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LUC #40: Engineering Profit: A Deep Dive into Dynamic Pricing Systems

Plus, how OAuth 2.0 works, tips and strategies for effective debugging, and how the most prominent Git branching strategies work

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Engineering Profit: A Deep Dive into Dynamic Pricing Systems

What do Uber, Amazon, airlines, and Facebook all have in common?

Dynamic pricing.

It’s a major function of their business models. And it’s not just them either. Dynamic pricing is a key strategy for a lot of companies. Maybe even the company you’re currently working for.

Behind dynamic pricing sits a system that leverages software engineering, data science, and business strategy to help companies achieve business outcomes.

Today we’ll look at what dynamic pricing provides and the system behind it.

Let’s dive in!

Dynamic Pricing in Practice

Dynamic pricing is a key strategy for businesses to increase profitability.

It enables companies to maximize revenue during periods of high demand by increasing pricing while demand is high.

In times of low demand, dynamic pricing allows companies to lower prices to stimulate demand and maintain a consistent revenue stream. This ensures that they generate sales even during periods of lower demand.

It also helps businesses conduct competitive pricing by allowing them to make adjustments based on competitors' strategies.

And for businesses with physical products, dynamic pricing is essential to inventory management. It helps in optimizing stock levels, consequently minimizing potential losses.

Depending on the business’s needs and system, the above changes in price are often in real-time or near real-time.

I did say that Uber, Amazon, airlines, and Facebook all use dynamic pricing. Here’s how:

Uber adjusts fares based on rider demand and driver availability, leading to higher prices during peak times.

Amazon changes product prices frequently, considering competition, demand, and availability.

Airlines vary ticket prices based on booking time, demand, and seat availability.

Facebook uses dynamic pricing for its advertising slots, with costs fluctuating based on demand, ad placement, and competition.

And it’s not just these companies. Through these examples, you can see how dynamic pricing is actually used by a lot of companies. By understanding the business context, you’re now better equipped as an engineer to strategize and implement the technical solution, helping achieve business outcomes.

Understanding the Architecture of Dynamic Pricing Systems

Dynamic pricing carefully balances market demand, competitor pricing and inventory levels while adjusting prices in real or near-real time.

Beyond evaluating these factors, engineers must also build systems that can process large datasets quickly and automate scalable pricing decisions.

Dynamic pricing generally follows the ELT process (extract, transform, load).

It begins with data collection and analysis, capturing real-time sales, customer behavior, inventory status and competitor prices.

Data pipelines must be created to handle this influx from diverse sources, ensuring both the precision and speed necessary for the pricing algorithms.

After collection, efficient data storage is necessary for quick querying and analysis.

To improve real-time analytics performance, data warehouses should be optimized for analytical queries and scalable to accommodate data growth, as well as caching mechanisms and data indexing.

Moving from storage to decision-making, well-designed algorithms are the backbone of any efficient dynamic pricing strategy.

Many approaches can be taken, here are some of the most notable:

Rule-based systems

This is the simplest approach where price adjustments are based on predefined criteria.

Time-series forecasting

This approach analyzes historical data to set future pricing. ARIMA (AutoRegressive Integrated Moving Average) and Prophet (a forecasting tool developed by Facebook) are commonly used for time-series forecasting.

The challenge here is to accurately model and forecast pricing trends, taking into account seasonal variations and market shifts.

Machine learning models

Regression models, decision trees, and neural networks are often used to determine pricing based on historical information.

Multi-armed Bandit Algorithms

When you have multiple pricing strategies available, multi-armed bandit algorithms can be used to determine which one will provide the most revenue.

Challenges and Considerations

Crafting dynamic pricing systems comes with its own set of challenges.

Key among these is the need for scalability, ensuring the system can manage large data volumes, particularly during peak times.

Preserving data privacy and security is equally important while requiring systems to comply with legal and ethical standards.

Additionally, the design must consider how swift changes in pricing could influence customer perceptions and the overall brand reputation.

Each of these factors plays a crucial role in the successful implementation and operation of a dynamic pricing strategy.

Wrapping Up

Twenty years ago, dynamic pricing systems were rarely seen. Nowadays, a lot of companies have them.

The growing adoption and continuous development of dynamic pricing systems highlight the growing role of engineering in modern business.

How OAuth 2.0 Works (Recap)

OAuth 2.0 is an authorization framework that enables applications to access a user’s data on another service without sharing the user’s password.

It’s essentially a digital handshake between the app, service, and user, with everyone agreeing on what is shared.

The process generally follows 6 steps:

🔶 1) Request access
🔶 2) Redirect to service
🔶 3) Permission request
🔶 4) Authorization code
🔶 5) Exchange code for token
🔶 6) Use the token

There are typically 4 components involved in the process:

🔷 Client (app wanting access)
🔷 Resource owner (user)
🔷 Authorization server
🔷 Resource server

OAuth 2.0 provides multiple grant types to cater to different use cases. These grant types dictate how the application gets an access token. For most web applications, the Authorization Code Grant is the recommended and most secure method to obtain access tokens.

How the Most Prominent Git Branching Strategies Work (Recap)

When formulating your branching strategy, take the most relevant features from the strategies below and apply your own set of tweaks. Every project and team has its own unique needs and boundaries, which should be reflected in their Git branching strategy.

🔶 Feature Branching: A popular method where each feature gets its own branch to ensure that changes are isolated and code reviews are simplified.

🔶 Gitflow: has two permanent branches — a production and a pre-production branch, often referred to as the “prod” and “dev” branches. Features, releases, and urgent bug fixes get temporary branches. It’s a great approach for scheduled releases and handling multiple production versions.

🔶 GitLab Flow: A blend of feature and environment-based branching. Changes merge into a main branch, then to branches aligned with the CI/CD environment stages.

🔶 GitHub Flow: Similar to feature branching but with a twist. The main branch is always production-ready, and changes to this branch set off the CI/CD process.

🔶 Trunk-based Development: Branches are short-lived. Changes merge into the main branch within a day or two, and feature flags are used for changes that require more time to complete. This is ideal for large teams with disciplined processes.

Tips and Strategies For Effective Debugging

1) Define the problem
Identify the problem’s symptoms, and compare expected versus actual outcomes. Determine its scope, assess its severity and impact, and note steps to reproduce it. This clarity streamlines the troubleshooting process.

2) Reproduce it
Reproducing the bug is often the most effective way to pinpoint its cause. However, if this can't be done, try checking the environment where it occurred, search the error message online, assess the system's state at the time, note how often it happens, and identify any recurring patterns. These steps can offer vital clues.

3) Identify the cause
Logs are a big help in the debugging process; if they're insufficient, add more logs and reproduce the issue. Some additional strategies are to use debugging tools for insights, test components in smaller chunks, and try commenting out code sections to pinpoint the problem area.

4) Provide a postmortem
When a bug's cause is identified and resolved, thoroughly document the issue, the fix, and ways to prevent it in the future. Sharing this knowledge with the team is important to ensure everyone is informed and can benefit from the lessons learned, promoting a proactive approach to future challenges.

That wraps up this week’s issue of Level Up Coding’s newsletter!

Join us again next week where we’ll explore what is gRPC and when to use it, Semantic versioning (SemVer), and how SQL injections work, and how to protect your system from them.