Bayesian vs CatBoost: Decoding Purchase Intent for Smarter E-Commerce Pricing

Many pricing tools look at what people do. But that is not enough. To price well, we need to know why they do it. That is called purchase intent. If we miss it, the model might guess wrong and lose sales. CatBoost and XGBoost are fast and reliable, making them well-suited for delivering results quickly. But they do not explain how decisions are made Bayesian models take a different path:

Slower to train
Faster to run
Built to show how each signal connects

This article walks through both types of models and shows you what happens when you really pay attention to intent. The result? More conversions, higher margins, and pricing that just feels fairer all around.

Why Purchase Intent Recognition Is the Missing Link in Dynamic Pricing

The Problem with Behavior-Only Models

Most pricing tools stop at clicks and carts, but pricing is not just about what people do; it is about why they do it. A person may be curious or comparing prices. They might be checking shipping costs before leaving.

If your model treats all actions the same, it starts learning from a lot of noise. That leads to problems like:

Overfitting: The model reacts to patterns that do not lead to sales.
Mispriced offers: Giving discounts to people who were not going to buy. Serious buyers get ignored.
Wasted margin: Giving incentives where they are not needed. It cut the profits.

For a business leader, this means lost revenue, weaker margins, and frustrated customers.

This funnel graphic shows how broad behavioral signals (such as clicks and views) are filtered down into a narrower, more focused stream of intent-rich signals.

Why Intent Recognition Is Non-Negotiable

Intent recognition helps you:

Filters out noise and highlights users who are close to converting. Ignore casual browsers who are not likely to convert.
Use models to spot buyers: set prices that match their intent, not just their behavior.
It is not just a nice-to-have. If you care about pricing that works, intent is non-negotiable. Better signals = better pricing.

Current Landscape: CatBoost and XGBoost Dominate, But Are They Enough?

Most pricing models use machine learning to monitor user behavior. CatBoost and XGBoost are popular and fast, delivering accurate results. But pricing now needs intent, not just speed. Are they still enough?

CatBoost for Intent Prediction

CatBoost handles clicks, views, and cart data well. It is designed for structured user behavior.

It handles categories natively: You do not need to preprocess fields such as product type or user segment. It reads them as-is.
It is strong on tabular data: It finds patterns in shopping data when you add time or session details.
It is good at identifying intent: CatBoost can distinguish between casual browsing and serious buying. It can tell who’s ready to buy.

CatBoost is the most accurate for data with user intent. LightGBM and XGBoost are faster, harder to interpret.

Comparison Table:

The takeaway is clear: Speed alone is not enough; pricing models must also explain their decisions.

Model	Accuracy	Speed	Handles Categories	Easy to Explain
CatBoost	High	Medium	Yes	Medium
LightGBM	High	Fast	No	Low
XGBoost	Medium-High	Fast	No	Low

XGBoost is fast. It is suitable for big datasets and quick updates. But it is not easy to explain.

It Learns fast: it is well-suited for frequent updates.
It has built-in regularization that prevents the model from overfitting to noisy data.
It is difficult to explain: You can’t tell which part of the data is helping.
It misses small clues: such as who’s just browsing and who’s ready to buy.

Behavior alone is not enough. Pricing needs to understand intent, not just what users do. You need models that explain their decisions, not just make guesses. That is where intent-aware tools help. And where older models start to fall behind.

The Bayesian Alternative: Precision, Interpretability, and Speed.

Why Bayesian Programming Is Criminally Underused

They show their work: Bayesian models do not just give answers. They explain how they got there. Every prediction is supported by clear logic.
Fast when it is deployed: Bayesian models run quickly once deployed and, unlike black-box models, show precisely how signals connect.
Accurate and clear: They are as precise as CatBoost, but easier to explain.
Built for intent: Bayesian models use fewer but smarter inputs to tell who is browsing and who is ready to purchase.

Latency vs accuracy comparison

You need to know:

Bayesian: It combines speed with clarity. It is always easy to understand. It is built for real-time pricing
CatBoost: It provides high accuracy. But runs slower and is difficult to explain
XGBoost: It is the fastest. But it may miss minor signs of intent.

Your Experience: Matching Top Models with Bayesian Estimator

Benchmarked against top models: Tested against CatBoost and LightGBM. It is also benchmarked with XGBoost. They perform well in pricing and conversion.
Trust leads to better decisions: When teams understand the model, they trust the output. Quicker setup. Fewer mistakes.
External proof: Claude.ai ran Bayesian estimators on real datasets. The results were strong and easier to explain.

def get_cpds(self) -> Dict[str, pd.DataFrame]:
      “””Extract conditional probability distributions for analysis.”””

      if not self._fitted:
          self.fit()

      cpds = {}

      for cpd in self._model.get_cpds():

          if cpd.variable == ‘intent’:

              # Convert CPD to readable DataFrame
              values = cpd.values
              state_names = cpd.state_names

              # Build multi-index for parent states
              parent_combos = []

              if cpd.evidence:
                  import itertools

                  parent_states = [
                      state_names[parent]
                      for parent in cpd.evidence
                  ]

                  for combo in itertools.product(*parent_states):
                      parent_combos.append(combo)

                  index = pd.MultiIndex.from_tuples(
                      parent_combos,
                      names=cpd.evidence
                  )

                  df = pd.DataFrame(
                      values.T,
                      index=index,
                      columns=[
                          f”intent-{s}”
                          for s in state_names[‘intent’]
                      ]
                  )

              else:
                  df = pd.DataFrame(
                      values,
                      columns=[‘Probability’],
                      index=[
                          f”intent-{s}”
                          for s in state_names[‘intent’]
                      ]
                  )

              cpds[cpd.variable] = df

      return cpds

Now that we have compared traditional ML models with Bayesian approaches, let us see how these ideas come together in practice with MarginBoost.

Inside MarginBoost: A Framework for Intent-Aware Pricing

What Is MarginBoost?

MarginBoost is an in-house framework that spots buying intent using Bayesian logic.

At Flexiana, we developed MarginBoost, built to be fast, simple, and easy to explain, with a pricing module powered by Bayesian Optimization.

It works by combining:

CPTs to map user behavior
Mutual information to find what matters most
Marginal effects to adjust prices with precision

It does not need tons of data. Just the right signals.

Architecture diagram of MarginBoost

Real-World Use Case

Challenge: Too many users dropping off. Pricing did not match intent.

Fix: MarginBoost flagged high-intent users and adjusted prices in real time. The results speak for themselves.

Outcome:

30% better pricing precision
Fewer users leaving
More conversions

DIY Guide: Build Your Own Bayesian Intent Estimator

Setup Instructions with pgmpy

We can integrate this for you, but if you would rather build it yourself, here is how to get started:

To build and train your Bayesian model, start by setting up your environment with the pgmpy library.

1. Import libraries and define the model

Use BayesianModel to connect user actions to intent. For example:

from pgmpy.models import BayesianModel

  model = BayesianModel([
      (‘Search’, ‘Intent’),
      (‘Basket’, ‘Intent’)
  ])

2. Detect key user attributes

Traffic source
Search use
Filtering actions
Basket activity
Checkout behavior
Shipping research
Purchase history

These signals help your model tell who is just browsing and who is ready to buy.

Training and Validation

Split your data: Use 80% for training. Use 20% of the data to test intent prediction
Enable adaptive pricing: Keep training to stay in sync with evolving user behavior changes.

Data Split

80/20

→

Training

→

Validation

→

Pricing
Engine

Training Flow Chart

For decision-makers, the takeaway is that Bayesian intent models can be deployed with minimal data and deliver immediate business impact.

Understanding What Matters: CPTs and Signal Strength

These tools do not just crunch numbers; they give teams clarity on which signals truly drive purchases.

Conditional Probability Tables (CPTs)

CPTs help you understand each user’s behavior and its impact on intent.

Conditional Probability Tables (CPTs) are like cheat sheets for understanding what your users are up to. Once you train your Bayesian model, you get a table for every input. Each one shows how likely someone is to buy based on their behavior, such as adding items to their cart or skipping the search bar entirely.

This aids in identifying patterns:

Patterns start to jump out. If someone is filtering by price, they are likely ready to buy
Starting checkout means they are about to buy.
If they are double-checking shipping info, they may still be confused.

CPTs turn all that behavior into numbers you can use. No more guessing games, you know precisely which actions matter most.

CPT matrix for top attributes

Attribute	Value	Intent = High	Intent = Low
Basket	Yes	0.78	0.22
Checkout	No	0.90	0.10

Mutual Information

See which actions actually matter.

Mutual information shows how much one behavior tells you about another. In this case, how much a user action tells you about their intent to buy.

The higher the score, the stronger the link. For example:

“Added to cart” usually has a high score: It is closely tied to buying
“Scrolled the homepage” has a low score: it does not say much

This helps you rank behaviors by impact. So you can stop guessing and focus on the signals that actually matter.

Heatmap: Attribute Importance Scores

Sensitivity Analysis

Change one user action and see how predictions shift. Change just one thing: where your traffic comes from or what happens during checkout. Also, check how the model responds

It is a quick way to spot which moves make the most significant difference, and where a few minor changes can really boost your results.

Impact of Attribute Shifts on Intent Probability

Marginal Effects

Marginal effects show how one action changes the chance someone will buy.

It looks at two situations:

When someone takes an action (such as using filters).
When they do not.

Then it shows how much that difference affects the result.

For example:

“Added to cart” may increase the likelihood of purchase by 40%.
“Scrolled homepage” might not change much.

This shows which actions are essential and which ones are not.

Table: Marginal Effect Scores Across User Behaviors

User Behavior	Marginal Effect
Added to Cart	+40%
Started Checkout	+35%
Used Filters	+15%
Used Search	+5%
Scrolled Homepage	~0%

Structural Dependencies: Seeing the Network Behind Intent

Bayesian Networks Reveal Hidden Relationships

Think of Bayesian networks as maps of intent; they do not just predict, they reveal the hidden pathways behind user behavior.

Bayesian networks do not just predict; they also explain. They show how user behaviors intersect. They also show the ones that stay independent.

You can see:

Actions affecting intent directly
Behaviors working only when paired
Independent behaviors

This helps you understand the whole structure and the results.

Directed Acyclic Graph of Attribute Dependencies

Implications for Feature Selection

When you know how features interact, you can:

Remove extra features
Keep the important ones
Make the model work better

Less data. Better guesses. Clearer thinking.

Putting Bayesian Models into Action

To use your model in real systems, follow these steps:

Save the model: Use tools like joblib or pickle to store it
Build a service: Wrap the model with a simple app using
- FastAPI
- Flask
- Cloud function
Create an API: Add a /predict route to enable other tools to send data and retrieve results.

Simple Example

from fastapi import FastAPI, Request
  import joblib

  model = joblib.load(“model.pkl”)
  app = FastAPI()

  @app.post(“/predict”)
  async def predict(request: Request):
      data = await request.json()
      return {
          “result”: model.predict(
              [data[“features”]]
          ).tolist()
      }

This setup lets your model make real-time predictions. Use it for pricing and recommendations. It also works for other real-time tasks.

Integration Options: Plug MarginBoost into Your Stack

MarginBoost works with any setup. It is easy to connect.

API-Ready Modules for Real-Time Scoring

It scores user behavior in real time. It works during search and browse. Also runs at checkout.
Its endpoints are fast and straightforward.
It handles high traffic without decreasing speed.
It connects easily to your data layer or CDP.

It works with Top E-Commerce Platforms

It works with Shopify and WooCommerce. Also supportsMagento
It supports custom stacks like React and Next.js. Also workswith Vue.
It integrates smoothly with headless storefronts.
It is compatible with most tag managers and event pipelines.

Integration flow with Shopify/Magento

How MarginBoost Works with E-Commerce Platforms

MarginBoost integrates seamlessly with major platforms such as Shopify, Magento, and WooCommerce, providing real-time pricing and intent scores as your customers shop.

Here is what it looks like:

First, someone browses a product or heads to checkout. That action triggers the process.
Next, the store sends MarginBoost the product ID, user signals, and the contents of the cart via an API call.
MarginBoost jumps in, crunches the numbers, and returns a score, say, how likely the person is to buy, or what price might seal the deal.
The store uses that information and responds immediately by adjusting prices, but no more than the last displayed price, making a new offer, or suggesting related products.

Let’s look at an example.

Someone adds a high-margin product to their cart on Shopify. MarginBoost spots this and says, “Hey, this person’s likely to buy.” The store immediately displays a bundle deal or upsell based on the shopper’s intent.

MarginBoost Integration Flow

Custom vs Off-the-Shelf Deployment

Deployment Options

We handle setup, tuning, testing
Use our open-source starter kit. It comes with scoring logic and wrappers. Also includes platform adapters

Quick Setup and Clear Results

Implementation is typically completed within a week.
It needs minimal engineering time.
It gives instant insight into high-intent users.

Note: They say setup takes less than a week, but let’s be real – it depends on how complex your platform is and how ready your data is. Sometimes, getting everything up and running takes a couple of months. It is smart to start with a pilot first. That way, you can see how it works before rolling it out across the organization.

Keeping Your Model Up to Date

To maintain accuracy, your model needs regular updates:

Retrain regularly: Update monthly to reflect changing seasons and habits.
Monitor data changes: If user behavior shifts, your model may begin to make errors. You need to identify it early.
Track updates: Save each model update so you can compare results or go back if needed.

Off-the-shelf tools do not offer this control. Custom models let you control everything, keeping you proactive.

Fair Pricing That Builds Trust

If dynamic pricing is fair, it can be beneficial. It is not a strategy to change rates just because someone is in a hurry or unaware. It is taking advantage.

Fair pricing uses real signals. To get it right, teams require models they can explain. If you know why a price was set, you can catch problems and fix them. That helps keep pricing fair.

Bayesian models are transparent, so you can demonstrate that your pricing is not unfair based on location, age, or device type. When people see how things work, they trust you more. And that trust? It keeps them coming back.

In the long run, ethical pricing is not just the right thing to do. It is a good business.

Conclusion – Bayesian Models Are the Future of Intent-Aware Pricing

In digital business, pricing involves more than simply numbers. It is crucial to identify ready buyers and know the motivation behind their purchase. Indeed, human behavior can be predicted by machine learning models such as XGBoost and CatBoost. But they do not explain the reasons. They are correct, but they are like a black box.

The Bayesian model changes that. They show everything necessary to users. When data changes, they quickly adapt and handle uncertainty. You can therefore base pricing on actual intent rather than relying solely on clicks or views.

This is not just another upgrade. It is a shift toward pricing that reflects how people actually think and buy. At Flexiana, Bayesian models are the future of intent-driven commerce.

If you want fast and transparent pricing based on real insights, choose Bayesian models.

The Future of Intent-Driven Shopping

Bringing the Pieces Together

Intent modeling identifies when someone is ready to purchase.
Generative AI creates customized offers and messages instantly.
Recommendation engines are shifting from static lists to more personalized recommendations.

These tools will work together as one system. They guide each user journey based on what they are likely to do next.

Early Signs of Change

Personalized discounts: Prices change based on
User actions
Urgency to buy
Loyalty
AI-driven negotiation: In response to indications from the buyer, intelligent agents instantly modify offers.
Customer lifetime value optimization: Retention strategies are designed for high-value users.

It is Important Because

Good timing means more sales
Smart pricing protects profits
Personalized paths keep users coming back

E-commerce will move from fixed groups to flexible paths customized according to users’ behavior. Clicks, pauses, and visits become signals. These signals help systems learn and adjust automatically.