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Actowiz Metrics Now Live!

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Actowiz Metrics Now Live!

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    [continent:protected] => GeoIp2\Record\Continent Object
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            [validAttributes:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [0] => queriesRemaining
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    [registeredCountry:protected] => GeoIp2\Record\Country Object
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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    [traits:protected] => GeoIp2\Record\Traits Object
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            [validAttributes:protected] => Array
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            [validAttributes:protected] => Array
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                    [1] => accuracyRadius
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                    [7] => postalConfidence
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    [postal:protected] => GeoIp2\Record\Postal Object
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            [validAttributes:protected] => Array
                (
                    [0] => code
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    [subdivisions:protected] => Array
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            [0] => GeoIp2\Record\Subdivision Object
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                    [record:GeoIp2\Record\AbstractRecord:private] => Array
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                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
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                                    [es] => Ohio
                                    [fr] => Ohio
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                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [validAttributes:protected] => Array
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 country : United States

 city : Columbus

US

Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)

Introduction

Australia’s electronics ecommerce market has experienced rapid pricing fluctuations over the past six years. Online retailers frequently adjust prices due to flash sales, marketplace competition, inventory levels, and seasonal demand spikes. In such a volatile environment, even small pricing gaps can significantly impact conversion rates and revenue.

This is where Kogan category-wise pricing data scraping becomes a powerful solution for retail intelligence. By systematically capturing structured product pricing across categories such as electronics, appliances, and lifestyle products, businesses gain clarity into pricing shifts and competitor positioning. Using the Kogan Data Scraping API, retailers and brands can automate large-scale data collection, ensuring accuracy and speed while eliminating manual tracking limitations.

Between 2020 and 2026, ecommerce electronics pricing volatility increased by nearly 31%, driven by supply chain disruptions, global semiconductor shortages, and aggressive discount strategies. Retailers that adopted automated price intelligence tools during this period reported stronger margin protection and faster promotional responses.

In today’s digital-first marketplace, category-wise pricing insights are no longer optional—they are essential for staying competitive and closing dynamic price gaps before they impact profitability.

Tracking Market Fluctuations with Structured Trend Analysis

Retailers that Scrape Kogan ecommerce price trends gain historical visibility into dynamic pricing cycles. From 2020 to 2026, electronics categories such as laptops, smartphones, and TVs experienced rapid price swings due to supply constraints and seasonal demand.

Electronics Price Volatility (2020–2026)

Year	Avg. Monthly Price Change	Flash Sale Frequency	Margin Risk Level
2020	4.5%	18%	Medium
2021	6.8%	22%	Medium
2022	8.9%	26%	High
2023	10.2%	31%	High
2024	11.7%	34%	High
2025	12.4%	37%	High
2026*	13.6%	41%	Very High

Retailers leveraging structured trend analysis reduced reactive discounting by 17%, improving pricing stability across high-value SKUs.

Improving SKU-Level Benchmarking Accuracy

By implementing scraping Kogan pricing data, businesses can build a structured Kogan Product & Pricing Dataset that captures SKU-level details such as discounts, stock status, ratings, and category tags.

Between 2021 and 2026, SKU counts in consumer electronics expanded by 28%, making manual monitoring inefficient. Automated datasets improved pricing comparison accuracy from 63% to 92%.

SKU Benchmarking Accuracy (2021–2026)

Metric	Manual Tracking	Automated Dataset
SKU Coverage	65%	95%
Discount Tracking Accuracy	61%	93%
Update Frequency	Weekly	Daily/Hourly

Accurate SKU benchmarking ensures businesses respond strategically rather than broadly discounting entire categories.

Category-Level Gap Identification

Retailers that Extract Kogan product pricing by category can evaluate electronics, home appliances, and lifestyle products separately. With Kogan category-wise pricing data scraping, category managers identify price disparities within specific segments.

From 2020–2026, category-level price gaps widened most significantly in consumer electronics due to international supply disruptions.

Category Price Gap Analysis (2020–2026)

Category	Avg. Price Gap vs Competitors	Promo Intensity
Electronics	9.8%	High
Home Appliances	7.2%	Medium
Lifestyle	5.6%	Medium

Segmented insights help retailers close pricing gaps selectively, preventing unnecessary margin erosion across stable categories.

Advanced Competitive Intelligence Integration

With Kogan pricing intelligence via web scraping, combined with broader Ecommerce Data Scraping strategies, businesses can centralize competitor pricing feeds into BI dashboards.

From 2022–2026, retailers using integrated pricing dashboards achieved:

• 19% faster promotional response
• 14% improvement in margin retention
• 22% higher inventory turnover in electronics

Competitive Response Efficiency (2022–2026)

Metric	Without Intelligence	With Intelligence
Response Time	3–5 Days	12–24 Hours
Margin Loss	10.5%	6.1%
Conversion Stability	72%	86%

Integrated pricing intelligence supports informed decision-making rather than reactive markdown cycles.

Appliance Segment Monitoring for Revenue Stability

Retailers conducting Kogan home appliance prices data Extraction gain insights into refrigerators, washing machines, and kitchen appliances—categories with high-ticket values and competitive discounting.

From 2020–2026, appliance discount rates increased by 24% due to bundled offers and seasonal clearance events.

Appliance Discount Trends (2020–2026)

Year	Avg. Discount Rate	Clearance Events
2020	8%	12
2022	11%	16
2024	14%	19
2026*	17%	23

Data extraction enables retailers to time promotions strategically rather than reactively matching competitor clearance campaigns.

Real-Time Category Monitoring for Lifestyle Segments

By choosing to Scrape Kogan lifestyle product pricing data, retailers can analyze pricing patterns across fitness equipment, furniture, and personal accessories. When paired with Real-Time Price Monitoring, dynamic alerts ensure rapid response to price shifts.

Between 2021 and 2026, lifestyle category pricing fluctuations averaged 8–10% monthly during peak sale periods.

Lifestyle Category Performance (2021–2026)

Metric	Without Monitoring	With Real-Time Alerts
Price Gap Duration	6 Days	1–2 Days
Revenue Leakage	9%	4%
Promotion Optimization	Moderate	High

Continuous monitoring ensures consistent competitiveness across non-electronics verticals.

Strengthening Dynamic Pricing Algorithms with Historical Intelligence

One of the biggest advantages of structured category-wise pricing intelligence is the ability to power dynamic pricing engines with historical depth. Between 2020 and 2026, electronics pricing on Kogan.com showed recurring discount cycles aligned with major sales events such as EOFY, Black Friday, Cyber Monday, and Boxing Day. Retailers using six-year historical archives improved forecast accuracy by 21% compared to businesses relying only on short-term trend data.

By analyzing historical datasets, pricing teams can:

• Predict peak markdown periods
• Identify repeat flash sale timing
• Estimate competitor price elasticity
• Detect long-term pricing thresholds

Historical Pricing Impact (2020–2026)

Metric	Short-Term Data Only	6-Year Historical Dataset
Forecast Accuracy	69%	90%
Promo ROI Accuracy	72%	88%
Margin Stability	Moderate	High

Longitudinal data ensures pricing adjustments are predictive rather than reactive, reducing unnecessary discount escalation.

Marketplace Seller Competition Analysis

Beyond first-party pricing, third-party marketplace sellers contribute significantly to price variability. Between 2022 and 2026, marketplace seller participation in electronics categories grew by approximately 33%, increasing price competition within individual product listings.

Category-wise scraping allows retailers to isolate:

• Seller-level price differences
• Buy-box positioning shifts
• Discount stacking strategies
• Shipping cost variations

Retailers who monitored marketplace seller competition reduced buy-box loss rates by 16%. Structured category monitoring ensures brands remain visible and competitive without resorting to blanket discounts.

Inventory Optimization Through Pricing Insights

Dynamic price gaps often correlate directly with inventory pressure. Between 2020 and 2026, high-stock electronics SKUs experienced 18–25% deeper discounts during clearance phases. Retailers leveraging pricing intelligence aligned inventory management with competitor discount cycles.

Inventory & Pricing Correlation (2020–2026)

Scenario	Avg. Discount Depth	Inventory Turnover Improvement
Overstock Clearance	22%	28%
Balanced Inventory	9%	14%
Limited Stock	5%	8%

When price scraping integrates with stock-level analytics, businesses can strategically clear inventory without triggering price wars.

Executive-Level Decision Support

Structured pricing datasets also support executive reporting and long-term strategy formulation. From 2020–2026, retailers leveraging automated category-wise intelligence achieved stronger annual pricing consistency and reduced earnings volatility.

Executive dashboards powered by pricing data enable:

• Quarterly competitive benchmarking
• Category contribution margin tracking
• Pricing risk assessment modeling
• Budget allocation forecasting

Rather than responding impulsively to competitor discounts, leadership teams gain data-backed clarity to maintain sustainable pricing frameworks.

This extended intelligence layer transforms category-wise price scraping from an operational tool into a strategic growth driver for ecommerce enterprises.

How Actowiz Solutions Can Help?

Actowiz Solutions provides advanced ecommerce intelligence services, including Extract Kogan electronics pricing data solutions powered by scalable infrastructure. Our expertise in Kogan category-wise pricing data scraping ensures comprehensive SKU-level, category-level, and historical trend coverage from 2020–2026 and beyond.

We deliver:

• API-based structured pricing feeds
• Category-wise competitor benchmarking
• Automated flash sale detection
• Historical price archive datasets
• Real-time dashboard integration
• Predictive pricing analytics

Our solutions empower ecommerce retailers, brands, and distributors to eliminate pricing blind spots and close competitive gaps efficiently.

Conclusion

Dynamic ecommerce markets demand intelligent pricing strategies. Leveraging Web Scraping, Mobile App Scraping, and access to a structured Real-time dataset enables retailers to detect price gaps instantly and respond with precision.

By investing in automated category-wise pricing intelligence, businesses can reduce margin leakage, strengthen SKU-level competitiveness, and maintain consistent promotional efficiency.

Partner with Actowiz Solutions to transform your ecommerce pricing strategy and stay ahead in a rapidly evolving digital marketplace.

You can also reach us for all your mobile app scraping, data collection, web scraping , and instant data scraper service requirements!

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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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    [traits:protected] => GeoIp2\Record\Traits Object
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            [validAttributes:protected] => Array
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                    [9] => isLegitimateProxy
                    [10] => isp
                    [11] => isPublicProxy
                    [12] => isResidentialProxy
                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
                    [16] => mobileNetworkCode
                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
                    [21] => userType
                )

        )

    [city:protected] => GeoIp2\Record\City Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => names
                )

        )

    [location:protected] => GeoIp2\Record\Location Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [validAttributes:protected] => Array
                (
                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
                    [3] => longitude
                    [4] => metroCode
                    [5] => populationDensity
                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

        )

    [postal:protected] => GeoIp2\Record\Postal Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => 43215
                )

            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => confidence
                )

        )

    [subdivisions:protected] => Array
        (
            [0] => GeoIp2\Record\Subdivision Object
                (
                    [record:GeoIp2\Record\AbstractRecord:private] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                        (
                            [0] => en
                        )

                    [validAttributes:protected] => Array
                        (
                            [0] => confidence
                            [1] => geonameId
                            [2] => isoCode
                            [3] => names
                        )

                )

        )

)

 country : United States

 city : Columbus

US

Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)

Start Your Project

+1 ▼

Select Service Web Scraping Real-Time API Access Data Dashboards Datasets Review & Sentiment Analysis Price Monitoring Custom Solution

Select Industry E-Commerce Quick Commerce Food Delivery Travel & OTA Grocery & Retail Real Estate Others

Target Country Web Scraping Services Mobile App Scraping Web and App Scraping API Datasets Dashboard

A$лвR$C$CHF¥Kčkr€£HK$FtRp₪₹kr¥₩Mex$RMkrNZ$₱złleikrS$฿₺$R Budget Range $500 - $1,000 - Starter Budget (POC/Single Site) $1,000 - $2,500 - Basic Plan (Small Business Needs) $2,500 - $5,000 - Growth Phase (Multi-site/Data Volume Projects) $5,000 - $10,000 - Business-Scale (Automation/API Integrations) $10,000 - $25,000 - Enterprise Projects (Real-Time Systems, Global Data) $25,000 - $50,000 - Enterprise+ (Custom Dashboards, Ongoing Support) $50,000+ - Strategic Partnership (End-to-End Data Ops)

Project Timeline * Immediate 1–3 Months Planning Phase

Additional Trust Elements

✨ "1000+ Projects Delivered Globally"

⭐ "Rated 4.9/5 on Google & G2"

🔒 "Your data is secure with us. NDA available."

💬 "Average Response Time: Under 12 hours"

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape → Structure → Analyze → Visualize

Explore Solutions Get a Custom Demo

Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.

Find Insights Use AI to connect data points and uncover market changes. Meanwhile.

Move Forward Predict demand, price shifts, and future opportunities across geographies.

Ecommerce

Q-Commerce

Travel & OTA

Food Delivery

Real Estate

Social Media

Trusted by Global Leaders – Secured by International Standards

Industry:

Fintech / Digital Payments

Result

Accurate daily voucher &

cashback visibility across platforms

★★★★★

“Actowiz Solutions helped us automate daily voucher and cashback data collection across PhonePe, Paytm, Flipkart, and Hubble. The API-driven delivery significantly improved offer accuracy and operational efficiency.”

Product Manager, Fintech Platform (India)

✓ Daily voucher & cashback tracking via Push & Pull APIs

View Case Studies

Industry:

Coffee / Beverage / D2C

Result

2x Faster

Smarter product targeting

★★★★★

“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”

Operations Manager, Beanly Coffee

✓ Competitive insights from multiple platforms

View Case Studies

Industry:

Real Estate

Result

2x Faster

Real-time RERA insights for 20+ states

★★★★★

“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”

Data Analyst, Aditya Birla Group

✓ Boosted data acquisition speed by 3×

View Case Studies

Industry:

Organic Grocery / FMCG

Result

Improved

competitive benchmarking

★★★★★

“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”

Product Manager, 24Mantra Organic

✓ Real-time SKU-level tracking

View Case Studies

Industry:

Quick Commerce

Result

2x Faster

Inventory Decisions

★★★★★

“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”

Aarav Shah, Senior Data Analyst, Mensa Brands

✓ 28% product availability accuracy

✓ Reduced OOS by 34% in 3 weeks

View Case Studies

Industry:

Quick Commerce

Result

3x Faster

improvement in operational efficiency

★★★★★

“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”

Business Development Lead,Organic Tattva

✓ Weekly competitor pricing feeds

View Case Studies

Industry:

Beverage / D2C

Result

Faster

Trend Detection

★★★★★

“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”

Marketing Director, Sleepyowl Coffee

Boosted marketing responsiveness

View Case Studies

Industry:

Quick Commerce

Result

Enhanced

stock tracking across SKUs

★★★★★

“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”

Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

View Case Studies

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★

'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"

Thomas Galido

Co-Founder / Head of Product at Upright Data Inc.

2 min

★★★★★

“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”

Iulen Ibanez

CEO / Datacy.es

1 min

★★★★★

“Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing highly recommended!”

Febbin Chacko

-Fin, Small Business Owner

1 min

See Actowiz in Action – Real-Time Scraping Dashboard + Success Insights

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

Price Drop + 12 min
in 6 hrs across Lel.6

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.

✔ Scraped Data: Price Insights Top-selling SKUs

Request Demo Access

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

With hourly price monitoring, we aligned promotions with competitors, drove 17%

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

Actowiz Insights Hub

Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place

All

Blog

Case Studies

Infographics

Report

Feb 15, 2026

How Kogan Category-Wise Pricing Data Scraping Fixes Dynamic Electronics Price Gaps

Use Kogan Category-Wise Pricing Data Scraping to track dynamic electronics price gaps, monitor competitors, and protect retail profit margins.

Flink.de Quick Commerce Berlin Price Monitor

Actowiz Solutions enabled real-time Flink.de price monitoring across Berlin to track hyperlocal pricing, promotions, and stock shifts in quick commerce.

Manual Data Collection vs Automated Scraping — Which Saves More Time & Cost?

Discover the key differences between manual data collection and automated web scraping. Learn which method saves more time, reduces costs, and improves efficiency for your business in 2026.

Web Scraping Amazon Robot Vacuum Data To Solve Competitive Pricing And Market Positioning Challenges

Web Scraping Amazon Robot Vacuum Data to track prices, ratings, reviews, and trends for competitive intelligence and smarter retail decisions.

Feb 15, 2026

How Kogan Category-Wise Pricing Data Scraping Fixes Dynamic Electronics Price Gaps

Use Kogan Category-Wise Pricing Data Scraping to track dynamic electronics price gaps, monitor competitors, and protect retail profit margins.

Feb 14, 2026

How Coles vs Woolworths Citrus Fruit Price Scraping Solves Supermarket Price Undercutting Issues

Use Coles vs Woolworths Citrus Fruit Price Scraping to detect undercutting, monitor citrus trends, and optimize supermarket pricing decisions.

Feb 13, 2026

How Samsung Product Data Extraction Eliminates Manual Tracking Errors And Improves Retail Intelligence

How Samsung Product Data Extraction reduces manual errors and enhances retail intelligence with accurate, real-time product insights.

Read More

Flink.de Quick Commerce Berlin Price Monitor

Actowiz Solutions enabled real-time Flink.de price monitoring across Berlin to track hyperlocal pricing, promotions, and stock shifts in quick commerce.

How We Helped a Leading Retail Brand Scale Pricing Intelligence with an eMAG Product Data Scraping API

Discover how we helped a leading retail brand scale pricing intelligence using our eMAG Product Data Scraping API for real-time insights.

The Global Arbitrage Map - Scraping Price Deltas Between Amazon US, UK, and EU

Scraping Price Deltas Between Amazon US, UK, and EU enables real-time tracking of regional price gaps, currency shifts, and competitive pricing trends.

Read More

Manual Data Collection vs Automated Scraping — Which Saves More Time & Cost?

Discover the key differences between manual data collection and automated web scraping. Learn which method saves more time, reduces costs, and improves efficiency for your business in 2026.

10 Ways Data Scraping Drives Business Growth & Market Intelligence

Discover 10 powerful ways data scraping boosts business growth, from competitive price intelligence and demand forecasting to inventory tracking and market monitoring.

Grocery Price Movement Tracker – Walmart, Instacart & Target

Real-time grocery price changes across Walmart, Instacart and Target. Track top SKU drops, increases and hourly volatility with Actowiz Solutions.

Read More

Web Scraping Amazon Robot Vacuum Data To Solve Competitive Pricing And Market Positioning Challenges

Web Scraping Amazon Robot Vacuum Data to track prices, ratings, reviews, and trends for competitive intelligence and smarter retail decisions.

Baby Products API-Driven Price Intelligence - Analyzing Inflation’s Impact on Baby Products

This report examines inflation’s impact on baby products using Baby Products API-Driven Price Intelligence to provide accurate pricing insights and trends.

UAE E-Commerce & Quick Commerce SKU Data Analysis - Price, Stock & Demand Insights

UAE E-Commerce & Quick Commerce SKU Data Analysis delivers insights on pricing, availability, trends, and performance to optimize catalogs and growth.

Read More

Our perks are irreplaceable

Time Zone Flexibility

Benefit from the ease of collaboration with Actowiz Solutions, as our team is aligned with your preferred time zone, ensuring smooth communication and timely delivery.

Clear Communication

Our team focuses on clear, transparent communication to ensure that every project is aligned with your goals and that you’re always informed of progress.

Uncompromising Quality

Actowiz Solutions adheres to the highest global standards of development, delivering exceptional solutions that consistently exceed industry expectations

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