Live opening · Posted 8 hours ago

CX Analyst & Product Insights (B2C, Tier-1) (

Staffnix · Pune City, Maharashtra, India (On-site)
Linkedin No
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At a glance

The key details from the original listing.

Posted 8 hours ago
CompanyStaffnix
LocationPune City, Maharashtra, India (On-site)
Work modeNo
SkillsPython
SourceLinkedin
ListedPosted 8 hours ago

About the role

Description supplied by the original job listing.

Experience 4-7 year's only.
Strong Consumer Insights / CX Analytics / Product Insights profile (behavioral/user/product/UX analytics)
2
Mandatory (Experience 1) - Must have 4+ years of experience in Customer Experience (CX) Analytics, Consumer Insights, Product Analytics, Behavioral Analytics, or UX Analytics, working on B2C digital products (Web/App) with a focus on understanding customer behavior, customer journeys, and experience improvement.
3
Mandatory (Experience 2) - Must have hands-on experience analyzing customer journeys, behavioral funnels, digital interactions, session-level behavior, clickstream data, and experience metrics to identify pain points, drop-offs, and optimization opportunities.
4
Mandatory (Experience 3) - Must have experience using CX and Digital Experience Analytics platforms such as Medallia, ContentSquare, Google Analytics (GA4) or similar tools for customer behavior analysis, journey mapping, and insight generation.
5
Mandatory (Experience 4) - Must have experience working closely with Product, Design, CX, Business, Marketing, and Research teams to generate insights, validate hypotheses, and drive customer experience improvements.
6
Mandatory (Experience 5 ) - Strong working knowledge of SQL and Python for data extraction, querying, analysis, exploratory data analysis, automation, and behavioral data interpretation. Ability to independently analyze large datasets, identify patterns, and generate actionable business insights.
7
Mandatory (Experience 6): Understanding of hypothesis-driven thinking and basic experimentation/A/B testing concepts, including interpreting results and distinguishing between correlation and causation.

Work arrangement
No

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