Dynamic Price Optimization for Ecommerce Platform

G. Uma Bhargavi, M. Balakrishna, H. Shivani, B. Bramhani and D. Pavithra

Department of Computer Science and Engineering Ravindra College of Engineering for Women, Kurnool, Andhra Pradesh,

India

Keywords: e‑Commerce Optimization, Dynamic Pricing, AI‑driven Pricing, Reinforcement Learning, Demand

Forecasting, Competitive Pricing Analysis.

Abstract: An adaptable price adjustment system plays a vital role in developing both e-commerce platform revenues

and interaction with customers. The research adopts an original AI- based strategy to optimize pricing

systems. The solution merges several operational functions that allow real-time price changes as well as

reinforcement learning-based optimization of prices and competitive pricing analysis and market demand

forecasting capabilities. The model employs automatic price mechanisms that adjust product costs in response

to customer interactions and market demand as well as competitive rate alterations by using ML and DL

techniques for integration. Through experimental tests reinforcement learning-based pricing methods

generated an 18% increase of revenue together with 10.2% growth in customer conversion and delivered 13%

better profit margin results. The e-commerce platform provides large companies with a flexible real- time

pricing system that operates rapidly. Through the research evaluation it is shown that AI-based price strategies

create a maximized market position and boost long-term profit while maintaining fair prices across market

changes.

1 INTRODUCTION

The competitive nature of e-commerce demands

digital competitors to use pricing strategies that

develop client relationships together with elevated

sales numbers and profits. Operating online stores

presents a unique set of challenges since markets

show less active demand along with unusual customer

behaviors and demanding business competition.

Traditional retail stands apart from online operations

since they operate according to different principles.

Tradition-based pricing methods that conduct cost

addition or use fixed markup margins fail to work

with rapidly changing conditions found in online

markets. Organizations with poor pricing

development experience both low sales numbers and

profitability losses because of missed revenue

opportunities.

The advanced pricing system known as "dynamic

pricing strategy helps businesses solve modern-day

business issues. The system uses real-time dynamic

pricing operations to maintain automated price

control for products. The operations system tracks six

fundamental trading factors which include both

customer patterns and market price rates together

with stock availability and seasonal trends along with

economic data points. Through this method

organizations maximize their revenue by maintaining

fair prices available to customers at every time

regardless of demand circumstances.

E-commerce platforms analyze past sales data

using artificial intelligence infrastructure that

machine learning methods have enhanced. The

platforms leverage this information to predict

upcoming demand needs before they make instant

automatic pricing changes. Time-series forecasting

technology together with deep neural networks using

reinforcement learning enable the creation of market-

based pricing solutions that follow client behavior

patterns. Companies utilize natural language

processing to obtain pricing strategies which exist

across websites and online marketplaces that their

competitors use.

The main area of study for this research paper

investigates an artificial intelligence-based dynamic

pricing solution intended to enhance online retailer

price performance and generate additional revenue.

The evolved pricing solution integrates multiple AI

prediction techniques with reinforcement learning to

create this framework. This pricing solution reaches

Bhargavi, G. U., Balakrishna, M., Shivani, H., Bramhani, B. and Pavithra, D.

Dynamic Price Optimization for Ecommerce Platform.

DOI: 10.5220/0013907300004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 4, pages

18-23

ISBN: 978-989-758-777-1

its highest possible level of prices as its final

outcome. This framework successfully resolves real-

time data management and computational complexity

as well as pricing fairness because it was designed for

e-commerce applications at scale.

2 LITERATURE SURVEY

The importance of dynamic pricing has become

essential for e-commerce operations because

scientists developed their research from basic rule-

based systems into Artificial Intelligence

optimization solutions. The market needed better

pricing methods when demand-specific techniques

together with cost-plus calculation failed to adapt to

fast market changes (S. Ikeda, et al. 2023). Research

confirms that fixed pricing techniques triggered

monetary losses whhile damaging customer

relationships so dynamic pricing solutions must be

immediately implemented (Q. Liu, et al. 2021) (H.

Xiao, et al. 2023). By integrating deep learning with

regression techniques businesses create predictive

models that drive major economic expansion and

significant increases in conversion counts (X. Xu, et

al. 2020). The use of time- series forecasting methods

requires historical data to establish pricing best

practices which boost operational effectiveness (J.

Luo , et al. 2020). System maintenance and

systematic data collection form essential

requirements for achieving successful machine

learning-based pricing execution due to performance

modifications caused by market situations (H. Obeid,

et al. 2023).

The redesigned versions of RL technology

function as a sophisticated system for dynamic

pricing which enables marketplace operations to

achieve optimized pricing methods. The dynamic

pricing attributes in RL-based models initiate price

alterations because of market price variations and

customer behavior modifications and competitor rate

movements ( S. Limmer, et al. 2019). The literature

confirms that training systems with reinforcement

learning produces superior pricing outcomes than

conventional approaches because these methods

generate maximal future revenue and dominant

market share (Luo, et al. 2017). These systems cannot

be implemented due to both computational

complexity and real-time scalability issues according

to (K. Valogianni, et al. 2020).

The integration of external market data consisting

of competitor prices and current market demands

provides the key to boost pricing dynamics in markets

(M. Jaswanth, et al. 2022). Game theorists

constructed auctions through pricing models to create

market competition protection methods per their

research findings (G.P. Ramesh, et al. 2022). Profit

growth happens through automated market responses

and price adjustments made possible by Fake-Time

competitive pricing systems using AI (M. Yin, et al.

2024). The current real-time data accuracy systems

remain challenging to handle since they need

organized approaches for cost reduction measures (H.

Zhu, et al. 2022).

Research establishes multiple operational and

ethical obstacles that occur with AI-powered dynamic

pricing systems even though they function

successfully across many business domains. System

performance declines when real-time pricing systems

handle large quantities of data therefore their

implementation requires cloud-based infrastructure to

preserve responsiveness. Studies demonstrate that

algorithmic pricing fairness has emerged as a main

issue since AI-based price selection processes reveal

bias patterns as noted by (L. Chen, et al. 2023). The

development of fairness-aware dynamic pricing

systems both prevents automated pricing

discrimination and increases pricing system

transparency according to (Z. Azadi, et al. 2019).

Studies confirm that AI-based dynamic pricing

systems generate higher revenues by using machine

learning and reinforcement learning to tweak

business positions in the market. The future

development requires addressing three main

problems involving computational complexity along

with fair pricing rules and immediate decision

execution. The proposed research develops adaptable

pricing system framework using real-time AI

optimization techniques which surpass present

methodologies.

3 METHODOLOGY

In order to optimize product prices on e-commerce

platforms in real-time, the suggested framework

combines machine learning, reinforcement learning,

and market data collected in real-time. Demand

forecasting, competitive pricing research,

optimization based on reinforcement learning, and

real-time pricing adjustment are the main components

of our methodology, as shown in figure 1.

Dynamic Price Optimization for Ecommerce Platform

Figure 1: Proposed methodology.

3.1 Demand Forecasting Module

The demand forecasting module of dynamic pricing

constitutes an essential component by employing past

sales data with market trends and external elements to

create future client demand predictions. The use of

accurate demand forecasts enables e- commerce

systems to dynamically optimize prices in order to

guarantee profitability together with competitive

standing. The implementation of exponential

smoothing and moving averages becomes inadequate

for predicting complex non- linear demand patterns.

The lack of precise accuracy in forecasts gets solved

by implementing advanced machine learning

techniques such as ARIA models together with Long

Short-Term Memory (LSTM) networks.

The demand function is typically modeled as:

Dt = f(Pt, Ht, Ct) (1)

where:

• Dt is the predicted demand at time t,

• Pt represents the current product price,

• Ht is the historical sales data and past trends,

• Ct includes contextual variables such as

promotions, holidays, and economic

indicators.

LSTM networks stand as highly effective predictive

models since they maintain the capability to detect

extended dependencies across time-series sequences.

The LSTMs maintain sequential data input for

learning how demand patterns change because of

prices together with environmental influences. BPTT

backpropagation trains the network while the

optimization process minimizes the forecasting errors

of parameters. The demand forecasting method

ARIMA recognizes linear patterns and seasonal and

trended linear patterns in order to provide short-term

forecasting capabilities.

The module incorporates external data such as

rival price information along with weather and social

media mood data for enhancing demand forecast

precision. The system continuously updates its model

with present sales data to adapt to market changes

which allows proactive price adjustments. The

demand forecasting module serves as the

fundamental element of dynamic pricing because it

provides data-based recommendations that match

actual market demand adjustments.

3.2 Competitive Pricing Analysis

The Competitive Pricing Analysis Module functions

as a major component for enabling e-commerce

platforms to modify prices according to competitor

pricing approaches. E-commerce markets with high

competition levels lead customers towards cross-

platform product price comparison before they buy

anything. After tracking real-time competitor pricing

and adjusting prices to match it will improve revenue

while boosting market share and retaining customers.

A system which integrates web scraping

technology with APIs along with AI capabilities

enables the collection and assessment of stable

competitor price data needed for analytical pricing

strategies. The Natural Language Processing (NLP)

application and machine learning techniques permit

the module to extract pricing information from

opponent domains and customer opinion websites

and additional online platforms.The competitive

pricing strategy can be formulated as:

Pt = αPc + (1 − α)Po (2)

where:

• Pt is the optimized price at time t,

• Pc is the competitor's price at time t,

• Po is the original price set by the retailer,

• α is a weighting factor (0 ≤ α ≤ 1) that

determines the influence of competitor prices.

A more sophisticated approach uses game theory to

model competitor interactions:

P ∗= argPmax(t = 1∑Tπ(P, Pc)) (3)

where:

• Pt∗ is the price that maximizes the profit

function π\piπ,

• Pc represents competitor pricing data.

3.3 Reinforcement Learning-Based

Price Optimization

The core element of the dynamic pricing framework

within the Reinforcement Learning-Based Price

assessment in the DQN system. Optimization Module

Demand

Forecasting

Module

Com

etitive

ricin

anal

sis

Reinforcement learning-based

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

ensures automatic price changes through responses to

customer behaviors and market modifications. The

model within reinforcement learning (RL) achieves

superior pricing outcomes than traditional practices

by continuously optimizing procedures while

meeting customer satisfaction needs and reaching

maximum revenue levels.

An RL model seeks to discover π as the optimal

policy to maximize total discounted revenue

throughout the duration.

where:

π ∗= argπmax(t = 1∑TγtRt) (4)

• γ is the discount factor (0 < γ≤ 1) that

determines the importance of future rewards.

Q-Learning stands as a prime reinforcement learning

algorithm used for pricing optimization through its

function of estimating state-action rewards.

Q(St, At) = Q(St, At) + η[Rt + γA′maxQ(St + 1

, A′) − Q(St, At)] (5)

where:

• (St,At) is the value of selecting action At in

state St,

• η is the learning rate that controls how

much new information overrides

old information,

• max 𝑄

(

𝑆

𝑡+1

, 𝐴

′

)

is the estimated maximum

future reward.

Many pricing decisions enable the pricing

model to improve its Q-values through

observations of actual outcomes.

Deep Q-Networks (DQN) serve as the solution for

managing extensive state-action spaces in e-

commerce pricing operations. A deep neural network

(DNN) functions as an alternative to traditional Q-

tables for approximate Q-function assessment in the

DQN system.

Q(St, At; θ) ≈ A′maxQ(St + 1, A′; θ) (6)

where θ represents the neural network parameters.

The optimized price Pt∗ is determined using the

trained RL model:

Pt ∗ = argAtmaxQ(St, At) (7)

The chosen pricing system uses selection models to

maximize long-term revenue and adjust according to

market changes.

The module implements reinforcement learning to

support continual learning and adaptive pricing

methods which achieve competitive rates while

optimizing revenue expansion

3.4 Real-Time Pricing Adjustment

This module performs instantaneous price

modifications that rely on present market trends and

customer interactions together with available stock

and external environmental factors. Through this

module e-commerce platforms can swiftly change

rates to accommodate market quantity shifts and their

competitors' offers and standard seasonal patterns and

economic forces. Organizations reach their highest

revenue potential and improved customer interaction

through dynamic pricing because it enables them to

offer competitive market rates.

The system implements price changes that occur

automatically and constantly respond to different

operational elements at once. A company needs to

compute pricing through the following method:

P ∗= argPmax(t = 1∑TRt(P)) (8)

where:

• Pt∗ is the optimal price at time t,

• Rt(P) represents the revenue function

dependent on price,

• T is the total time horizon for price

adjustments.

4 RESULTS AND DISCUSSION

Table 1: Dynamic pricing results.

TimeP

eriod

Revenue

(Dynamic

Pricing)

Revenue

(Static

Pricing)

Conversion

Rate

(Dynamic

Pricin

)

1 5000 5000 2.5

2 5300 5100 2.7

3 5800 5200 2.9

4 6200 5300 3.2

5 6800 5400 3.6

6 7400 5500 3.9

8000 5600 4.1

8 8600 5700 4.4

9 9200 5800 4.6

10 9900 5900 4.9

A performance evaluation of the proposed Dynamic

Price Optimization Framework consisted of testing

Static Pricing, Rule-Based Pricing, Machine

Dynamic Price Optimization for Ecommerce Platform

Learning Pricing and Reinforcement Learning- Based

Pricing strategies. A set of evaluation metrics

includes Revenue Increase (%), Customer

Conversion Rate (%), Profit Margin Increase (%)

together with Market Competitiveness Score as

presented in table1.

Figure 2: Revenue comparison.

Reinforcement learning pricing produces the most

significant increase in revenue at 18% according to

the research while machine learning brings 12%

growth and rule-based pricing generates 5% growth.

Figure 2 demonstrates how reinforcement learning

achieves price adjustments to real-time demand

variations and competitor activities thus resulting in a

revenue increase of 18% which exceeds machine

learning by 6% as well as rule-based pricing by 13%.

Figure 3: Conversion rate comparison.

When customers face pricing, options designed

through reinforcement learning methods they have

the highest conversion rate at 10.2% followed by

those using machine learning approaches which reach

7.5%. Figure 3 shows the importance of adaptive

pricing approaches since static pricing and rule-based

pricing both fail to maximize customer conversions

(figure 3).

Profit margins demonstrate significant growth

through AI pricing models because reinforcement

learning generates a 13% increase above machine

learning-based pricing results which achieve an 8%

margin. The dynamism of AI pricing algorithms

makes revenue maximization possible through

balanced prices which keep competition in check

according to figure4.

Figure 4: Profit margin increase by pricing strategy.

Figure 5: Market competitiveness.

A product's pricing strategy success to retain

market competitiveness is evaluated through the

Market Competitiveness Score. In Figure 5

reinforcement learning-based pricing

demonstrates the capability to respond

automatically to market shifts as well as

competitor price changes. It scores 9.3 out of 10.

A research study indicated that e-commerce

businesses obtain superior outcomes through AI-

based dynamic pricing systems especially

reinforcement learning methods. The ability to

make price modifications immediately according

to market conditions and customer preferences and

competitor behavior ensures both sales revenue

optimization and customer satisfaction

improvement. Future development will

concentrate on improving user experiences

through produce better predictions and multi-

channel approaches and individual pricing

methods.

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

5 CONCLUSIONS

Dynamic pricing optimization stands as a vital

condition that improves e-commerce platforms

through increased competitiveness and better

profitability and customer retention. The research

implemented an AI-powered dynamic pricing system

which unifies forecasting systems with competitive

analysis and reinforcement learning methods and

real-time pricing adaptions. AI-based pricing

algorithms prove superior to static and rule- based

pricing approaches since they generate better revenue

figures and improved profit margins and better

customer conversion numbers. Reinforcement

learning-based dynamic pricing models yield the

greatest revenue increase of 18% because of their

capability to perform automatic price decision

optimization. A customer conversion rate improves

substantially (10.2%) when pricing models use AI to

respond dynamically to market demand and

competitor movements. The utilization of

reinforcement learning-based optimization methods

results in a 13% improvement of profit margins

because dynamic pricing effectively maximizes long-

term profitability. This module for real-time pricing

adjustment brings excellent scalability and quick

responsiveness to e-commerce operations at large

scales.

REFERENCES

G.P. Ramesh, Pallavi, H. Abdullah and B.D.

Parameshachari, "Design and Comparative Analysis of

Microstrip Patch Antenna by Using Various Materials

in HFSS", Distributed Computing and Optimization

Techniques: Select Proceedings of ICDCOT 2021, pp.

303-312, 2022.

H. Zhu, "A simple heuristic policy for stochastic inventory

systems with both minimum and maximum order

quantity requirements", Annals of Operations

Research, vol. 309, no. 1, pp. 347-363, 2022.

H. Xiao, L. Chen and Z. Dai, "Automatic Pricing and

Replenishment Strategy for Vegetable Products Based

on ARIMA Prediction and Particle Swarm

Optimization Algorithm," 2023 IEEE International

Conference on Electrical, Automation and Computer

Engineering (ICEACE), Changchun, China, 2023, pp.

1095- 1099, doi: 10.1109/ICEACE60673.2023.10442

292.

H. Obeid, A. T. Ozturk, W. Zeng et al., "Learning and

optimizing charging behavior at PEV charging stations:

Randomized pricing experiments and joint power and

price optimization", Applied Energy, vol. 351, pp.

121862, 2023.

J. Luo and C. Sun, "Optimization of charging pricing

strategy based on user behavior and time-of-use tariffs",

Journal of Southeast University, vol. 51, no. 6, pp.

1109-1116, 2021.

K. Valogianni, W. Ketter, J. Collins et al., "Sustainable

electric vehicle charging using adaptive pricing",

Production and Operations Management, vol. 29, no. 6,

pp. 1550-1572, 2020.

L. Chen, T. Dong, J. Peng and D. Ralescu, "Uncertainty

analysis and optimization modeling with application to

supply chain management: a systematic review", Math

ematics, vol. 11, no. 11, pp. 2530, 2023.

Luo, Y. F. Huang and V. Gupta, "Stochastic dynamic

pricing for EV charging stations with renewable

integration and energy storage", IEEE Transactions on

Smart Grid, vol. 9, no. 2, pp. 1494-1505, 2017.

M. Jaswanth, N. K. L. Narayana, S. Rahul, R. Subramani.

and K. Murali., "Product Price Optimization using

Least Square Method," 2022 IEEE 2nd International

Conference on Mobile Networks and Wireless

Communications (ICMNWC), Tumkur, Karnataka,

India, 2022 pp.1- 5, doi: 10.1109/ICMNWC56175.20

22.10031834.

M. Yin, M. Huang, D. Wang, S.C. Fang, X. Qian and X.

Wang, "Multi-period fourth-party logistics network

design with the temporary outsourcing service under

demand uncertainty", Computers & Operations

Research, vol. 164, pp. 106564, 2024.

Q. Liu, L. Ma, L. Li and Y. Liu, "Optimal Robust Real-time

Pricing Algorithm Based on Utility Maximization for

Smart Grid," 2021 4th International Conference on

Energy, Electrical and Power Engineering (CEEPE),

Chongqing, China, 2021, pp. 836-841, doi:

10.1109/CEEPE51765.2021.9475651.

S. Limmer and T. Rodemann, "Peak load reduction through

dynamic pricing for electric vehicle charging",

International Journal of Electrical Power & Energy

Systems, vol. 113, pp. 117-128, 2019.

S. Ikeda, N. Nishimura and S. Umetani, "Operating Range

Estimation for Price Optimization," 2023 IEEE

International Conference on Data Mining Workshops

(ICDMW), Shanghai, China, 2023, pp. 31-36, doi:

10.1109/ICDMW60847.2023.00012.

X. Xu, D. Niu, Y. Li et al., "Optimal pricing strategy of

electric vehicle charging station for promoting green

behavior based on time and space dimensions", Journal

of Advanced Transportation, vol. 1, pp. 8890233, 2020.

Z. Azadi, S.D. Eksioglu, B. Eksioglu and G. Palak,

"Stochastic optimization models for joint pricing and

inventory replenishment of perishable products",

Computers & industrial engineering, vol. 127, pp. 625-

642, 2019.

Dynamic Price Optimization for Ecommerce Platform