Make Every Reader's Journey Unique
Staying relevant amid endless content is a constant struggle for publishers. Broadcast-style feeds showing all users the same trending articles inevitably lead to disengagement over time.
We realized early the need to make every reader's journey unique. To thrive in a crowded market, we offer you to achieve true personalization at scale.
The Problem
Without personalization, you will face mounting challenges:
- Plummeting clickthrough rates as headlines grow repetitive and stale, even on tentpole topics.
- Mass subscriber churn as static feeds fail to retain users beyond a month or two. Readers crave relevance.
- Minimal social sharing due to misaligned content matching. Readers won't share what doesn't resonate.
- Difficulty segmenting and targeting amid limited and siloed user data.
The end result? Lost revenue from lower ads, subscribers, and engagement. Continuing as-is meant falling further behind nimbler disruptors.
The FirstBatch Solution
To reverse course, FirstBatch enables you to overhaul underperforming content recommendation engines using AI-powered User Embeddings.
Rather than sparse and siloed user data, User Embeddings analyzes real-time signals like reads, clicks, shares, and reactions to generate dynamic experiences for each reader.
These "embeddings" locate users in high-dimensional vector space near content predicted to match their taste based on activity patterns. This enables hyper-personalized recommendations that actually adapt as reader interests grow and evolve.
Transforming Your Business
The impact across your business will be profound:
Higher clickthrough rates. Headlines stay relevant as reader embeddings capture nuanced preferences no survey could ever reveal.
Lower churn. Personalized feeds keep subscribers engaged all year long by continuously matching their needs.
More social sharing. Relevant content resonates when shared because it fits readers' follower interests.
Higher ad rates. Contextual ad targeting significantly lifts performance by optimizing placement.
The Secret Sauce
For example, two proprietary algorithms can make this transformation possible:
Gradual Relevance
Uses a multi-phase approach to shift readers into content spaces aligned with their interests:
- Cold Start Phase: Explore randomized articles to determine preferences
- Sampling Phase: Gets feedback across categories to build a taste profile
- Discovery Phase: Initial personalization based on established preferences
- Tailoring Phase: Fine-tuned recommendations catered to each reader
By gradually evolving relevance, it balances discovery serendipity with precision over time. New readers explore, while your users get 100% tailored articles.
Optimizing Clickthrough Rates
Getting readers to click on headlines is crucial, but a challenge with one-size-fits-all content. This Headline Tuner algorithm might be the answer.
It works in stages to optimize clicks:
- Scout Phase: Tests headline styles with randomized articles to determine initial preferences.
- Experiment Phase: Explores user reactions to different headline types and topics.
- Align Phase: Aligns headlines to resonating patterns for each user.
- Personalize Phase: Generates personalized, optimized headlines matched to user interests
Rather than guesswork, Headline Tuner uses reader embeddings to scientifically determine headline resonance and refine it over time.
The algorithm learns exactly what makes each user click. The result? Industry-leading clickthrough rates.
With AI-powered algorithms like Gradual Relevance and Headline Tuner, you can deliver truly personalized experiences - adapting content and headlines to each reader's evolving interests over time. The result is higher engagement, loyalty, and clicks by making every reader's experience unique.