Threads rolls out Dear Algo

Hi ,

Meta just launched "Dear Algo" for Threads.

Users can now personalize their content-recommendation algorithms by telling them what they want to see.

Post publicly. Algorithm adjusts your feed. Lasts three days.

Testing in U.S., U.K., Australia, and New Zealand.

Let me share today's multi-channel automation framework and AI churn prevention tactics first. After that, we'll look at what algorithm personalization means for social media.

🔥 Prompt of the Day 🔥

Marketing Automation Distribution System: Use ChatGPT or Claude

Act as a Marketing Automation Specialist.

You are an expert in multi-channel communication strategies, specifically Email, SMS, and QR code integration.

I need a complete distribution plan for an automated review system that maximizes responses without being intrusive.

Essential Details:

• Business Type: [YOUR INDUSTRY]

• Customer Touchpoints: [WHERE CUSTOMERS INTERACT]

• Current Review Collection: [HOW YOU GET REVIEWS NOW]

• Email Open Rate: [CURRENT PERCENTAGE]

• SMS Usage: [YES/NO/FREQUENCY]

• Average Customer Journey: [DURATION FROM PURCHASE TO COMPLETION]

Design one automated review distribution plan including:

• QR Code Placement Strategy (where to place QR codes in physical or digital customer experience: packaging, front desk, digital receipt, post-service touchpoint)

• Channel Logic Flow (when to use email vs. SMS to maximize open rates, timing based on customer behavior, preferred communication method by customer segment)

• Fallback Logic Plan (if user doesn't open email within 48 hours, send follow-up SMS; if no response to SMS within 24 hours, try different channel; escalation sequence until response or opt-out)

• Message Sequencing (first touchpoint content, follow-up variations, final attempt messaging)

• Opt-out Management (how to respect preferences, unsubscribe handling, channel preference updates)

✅ Tips & Tricks Thursday ✅

AI Customer Churn Prevention

Losing customers costs 5x more than keeping them.

Most businesses notice departures too late. Customer already canceled. Relationship already broken. Recovery attempts feel desperate.

Here's how to prevent churn before it happens.

The Problem

Traditional churn detection is reactive. Customer stops using product. Customer cancels subscription. Customer doesn't return.

By the time you notice, they've already decided to leave. Win-back campaigns rarely work because the decision is final.

Prevention requires spotting warning signs early. Before they decide to leave.

Train AI on Churn Patterns

Look at customers who've left previously. What did they do before leaving?

Usage dropped 40% in final month. Support tickets increased. Login frequency decreased. Feature engagement changed. Payment issues appeared.

Train AI to recognize these patterns. Not just one signal. Combinations of behaviors that predict departure.

Monitor Current Customers

Run AI monitoring on active customer base. Flag accounts showing similar patterns to customers who left.

Usage declining? Flag it. Support tickets increasing? Flag it. Engagement dropping? Flag it.

Don't wait for cancellation. Catch the warning signs weeks before they decide to leave.

Trigger Retention Campaigns

When AI flags at-risk customers, trigger retention campaigns immediately.

Not generic "we miss you" emails. Personalized interventions based on specific warning signs.

Usage dropping? Offer onboarding help or training. Support tickets increasing? Schedule check-in call with success team. Engagement declining? Highlight unused features that solve their problems.

Personalize Win-Back Offers

Base retention offers on customer value and history.

High-value customers get premium interventions. Personal calls. Custom solutions. Executive attention.

Lower-value customers get automated campaigns. Helpful content. Feature highlights. Usage tips.

Match intervention intensity to customer lifetime value.

Keep Measuring

Track which retention tactics work. Which warning signs actually predict churn. Which interventions prevent departures.

Some signals are false alarms. Some interventions don't work. Refine based on results.

Retention campaigns that save 30% of at-risk customers are worth the investment. Campaigns that save 5% aren't.

Why This Matters

Acquiring new customers costs 5x more than retaining existing ones.

Losing a customer loses all future revenue from that relationship. Not just this month's payment. Years of potential value.

Prevention is cheaper than acquisition. Spotting warning signs beats desperate recovery.

Most businesses don't measure churn patterns. They react after customers leave. They miss the opportunity to intervene early.

AI makes early detection possible. Train it on past departures. Monitor current customers. Trigger interventions before decisions are made.

Prevention beats recovery every time.

Did You Know?

AI systems can now process and analyze entire genomes to identify rare disease-causing mutations with unprecedented precision, diagnosing conditions that once left families searching for answers for years without ever receiving a definitive diagnosis.

🗞️ Breaking AI News 🗞️

Meta Launches "Dear Algo" for Threads

Meta announced "Dear Algo," an AI feature that lets Threads users personalize their content-recommendation algorithms using natural language.

Testing now in U.S., U.K., Australia, and New Zealand.

What Changed

Social media algorithms historically work as black boxes. Users see what the algorithm decides to show them. No direct control. No transparency. No personalization beyond likes and follows.

Meta is making algorithms conversational.

Users can now tell the algorithm directly what content they want to see. Write it in plain language. Algorithm adjusts accordingly.

How It Works

Create a public post on Threads starting with "Dear Algo."

Explain what content you want to see more or less of.

Example: "Dear Algo, show me more posts about AI development and fewer posts about celebrity news."

Once you share the request, Dear Algo adjusts your feed for three days.

You can also repost someone else's Dear Algo request to apply their content preferences to your own feed.

Why Three Days

Meta says three-day duration keeps you "connected to the most current conversations."

Temporary personalization means algorithm doesn't lock into preferences permanently. Allows testing different content preferences without committing long-term.

The Inspiration

Meta was inspired by a trend of people publicly sharing posts with "Dear Algo" phrase.

Users were already trying to influence algorithms by publicly stating preferences. Meta built an official feature around this behavior.

Strategic Context

Threads launched July 2023 to compete against Twitter (now X, part of xAI-SpaceX merger).

Threads reached 400 million monthly active users as of last month. Started rolling out ads globally.

Meta plans to spend $115-135 billion this year on AI-related capital expenditures. Nearly double last year's spending.

CEO Mark Zuckerberg told analysts Meta plans to debut and test new AI products and features throughout 2026.

Other Recent Meta AI Moves

Tuesday: Released AI features for Facebook that animate profile photos and alter images with Meta AI digital assistant.

Ongoing: Incorporating AI across all Meta apps as part of massive AI infrastructure investment.

Why This Matters

This is the first major social platform to let users directly instruct algorithms using conversational language.

Traditional algorithm control is indirect. Like, follow, hide, block. Algorithm infers preferences.

Dear Algo is direct. Tell the algorithm what you want. Algorithm listens.

If this works, expect other platforms to copy it. Instagram, Facebook, TikTok, YouTube could all add conversational algorithm control.

Users get more control over what they see. Platforms get explicit preference data instead of inferred signals.

The Questions

Does three days provide enough time to evaluate if preferences work?

Will users actually write Dear Algo posts or will adoption remain low?

Can algorithms accurately interpret natural language preferences and map them to content?

Will this reduce engagement (users see only what they ask for) or increase it (users see more relevant content)?

Does public preference sharing create social pressure to request "acceptable" content?

Testing over next few months will answer these questions.

Over to You...

Would you use Dear Algo to personalize your Threads feed or let the algorithm decide?

Hit reply and tell me what you'd ask for.

To algorithm control,

Jeff J. Hunter 
Founder, AI Persona Method | TheTip.ai

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