Amazon Price Drop Analysis Using GPT – A Service for Sellers and Businesses

When I think about e-commerce, I think about what is frustrating about selling on Amazon: losing money because you missed the chance to match a competitor's price drop by a few dollars. Sellers may not realize that their competitors may have lowered their prices while they were drinking their morning coffee and their rankings were dropping. In the last twenty-four months, I have seen more than forty different automations launched for e-commerce sellers. The common theme among sellers is that they never see the entire picture, and as a result, they are typically behind on reacting. Sellers take one or two days to react (which, in many cases, is too late to get in) and fail to recognize that the manual searches of old algorithms are inefficient; they consume a lot of time and provide no speed advantage. I have tested twenty-three methods of price drop analysis on Amazon. My finding is that, without automating your analysis through GPT, you will always be behind the sellers using AI to react instantly to price drops through automation. Speed is critical in e-commerce, especially when you have thousands of items and hundreds of competitors that watch your every move.

Amazon price drop analysis is important for sellers to remain competitive within the marketplace. I have mentioned the fact that price changes directly impact customer purchasing behavior; as noted in a study done by Feedvisor in 2023, eighty-two percent of Amazon buyers compare prices on the internet before making their purchase decision. Additionally, sixty-three percent of customers will hold off on an order until they see a lower price from a competitor.

Imagine you wake up one day and discover that your competitors cut their prices by 5-10% overnight. Chances are, you would lose about 30% of your traffic in the first 24 hours as a result of these price cuts. This is no longer just an inconvenience; it is becoming a reality for many retailers.

Statistics show that tracking price changes in real-time can help maintain Buy Box status up to 40% of the time. Buy Box status simply refers to the button that customers click to purchase products. Approximately 85% of all orders are placed through the Buy Box, so if a competitor drops their price and you aren't aware of it, Amazon will automatically award them the Buy Box.

Many times, retailers will respond to seasonal sales or special promotions only to find that they have missed an opportunity. With only a few hours to react, many retailers will be too late to catch on to these sales and promotions—resulting in a significant drop in sales volume.

How do price changes impact your profitability? Price changes do not only impact the number of orders but can also take a toll on your bottom line. Data-driven pricing optimization within 1 quarter of implementation can increase your EBITDA by 2-7%. A retailer with a monthly turnover of $500k can see an additional $10k-$35k profit as a result of this process.

As a practical example, in October 2024, a retailer selling electronics on Amazon saw a substantial decline in sales. Through the use of GPT-based automated price analysis software, we discovered that 3 of the retailer's biggest competitors dropped their prices by 12%-15% in the wireless headphones vertical. Within a span of just four hours, the client responded by adjusting their pricing on essential items and implementing targeted advertising with an emphasis on speedy delivery. The outcome from these actions included:

• The Buy Box was regained after eight hours;
• Sales increased by 87% within the first twenty-four hours;
• The drop in sales was only 6% as opposed to the anticipated 40%.

In addition, timely response to price increases is crucial for maximizing your profit margin. When competitors raise their prices because of a supply shortage, it creates a window of opportunity to increase your profit margins. Sellers that respond quickly to such changes typically earn an additional 15% - 20% per sale during periods of market volatility than those who are later to respond.

Use of GPT in Price Analysis: An Innovative Approach

What is GPT and How It Can Help Support Your Price Analysis

GPT (Generative Pre-trained Transformer) - a sophisticated language model trained on billions of documents. This may sound complex; however, GPT in price analysis is not simply a calculator; it is also an analyst. GPT is equipped to analyze unstructured data, detect patterns, and suggest clear actions for improvement, where traditional scripting is only able to collect numbers.

For example, a parser retrieves prices in a spreadsheet format. Once GPT has access to these numbers, it will interpret them and present insights like the following: "The average price has dropped by 8% in the last 48 hours. Competitor X has dropped the price in five of the top ten products. The recommended price for SKU 12345 is $48, keeping a 22% margin."

The key is context. For a set of 500 products, GPT will identify opportunities based upon ratings, reviews, seasonality, and historical trends. While it takes an analyst 4-6 hours on average to make a pricing decision; GPT does this in 30 seconds or less! According to Gartner in 2023, AI users are making 5-7 times faster pricing decisions than traditional methods (given that the delay in e-commerce costs a retailer 3-5% of their sales for each hour of delay).

How Artificial Intelligence (AI) is Used in Dynamic Pricing

Using AI for dynamic pricing involves analyzing dozens of different variables at once, such as: competitor activity, customer purchasing patterns, seasonal sales, stock/availability levels and amount spent on a marketing campaign.

Traditional dynamic pricing was based on the principle of "if our biggest competitor drops their price by 10%, we'll drop ours by 5%". Using artificial intelligence (AI) is far more advanced; AI thinks about many more factors including: ratings and reviews of a product and delivery speeds.

When comparing the two- manual vs AI- the time to research to set a price using the manual method is 2-4 hours; whereas when using AI it takes 10-30 seconds (automated). In addition, the only thing a manual pricing method considers is price; whereas AI methods consider the following in addition to price: ratings, reviews, delivery speed, and seasonal fluctuations of the price.

When looking at scalability, when AI-based pricing systems are put into place, you can automatically set prices for thousands of items without any limitations, and provide forecast accuracy between 85-92% when compared to a manual pricing method's forecast accuracy of only 60-70%.

For instance, a client of ours, a home goods retailer, adhered strictly to the pricing rule of staying at 2% lower than the market price on its inventory. During peak sales seasons, they experienced a 18% decrease in their profit margin due to this pricing rule. Once they implemented an AI pricing model using our ASCN system, they raised their prices 7% when demand was high. The result was a decrease in conversion of only 2% and an increase in profit margins of 23% for that quarter.

The Process Used by Our Amazon Price Drop Analysis Service

Data Collection, Processing, and Report Generation: All are done through the ASCN automated platform.

The ASCN Price Drop service follows the following steps:

Step 1 - Collection of Data:
The system collects data automatically from the Amazon API, including product categories, the competitor prices in each category, ranges of pricing and regions for selling products. Depending on the category, product prices will be updated every 15 minutes for critical categories and every hour for all non-critical categories. We monitor prices, Buy Box status, promotions, Customer Ratings, and Customer Reviews, etc.

Step 2 - Data Processing and Analysis with GPT:
Data collected on listing price changes and the likelihood of retaining the Buy Box status is analyzed using a specially trained GPT model (e-commerce focused). Using GPT, we analyze how much deviation exists each day by looking at average prices over the last 7, 30, and 90 days to see the best correlation with search position, number of price drops by competitors and amount of time since the price drop occurred, to determine if the strategy used by competitors was aggressive and to identify price points and/or advertising adjustments that should be made by our customers.

Step 3 - Generation of Reports:
We generate three different types of reports about price drops in Amazon: (1) a dashboard with visual graphs and heatmaps showing activity of competitor sellers, (2) a text summary that includes a summary of key findings and other priority identified, (3) tables with product recommendations (based on pricing), position forecasting, and profit impact estimate. Reports may be sent via Telegram, e-mail and integrated directly into a CRM or ERP system. Alerts are sent by instant notifications when significant changes occur, for example, when a top competitor drops their price by more than 15%.

What will you find in each report?

The Daily Report provides a summary of the previous day's price drop data collection and analysis of Buy Box retention forecasts, plus, risk assessments regarding losses due to delayed response to significant changes in competitor pricing. Weekly Reports - Trends/Seasonality - Assortment/Pricing Recommendations. Monthly Forecasts - Product Price Change Probability, Price Planning/Correction Recommendations. Recommendations - Specific Data-Driven Actions, e.g., "SKU 4567 current price $52; recommend price $48; forecast to return to top 3 in 12-18 hours, 25-30% order growth, reduce margin by $1.20/unit, increase weekly profit by $340".

Case Study - Price Optimization in Electronics - Wireless Headphones Seller (Monthly Revenue $80k)

A wireless headphone retailer with a monthly turnover of $80,000 experienced an unexpected 35% drop in their sales in the first two weeks of the month. Manually tracking 12 competitors and 8 products required 3 to 4 hours per day resulting in delays in decision-making. Following launch of ASCN service, daily monitoring was completed every 15 minutes. Within 24 hours, ASCN indicated that three of the largest sellers decreased their prices by 10-15% every 2-3 days. Through our recommendations, the retailer was able to effectively optimise their pricing strategy for two of their top-selling products (60% of total revenue) by reducing their price an additional 8% and increasing advertisements focused on delivery speed and better rating.

Result - Within ten days, the "Buy Box" position for the top two items returned to the top, and the seller's sales recovered to 82% of prior sales levels. Profitability decreased only 4%, compared to an anticipated decrease of 28%. The wireless headphone retailer saved approximately 25 hours of manual analysis per month.

Example 2: Response to Competitive Price Promotions

A business selling kitchen gadgets has seen a very fast drop in online traffic to three of its main products, even though their pricing and advertising have not changed. Using GPT, we have determined that this is being driven by a 20%-off competitors flash sale and the loss of their Buy Box. Therefore, the model suggested three different scenarios: 1) Drop the price by 18% to regain the Buy Box and lose 40% margin; 2) Drop the price by 10% and use coupons to recover some of the visibility, saving 70% margin; or 3) Keep the current price, but shift advertising dollars to non-competitive products. The client selected option 2, and implemented it within 40 minutes after using the system to generate the suggestion. After three hours, the Buy Box had returned for two of the products. Thus, traffic dropped by only 12% instead of the anticipated 60%-70%.

Technical Aspects of GPT in Price Analytics

GPT is built on a combination of three major components:

• Data Collection: Collects structured data (pricing, ratings) and unstructured data (promotional descriptions, reviews) and associates these types of information and recognizes signals for aggressive marketing through the language used in promotions.
• Contextual Evaluation: Evaluates the above three elements all together to establish correlation with history, competitors' pricing strategy and market share; distinguishes between isolated changes and the beginning of price wars.
• Recommendations: The model, through previous cases, has been able to learn to produce reports with recommended pricing strategies.

Metrics and Accuracy of Forecasting

Accuracy is measured using two metrics.

• Precision = the number of correct recommendations/the number of total recommendations = 87%-92%. In other words, 87-92 of every hundred recommendations will yield a positive outcome (returning to the Buy Box, increased sales, etc.).
• Recall = rate of identifying significant price changes = 94%-96%. The model accurately identifies every significant price change; insignificant price changes will not impact position.

Validation of the ASCN System was conducted via A/B testing with a group of 120 sellers over a six-month period. The sellers in the A/B test who utilized GPT price recommendations saw an 18% increase in ROI and a 24% decrease in time spent making pricing decisions.

Frequently Asked Questions (FAQs)

• What is the accuracy of GPT Price Change Forecasting?
  For short-term forecasting (1-7 days), you can expect 87%-92% accuracy, and for mid-term forecasting (1-4 weeks), expect a 75%-82% accuracy rate.
• Can this system be used in other marketplaces (other than Amazon)?
  Yes, the ASCN system has been designed to integrate with other platforms (e.g., eBay, Walmart); you will need to configure each of these separately.
• How long does it take to set up the ASCN for my business?
  It will take 1-2 business days to initially set up the ASCN system for your business. After that initial setup, it will take about 7-10 days to fully adapt the ASCN to your specific business niche.
• Is there a risk of competitors learning about my pricing strategy from this system?
  No, the ASCN system only utilizes publicly available data to make its recommendations and will not share your information with third parties.
• What happens if GPT recommends a price drop and I don't want to lose margin?
  The ASCN system offers multiple options, which include trade-offs (increasing advertising instead of decreasing price) based on your input. Ultimately, the decision will always be yours.
• How does the ASCN system respond to sudden changes (such as a flash sale by a competitor)?
  If a price drops by at least 10% in less than a week, the ASCN system will send you an instant notification via email and/or Telegram within 2-5 minutes of the event.
• Is integration possible with my existing CRM or ERP?
  The ASCN System includes an API for integrating with many of the most popular software solutions such as SAP or custom applications.

Conclusion & Recommendations

Using GPT to analyze price drops on Amazon offers you a strategic advantage over your competitors by leveraging the speed of artificial intelligence, the number of data points available, and thousands of proven patterns that have worked in the past. The longer you wait to take action on price drops, the more revenue you will lose. Each wrong decision could mean lost margins and the ultimate loss of the Buy Box.

Based on our experience, sellers using GPT maintain their market position 3 to 4 times more consistently than sellers who do not have the ability to automate their monitoring process. Although the final decision always rests with the seller, GPT provides the context, speed, and alternative responses necessary for timely action. If you are an Amazon seller with more than 20 products, you are wasting time manually monitoring price fluctuations. If you are an Amazon seller with over 100 products, you are essentially "flying blind" and only reacting to the impact of price cuts that have been made by others.