As the CEO of an analytics and personalization technology company specializing in the video streaming industry, I often field questions like, “Which analytics vendor is the best?”, “Why should I choose your product?”, and “What sets your offering apart from the rest?” At the heart of these queries lies a singular objective: optimizing video businesses through a data-driven approach.
Yet, there isn’t a one-size-fits-all response to these questions. Typically, video streaming companies find themselves integrating several analytics products, each uniquely crafted to address specific challenges.
Analytics offers a data-driven perspective, enabling streaming platforms to delve deep into user behaviors, evaluate content performance, measure marketing outcomes, and much more. By leveraging the strengths of these analytics solutions, platforms can make informed decisions, enhance their services, and design a user experience that truly aligns with their audience’s preferences.
At its core, analytics theoretically transforms vast swathes of raw data into actionable insights, driving growth, heightening user satisfaction, and ensuring lasting success. However, the manner in which you structure your analytics stack is pivotal. It determines whether you reap these benefits or find yourself mired in a maze of overlapping analytics products that introduce data inconsistencies. Such a scenario can erode confidence and hamper decisive action.
I aim to share my insights to demystify this multifaceted market. My hope is that this knowledge will guide video streaming services in selecting the right analytics that resonate with their business model, product evolution, and broader organizational objectives.
Now, let’s delve first into a comprehensive analysis of the crucial analytics categories that every video streaming service should contemplate:
DISCLAIMER 1: This categorization is not meant to be the definitive method for classifying analytics in video streaming. However, it aligns closely with how most video streaming services and vendors typically organize their data practices.
DISCLAIMER 2: While I aim to provide examples of vendors in these categories, it is not my intention to list every vendor or conduct a comprehensive evaluation of them.
1 . User Acquisition & Monetization Analytics
Goal:
Understand and optimize the sources of user acquisition, track revenue streams, and calculate metrics related to user value.
Main Characteristics:
- User Acquisition Tracking: Monitor the sources (e.g., organic, paid ads, referrals) from which users are coming to your video service and their associated costs.
- Revenue Analysis: Track revenue streams, whether from subscriptions, ads or other sources.
- Customer Lifetime Value (CLTV): Calculate the total net profit a video streaming business expects to earn from a user or subscriber throughout their relationship.
- Cost Per Acquisition (CPA): Determine the cost to acquire a new user or subscriber, considering all associated expenses.
- Retention Rate: Measure the percentage of acquired users who continue to use the video streaming service over a given period.
- Churn Rate: Track the percentage of users who stop using the video service over a given period.
Why It’s Relevant:
- Optimized Marketing Spend: By understanding which acquisition channels are most effective, video businesses can allocate their marketing budget more efficiently.
- Revenue Growth: Identifying the most lucrative revenue streams allows for strategic focus and growth.
- Long-term Planning: Metrics like CLTV provide insights into the long-term value and sustainability of the user base.
Vendor Examples:
Evergent, Cleeng, Aptitude, JUMP
2.Content Performance Analytics
Goal:
Evaluate the performance of individual video content pieces.
Main Characteristics:
- Track views, average watch time, and completion rates.
- Monitor content sharing and virality metrics.
- Analyze feedback and ratings for content.
Why It’s Relevant:
- Helps in content curation and recommendation.
- Assists in making informed decisions about content renewal or removal.
- Provides insights into content trends and user preferences.
Vendor Examples:
Jump, Conviva, Mux, NPAW, Brightcove
3.Product Analytics
Goal:
Understand how users interact with the video product, identify areas of improvement, and drive feature optimization.
Main Characteristics:
- Feature Usage (search, continue watching, Offline viewing, etc)
- User Journey/flows (registration, subscription, activation, engagement, churn)
- A/B Testing
- Retention Analysis
- Error Tracking
- Feedback Collection
Why It’s Relevant:
- Directly impacts feature development and UX optimization.
- Helps in identifying growth opportunities and reducing churn.
Vendor Examples:
Mixpanel, Heap, Amplitude, Jump
4.Predictive Analytics for Churn Prediction
Goal:
Predict which users are likely to stop using the service in the near future, allowing for proactive retention strategies.
Main Characteristics:
- Data Collection
- Modeling
- Feature Importance
- Real-time Analysis
- Alerts & Reporting
Why It’s Relevant:
- Proactive Retention: Identify users at risk of churning for targeted engagement.
- Resource Allocation: Focus retention efforts on high-risk segments.
- Improved Understanding: Gain insights into why users might leave.
Vendor Examples:
Jump, Cleeng, NPAW
5.Quality of Experience (QoE) Analytics
Goal:
Ensure optimal video playback quality.
Main Characteristics:
- Monitor buffering rates, video start times, and playback failures.
- Track video resolution and bitrate changes.
- Analyze network conditions and device capabilities.
Why It’s Relevant:
- Directly impacts user satisfaction.
- Helps in troubleshooting and improving video delivery.
- Aids in optimizing content for different devices and network conditions.
Vendor Examples:
Conviva, Mux, NPAW, Jump
6.Content Personalization Performance Analytics
Goal:
Measure the effectiveness of content personalization algorithms and strategies.
Main Characteristics:
- Recommendation Accuracy
- Diversity & Novelty
- Engagement Metrics
- Algorithm Performance
Why It’s Relevant:
Enhanced User Experience, Increased Engagement, and User Retention.
Vendor Examples:
Jump, ThinkAnalytics, ContentWise
7. Marketing Performance Analytics
Goal:
Measure the effectiveness and ROI of marketing campaigns and strategies.
Main Characteristics:
- Campaign Tracking
- Channel Effectiveness
- CAC
- CLV
- CRO
- Retention Metrics.
Why It’s Relevant:
Optimized Spend, Growth, and Strategic Decision Making.
Vendor Examples:
HubSpot, Google Analytics, Kissmetrics, Jump
8.Ad Performance Analytics
Goal:
Maximize ad revenue and optimize ad delivery for your AVOD service.
Main Characteristics:
- Track ad impressions, click-through rates, and conversion rates.
- Monitor ad buffering and playback issues.
- Analyze user engagement with ads and ad skip rates.
Why It’s Relevant:
- Directly impacts Ads monetization strategies.
- Helps in targeting ads to the right audience.
- Assists in optimizing ad placement and frequency.
Vendor Examples:
Nielsen, Comscore, Jump
9.Security and Anti-Piracy Analytics
Goal:
Protect content from unauthorized access and distribution.
Main Characteristics:
- Monitor for suspicious user behavior and access patterns.
- Track content distribution outside the platform.
- Analyze potential vulnerabilities in the platform.
Why It’s Relevant:
- Ensures content creators receive their due revenue.
- Protects the platform’s reputation and user trust.
- Helps in compliance with content licensing agreements.
Vendor Examples:
Verimatrix, Irdeto
Conclusion
The success of video streaming services hinges on a multifaceted approach to analytics. From understanding user behavior to optimizing marketing strategies, analytics tools provide the insights needed to navigate the competitive landscape and ensure sustained growth. As the industry continues to evolve, leveraging these analytics solutions will be paramount for platforms aiming to stay ahead of the curve.
The challenge lies in implementing the data infrastructure to handle all these data points. Furthermore, the fragmentation of the analytics vendor ecosystem makes it extremely difficult to manage all these data categories with a single tool. Typically, video streamers resort to multiple analytics tools and dashboards to address these diverse needs.
Returning to the questions that opened this article: “Which analytics vendor is the best?”, “Why should I choose your product?”, and “What sets your offering apart from the rest?” – there isn’t a one-size-fits-all answer.
I would recommend evaluating potential vendors and selecting the one that covers most of the analytics categories you need in a single tool. Doing so will grant you the highest level of data accuracy, a consolidated view of your business on one dashboard, and the ability to correlate different data points seamlessly. All these benefits will contribute to greater value for your business at a reduced cost.
If you’d like to continue the conversation, please reach out to me. I love this topic! 🙂
Jeronimo Macanas
CEO & Co-founder
JUMP Data-Driven Video
