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Geneo

marketing analytics predictive modeling

Analyzed across ChatGPT, Perplexity & Google AIO
Analyzed 10/16/2025

Are you in the answers when your customers ask AI?

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Brand Performance Across AI Platforms
All 24 brands referenced across AI platforms for this prompt
Keends
1
3
Sentiment:
Score:95
adjust
1
1
Sentiment:
Score:84
Amazon
1
0
Sentiment:
Score:69
4MailChimp
0
2
Sentiment:
Score:69
5acquia
0
2
Sentiment:
Score:69
Referenced Domains Analysis
All 27 domains referenced across AI platforms for this prompt
ChatGPT
Perplexity
Google AIO
#1keends.com faviconkeends.com
ChatGPT:
1
Perplexity:
1
Google AIO:
1
3
#2arxiv.org faviconarxiv.org
ChatGPT:
2
Perplexity:
0
Google AIO:
0
2
#3acquia.com faviconacquia.com
ChatGPT:
0
Perplexity:
1
Google AIO:
1
2
#4lift-ai.com faviconlift-ai.com
ChatGPT:
0
Perplexity:
1
Google AIO:
1
2
#5getrecast.com favicongetrecast.com
ChatGPT:
0
Perplexity:
1
Google AIO:
1
2

AI Search Engine Responses

Compare how different AI search engines respond to this query

ChatGPT

3640 Characters

BRAND (24)

IBM
Adobe
Amazon
Salesforce
MailChimp
Improvado
Amplitude
Investopedia
Factors AI
CMSWire
Taboola
qlik
itransition
nativo
emfluence
adjust
acquia
champlain
Keends
Recast
Lift AI
Harvard Business School Online
Vaia
GreenM

SUMMARY

ChatGPT provides an educational overview of predictive modeling in marketing analytics, focusing on statistical techniques and machine learning algorithms for analyzing historical data. The response emphasizes key applications like customer segmentation and personalized marketing, with structured explanations and practical examples of how businesses can use these techniques to make data-driven decisions and optimize marketing strategies.

Perplexity

2900 Characters

BRAND (24)

IBM
Adobe
Amazon
Salesforce
MailChimp
Improvado
Amplitude
Investopedia
Factors AI
CMSWire
Taboola
qlik
itransition
nativo
emfluence
adjust
acquia
champlain
Keends
Recast
Lift AI
Harvard Business School Online
Vaia
GreenM

SUMMARY

Perplexity delivers a comprehensive analysis of marketing analytics predictive modeling, defining it as using historical and current data to build mathematical or machine learning models for forecasting future customer behaviors. The response provides detailed coverage of key aspects including objective definition, data collection and processing, with emphasis on practical business applications like churn prediction and cross-sell opportunities.

Google AIO

441 Characters

BRAND (24)

IBM
Adobe
Amazon
Salesforce
MailChimp
Improvado
Amplitude
Investopedia
Factors AI
CMSWire
Taboola
qlik
itransition
nativo
emfluence
adjust
acquia
champlain
Keends
Recast
Lift AI
Harvard Business School Online
Vaia
GreenM

SUMMARY

Google AIO offers a concise, neutral explanation of marketing analytics with predictive modeling, highlighting the use of historical data, machine learning, and statistical algorithms to forecast future outcomes. The response briefly covers the core concept and mentions common applications like customer behavior prediction, campaign performance optimization, and budget allocation for improved ROI.

Strategic Insights & Recommendations

Dominant Brand

No specific brands are prominently featured across the platforms, with only minimal mentions of Adjust and Keends in ChatGPT's response.

Platform Gap

ChatGPT focuses on educational structure with practical applications, Perplexity provides comprehensive technical depth, while Google AIO offers a more concise overview with broader business context.

Link Opportunity

All platforms provide substantial link opportunities with ChatGPT offering 6 links, while both Perplexity and Google AIO provide 14 links each for deeper exploration.

Key Takeaways for This Prompt

All platforms emphasize the importance of historical data and machine learning algorithms in predictive modeling for marketing.

Customer segmentation and personalized marketing emerge as primary applications across all responses.

The responses collectively highlight the business value of data-driven decision making and ROI optimization.

Technical implementation details vary significantly between platforms, with Perplexity providing the most structured approach to model development.

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