Competitive Intelligence 2026: Mobile Proxies, Strategies, and Automation for Brands

Introduction: Why This Topic Matters and What Readers Will Learn

Competitive intelligence in e-commerce and performance marketing is maturing rapidly. By 2026, it's not the loudest brands that win, but those who can see the market better, make faster decisions, and base them on verified data. Paradox? Data has increased, but value lies in its relevance and localization. Assortment and prices change from city to city, ad creatives are displayed based on interests and regions, and search positions depend on devices and location signals. Without a regional perspective, you see only a shadow of reality.

Mobile proxies have become a key infrastructure element of competitive intelligence. They allow for market research just like real users from specific cities and mobile networks, rather than an abstract data center. This is not about “cheating” systems: it’s about accurately localizing measurements, conducting tests, maintaining speed, and making precise requests for open data. When comparing prices and creatives, we must see them as target audiences do.

This guide will cover the topic from A to Z: from the basics of Competitive Intelligence (CI) and OSINT to advanced scenarios: real-time monitoring of competitor prices on Wildberries, Ozon, Amazon, analyzing creatives in Facebook Ads Library and Yandex.Direct, tracking assortments and stock status, SERP monitoring in Google and Yandex across 50+ cities, and systematic work with reviews and reputation. We’ll discuss tools, architecture, Python automation, BI integrations, legal aspects, and common mistakes. You’ll receive step-by-step instructions, checklists, frameworks, and real cases with numbers.

The goal is simple: to provide you with a real lever of competitive advantage that you can implement in the nearest sprint.

Basics: Fundamental Concepts (For Beginners)

What is Competitive Intelligence in E-Commerce

Competitive Intelligence (CI) is the systematic collection, analysis, and dissemination of information about the market, competitors, customers, and sales channels to make strategic and tactical decisions. In e-commerce, CI relies on public sources: product cards, prices and promotions, ad creatives, search results, reviews and ratings, social media content, public reports.

OSINT vs CI

OSINT (Open Source Intelligence) refers to the collection of open data. CI includes OSINT but adds interpretation, business context, and action. The formula is simple: data without decisions is an expense, not an asset.

Why Mobile Proxies are Important

Mobile proxies provide internet access through mobile operators' IP addresses. Their specificity—real geolocation and typical device behavior—allows for accurately viewing regional prices, product availability, local creatives, and search results. Static data center IPs often show an “averaged” or “suspicious” picture that a real user does not see.

Key benefits of mobile proxies for CI: precise geo-localization by cities and operators; stable “sticky” sessions that resemble real user behavior; a high probability of correct regional content delivery; and the ability for QA checks across devices (mobile results vs. desktop).

Ethics, Legal Frameworks, and What is Allowed

The collection of open data is permissible under certain conditions: do not collect personal data without grounds; respect robots.txt and terms of service; avoid overloading resources; use official APIs when possible; document the data source and time; do not bypass paid or restricted areas or protective mechanisms. In the EU, GDPR and ePrivacy apply; in Russia, 152-FZ; in the US and several countries, case law is developing that recognizes the legality of collecting public information if conducted lawfully. In summary: 'open and legitimate,' 'carefully and fairly,' 'for a beneficial purpose.'

How Proxies Fit into the CI Stack

A typical stack includes: data sources and APIs; network and proxy layers (including mobile); collection (scripts, orchestration); cleansing and normalization; storage (DWH, lakehouse); analytics and models; visualization and alerts; decision-making and experimentation. Proxies are part of the network layer, ensuring correct geo-reporting and reproducibility of measurement metrics.

Three Levels of CI Maturity

• Level 1: Manual. Analysts manually check prices and creatives from the necessary cities through mobile proxies, recording data in spreadsheets.
• Level 2: Semi-Automatic. Python scripts collect data on a schedule, proxies are managed via API, and data is sent to the database and BI.
• Level 3: Production. Streamlined integration, SLA on metrics, real-time alerts, on-the-fly A/B decisions (e.g., automatic price reevaluation).

Deep Dive: Advanced Aspects of the Topic

CI Pipeline Architecture

We aim for a robust system: declarative task planning (e.g., Airflow), environment isolation, retries and deduplication, idempotency in uploads, version control of schemas. For data—separation into bronze-silver-gold: raw, cleansed layers, and business aggregates. For displays—subsystems for marketing, pricing, assortment, and SEO.

Network and Proxy Strategy

The essence of mobile proxies is not to constantly change IPs, but to provide correct regional content. We use “sticky” sessions of 10-30 minutes to view consistent outputs and avoid fragmenting measurements. City, operator, device type—parameters we retain in the metadata of each sample. We avoid excessive parallelism: better to do less but do it well.

Normalization and Entities

Key entities: product (SKU), competitor SKU (cross-matching matrix), price (list, promo, coupons), availability (in stock, out of stock), ad creatives (texts, images, formats), SERP positions (mobile/desktop), reviews (text, sentiment, topics). Normalization includes deduplication, SKU mapping, brand and attribute unification, and promo mechanics dictionaries.

Data Quality and Credibility

We introduce rules: threshold checks (minimum and maximum prices), currency validation, collection time monitoring, geolocation markers, and missing value share control. Any anomaly (e.g., price = 0) goes into quarantine for manual verification. For reviews—filtering out spam and auto-generated templates, marking suspicious spikes.

Analytical Frameworks

• OODA for CI: Observe (collection), Orient (context), Decide (rule/hypothesis), Act (change in price, creative, promotion). We close the cycle every week or every day in fast-moving categories.
• 3x3 Data Fit: Coverage (cities, operators, devices), Depth (update frequency, granularity), Compliance (robots, ToS, load limits).
• SKU-Window: focus on 20% of SKUs that yield 80% of turnover, expanding to 'perimeter' based on seasonality.

2026 Trends

• Regionalization of prices and creatives is intensifying: marketplaces and advertising platforms are segmenting audiences more finely.
• Enrichment of open data with insights through LLM: automatic summaries of reviews, thematic clustering, and detecting weak signals.
• Ethics and compliance are becoming a competitive advantage: 'clean' processes reduce risks and accelerate implementation.
• Real-time alerts for prices and stock on top-SKUs are becoming standard.

Practice 1: Real-Time Price Monitoring on Marketplaces

Task

To obtain relevant prices and promotional configurations from competitors on Wildberries, Ozon, and Amazon, with a regional perspective and frequency ranging from 15 minutes to 24 hours, in order to manage RRP, discounts, coupons, and product uploads.

Legal and Technical Foundations

It is preferable to use official APIs for sellers where possible or publicly available catalogs and product cards compliant with robots.txt and ToS. Data collection should be done carefully, with request limitations and respect for platforms. Mobile proxies are used for accurate geolocation and to verify how prices and mechanics appear to buyers in specific cities.

Solution Architecture

• A list of target competitor SKUs and matchings: mapping your SKU to competitors.
• Task scheduler: update critical SKUs more frequently.
• Mobile proxies: “sticky” sessions of 10-30 minutes per city.
• Price and promo collection: product card, cart (where permissible), pop-up coupons, subscription offers.
• Normalization: currency, promo lines, category.
• Storage: time-series and layers for BI.
• Alerts: if a competitor reduces the price by X%, send a notification to the channel.

Step-by-Step Scenario

1. Define the priority SKU pool (top 200 by turnover).
2. Collect competitor links on WB, Ozon, Amazon, or their SKU/ASIN.
3. Set up mobile proxies by cities: Moscow, St. Petersburg, Yekaterinburg, Novosibirsk, Kazan, etc.
4. For each city-SKU pair, make a careful request to the product card, extract price, discount, coupons, availability status.
5. Record metadata: city, operator, time, device (mobile/desktop).
6. Save to the database and calculate the minimum, median, and promo prices for the market.
7. Set up alerts and check for false triggers.

Mini Example in Python: Careful Request with Mobile Proxy

import requests

target_url = "https://example-marketplace/product/sku123"

proxies = {"http": "http://user:pass@proxy_host:proxy_port", "https": "http://user:pass@proxy_host:proxy_port"}

headers = {"User-Agent": "Mozilla/5.0 (Linux; Android 14) Mobile"}

resp = requests.get(target_url, headers=headers, proxies=proxies, timeout=30)

if resp.ok: print("OK", len(resp.text))

Quality Checklist

• Check currency and taxes, discounts, and coupons.
• Save the city and time of the request.
• Monitor anomalies (price = 0, outlier).
• Compare prices within a time window, not moment to moment.

Result

You receive an operational 'heat map' of competitor prices by region and automatic prompts for adjustments to your pricing and promotional strategy.

Practice 2: Analyzing Creatives in Facebook Ads Library and Yandex.Direct

Task

Identify the best creative strategies from competitors, understand messages and offers by region, and compile a library of patterns for testing.

Data Sources

• Facebook Ads Library: a public library of active ads by pages, keywords, and geography.
• Yandex.Direct: semantics, display regions, texts and extensions; available previews and reports through API.

Approach

1. Define your target list of brands and key topics.
2. Collect active ads: creatives, texts, formats, landing pages.
3. Document regional parameters (city, language, currency, if available).
4. Classify creatives by hypotheses: price, gift, installment plan, scarcity, UGC, expertise.
5. Summarize patterns and rank them by frequency and duration of display (indicative of effectiveness).

How Mobile Proxies Help

They allow for viewing previews and regionally-targeted interface elements as a user from the desired city would see them. Important: use official tools and preview modes where available, and comply with platform rules.

Mini Example: Requesting from the Open Ads Library

import requests

url = "https://graph.facebook.com/v19.0/ads_archive"

params = {"search_terms": "brand", "ad_type": "all", "ad_reached_countries": "RU", "access_token": "YOUR_TOKEN"}

resp = requests.get(url, params=params, timeout=30)

print(resp.status_code)

Creative Analysis Template

• Problem/Offer/Evidence/Call to Action (POECA): identify key elements.
• Format: static, carousel, video, story.
• Triggers: price, scarcity, social proof, new arrival.
• Regional markers: local holidays, slang, delivery times.

Result

A set of hypotheses for A/B tests and a message map by region, backed by real examples from competitors.

Practice 3: Tracking Assortment and Stock Status

Task

Understand what competitors are selling, where and in what volume products are available, which SKUs are going out of stock and when they return. This is key to tactical windows of opportunity: if a competitor's stock drops—boost your offer.

Methodology

1. Gather a competitor's catalog: categories, SKUs, attributes, prices.
2. Document stock status and availability windows (in stock, out of stock, pre-order).
3. Correlate with price and promotional spikes.
4. Identify 'thin spots': SKUs where competitors often go out of stock.

Use of Mobile Proxies

Stock often depends on warehouse and city of delivery. Mobile proxies provide visibility into local stock and availability by specific regions. It's important to retain city and delivery parameters in requests, where allowed by the interface.

Pseudocode for Stock Tracking

For each city: for each competitor SKU: request card and availability block; extract status; log to database with timestamp; aggregate daily availability windows and trends.

Analytics

• Stock-to-Price: how competitors change prices with low stock.
• Back-in-Stock Alerts: competitor returns—adjust bids and discounts accordingly.
• Assortment Gaps: missing subcategories among competitors represent your opportunities.

Result

You know where and when competitors are weak and prepare targeted promos for regional windows of opportunity.

Practice 4: Monitoring SERP Positions in Google and Yandex from 50+ Cities

Task

Daily visibility of positions for key queries in mobile and desktop results across different cities, to manage SEO and local campaigns.

Legal and Technical Context

Direct parsing of SERPs is limited by search engine terms. For large-scale measurements, use licensed SERP data providers that offer APIs and comply with rules. Use mobile proxies for targeted manual validations and QA, to ensure that data reflects the real situation in the city.

Process

1. Gather semantic clusters (brand, category, informational).
2. Set up daily position collection by cities through authorized SERP API providers.
3. Conduct manual quality checks through mobile proxies on a sample of queries.
4. Aggregate data into displays: position, pixel visibility, presence of snippets.
5. Add alerts: drop in position by more than N points.

Mini Example of Queries to Third-Party SERP API

import requests

params = {"q": "buy sneakers", "device": "mobile", "location": "Moscow", "api_key": "YOUR_KEY"}

r = requests.get("https://serp-provider/api/search", params=params, timeout=30)

print(r.json().get("organic", [])[:3])

Metrics

• Visibility Score (weighted visibility in the top 10).
• Share of Outcome (traffic share from the result considering blocks).
• Local Winner Map — a map of cities where you outshine competitors.

Result

A dynamic map of positions by cities, driven by content, link campaigns, and local factors.

Practice 5: Analyzing Reviews and Competitors’ Reputation

Task

Automatically gather reviews from marketplaces and open platforms within rules, assess sentiment and themes, uncover product insights and risks.

Methodology

1. Collect open reviews while adhering to platform terms and limitations.
2. Cleansing: removing spam, templates, duplicates.
3. Classification: themes (quality, delivery, packaging, sizing), sentiment (positive, neutral, negative).
4. Summarization: insights by category, city, and time.

Pseudocode for Simple Sentiment Analysis

reviews = load_reviews()

for r in reviews: r.lang = detect_lang(r.text); r.sentiment = simple_model.predict(r.text); r.topics = topic_model(r.text)

aggregate_by(city, sku, sentiment)

Role of Mobile Proxies

Some reviews and ratings are displayed with local nuances (e.g., filters by delivery). Mobile proxies help to look at the product card through the lens of an urban shopper and gather correct metadata.

Conclusions

• Identify 'hidden' reasons for returns and dissatisfaction.
• Repeated complaints about competitors present product opportunities.
• Regional differences in perception will suggest local FAQs and content.

Common Mistakes: What NOT to Do

• Ignore Compliance. Disregarding ToS, robots.txt, and ethical constraints can lead to blocks and reputational risks.
• Chase Quantity. 50 cities × 24 times a day without a goal leads to noise and expenses. Focus on ROI of hypotheses.
• Mix Geos. Requests without stable sessions and city fixation distort measurements.
• Neglect Promo Normalization. Different types of discounts cannot be summed up 'as is'—bring them to a unified model.
• Alerts without Validation. Any automated response must have protection against noise signals.
• Stagnate at Data Collection. Collecting data for the sake of it doesn't solve problems—close the cycle with actions.

Tools and Resources: What to Use

Network and Proxies

• Mobile Proxies with API: city selection, operator, 'sticky' session, metadata. Look for providers with transparent policies and logging.
• Fallback Types: residential IP for tasks without strict mobile specificity; data center IPs for service calls.

Collection and Browser Automation

• Requests, httpx: lightweight requests.
• Playwright / Selenium: for real client rendering. Use moderately and within platform rules.

Orchestration and Data

• Airflow, Prefect: scheduling, retries, dependencies.
• Kafka / PubSub: streaming event delivery (e.g., 'price changed').
• DWH/Lakehouse: BigQuery, Snowflake, ClickHouse, DuckDB — tailored to your workload profile.

Analytics and ML

• Python Stack: pandas, Polars, scikit-learn for basic analytics.
• NLP: sentiment and topic analysis of reviews using prebuilt models.

BI and Alerts

• Power BI, Tableau, Metabase: displays for marketing and sales.
• Alerting: notifications in Slack/Telegram on triggers.

Documentation and Quality Control

• Data Contracts: schemas and field expectations.
• Quality Monitoring: share of empty fields, anomalies, latency.

Case Studies and Results: Real Application Examples

Case 1: Dynamic Reevaluation of Top SKUs

Category: electronics, 180 SKUs. Regional price monitoring on WB and Ozon every 60 minutes in 12 cities using mobile proxies and “sticky” sessions. Result over 8 weeks: conversion growth of 6.8% in cities with targeted adjustments, a 9% reduction in over-discounts, and a 3.2% savings on promotional budget as a percentage of category turnover. The key was focusing on 40 SKUs that accounted for 70% of revenue and a 'safety step' algorithm for prices.

Case 2: Creative Pattern and CPA

Category: fashion. Analyzing ad libraries from 7 competing brands. A sustainable storytelling pattern with UGC and local delivery accents was identified. Testing in two cities increased CTR by 24% and reduced CPA by 11% over three weeks. An important nuance—outlier events in the region were factored into the messaging and timing of publication.

Case 3: Windows of Opportunity from Out-of-Stock

Category: children's products. Tracking competitors' stocks in 10 cities. Recurring stock breaks were noted for 5 key SKUs on Wednesdays. Targeted promos on these days resulted in a 14% revenue increase in affected regions without increasing discounts.

Case 4: Local SEO Gap

Category: home appliances. Monitoring SERP through a provider with QA validation using mobile proxies. Cities were identified where competitors outperformed in local informational queries. Local guides and stock availability pages yielded a 17% visibility increase and a 9% increase in organic traffic over two months.

FAQ: 7–10 In-Depth Questions

Can Mobile Proxies Bypass Website Protection?

The goal of mobile proxies in CI is correct localization of measurements and QA. We do not bypass protections, do not circumvent paid or restricted sections, and respect ToS and robots.txt. For large-scale tasks, use official APIs and authorized data providers.

How Many Cities Should Be Covered?

Base it on contribution to turnover and logistical mapping. Start with 8-12 cities, then expand to clusters with anomalous price and demand behavior.

How Often Should Data Be Updated?

Top SKUs and key queries—every hour or every 3 hours. Long tail—daily or several times a week. Focus on useful frequency, not maximum frequency.

What are 'Sticky' Sessions for?

To ensure measurements are reproducible: one session per city and time window reflects user experience, reduces noise, and eliminates context mixing.

Can Data Center Proxies Be Used Instead of Mobile Ones?

For some tasks—yes, especially for non-geo-dependent API requests. But for localized content and mobile results, mobile proxies provide a more accurate picture.

How to Handle Reviews and Personal Data?

Only collect open reviews within platform rules; do not extract personal data without legal grounds; comply with local laws and platform policies.

How to Integrate CI with BI?

Standardize schemas, build displays for teams (pricing, marketing, SEO), automate alerts, document SLAs for data and metric catalogs.

What KPIs Should Be Set for the CI Team?

Data accuracy and freshness, coverage of key cities and SKUs, proportion of hypotheses reaching A/B tests, impact on revenue and margins, and speed of responsiveness to market changes.

How Dangerous are Temporary Anomalies?

High when collected frequently: check anomalies with re-measurement, filter by median of the window, eliminate false triggers.

Where to Save and Where Not?

Save on noisy segments and rare SKUs; do not skimp on network quality, compliance checks, and promo normalization.

Conclusion: Summary and Next Steps

Competitive intelligence in 2026 is a discipline of speed, localization, and compliance. Mobile proxies play a key role in ensuring an accurate regional picture: prices, availability, creatives, and search positions must be measured as real users see them. But proxies alone are merely a component. Value is born from the process: collection within rules, normalization, analytics, alerts, and actions that close the loop.

Your next steps: define priority SKUs, cities, and metrics; set up mobile proxies with 'sticky' sessions; build a minimal pipeline for prices and assortments; add creatives and SERP; integrate displays into BI and set alerts; introduce a weekly OODA cycle. Start small, but do it right. In 6-8 weeks, you'll see how CI becomes a source of transparent decisions that drive revenue.