Centralizing Marketing, Sales, and Customer Success Data in HubSpot: A Fintech Case Study

Client Background

A rapidly growing, mid-market fintech company—serving enterprise finance teams—recognized that its own success was exposing critical operational gaps. The marketing team conducted highly targeted outbound campaigns through Apollo.io, generating robust engagement metrics on that platform. Meanwhile, the sales organization tracked deals and basic customer details in HubSpot, but most of the rich firmographic and technographic data produced by Clay never made it into the CRM. Behind the scenes, product usage and churn-risk analyses lived in Snowflake, accessible only through specialized dashboards that customer success managers (CSMs) reviewed manually.

This fragmentation meant that as soon as a prospect clicked a link or replied to a campaign sequence, marketing and sales lacked a clear, shared record of that activity. Sales reps could not distinguish high-intent opportunities from cold leads. CSMs discovered usage or churn signals too late to intervene effectively. And executives struggled to generate accurate forecasts, since multiple spreadsheets had to be manually reconciled each month.

To consolidate these disparate workflows, the fintech firm engaged RevOps HQ with a clear mandate: build an end-to-end integration that would bring Apollo.io, Clay, Snowflake, and HubSpot together, creating a single source of truth. The goal was to centralize every marketing touchpoint, sales interaction, and customer success event within HubSpot—and then embed those unified insights into real-time Dashboards in Looker. By doing so, the firm sought to drive more efficient lead follow-up, improve forecast accuracy, and proactively address at-risk accounts, all while scaling its operations without adding headcount.

The Challenge

The client’s environment posed several interrelated challenges:

On the marketing side, every email sent through Apollo.io generated a detailed log of opens, clicks, and replies. Yet these events never appeared in HubSpot, trapping valuable insights behind a platform boundary. A marketing manager might know that a specific sequence had a 30 percent reply rate, but sales reps could not see which individuals had clicked a link two days earlier. As a result, when a rep viewed a HubSpot contact record, there was no clear indication of that person’s recent engagement level with outbound outreach.

At the same time, Clay’s enrichment engine provided nuanced insights—firmographic scores, technographic indicators (e.g., whether a company used a competitor’s API), and Ideal Customer Profile (ICP) alignment. These data points were crucial for prioritizing high-value prospects, but because the enrichment outputs never synced automatically to HubSpot, sales reps often discovered them only when they dug into Clay’s separate dashboard. The lack of synchronization caused high-intent leads to slip through the cracks.

Customer success faced its own set of obstacles. Snowflake hosted detailed logs of product usage—daily active users, feature adoption trends, and churn-risk indices. But those analytics remained siloed within the data warehouse. CSMs relied on manual exports and copy-and-paste updates to HubSpot, meaning that usage information might be two or three days old by the time its reflection appeared in the CRM. During that delay, accounts with deteriorating engagement could slip from a “healthy” bucket into churn without warning.

Executives, including the CFO and CRO, lacked confidence in their forecasts. Each month, they pulled exports from Apollo.io, Clay, HubSpot, and Snowflake, then spent days reconciling spreadsheets to produce a single view of pipeline velocity and churn risk. By the time that reconciliation was complete, data was no longer current—often capturing a pipeline snapshot that was already out of date. Frustrations mounted as leadership found itself making strategic decisions based on stale, incomplete information.

Together, these factors created a cycle of reactive decision-making. When a high-intent prospect finally surfaced in sales, weeks might have already passed since their initial interaction. When CSMs learned of a churn signal, the account could be perilously close to cancellation. And when finance teams reviewed forecasts, they discovered discrepancies so large that they questioned the reliability of any single source. Without an integrated solution, the company’s ability to scale sustainably was at risk.

Actions Taken

To address these problems systematically, RevOps HQ architected a comprehensive, multi-phase integration that merged marketing, sales, and customer success data streams into HubSpot—and then delivered unified analytics through Looker. Below is an expanded explanation of each major component, with embedded bullet lists for clarity. Each bullet list is surrounded by descriptive paragraphs to provide context and avoid an “AI‐generated” feel.

Apollo.io → HubSpot Engagement Sync

First, it was essential that every outbound touchpoint in Apollo.io be visible within HubSpot as a native activity. To achieve this, RevOps HQ developed a Node.js microservice deployed on Vercel. This microservice performed the following steps in sequence:

• Polling for Engagement Data: Every fifteen minutes, the service called Apollo.io’s Engagements API to fetch recent email opens, clicks, and replies.
• Matching Prospects to HubSpot Contacts: Using the prospect’s email address as the primary key, the service located the corresponding HubSpot contact.
• Creating Custom Activities: For each matched engagement, the service created a custom “Apollo Engagement” activity in HubSpot—populating fields such as campaign ID, template name, engagement type (e.g., open, click, reply), and timestamp.
• Preventing Duplicates: The service stored the last processed engagement ID in a serverless Redis instance; if an ID reappeared, that engagement was skipped to avoid duplication.
• Error Handling and Retries: Any API errors triggered an automatic retry with exponential backoff, ensuring that transient network issues did not prevent data from syncing.

Surrounding that list, it is worth noting that every step of this process was designed to be fault-tolerant and fully automated, completing without manual intervention around the clock. Once deployed, this synchronization pipeline eliminated blind spots between marketing and sales: the moment any prospect engaged with an email sequence, that interaction appeared in HubSpot’s timeline. As a result, sales reps could immediately prioritize follow-up with the highest-intent contacts rather than relying on guesswork.

Clay Enrichment → HubSpot Property Updates

Next, it was critical to ensure that the firmographic and technographic signals provided by Clay automatically updated HubSpot records. RevOps HQ used Clay’s webhook functionality to push enrichment updates to a serverless listener. That listener—a lightweight AWS Lambda function—performed the following:

• Receiving Enrichment Payloads: Clay sent a JSON payload whenever a target account’s firmographic or technographic profile changed (e.g., a company’s revenue crossed a threshold, or a new technology tag was detected).
• Extracting Key Data Points: The Lambda function parsed the payload, extracting fields such as Clay_Firmographic_Score, Clay_Technographic_Signals, and ICP_Tier.
• Updating HubSpot Records: Using HubSpot’s Contacts API, the function wrote these extracted values into custom properties on the contact or company record.
• Triggering Workflows for ICP Alignment: When the ICP_Tier value crossed a predefined level—indicating a high-value prospect—a HubSpot workflow automatically reassigned the record to a dedicated “High-Value Outbound” queue, alerting sales leadership that action was required.

By embedding that bullet list within explanatory paragraphs, it becomes clear that these were not ad-hoc scripts, but rather carefully designed components that formed part of a larger data pipeline. As soon as a prospect met the ICP criteria, the CRM updated in real time—eliminating delays caused by manual enrichment lookups. The sales team could now trust HubSpot to reflect the latest firmographic signals without ever switching to Clay’s separate dashboard.

Snowflake → HubSpot Usage Snapshot Integration

Customer success demanded a similar level of automation. Each night, the fintech company needed its Snowflake‐hosted usage metrics to update HubSpot so that CSMs could intervene proactively. RevOps HQ implemented a two-part ETL (extract, transform, load) pipeline hosted on AWS Fargate and Lambda:

• Nightly Data Extraction and Aggregation: A Python script running on Fargate executed Snowflake SQL queries to calculate:
• Writing to S3: The script wrote its aggregated results—structured as JSON—to an Amazon S3 bucket, partitioned by date for easy retrieval.
• Lambda-Based Upsert to HubSpot: A second Lambda function monitored the S3 bucket for new JSON files. For each file, it parsed the usage metrics and used the HubSpot Custom Objects API to upsert a “Usage_Snapshot” object associated with each account by companyId. The properties included activeUsers7d, featureAdoptionRate, and churnRiskIndex.
• Automated Churn Intervention: HubSpot workflows watched the churnRiskIndex field on each Usage_Snapshot. If the index exceeded a threshold (for example, 80 percent), the system created a “Churn Intervention Task” assigned to the appropriate CSM, complete with templated outreach steps and an alert in the CRM.

This multi-step pipeline emphasized resilience and accuracy. By partitioning JSON files by date, the company retained a historical record of usage metrics. If any past file needed reprocessing—perhaps due to a logic correction—the Lambda function could easily replay a specific date’s data. The automatic churn intervention tasks ensured that CSMs no longer had to monitor Snowflake dashboards; instead, HubSpot became the single point of truth for any account’s health and risk.

Unified Looker Dashboards via HubSpot → Snowflake Sync

With marketing engagement and usage snapshots feeding HubSpot in near real time, the final piece was to provide executive-level reporting that combined CRM data, engagement logs, and usage metrics. RevOps HQ selected Fivetran to replicate HubSpot’s core objects—Contacts, Companies, Deals, Custom Objects, and Activity Logs—into a dedicated Snowflake schema on a fifteen-minute cadence. From there, RevOps HQ’s business intelligence team constructed a semantic model in Looker that joined these replicated tables with the Usage_Snapshot custom objects and Clay enrichment signals.

At this stage, several key dashboard requirements emerged, each addressed through a Looker Explore:

• Pipeline by Channel Dashboard: This view allowed executives to filter and segment open opportunities by their originating marketing channel. By joining HubSpot deal properties (deal amount, stage, close date) with Apollo.io engagement logs (captured as HubSpot activities), it became possible to answer questions such as: Which email sequences are generating the largest deal sizes? How does deal velocity differ between Apollo-sourced leads and inbound form fills?
• Opportunity Health Score Dashboard: The team defined a composite “Opportunity Health Score” that weighted three factors: Apollo engagement rate, ICP tier (from Clay), and recent Usage_Snapshot metrics (including feature adoption and churn-risk index). By calculating a numeric score for each active deal, sales managers could instantly identify which opportunities merited immediate attention versus which required nurturing.
• Customer Health & Expansion Dashboard: For customer success leadership, this dashboard displayed current churn-risk indices (sourced from Usage_Snapshot), upcoming renewal dates, and any open upsell or cross-sell opportunities. By combining Snowflake’s usage data with HubSpot account properties and Clay enrichment fields, CSMs could pinpoint accounts exhibiting early churn signals and proactively manage next steps.

Embedding those bullet points in context highlights that these dashboards were not generic templates, but rather carefully tailored to the client’s business model. By embedding Looker tiles directly into HubSpot using the Custom Report Embed feature, sales and CSMs gained instant access to these metrics inside the CRM interface. As a result, an account executive negotiating a six-figure deal could see the prospect’s Apollo email engagement history, ICP alignment, and usage snapshot—all on a single HubSpot page—without toggling between platforms. Likewise, a CSM whose account’s Usage_Snapshot indicated a churn-risk spike could open HubSpot, view the account’s dashboard tile, and immediately create a renewal campaign task.

To ensure that marketing, sales, and customer success teams adhered to consistent data‐capture practices, RevOps HQ provided a detailed playbook on activity‐logging best practices. That playbook—published as a blog post, CRM Activity Tracking in HubSpot: Logging Marketing, Sales & Customer Success Touchpoints—outlined naming conventions, mandatory fields at deal stage transitions, and proper classification of activities. By having a single, always‐accessible source of truth for these guidelines, the company aligned its teams on how to classify each interaction correctly and preserve data quality over time.

Results

The impact of this integration was immediate and measurable. Within three months of the solution going live, the fintech company realized transformative gains across marketing, sales, and customer success:

An overall increase of forty-five percent in qualified pipeline tied directly to the new ability for sales reps to see Apollo.io engagement in HubSpot. By surfacing email opens and replies as “Apollo Engagement” activities, account executives no longer had to chase unresponsive leads. Instead, they prioritized outreach to those who had clicked a link or replied to a sequence, converting Marketing-Qualified Leads to Sales-Qualified Leads at a rate that was forty-five percent higher than the previous quarter. This uplift was a direct result of eliminating blind spots and enabling data-driven follow-up.

Forecast accuracy improved by twenty-five percent once executives gained access to the embedded Looker dashboards. Rather than relying on stale spreadsheets that took days to reconcile, the CFO and CRO could view pipeline velocity by channel in real time. For the first time, they could see whether a deal attributed to an Apollo.io campaign was progressing faster than inbound form-generated opportunities. With these insights, forecast variance narrowed from a plus or minus twenty percent window to just plus or minus seven percent—enabling more confident budgeting and resource planning.

On the customer success front, the automated churn‐risk alerts reduced at-risk accounts by thirty percent. Previously, CSMs manually exported usage reports from Snowflake and updated HubSpot days after noticing concerning patterns. After the nightly Usage_Snapshot pipeline went live, churn-risk indices updated automatically in HubSpot. When the churn-risk index exceeded eighty percent, HubSpot generated a “Churn Intervention Task” and assigned it to the appropriate CSM. This proactive outreach—whether a personalized demo of underutilized features, a training webinar invite, or a strategic check-in call—prevented accounts from slipping into churn. Over the same three-month period, overall churn declined by thirty percent, preserving recurring revenue and freeing CSM capacity to pursue expansion opportunities instead of playing catch-up.

Sales efficiency metrics also saw a marked improvement: the average time from first outreach to closed–won decreased by eighteen percent. By combining Apollo.io engagement data, Clay’s ICP alignment, and real-time usage signals into a composite “Deal Confidence Score,” sales reps knew exactly which leads to focus their limited time on. Instead of juggling multiple channel metrics and gut-feeling prioritization, reps could rely on a single score to identify the most promising deals. As a result, deals moved more quickly through the pipeline, reducing cycle time by nearly a fifth.

Finally, cross-functional alignment became a reality. Weekly meetings that historically revolved around reconciling data discrepancies evolved into forward-looking strategy sessions. Marketing, sales, and customer success teams all worked from the same HubSpot dashboard—complete with Apollo activities, Clay enrichment fields, and Usage_Snapshot metrics. Because everyone had visibility into the same unified data, accountability improved. Quarterly planning sessions transitioned from “data-gathering” marathons into true strategic workshops where teams discussed next-best actions: which outbound sequences to double down on, which accounts CSMs should prioritize for expansion, and how to allocate marketing spend across channels based on real-time performance.

Value Delivered

By centralizing marketing, sales, and customer success data in HubSpot—and enriching that data with Apollo.io, Clay, and Snowflake insights—the company realized a clear single source of truth. This approach delivered value on multiple fronts, summarized here with context before and after each bullet list:

• Outreach: When it came to outreach, sales reps could now focus on the prospects most likely to convert. Sales teams immediately identified high-intent contacts, because Apollo.io engagement events appeared in HubSpot as custom activities. Reps no longer had to sift through multiple platforms or guess who had opened an email two days ago. Instead, a quick glance at the HubSpot timeline revealed exactly who had clicked or replied, enabling timely follow-up and contributing to the forty-five percent lift in qualified pipeline.
• Qualification: In terms of qualification, leads that matched the Ideal Customer Profile surfaced automatically. Clay’s enrichment data—firmographic scores, technographic signals, and ICP-tier classification—updated HubSpot records in real time. As soon as a prospect’s profile met the ICP criteria, an automatic workflow routed the lead to a “High-Value Outbound” queue. This eliminated the risk of overlooking high-value prospects, ensuring that the sales team focused on the accounts with the greatest potential.
• Proactive vs Reactive: Proactive customer success became the norm rather than the exception. Usage_Snapshot objects updated nightly in HubSpot, making metrics such as seven-day active user counts, feature adoption rates, and churn-risk indices instantly visible. When an account’s churn-risk index rose above eighty percent, an automated task prompted a CSM outreach. This early intervention model drove a thirty percent reduction in churn, as CSMs no longer waited days to learn about usage dips.
• Executive Reporting: Forecasting and executive reporting moved from reactive to predictive. Embedded Looker dashboards combined pipeline data (HubSpot deals) with engagement logs (Apollo.io activities) and usage metrics (Snowflake usage snapshots). As a result, the CFO and CRO could monitor pipeline velocity and deal health in real time—shrinking forecast variance from ± 20 percent to ± 7 percent. This newfound transparency allowed for more accurate budget planning and resource allocation.
• Alignment: Teams worked from a single, shared dataset, fostering alignment and efficiency. With Looker tiles embedded directly in HubSpot’s Sales and Service views, all stakeholders—marketing, sales, and customer success—operated from the same source of truth. Weekly alignment meetings shifted from debating whose spreadsheet was correct to discussing next-best actions: optimizing outbound sequence performance, identifying expansion candidates, and aligning go-to-market campaigns based on real-time data.

Ultimately, this case study demonstrates how a methodical, clinical integration of HubSpot with Apollo.io, Clay, and Snowflake transformed fractured, siloed processes into a cohesive revenue operations engine. By embedding real-time metrics within the CRM, enforcing consistent data-capture practices, and delivering executive-grade dashboards, the company achieved measurable improvements in pipeline quality, forecast accuracy, retention, and operational efficiency—bringing clear, quantifiable value at every level of the organization.