect; it's happening now. Many retailers already implement AI solutions to enhance customer experiences, optimise inventory management, and streamline operations. For example, our clients have been using AI for years in stock control, but no one is talking about it, and we need to.

I heard about chatbots handling customer queries in a retail firm a few days ago. It was expensive, but as AI becomes more accessible and affordable for businesses of all sizes, we will soon see it in almost all stores.

How AI is Transforming Australian Retail

Personalised Customer Experiences

AI will revolutionise how retailers understand and cater to individual customer preferences, offering a more personalised shopping experience. And it will not take long. If such a system is live in 10,000 shops in one month, that AI will have 800+ years of experience at the end of the month.

Smart Inventory Management

AI is already in our POS system and crucial in predicting demand and optimising stock levels, helping retailers reduce waste and improve efficiency. It has proven vital for retailers who often struggle with inventory management. AI-powered systems can analyse every stock item in the shop with its historical sales data, seasonal trends, and even external factors, like weather.

AI-Powered Customer Service

Chatbots and virtual assistants are improving customer support across online platforms. Currently, 47% of consumers feel comfortable using AI for product selection, while 75% remain cautious about AI handling high-value purchases.

Enhancing In-Store Experiences

AI is set to transform the in-store experience. Retailers use emotional recognition tools to detect customer frustration and seamlessly transfer to human support. This blend of AI and human interaction could be a game-changer for small retailers looking to provide personalised service while optimising staff resources.

Practical Considerations

Cost vs ROI for Retailers

Implementing AI solutions today requires a significant investment, but this is rapidly changing. DeepSeek-R1 is roughly comparable to ChatGPT GPT-4 Turbo, and it is $2.19/128k token, while ChatGPT is $30/128k token, a cost savings of about 93%.

Future Trends in Australian Retail with AI

Looking ahead, the future of AI in retail is bright. By 2030, AI is expected to create 200,000 jobs and $115 billion in economic value, which presents enormous opportunities for retailers of all sizes. That is almost 10% of the Australian economy now.

Voice Commerce

The growth of smart speakers and voice-activated shopping is expected to continue, offering new ways for customers to interact with retailers. I know some clients who now use them in the shop as a translator, and I do, too here. It has helped.

Conclusion

AI is undeniably reshaping the landscape of Australian retail. It is now transforming shopping experiences, retail operations, and customer engagement strategies. We intend to explore this topic here. I encourage our readers to share their thoughts on how they see AI shaping their shopping experiences or what trends they're most excited about.

Let's see how it goes forward together.

I hope you enjoy the new section, "AI Musings."