Senior Customer Intelligence Manager

Zillow

3d•$112,700 - $189,400•Remote

About The Position

Zillow’s Frontline Product Engagement (FPE) team connects customer experience to business impact. We sit at the intersection of operations, product, and data—helping leaders understand what’s working, what’s broken, and what matters most to our customers and agents. Our mission is to reduce avoidable contacts, improve experience quality, and influence product decisions that deliver measurable value for both customers and the business. We turn frontline insights and the voice of the customer into actionable product and experience improvements. Our work bridges Customer Experience Operations and Product teams, ensuring that customer feedback and support signals directly inform how Zillow builds and scales customer experiences. As the Senior Customer Intelligence Manager for Frontline Product Engagement, you will build the measurement and data foundations that connect customer signals to product and operational outcomes. You’ll translate contact center and customer feedback into quantified opportunities, define how success is measured, and deliver post-launch impact analysis that shapes prioritization and investment decisions. You will play a key role in developing Zillow’s AI-driven customer intelligence platform by partnering with Data Engineering and Data Product teams to shape data models, validate sources, and establish trusted, reusable metrics across integrated systems. You’ll bring structure to ambiguity, align stakeholders around clear definitions and tradeoffs, and communicate insights in a way that helps leaders confidently improve experience quality while reducing cost-to-serve.

Requirements

7+ years of experience in customer intelligence, business analytics, product insights, or strategy/operations, with a strong track record of translating customer signals into measurable business impact.
Advanced SQL and strong data platform fluency (Databricks, Snowflake, or similar), with experience working across complex, multi-source datasets.
Strong data strategy and technical acumen—able to partner closely with Data Engineering and Data Product teams on data models, schemas, and analytics-ready pipelines without owning engineering execution.
Proven ability to integrate customer and operational data (CRM, telephony, surveys, knowledge base, product usage) to generate actionable insights and quantify outcomes such as contact deflection, cost-to-serve reduction, and CX improvement.
Expertise in measurement and impact analysis, including defining ROI and success frameworks and evaluating post-launch results of customer-facing product and CX initiatives.
Strong focus on data quality and trust, ensuring accuracy, consistency, and confidence in metrics used for executive decision-making.
Exceptional communication and storytelling skills, with the ability to translate complex analysis into clear, executive-ready insights.
AI-forward mindset, using modern AI tools and techniques to accelerate analysis, synthesize unstructured data, and support scalable customer intelligence systems.

Nice To Haves

Experience building or managing customer insights or VoC platforms.
Knowledge of Salesforce Service Cloud, Agentforce, Data Cloud, and self-service ecosystems.
Exposure to contact center platforms (Genesys, NICE, HighSpot, Aspect), Help Center design, or search/content quality.
Experience working with LLM or AI/ML-based workflows (agent assist, summarization, classification, retrieval-augmented generation).
Comfort with BI/dashboarding tools and data-governance practices.

Responsibilities

Turn customer signals into decisions
Translate contact drivers, customer feedback, and frontline insights into quantified opportunities that inform product roadmaps and CX prioritization.
Map and measure customer journeys across channels (self-service, assisted support, product) to identify friction points, deflection opportunities, and experience improvements.
Build customer intelligence foundations at scale
Partner with Data Engineering and Data Product teams to develop a scalable customer intelligence data layer across systems such as Salesforce, Genesys, Gong, knowledge content, and surveys—enriched with customer, segment, and revenue context.
Establish canonical definitions, metric specifications, and data contracts (e.g., contact rate, deflection, repeat contact, transfers, resolution) so teams operate from shared truth.
Enable trusted measurement and instrumentation
Define measurement plans for product and CX initiatives (baselines, success metrics, attribution approach) and partner with Product/Engineering to ensure required fields and instrumentation are in place before launch.
Validate data sources, metrics, and analytics to ensure accuracy, consistency, and executive confidence—supported by monitoring, QA checks, and clear documentation.
Quantify impact and drive a learning loop
Develop models, analyses, and dashboards that translate customer issues into measurable outcomes, including contact deflection, cost-to-serve reduction, experience quality, and satisfaction.
Deliver post-launch impact readouts that explain what changed, why it changed, and what to do next—highlighting residual opportunity and tradeoffs.
Influence through clear, executive-ready narratives
Synthesize findings into crisp, decision-ready stories and recommendations that help leaders align and act.
Run an insights-to-action cadence with CXO and Product partners: quantify opportunity size and ROI, recommend priorities, track decisions, and report outcomes over time.