Why Relying Solely on Historical Data Hinders Price Optimization
In the fast-paced world of retail, the art of pricing has evolved dramatically over the years. Sellers have traditionally relied on historical data as their compass for setting prices. However, as markets become increasingly dynamic, customer behavior more unpredictable, and competition fiercer, the limitations of historical data-driven pricing have become apparent.
The role of historical data in price optimization
Historical data has long been a cornerstone of pricing strategies in many industries. It offers a treasure trove of information about past sales, customer behavior, and market trends.
One of the primary functions of historical data is to serve as a reference point. Businesses can analyze past pricing strategies and their outcomes to identify patterns and trends. Moreover, historical data is instrumental in establishing baseline performance metrics. By studying past sales data, companies can determine average selling prices, sales volume, and revenue figures.
These benchmarks provide a foundation for setting pricing goals and measuring the impact of pricing changes over time.
The limitations of historical data
Based on our discussions with numerous Amazon sellers and e-commerce pricing departments, many of them employ a machine learning model primarily utilizing mainly historical data to optimize their product pricing. However, this approach is far from perfect.
Harvard Business Review (HBR) highlights that retailers using a machine learning model reliant on historical data for pricing decisions usually achieve only a 1% or less improvement in revenue and profit growth. According to HBR, there are two main reasons for this:
- Lack of necessary price variation: sellers often neglect the importance of price experiment, or they closely follow competitor prices. Therefore, the historical price data lacks the necessary variation to compute price elasticity.
- Confounding effects can result in wrong interpretation: Aspects that influence both price and demand cannot be easily identified retroactively. For example, a sudden surge in demand due to a viral social media trend or a competitor's flash sale can't be predicted by looking at past sales figures alone.
Historical data, while valuable, has an inherent flaw – it looks backward, not forward.
The value of ongoing price experiments
You may wonder, what should you do if historical data isn't the superpower? The answer is to systematically and continuously A/B test your product prices.
Continuous price testing emerges as a pivotal strategy for many sellers and pricing departments. The ability to adapt and refine pricing strategies in real-time becomes crucial in today's dynamic business landscape.
With price testing, businesses can proactively experiment with different pricing models, promotions, and strategies. This approach enables them to optimize pricing dynamically and discover what resonates most with their target audience. It provides the agility needed to seize opportunities like sudden surges in demand or respond promptly to competitive threats.
Moreover, price testing empowers organizations to continuously gather valuable insights from customer behavior and preferences, guiding data-driven decisions.
Price optimization: what you should do?
To help you address your pricing challenges, we have drawn some valuable advice based on our experience working with several leading sellers in the e-commerce industry.
1. Group your products into at least 2 categories
If you have thousands of products to manage, you should primarily focus on the products that generate the most profit. Therefore, grouping your products into different categories based on their profitability or popularity is an efficient way to manage your pricing tests.
You can then decide which groups you want to rely on AI to fully automate the pricing experiments, which ones you want to trigger the tests for after analyzing pricing performance each time, and which ones you want to leave as they are for now.
In 'A Step-by-Step Guide to Real-Time Pricing' published by Harvard Business Review, it also suggests the importance of grouping your products to top sellers, midrange sellers, or long-tail products to focus your time and resources on the most profitable products.
2. Experiment different pricing strategies
If your prices have historically proven to work wonderfully, congrats! However, once a strong competitor enters the market or customers suddenly change their buying behavior, it might be too late for you to become aware of the impact and make price adjustments accordingly.
The need to systematically experiment with your prices to measure price elasticity is also emphasized in Harvard Business Review. Historical data mainly provides a backward perspective. Plus if sellers do not test their pricing strategies often, historical data may not offer sufficient variation in prices and can be influenced by confounding factors.
Thus, pricing experimentation is always recommended for making informed pricing decisions, ensuring that you always have the sweet pricing spot and avoiding potential pitfalls associated with relying solely on historical data.
3. Analyze your performance, regroup your products and test again
Once you've grouped your products and conducted pricing experiments, the journey doesn't end there. It's crucial to continuously monitor your pricing strategies and their impact on your business.
Consider revisiting your product categorizations to ensure they still align with current profitability and popularity trends. If you notice shifts in customer behavior, competitive dynamics, or any other relevant factors, be ready to regroup your products accordingly.
Testing should be an ongoing practice, not a one-time event. By regularly reassessing and refining your pricing strategies, you can adapt to changing market dynamics and maintain a competitive edge in your industry. Like what is stated in the Harvard Business Review, 'Retailers often worry that it is too costly to experiment, but our results show that it is even more costly not to.'
A pricing platform combines rule-based approach with AI technology
To overcome the challenges mentioned above, the key lies in having a powerful tool that enables you to systematically and continuously conduct pricing experiments to seize opportunities and respond effectively to competitive threats.
Not only that, it should also enable you to experiment with various price points based on your existing price algorithm and make every pricing decision transparent and easy to understand.
Our Amazon Pricing Engine (APE) can fulfill these needs. Priceloop’s APE combines rule-based pricing with AI pricing, allowing sellers to define specific pricing rules and strategies tailored to their unique needs and helping them automate the pricing tests and determine the optimal prices.
APE’s seamless integration of rule-based expertise and AI-driven insights not only empowers sellers to excel in the challenges of today's rapidly evolving marketplace but also equips sellers with the essential capabilities required to achieve sustained success amidst competitive forces and emerging opportunities.
