As businesses increasingly rely on data for decision-making, maintaining control over that data has become a critical priority. Many analytics platforms store sensitive information in third-party silos, creating duplication and reducing transparency. Enterprises now realize that without full control over their data and one single source of truth, their governance, security, and privacy are all compromised.
The lack of visibility into how insights are generated from third-party data silos—often through opaque, “black box” processes—raises serious doubts about the integrity of those insights. Without a direct connection to the data, it’s nearly impossible to trust the analytics produced. Data security isn’t just about compliance; it’s about ensuring that the insights you rely on are accurate and actionable.
Customer Data Security: Why Traditional Solutions Fall Short
Many analytics platforms store data in proprietary infrastructures or third-party silos, enabling quick querying but compromising security in the process. This approach may save time in the short term, but it can create long-term vulnerabilities for enterprises.
Here’s where traditional solutions fall short:
Data Duplication: Storing data outside your warehouse introduces multiple copies, increasing the risk of breaches.
Complex Data Protection: Special care must be given to protect PII (Personally Identifiable Information) from leaking. Privacy and compliance is a nightmare to guarantee.
Black Box Insights: Using platforms that hide the data processing and analysis mechanisms creates a lack of trust in the accuracy of your insights.
Fragmented Data: Data scattered across silos prevents a cohesive understanding of your customers’ complete journey through analytics, leading to inconsistent insights.
Recent statistics underscore the risks: 61% of companies have reported being breached due to vulnerabilities in third-party systems, and 80% of organizations have experienced a third-party data breach (Source: Prevalent). Enterprises are increasingly aware that they can’t maintain full data governance if their sensitive information is replicated and stored in third-party systems they don’t control. The lack of visibility into how data is processed also creates problems for organizations trying to ensure integrity in their reporting.
The Key Elements of Strong Data Security and Integrity
Focusing on customer data security allows businesses to address common risks, such as unauthorized access, data breaches, and regulatory violations. Here’s how securing your data benefits enterprises—from travel and hospitality brands, to ecommerce and media:
Data Control: You keep data in your own warehouse—under your data governance preferences—preventing unnecessary exposure and duplication.
Transparency: Full access to the data processing methods means you can trust the insights your business relies on.
Compliance Assurance: Adhering to industry standards like GDPR helps mitigate the risk of non-compliance and associated penalties.
Without data security, your insights can become unreliable, undermining decision-making across your organization. Ensuring that the data used for analytics is secure, transparent, and compliant isn’t just about protecting your business—it’s about making informed, accurate decisions.
How Kubit Protects Data Security and Privacy
Unlike traditional platforms that move or duplicate your data, Kubit keeps your data where it belongs—in your data warehouse. By querying the data directly rather than creating copies, Kubit reduces the security risks that come with third-party storage and gives you full transparency over your analytics process. Kubit is SOC2 Type 2 compliant, which ensures that sensitive data is protected through rigorous security controls.
With Kubit, you get:
Secure Analytics: No unnecessary data duplication, no third-party silos—just secure, direct access to your data.
Full Visibility: You can see how data is processed and understand how insights are generated.
Compliance Ready: Kubit is built to meet GDPR and other data privacy regulations, giving you peace of mind.
Kubit’s approach to customer data analytics security ensures that your organization has the control, transparency, and compliance necessary to make confident decisions backed by secure data.
Securing Customer Data for Accurate, Reliable Analytics
When it comes to customer data analytics security, the risks of relying on platforms that store or duplicate your data in third-party systems are clear. Without proper security, transparency, and control, your data can’t be trusted to drive decisions. By leveraging a solution like Kubit, which keeps your data in your warehouse and provides full visibility, you can protect your customer data while maintaining the integrity of your analytics.
LOS ALTOS, March 18, 2025 – Kubit, a leading customer journey analytics platform, has announced the launch of Kubit Lumos, an AI-powered analytics engine designed to revolutionize how enterprises extract value from their existing data warehouse.
Kubit Lumos leverages AI, natural language processing (NLP), and machine learning to streamline analytics reporting, optimize workflows, and ensure data quality—equipping product and data teams with in-depth, actionable insights to optimize product performance and the customer experience.
“Kubit Lumos is a game-changer for organizations looking to bridge the gap between complex data and decision-making,”said Alex Li, CEO of Kubit.“By embedding AI-powered analytics directly into our flexible and transparent platform, we’re helping teams make smarter, faster, and more confident business decisions—without the burden of data silos, rigid data models or expensive engineering resources.”
Core Functionality of Kubit Lumos:
Data Insights: Highlight key trends and insights from reports with AI-generated summaries, delivering meaningful context and helping you spot details at a glance.
Intuitive Language: Unlock deeper insights into your data with Natural Language Processing, simplifying the process of finding and understanding the insights you need.
Optimize Workflows: Identify repetitive queries, receive suggestions on the next best actions, and get recommendations for relevant saved entities.
Ensure Data Quality: Safeguard your data quality with query efficiency reports and real-time alerts for admins to address data issues promptly.
With Kubit Lumos, enterprises gain the power of AI without losing control of their data. Unlike traditional analytics tools that require data extraction, Kubit keeps customer journey insights directly within the source data infrastructure, ensuring full transparency, governance, and flexibility.
“Kubit Lumos ensures teams have access to trusted, AI-driven analytics while maintaining full visibility and confidence in their metrics,” added Li.
Kubit is the leading Customer Journey Analytics platform for enterprises. Through a revolutionary warehouse-native approach, Kubit empowers teams with self-service insights, rapid decision-making, and full transparency without engineering dependencies or vendor lock-in.
Unlike incumbent solutions, Kubit eliminates data silos and black boxes, allowing enterprises to analyze customer behavior directly from their existing data infrastructure like Snowflake, BigQuery, or Databricks. Kubit provides the flexibility to unlock actionable insights from custom data models with both clickstream events and operational data, without compromising governance or control.
Enterprises like Paramount, TelevisaUnivision, and Miro trust Kubit not only for its agility and reliability but also for its unmatched customer support with expert guidance and a customer-first approach that ensures success at every stage.
When people hear AI, they often think of artificial intelligence—a powerful technology transforming industries. But at Kubit, we view AI differently. For us, it stands for Analytics Intelligence, an approach that prioritizes data transparency over the complexities artificial intelligence can create when applied to misunderstood data.
Artificial Intelligence and Misunderstood Data: Adding to the Confusion
While artificial intelligence has immense potential, applying it to poorly understood or siloed data often worsens confusion. It’s similar to automating a flawed process—you’re simply accelerating the problem. When AI is layered on top of this kind of data, the results are typically conflicting insights, incomplete answers, and poor decision-making.
A 2023 report by Forrester found that 60% of decision-makers struggle to trust the data output from AI-driven systems. This is largely due to black-box AI systems, which offer no visibility into how data is processed. Business leaders are often left in the dark about how insights are generated, which breaks down trust and leads to inefficiencies. Moreover, relying on AI to run complex queries without understanding the data sources can be extremely expensive.
The solution? Embrace Analytics Intelligence and lean into data transparency.
Kubit’s Approach to Transparency: Analytics Intelligence in Action
At Kubit, we believe in empowering businesses with full visibility into their data, enabling them to understand and trust the information driving their decisions. Here’s how we make that happen:
1. Open Data Sources and Streamlined Systems
Traditional product analytics solutions, like Amplitude, operate within a black box, leaving users in the dark about how insights are generated. Kubit takes the opposite approach: we open up data sources so users can see exactly where their data comes from and how it’s processed. This transparency eliminates confusion and reduces errors caused by disconnected or misaligned systems.
2. SQL Visibility After Reports Are Built
A common frustration with AI-driven data solutions is the inability to understand how reports are created. At Kubit, we provide full SQL visibility after reports are built. Users can see the exact queries behind every report, giving them full transparency into the logic and calculations used.
This level of insight helps users:
Identify errors or inconsistencies in reports
Troubleshoot issues when data seems off
Refine reports and queries as needed, without guesswork
3. Self-Service Access to Data
Traditional AI systems often centralize data access, making business users reliant on data teams to pull reports or run queries. Kubit changes this dynamic by offering a self-service model, allowing users to access data independently, without needing a data team to mediate.
Benefits of self-service data include:
Faster decision-making
Less dependency on technical teams
Increased agility across departments
4. Removing Data Silos by Keeping All Data in the Warehouse
Data silos—isolated pockets of information stored in different systems—can cause discrepancies and confusion. Many AI systems only layer additional complexity on top of this fragmented data. Kubit solves this problem by keeping all data in the data warehouse, ensuring everyone operates from a single source of truth. This eliminates silos and allows teams to collaborate using consistent, reliable data.
A Future Built on Analytics Intelligence
As businesses grapple with increasing data complexity, relying solely on AI-driven black-box solutions can be risky. The better approach is Analytics Intelligence, which emphasizes transparency and accessibility.
Kubit’s focus on open data sources, SQL visibility, and self-service data access empowers companies to make smarter decisions, faster—without the confusion and inefficiencies artificial intelligence can sometimes create. By keeping all data in the warehouse, we ensure there are no silos, only clear, actionable insights that drive business success.
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Activate your warehouse data with complete analytics.
With recent shifts in the product analytics landscape, Kubit stands apart as the only warehouse-native solution that remains independent of any specific use case or integration partner. While some competitors are being acquired by larger platforms and shifting their focus to MarTech, Kubit continues to focus on delivering clear, accurate data insights directly from the customer’s cloud data warehouse. Instead of getting distracted by expanding into other fields, Kubit stays committed to its core mission: providing data transparency and control to help businesses democratize analytics and make smarter, faster decisions.
Why Focusing on a Data Visibility Solution Matters
The traditional method of moving data into third-party data silos and analytics tools (via ETL processes or SDK) has several drawbacks, and Kubit’s focused approach directly addresses these issues:
Scaling and resource constraints: As data requests increase, traditional platforms struggle to scale efficiently, often leaving teams waiting for insights.
Errors and confidence: Moving data to third-party tools introduces risks of inaccuracies, diminishing trust in the analytics provided.
Inefficiencies: Teams often waste time fixing issues introduced by complex integration processes.
Security concerns: Transferring sensitive data to external systems adds potential security vulnerabilities.
Kubit solves these problems by keeping everything within the customer’s chosen data warehouse. Our platform integrates directly with the source, avoiding the need to move data into multiple systems. This focus on data visibility, without unnecessary complexity, ensures that our users can trust their data—every time.
Speed and Agility
Large, one-size-fits-all platforms often suffer from long development cycles due to their complex roadmaps, which try to satisfy various customer needs across different industries. At Kubit, our focus allows us to be agile and responsive to customer requests. Rather than waiting years for features that are deprioritized in favor of more generalized tools, Kubit delivers quick, impactful updates that directly address the challenges facing data organizations today.
Faster implementation: Our platform has seamless No-Code integration, allowing customers to gain insights within 10 days.
Continuous innovation: We rapidly introduce new features weekly based on customer feedback, maintaining a competitive edge.
Staying Warehouse Independent
One of Kubit’s greatest strengths is our warehouse independence. While we partner with all major data platforms like Snowflake, BigQuery, Databricks, Redshift and ClickHouse, we remain agnostic, ensuring customers are not locked into any single ecosystem. This flexibility allows data teams to use the infrastructure that best suits their needs, without sacrificing performance or control.
By remaining warehouse-independent, Kubit enables customers to keep control of their data infrastructure while maximizing the value of their analytics.
Why Best-in-Class Beats All-in-One
All-in-one platforms often promise the world, but their focus is spread too thin. The favoritism of one specific set of data over another completely misses the point of data insights from a central warehouse. This skew in focus results in bloated systems full of features that most users don’t need. In contrast, Kubit’s best-in-class approach is laser-focused on solving real problems for data teams. Whether it’s providing product analytics, executive dashboards, or improving decision-making through data democratization, our mission is clear. Kubit is focused on being the best at what we do, providing superior data visibility and analytics solutions to every team throughout an organization.
Conclusion: Simplicity Drives Success
As the data analytics industry continues to evolve, it’s clear that simplicity and specialization are the keys to success. Kubit’s warehouse-native approach empowers data teams to maintain control of their data without the overhead or complexity of large, all-in-one platforms. By staying focused on data transparency and flexibility, Kubit delivers faster, more reliable insights, helping businesses make better decisions.
Recent events only validate our approach, and we’re excited to continue leading the charge in warehouse-native analytics, putting data visibility and customer needs at the forefront.
ClickHouse and Kubit are a match made in data heaven. Together, they form a powerful combination that enables fast, efficient, and scalable analytics for modern businesses. In this guide, we’ll explore how ClickHouse’s architecture works seamlessly with Kubit’s customer analytics platform, enabling you to leverage the full potential of your data.
ClickHouse Architecture Overview
ClickHouse is a columnar database management system (DBMS) designed for online analytical processing (OLAP). Its architecture is optimized for handling large-scale data queries, making it ideal for big-data applications.
Some key features of ClickHouse’s database architecture include:
Columnar data storage for faster query processing
Parallel execution for scalability and speed
Compression techniques that reduce storage costs
These features ensure that Kubit’s analytics platform performs at top speed, handling complex queries with ease.
How to Optimize Your ClickHouse Queries
To get the best performance from ClickHouse, especially when integrated with Kubit, it’s crucial to understand ClickHouse internals. This deep understanding allows you to optimize query performance by focusing on:
Using partitioning to break down large datasets for faster query response.
Optimizing index usage to narrow down the search space for queries.
Batch inserting data to avoid frequent minor updates that may slow down performance.
At Kubit, we leverage these techniques to ensure your data queries are fast and cost-efficient.
Benefits of ClickHouse’s Architecture
The architecture of ClickHouse makes it an ideal solution for businesses that require high-performance product analytics, such as those powered by Kubit. Its scalability allows it to handle millions of rows of data without compromising performance, ensuring seamless operations even at large data volumes. The system’s efficiency shines through with parallel query processing, delivering near-instant results for complex data sets. Additionally, the columnar storage format offers flexibility, giving businesses greater control over how their data is queried and analyzed to gain actionable insights.
These features help large-scale organizations democratize data, making it accessible to more teams across the business.
ClickHouse Use Cases
ClickHouse is the go-to solution for organizations that handle data from millions of users, particularly in consumer applications and high-volume SaaS products. Its performance is crucial for industries such as e-commerce and media, where businesses rely on real-time data to track customer behavior, and when capturing content performance and audience engagement is critical to driving user retention and growth.
SaaS companies use it to monitor product usage and engagement metrics at scale. Kubit leverages this power for both product and customer analytics, delivering fast, actionable insights that can be easily visualized in executive dashboards. This makes it the ideal platform for businesses looking to turn massive data streams into clear, strategic decision-making tools.
How to Get Started with ClickHouse
To begin utilizing ClickHouse with Kubit, simply integrate ClickHouse as your backend database for analytics. Kubit’s platform is designed to integrate with ClickHouse, allowing you to seamlessly:
Connect your data sources quickly
Access fast, real-time analytics
Optimize query performance for large datasets
With Kubit’s streamlined setup and expert customer success team, you’ll unlock ClickHouse’s full potential, effortlessly optimizing performance and scalability. Complex datasets become actionable, giving teams across your organization visibility into critical insights. This visibility allows for data-driven decisions and business improvements, empowering more of your team to operate effectively and align on strategic goals.
FAQs
What is ClickHouse?
ClickHouse is a columnar DBMS designed for fast query processing of large datasets in OLAP scenarios.
Which engine does ClickHouse use?
ClickHouse uses a MergeTree engine, allowing partitioning, indexing, and replication.
What language does ClickHouse use?
ClickHouse uses SQL as its query language, making it accessible to most data engineers.
What is a DBMS?
A DBMS (Database Management System) is software used to store, retrieve, and manage data.
Ready to move on from Amplitude, or want to explore other options? Read this blog post to get a better understanding of vendors that offer similar solutions to Amplitude, their target use cases, key features, and customer ratings.
Analytics for insight on digital product usage, customer journey, and business intelligence
What is Amplitude?
Amplitude is a product analytics platform that helps businesses build better digital products by tracking and understanding user behavior. Amplitude analytics help teams answer questions about what happened and why. These insights enable informed decision-making to drive growth.
Businesses use Amplitude to:
Analyze active users
Understand customer value
Accelerate monetization
Increase user engagement and retention
Improve the customer journey
Maximize user adoption
Drawbacks to using Amplitude
Choosing the right analytics platform for your use case is critical for success. Many analytics vendors are on the market, and each has its own strengths (and drawbacks), depending on the use case it’s implemented for. Some drawbacks to using Amplitude include:
It’s not warehouse-native. With Amplitude, you must move or replicate your data for analysis, so your data will not represent a 100% complete, up-to-date picture.
Higher cost of ownership. Amplitude requires additional engineering resources to transform data into a certain format, include historical data, expunge data, or add new schemas to it.
Limited permissions. While other product analytics vendors offer custom, role-based permissions, Amplitude offers only four out-of-the-box roles.
Data security. With Amplitude, your data must leave the walls of your CDW to be analyzed, potentially causing security or compliance risks.
Data analysis. Amplitude offers common analysis types but does not include capabilities such as and/or with filters, creating filters on the fly, out-of-the-box sampling, access to SQL behind each query, or creating histograms on the fly.
Top 9 alternatives to Amplitude, by use case
#1 Kubit
Use case: Warehouse-native analytics for optimizing digital products while ensuring data security, compliance, and scalability.
Kubit analytics platform helps companies gain valuable customer insights without moving their data into silos. This warehouse-native approach lowers the cost of ownership, frees up engineering resources, and delivers more accurate and complete self-service insights.
Key features:
User engagement: Find out which user behaviors lead to higher lifetime value and how to retain and grow your user base.
Feature engagement: See which product bets drive the highest engagement and create power users within your product.
Conversion analysis: Learn how users convert through critical funnels within your product and how to resolve areas that lead to drop off.
Consumption patterns: Understand which product bets and content to play up and which to sunset.
Use case: Analytics for learning how and why people engage, convert, and retain (across devices) to improve their user experience.
Mixpanel is a digital analytics platform that helps companies measure what matters, make decisions fast, and build better products through data with self-serve product analytics solutions.
Key features:
Product analytics: Track user behavior, KPIs, and core metrics with trends, retention, and flows.
Collaborative boards: Build analysis in collaborative boards that can include reports, text, videos, and GIFs.
Alerts: Get automated notifications when there are anomalies in metrics or when they fall outside of an expected range.
Filtered data views: Hide and filter data on a per-team basis to protect data privacy and reduce noise.
Use case: Analytics for improving the customer journey and testing new features and experiences.
Heap is a digital insights platform that helps companies understand their customers’ digital journeys so they can quickly improve conversion, retention, and customer delight.
Key features:
Session replay: Get insights about user behavior by replaying their session to understand where they experience friction.
Heatmaps: A visualization of a user’s behavior on the page, including what they click on, how far they scroll, and where they focus their cursor.
Autocapture: Capture all the data you need automatically, including every view, click, swipe, and form fill, for web and mobile.
Segments: Create user cohorts based on real actions taken on your site or app to understand how different users navigate your digital experience.
Use case: Product analytics, in-app guides, session replay, and user feedback.
Pendo is a product experience platform that helps teams deliver better software experiences and increase product adoption through onboarding users, tracking adoption analytics, monitoring usage patterns, and measuring churn rates.
Key features:
Product analytics: Collect app and user data and learn from the past to make informed decisions that improve product adoption.
Session replay: Watch video playbacks of user sessions to understand why users do what they do.
In-app guides: Deliver personalized guidance to customers directly inside your app.
Product-led growth: Drive better customer retention, conversions, and engagement with less time and expertise.
Use case: Analytics for increasing product adoption, improving onboarding, and supporting product-led growth.
Userpilot is an all-in-one platform for Product & UX teams. It combines product analytics, in-app engagement, and in-app surveys to help you increase product adoption through powerful in-app experiences, actionable product analytics, and user feedback.
Key features:
Feature tags and heatmaps: Tag certain UI elements and monitor how users interact with them; visualize data through color-coded heatmaps.
Custom event tracking: Track relevant milestones in a customer journey that reflect desirable user behavior, like downloading a Chrome extension, and then monitor how many users behave in that manner.
Analytics dashboards: Track product usage metrics such as the number of active users, sessions, average session duration, and feature adoption rate from a single view.
Funnel analysis: Track how users progress through different user funnels, enabling you to discover friction points in the user journey and optimize them to improve the user experience.
Use case: Analytics for websites, iOS/Android apps, and various frameworks that answer the “why” behind user actions.
Smartlook gathers brands’ app data together on one central dashboard to provide clear, data-driven decision-making for product managers, marketers, UX designers, and developers to reduce churn rates, boost conversions, identify and fix bugs, and improve UX.
Key features:
Session recordings: Evaluating user recordings can reveal issues with your app or website.
Events: Find out how often users perform certain actions that are important to you.
Funnels: Find out where and why your users are dropping off, so you can improve.
Heatmaps: Get an overview of where your users click and how far they scroll.
Use case: Freemium analytics service for gaining insight into website and app behavior, user experience, and marketing efforts.
Google Analytics collects website and app visitor data to help businesses determine top sources of user traffic, gauge the success of their marketing campaigns, track goal completion, discover patterns and trends in user engagement, and obtain visitor demographics.
Key features:
Built-in automation: Get fast answers to questions about your Google Analytics data, predict user behavior, and tap into powerful modeling capabilities.
Reporting: Dive deeper into the ways customers interact with your sites and apps with real-time reporting, acquisition reports, engagement reports, and monetization reports.
Advertising workspace: Understand the ROI of your media spend across all channels to make informed decisions about budget allocation and evaluate attribution models.
Explorations: Run deeper, custom analysis of your data without the limitations of pre-defined reports, and share insights with other users.
Use case: Analytics for insight on digital product usage, customer journey, and business intelligence.
Netspring is a product and customer analytics platform that brings the modeling flexibility and exploratory power of business intelligence to self-service product analytics, working directly off of any cloud data warehouse.
Key features:
Self-service: Self-serve answers questions from a rich library of product analytics reports, with the ability to pivot back and forth between any report and ad hoc visual data exploration.
Warehouse-native: Combine product instrumentation with any business data in your data warehouse for context-rich analysis.
SQL option: Avoid writing and maintaining complex SQL for funnel/path-type queries, but have the option of leveraging SQL for specialized analysis.
Product and customer analytics: Solutions for behavioral analytics, marketing analytics, operational analytics, customer 360, product 360, and SaaS PLG.
Use case: Combining product analytics, session replay, feature flags, A/B testing, and user surveys into an all-in-one, open-source platform.
Posthog enables software teams to capture events, perform analytics, record user sessions, conduct experiments, and deploy new features, all in one platform, helping engineers to design better, build better, develop better, and scale better.
Key features:
Product analytics: Funnels, user paths, retention analysis, custom trends, and dynamic user cohorts. Also supports SQL insights for power users.
A/B tests: Up to nine test variations, as well as primary and secondary metrics, can be used. Test duration, sample size, and statistical significance can be automatically calculated.
Session replays: Includes timelines, console logs, network activity, and 90-day data retention.
Surveys: Target surveys by event or person properties. Templates for net promoter score, product-market fit surveys, and more.
Kubit offers the market-leading self-service analytics platform that runs natively on Snowflake.
In today’s data-centric world, the ability to sift through large amounts of information and extract actionable insights quickly is not just an advantage—it’s a necessity. With IDC predicting that global data volume will surpass 160 zettabytes by 2025, a tenfold increase from 2017, having the ability to quickly access, analyze, and act on company data that you can trust will be a competitive differentiation point that organizations will not be able to ignore.
The Rise of Snowflake
This explosion of data has led to the creation of an entirely new generation of cloud data warehousing technologies, all positioned to help organizations have more flexibility and control of their data with a scalable cost model. Among these companies, Snowflake is a trusted leader of thousands of organizations, realizing the value and necessity of data for their business.
While there are numerous ways customers can derive value from Snowflake, this article, 8 Reasons to Build Your Cloud Data Lake on Snowflake, highlights several reasons why organizations turn to Snowflake to enable a more robust data practice in their organizations. The critical takeaway from this article is that when you store data in Snowflake, your experience is drastically simplified because many storage management functionalities are handled automatically. Yet, there are still some challenges and limitations in accessing and activating that data, which we will discuss here.
The biggest challenge and most common question is: How do non-technical (non-Snowflake) users access and use the data that is relevant to them?
The reality is that this question persisted long before cloud data warehousing was around. Company data was still held directly in databases, and any analysis required a database administrator or engineer to access it for the business. This is where product analytics was born.
The Birth of Self-Service Product Analytics
Product analytics emerged from the frustration of traditional data analysis methods. While querying databases for insights was possible, the process was slow and cumbersome, requiring significant technical expertise. Business intelligence (BI) tools offered some relief but were often rigid and pre-configured for specific reports. This meant limited flexibility for stakeholders who needed to explore data independently and answer unforeseen questions quickly. The rise of product analytics addressed this need for speed and exploration. It provided user-friendly interfaces and intuitive data visualizations specifically designed to analyze user behavior within digital products rapidly. This empowered stakeholders to delve deeper into user data, identify trends and pain points, and ultimately make data-driven decisions to optimize the product and user experience.
Product analytics has always been pivotal to understanding customer behaviors, enhancing product offerings, and driving user engagement. However, the landscape of data analytics has undergone a seismic shift with the advent of Big Data, escalating both the opportunities and challenges it brings.
Traditional product analytics tools, while offering some level of self-service analytics, essentially create data silos. This situation conflicts with the organizational drive and investment toward cloud data warehousing. The core issue with this setup is that data residing outside the warehouse leads to concerns about trust and integrity. Moreover, organizations find themselves duplicating efforts and squandering resources to manage and reconcile data across disparate locations.
Enter Kubit’s Snowflake-native Product Analytics
Kubit is the first Snowflake-native product analytics platform purpose-built to address the limitations and challenges inherent in traditional product analytics approaches. Specifically, providing a self-service analytics platform native to Snowflake allows organizations to access their complete dataset with flexibility, agility, and trust. There are other value drivers as well including but not limited to:
Self-Service Analytics Self-service analytics refers to the ability for non-technical users to access and analyze data without needing assistance from data engineers and analysts. This is made possible by Kubit’s intuitive and easy to use business interface that allows users to directly query and manipulate their data in real-time, without the need for SQL knowledge or complex ETL jobs.
Flexibility Kubit empowers organizations to analyze ALL of their data within Snowflake, going beyond mere clickstream analysis to encompass a wide array of sources including marketing, product, customer success, sales, finance, and operations. By aggregating this diverse data, organizations are equipped to delve into one of the most vital inquiries – why? It’s only through a holistic overview of all data points that teams can begin to unravel this question, paving the way for more informed decision-making.
Data Integrity The abundance and completeness of data for analysis becomes irrelevant if there’s a lack of trust in the data itself. Hence, it’s imperative that Kubit can directly access Snowflake, serving as the ‘single source of truth,’ to guarantee the accuracy and reliability of data throughout its lifecycle. This ensures compliance, operational excellence, and builds trust within any data-driven environment.
Total Cost of Ownership Gartner’s research indicates that organizations can reduce their Total Cost of Ownership (TCO) by up to 30% through migrating to cloud data warehouses. Kubit further enhances this advantage by assisting organizations in streamlining their analytics technology stack. This enables the reallocation of valuable resources, which are currently underutilized in efforts to create, manage, measure, and validate data and analytics with tools not designed for these tasks. Kubit also cuts down on double paying for storage and compute of data residing in yet another repository for analytics purposes.
The Real-world Impact The advantage of adopting a Snowflake-native strategy for self-service analytics lies in the ability of organizations to be operational within days, not weeks or months. This rapid realization of value empowers companies to immediately concentrate on their most crucial and impactful areas. For instance, this TelevisaUnivision case study illustrates how they focused on boosting retention rates for their ViX streaming service, showcasing just one of many successes where Kubit has facilitated the achievement of significant outcomes.
Implementation Insights Kubit offers far more than just self-service analytics software; it boasts a world-class team dedicated to ensuring customer success through comprehensive onboarding, enablement, training, and support. Our commitment goes beyond just providing technology; we actively lean in with our customers to help create value and success.
Looking Ahead: Benefits and Future Trends
The immediate advantages of leveraging Snowflake-native product analytics are evident, including improved decision-making capabilities and more profound insight into customer behaviors. Moreover, the long-term benefits herald a continuous shift towards predictive and prescriptive analytics, fundamentally transforming the future of business data interaction.
Get Started Today
What are you waiting for? Are you a Snowflake user ready to try Snowflake-native Kubit? Feel free to Take a Tour or Contact Us to discuss your specific goals and how Kubit can help you achieve them. Our team is here to provide personalized support and ensure a smooth onboarding experience.
If you want more information about our offering, including detailed features and implementation guidelines, check out our technical documentation. Whether you’re an experienced data analyst or a Product Manager just starting out, our resources are tailored to meet your needs and help you maximize the potential of your data.
In streaming and entertainment applications, content plays a significant role in the engagement, retention and monetization since that’s everything customers interact with. It can also be used as a critical tool to attract new customers, drive conversion and reactivate dormant ones.
Though content typically is not free. There are licensing and loyalty considerations, also the cost to promote certain content to the right audience. For example, using a free show to acquire new customers or drive them to sign up for a trial subscription; or target a specific cohort of users with episodes from certain genres to bring them back to the platform.
Even with sophisticated recommendation systems based on modern machine learning algorithms, the content team must conduct lots of experiments and measure their results to maximize the impact. It is a tricky balance since the audience’s taste changes frequently and can be easily influenced by the season or cultural atmosphere. That’s why content management becomes live operations.
Unfortunately, in most organizations, content analytics is typically overlooked and treated just as a reporting function and left for some pre-built reports to handle. Without self service and exploration, many enterprises couldn’t even connect the dots between content changes and long term customer behavior.
Problems with Siloed Product Analytics
Some organizations managed to get content insights using last generation product analytics platforms at the expense of very high cost.
Content data is massive and changes very frequently. Imagine a content database with every show and episode, with dynamic assignments to different channels, genres, carousels and promotions, along with confidential loyalty data. None of the information is available inside the digital application when a customer starts watching a video.
In order for these product analytics platforms to provide content insights, complex integration has to be completed to duplicate the content data into their data silos. Either the content data must be available inside the application on the devices, or special ETL jobs have to be built to sync it over periodically. Neither approach is ideal because of the dynamic nature of the content data itself: any kind of copying or syncing causes problems of stale data, or even worse, potential of wrong data.
There are also other vendors for each stage of a customer’s journey, like identity resolution, customer engagement and experimentation. The product analytics must have a copy of precious customer data from each and every vendor in order to deliver the insights. That is the root cause of all the headaches and issues.
There are criss-cross connections to be established through various approaches (e.g. ETL, API and storage sharing) and conduct heavy duty data copying. More often to anyone’s like, these connections can be broken, require maintenance, or even worse need to restate the historical data because of mistakes made. Just imagine the impact on the critical campaigns which require almost real-time insights.
Identity Resolution Becomes a Nightmare
Streaming and entertainment apps are all very sensitive about data security and privacy. As required by GDPR like policies, most customer identifiers are obfuscated or anonymous and require identity resolution vendors (e.g. Amperity, LiveRamp) to stitch them together.
Unfortunately identity resolution is not deterministic and often there are desires to play/test with different strategies in order to measure certain content campaigns more accurately or efficiently. If the resolved ids have already been copied into product analytics platform’s data silos, there is no chance to restate or re-evaluate. Frankly, it is even hard to imagine how these insights can be trusted because technically as a third party, a product analytics platform shouldn’t store customers’ PII information in the first place.
No Single Source of Truth
This one is really simple: with copies of data lying outside of the enterprises, how can anyone trust the insights where the analytics platform is a blackbox and there is zero transparency to understand how the insights are generated. Needless to say, there is no reconcilability whatsoever. It would really take some vote for confidence to rely on these findings to make content decisions, which often involves millions of dollars of budget.
Limited View on Customer Impact
Because some content data (like loyalty) is too sensitive to be sent to the digital app or the third party analytics vendor, some changes very fast and require constant restating or backfilling (like cataloging information), there can never be a complete 360 view of customer journey with siloed product analytics.
In addition, most media apps generate significant amounts of behavior data, like heartbeat events for video watching, which would lead to skyrocketing cost on such platforms which typically charge by data volume because they ingest customer data. Many content teams were forced to sample their data and live with partial insights which could lead to completely wrong results.
The Warehouse-native Way
All of these problems can be solved with the warehouse-native approach when the enterprise is committed to have full control of their data within a cloud data warehouse. By bringing all of the clickstream, identity resolution, impression, conversion and A/B test data from the vendors together and making their own data warehouse the Single Source of Truth, new generation of warehouse-native analytics platforms can connect directly to customer’s complete data model through effortless integration and ensuring both the integrity and self service perspectives required by content operations.
For the enterprise, they just need to collect their own customer data (including clickstream/behavior, content and operational data) and all vendors’ data into a central data warehouse which is under their full control. Often, access to vendors’ data can be achieved through Data Sharing protocols (available in most cloud data warehouses) instead of duplication with ETL or API.
There is no complex graph of data flow outside of the enterprise, especially between vendors. When there are data issues, only one place needs to be fixed and it is easily verifiable instead of coordinating with several third parties to pray that they will do the right thing since there is no visibility into their black boxes. There is no data backfilling, scrubbing or restating required.
Flexible Identify Resolution
Because all the data now goes to the enterprises where the customers are from, all available customer identifiers can be explicitly stated and used for analytics internally without the need for hashing and complicated matching (often guessing) algorithms.
Even better, the content team can experiment different identity resolution or attribution strategies on the fly, without the need to engage with vendors or reprocess any data. The ability of asking and validating “what if” questions before commitment gives complete confidence and flexibility.
Moreover, sensitive identity data can also be hidden or dynamically masked for warehouse-native analytics platform’s access since they don’t need to see the individual data as long as the underlying join works.
Exploration with Integrity
With One Single Source of Truth, and the ability to provide the SQLs behind every insight, the content team can now measure their content impact, explore customer insights in a self service manner while maintaining the highest level of integrity. There are never concerns about data volume or bringing in new data for analysis.
The transparency delivered by warehouse-native analytics makes it complimentary to any other BI, AI or machine learning tools, where they can not only reconcile the insights but also build on top of them. For example, a complex subscription retention analysis for different content cohorts can now be embedded into the machine learning algorithm for content recommendation as the KPI for the tuning purposes because the SQL is fully accessible.
Full Customer 360 View
With all the data about customers’ complete lifecycle stored in one place. warehouse-native analytics can easily analyze the impact from content campaigns with subscriptions, lifetime value, retention and reengagement data. Best yet, because all insights are generated dynamically, there are no ETL jobs to develop, no data to backfill when new data is required. That means that growth marketers don’t have to wait weeks or months for some data model changes required for specific vendors. Live customer insights with thorough depth is not a dream any more.
Summary
The days of data silos are long gone. With the convenience and advantages, warehouse-native analytics for content operations is an undeniable trend for enterprises with media and entertainment focused digital products. Getting reliable, trustworthy insights from a Single Source of Truth should be on the top of the mind for every serious content team.
There are many definitions out there. We’d like to think of Growth Marketing as an approach to attract, engage and retain customers through campaigns and experiments focusing on the everchanging motives and preferences of their customers. In practice, growth marketers build, deliver and optimize highly tailored and individualized messaging aligned with their customers needs through multiple channels. They are a cross functional team between Product, Marketing, Customer and Data. Product analytics play a significant role for this job with the focus on self service customer insights.
From customers’ lifecycle perspective, there can be several stages:
Acquisition
At the top of the funnel, customer acquisition is all about the strategy to target potential customers with tailored content through multiple channels with highest efficiency and fastest but most accurate measurements. The campaigns can be executed as ads, paid search, call-to-actions, free offers or discount coupons on various third-party channels. Often, there is a significant amount of budget allocated with these campaigns, which are also super dynamic.
Often the term “attribution” is used, which means to attribute every customer to the proper channel they come from in order to measure and find the most effective one. It requires constant monitoring, A/B testing and tuning to optimize acquisition channels on the fly in order to adapt to the market dynamics and get the best ROI.
Engagement
Once a new customer comes in, the focus now is to drive their engagement and collect more data to help build better experiences by enhancing their customer journey. Typically there are critical stages or funnels for a digital product like onboarding (tutorial), sign up, engaging with the core loop (e.g. watch a video, invite a friend, add to cart), and checkout. The goal of the engagement is to prompt customers with the most relevant and attractive content and push them through the desired sequence in order to keep them in the application.
Besides optimizing the user flow by improving design and usability, growth marketers typically rely on incentivized offers (first order discount, free trial), social/viral loop (invite a friend, refer someone) and loyalty programs to keep their customers engaging. All of these efforts require a deep understanding of customers’ journey (e.g. funnel, conversion, drop off) through product analytics in order to make the right decisions.
Reactivation
There will always be customers who become dormant or churn completely. In order to get them back into the application and retain, growth marketers utilize every possible communication channel at their disposal: email, push, SMS or even targeted ads to get their attention and get them back. Often some third party tools like Braze, a Customer Engagement Platform, will be utilized to deliver these messages. Though, product analytics will be the driver for these campaigns to identify different cohorts, target them and measure the ultimate results, which is not only about impression and open rate, but also the long term impact inside the application: e.g. retention, subscription attach, LTV (lifetime value).
Problems with Siloed Product Analytics
Those last generation product analytics platforms worked out for growth marketing needs at the time when they needed to run fast, but with some high cost.
Super Complex Data Flows
Since there are always different vendors for each stage of a customer’s journey, the product analytics must have a copy of precious customer data from each and every vendor in order to deliver the insights. That is the root cause of all the headaches and issues.
There are criss-cross connections to be established through various approaches (e.g. ETL, API and storage sharing) and conduct heavy duty data copying. More often to anyone’s like, these connections can be broken, require maintenance, or even worse need to restate the historical data because of mistakes made. Just imagine the impact on the critical campaigns which require almost real-time insights.
Identity Matching Becomes a Nightmare
When privacy concerns like GDPR arise, there are more and more limitations on what kind of customer identifiers can be shared with and between vendors themselves. Often the growth marketers get stuck in the middle of the battle between data engineering and security personnels. Eventually some aerobatic maneuvers have to be done on the data pipeline, which makes everything further more complicated and fragile.
No Single Source of Truth
This one is really simple: with copies of data lying outside of the enterprises, how can anyone trust the insights where the analytics platform is a blackbox and there is zero transparency to understand how the insights are generated. Needless to say, there is no reconcilability whatsoever. It would really take some vote for confidence to rely on these findings to make growth marketing decisions, which often involves millions of dollars of budget.
Limited View on Customer 360
For growth marketers, often just having the impression, conversion, CPI/CPM data is not enough at all. The deeper the insights into customer behavior, the better. For example, just measuring the open rate of a push campaign only scratched the surface, it is often desirable to understand what kind of content did the customer engage with, how long did they stay in the application, did they come back the week after, or if they converted to subscriber and if/when did they churn again.
In order to get this complete view of customer 360, operational data are required (e.g. Items and Subscriptions), but often it is almost impossible for traditional product analytics platforms to get these data because they are usually not part of the clickstream (behavior) data and will require very complicated ETL integration to send a copy out.
The Warehouse-native Way
All of these problems can be solved with the warehouse-native approach when the enterprise is committed to have full control of their data within a cloud data warehouse. By bringing all of the clickstream, campaign, impression, conversion data from the vendors together and making their own data warehouse the Single Source of Truth, new generation of warehouse-native analytics platforms can connect directly to the custom data model with effortless integration and ensuring both the integrity and self service perspectives required by growth marketers.
Simplest Data Integration
For the enterprise, they just need to collect their own customer data (including clickstream/behavior and operational data) and all vendors’ data into a central data warehouse which is under their full control. Often, access to vendors’ data can be achieved through Data Sharing protocols (available in most cloud data warehouses) instead of duplication with ETL or API.
There is no complex graph of data flow outside of the enterprise, especially between vendors. When there are data issues, only one place needs to be fixed and it is easily verifiable instead of coordinating with several third parties to pray that they will do the right thing since there is no visibility into their black boxes. There is no data backfilling, scrubbing or restating required.
Customizable Identify Resolution
Because all the data now goes to the enterprises where the customers are from, all available customer identifiers can be explicitly stated and used for analytics internally without the need for hashing and complicated matching (often guessing) algorithms.
Even better, enterprises can experiment different identity resolution or attribution strategies on the fly, without the need to engage with vendors or reprocess any data. The ability of asking and validating “what if” questions before commitment gives complete confidence and flexibility.
Moreover, sensitive identity data can also be hidden or dynamically masked for warehouse-native analytics platform’s access since they don’t need to see the individual data as long as the underlying join works.
Exploration with Integrity
With One Single Source of Truth, and the ability to provide the SQLs behind every insight, growth marketers can now explore customer insights in a self service manner while maintaining the highest level of integrity. The transparency delivered by warehouse-native analytics makes it complimentary to any other BI, AI or machine learning tools, where they can not only reconcile the insights but also build on top of them.
Full Customer 360 View
With all the data about customers’ complete lifecycle stored in one place. warehouse-native analytics can easily bring in any operational data (e.g. Items, Subscriptions or LTV) into the analyses. Best yet, because all insights are generated dynamically, there are no ETL jobs to develop, no data to backfill when new data is required. That means that growth marketers don’t have to wait weeks or months for some data model changes required for specific vendors. Live customer insights with thorough depth is not a dream any more.
Summary
The days of data silos are long gone. With the convenience and advantages, warehouse-native analytics for growth marketing is an undeniable trend for enterprises with customer focused digital products. Besides exploring customer 360, getting reliable, trustworthy insights from a Single Source of Truth should be on the top of the mind for every serious growth marketer.
In the fast-paced world of digital marketing and product analytics, understanding the intricacies of user behavior is not just advantageous—it’s essential. One of the most powerful tools at your disposal for dissecting user journeys is the funnel report. Effective use of funnel reports can illuminate the path to increased conversions, reveal bottlenecks in your user experience, and guide strategic decisions that drive business growth.
Here are the 5 unique capabilities of Kubit’s funnel report tool that have made it an indispensable asset for data-driven professionals aiming to unlock actionable insights from their user data.
Understanding Funnel Reports in Kubit
At its core, a funnel report is a visual representation of how users progress through a predetermined series of steps or actions on your website or app. This progression could relate to anything from completing a purchase to signing up for a newsletter.
Kubit’s Funnel Report offers these 5 capabilities to get the most out of your data:
Multi-step Funnel Creation: Craft funnels that reflect the complexity of real user journeys.
Partitioning Options: Slice your data by day, session, or custom conversion windows for nuanced analysis.
Deeper Conversion Insights: Break down funnel stages by various fields to uncover underlying patterns.
Advanced Visualization: Choose between step-by-step breakdowns or time-based line charts for dynamic report viewing.
Cohort Analysis: Right click and build users into cohorts for targeted behavioral analysis over time.
Use Cases for Funnel Reports
The applications of funnel reports in Kubit are diverse, mirroring the myriad pathways users can take towards conversion. Here are just a few scenarios where Kubit’s funnel reports can be most valuable:
Enhancing User Onboarding: Track new users’ progress through your onboarding sequence to identify and rectify any stumbling blocks.
Optimizing Product Engagement: Discover where users disengage or drop off when interacting with specific features or content.
Streamlining Conversion Paths: Measure the time it takes for users to move from one stage of your funnel to the next, and deploy strategies to accelerate this progression.
Analyzing Behavior Pre-Conversion: Understand the actions repeat users take before finally converting, providing insights into which features or content are most influential in driving conversions.
Through these use cases and beyond, Kubit’s funnel reports offer actionable insights that can powerfully impact business strategies and outcomes.
Real-World Success with Kubit Funnel Reports
Consider Influence Mobile, a customer that leveraged Kubit’s funnel reports to uncover a costly problem. By carefully analyzing their onboarding process and identifying friction points with Kubit’s tools, they significantly improved user retention. Furthermore, Kubit’s capabilities enabled them to detect patterns indicative of fraudulent activity, ensuring a secure and trustworthy platform for their users. Their success story underlines the potential of Kubit’s funnel reports to transform challenges into triumphs.
Getting Started with Funnel Reports in Kubit
Kubit simplifies the process of building and deploying funnel reports. To get started:
Define Your Conversion Goals: Determine what user actions or sequences you want to analyze.
Set Up Your Funnel Steps: Using Kubit, create a funnel that reflects these steps in your user’s journey.
Analyze and Iterate: Once your data starts flowing, use Kubit’s insights to refine your strategy and improve user outcomes.
Understanding how to interpret the data from your funnel reports is crucial. Look not just at where users are dropping off, but also why. This often involves cross-referencing funnel data with user feedback, usability tests, or other analytics reports.
Conclusion
Kubit’s funnel reports are a potent tool for anyone looking to enhance their understanding of user behavior and drive meaningful improvements in their conversion rates. Whether you’re just starting on your analytics journey or are looking to refine your approach with cutting-edge tools, Kubit offers a robust platform designed to elevate your analytics capabilities.
“The more people are looking at this data, the better. Everyone should be monitoring our most important conversions,” states a seasoned user of Kubit, underscoring the collective benefit of widespread engagement with analytics within an organization.
Ready to transform your data into actionable insights? Sign up for Kubit or reach out for a demo today, and discover how funnel reports can redefine the way you view your users’ journeys from first touch to conversion.
