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Understanding Price Prediction Models for Business Success

Understanding Price Prediction Models for Business Success

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In the world of business, pricing decisions can make or break a company's success. And where pricing is crucial, price prediction models are powerful tools where data meets decision-making.

Price prediction models serve as indispensable tools in the modern business landscape, leveraging data analysis to forecast pricing trends and inform strategic decision-making. These models play a crucial role in helping businesses optimize their pricing strategies, maximize profitability, and stay competitive in dynamic markets.

In this series, we delve into the world of price prediction models, examining their importance and practical applications in driving business success. At the heart of our exploration lies a specific project we undertook for a client whom we cannot name for confidentiality purposes, but that’s not important. 

Through this project, I aim to offer a deep dive into the intricacies of developing and implementing price prediction models, showcasing the decisions made, the challenges faced, and the tangible impact we’ve achieved from building a bespoke model for our client.

I want to share my insights from our project to provide a comprehensive understanding of how these models empower businesses to make informed pricing decisions and stay ahead in today's competitive landscape, so let’s jump right in!

A high-level understanding of price prediction models

Price prediction models utilize data analysis techniques to tell us the specific price for a given quote. These models are essential tools for businesses across various industries, helping them make informed pricing decisions to maximize profitability and maintain competitiveness. 

Typically, price prediction models incorporate historical pricing data, market trends, and other relevant variables to generate accurate predictions. They can range from simple linear regression models to more complex machine learning algorithms such as neural networks or ensemble methods. 

By understanding patterns and relationships within the data, these models help businesses anticipate changes in demand, adjust pricing strategies accordingly, and capitalize on market opportunities. 

While many existing models serve diverse business needs, our project demanded a solution tailored to our client’s unique requirements and capable of scalability. This pushed us out of our comfort zone, compelling us to innovate and devise a model that delivers unparalleled accuracy, efficiency, and scalability. And with that, we arrive at our project overview!

Project overview - Objectives, scope, and development

This project came to us as just another data science endeavor, scoped for three to six months with a primary focus on price prediction. Fortunately, our client had technically proficient personnel on their end that made collaboration and communication a much-needed breeze. 

We began by developing prototypes using common out-of-the-box algorithms like XGBoost and Random Forest, standard choices for price prediction tasks. While these models yielded accurate results, we chose to implement the solution in phases.

The first phase aimed to replicate pricing analyses conducted by human experts, mimicking their recommendations. Following stabilization, phase two focused on optimizing discounts to boost revenue and enhance quote win rates.

Initially, our client encountered resistance from the business side due to concerns about job displacement and risks associated with automated pricing. However, the objective was to streamline processes so senior analysts could prioritize high-value quotes, rather than waste their time and effort on smaller, less important ones.

Upon sharing initial model results, skepticism arose regarding reliability, marking a turning point in the direction of the project, evolving into a race to bridge the gap between complex algorithms and business comprehension. 

We realized that existing algorithms could not explain predictions comprehensively to meet the business's needs. While algorithms like Shapley and Captum Insights could identify influential parameters, they fell short of providing the level of explanation our client required.

Approaches to developing this model - Decisions and challenges

Since this part is going to be a little more detailed, I want to break it down into more digestible chunks for you.

1. Customized model development

Traditionally, creating separate models for each business category would have been impractical due to the sheer number of models required and the difficulty in managing them. Conversely, using existing algorithms would blend different categories, which wasn’t ideal either. 

So we had to develop a custom algorithm that allowed us to set parameters specific to our client's business needs.

Our goal was to provide transparency regarding the historical data informing our discount predictions. Our client faced specific constraints, such as varying discount patterns across customer categories like construction and industry clients, alongside different discount programs like rebates and sales discounts. Ensuring that discounts for one category didn't influence others was crucial.

Existing algorithms lacked the necessary detail, so we experimented with various approaches. Eventually, we settled on parametric decision trees, a modified version of traditional decision trees where we could customize certain nodes.

This approach yielded significant improvements in prediction accuracy, surpassing 90% from an initial 80%. 

2. Optimizing performance and scalability

We approached optimization differently, recognizing Python's limitations in concurrency and multithreading due to the Global Interpreter Lock. Scaling our project for hundreds of thousands of products across various geographies posed a significant challenge, with training for all combinations taking up to two days.

Acknowledging the need for production optimization, we implemented true multithreading to utilize all available CPUs. While libraries exist for certain cases, our price prediction algorithm's unique processes required a custom solution. Implementing multithreading reduced training time from over two days to just 3 hours.

At the risk of tooting my own horn, this system's configurability was impressive, allowing full resource utilization with larger systems boasting more CPUs. This scalability ensures straightforward adaptation to varying hardware configurations, further reducing processing time.

3. Adapting to changing market dynamics

Timing plays a crucial role, presenting both challenges and opportunities. The landscape shifted dramatically before and after COVID-19, impacting trends significantly. For instance, the surge in raw material costs compelled businesses to reconsider their pricing strategies to maintain profitability.

As is the case, implementing changes in large enterprises takes time due to the complexity of existing systems. Furthermore, the post-COVID landscape brought significant shifts, with the construction industry experiencing a boom and raw material prices soaring. This necessitated a recalibration of price analysts' strategies to adapt to the new dynamics.

Introducing changes like 'reduce all transformer discounts by 10%' or implementing specific adjustments for different geographies or product categories was a huge hurdle for the pricing team to adapt to new coding structures. However, we saw this as an opportunity to integrate these features into our new development.

With our system, adjustments could be made promptly. We provided levers for clients to fine-tune discounts, enabling them to make immediate changes, whether adjusting absolute discounts or implementing relative percentage reductions. 

While enterprise-level adjustments typically require a week to implement, our system streamlined the process, allowing changes to be executed within a day. This agility proved invaluable in navigating rapidly evolving market conditions.

The ability to implement changes on time highlighted a crucial, valuable feature of our price prediction system. Enhancing the system's usability became a priority alongside ensuring model accuracy. It was essential for stakeholders, including price analysts and business leaders, to recognize the system's benefits and actively utilize it.

4. Risk mitigation strategies

Incorporating guardrails was essential to address concerns about potential losses stemming from overly generous discounts. Drawing from past incidents, such as the case of a US real estate company facing bankruptcy after automating pricing, we prioritized risk mitigation.

To enforce these guardrails effectively, we implemented an additional model. This model automatically adjusts discounts if they exceed historical values, ensuring that pricing decisions align with past performance and mitigating the risk of significant losses. 

By proactively addressing potential risks, we safeguarded our client against potentially disruptive outcomes and enhanced the overall stability of the pricing system.

5. Overcoming business challenges

In many projects, especially this one, primary hurdles often stem from the business perspective. Convincing stakeholders required substantial effort, often involving multiple reports and thorough discussions. 

However, these obstacles served as valuable learning experiences.

Despite encountering significant resistance from the business side, overcoming these challenges only proved beneficial in the long run. Addressing these challenges helped us achieve project success and instill a deeper understanding of business needs and processes.

For those who deal with business hurdles, while initially daunting, navigating these obstacles is essential for driving meaningful change and achieving project objectives. By overcoming resistance and gaining insights into business operations, you not only ensure the success of a project but also lay the foundation for future endeavors effectively.

Looking ahead

In data science, innovations are always around the corner, particularly with the advent of large language models (LLMs) in areas like NLP, signaling significant advancements. 

However, in the domain of price prediction, reliance on traditional models analyzing historical data remains prevalent. While LLMs aren't yet widespread in this domain, anticipation for their integration, especially in forecasting and analyzing tableau data, hints at promising developments on the horizon.

For those wanting to explore deeper into this field, there are tons of resources to stay on top of developments in data science. Platforms like Medium host insightful blogs covering various aspects of the field. Additionally, participation in competitions on platforms like Kaggle provides hands-on experience and exposure to real-world problems. I’ve found these two to be extremely useful for my journey, so I’d definitely recommend starting here.

Even for those unable to actively participate, exploring kernels and notebooks shared by other users offers valuable insights into diverse problem-solving approaches. These resources serve as invaluable learning tools, ensuring you remain informed and continuously expand your knowledge in data science.

Exciting advancements are always on the horizon, whether in terms of ease of implementation, cost-effectiveness, or performance enhancements. Each innovation carries the potential to better serve businesses, driving continuous improvement and efficiency. As a passionate data scientist, I think this is a beautiful and assuring future to look forward to!

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