u/mitchiemam

Most TikTok slideshow operations run one of two setups. Pure keyword triggers ("comment X to get Y") capture leads but produce weak comment quality. Substantive open-ended questions get strong threads but prospects disappear with no keyword routing them into a direct message sequence.

One call to action has to trigger both signals.

The stacked call to action

Four parts: a keyword that triggers your automation, a specific resource promise, a substantive prompt about the commenter's situation, and language that makes it feel like a conversation.

Example: "Comment SKINCARE below and tell me your biggest skincare struggle this year. I'll send you the exact routine that worked for us once I see your comment."

The commenter who drops just "SKINCARE" still enters your sequence. The one who answers the prompt produces both signals at once.

Keyword design

Generic keywords ("YES", "INFO", "DM") get pattern-flagged by TikTok's spam detection. If multiple slideshows share a keyword, you lose the data that tells you which post triggered each response, and your qualification logic collapses downstream.

Three principles: match the keyword to the content category (SKINCARE for skincare, MONEY for finance), keep it 5 to 8 characters, and rotate across 30 to 50 active variants per operation.

The substantive prompt

"What skincare products have you tried?" gets generic answers. "What products promised hormonal acne results but didn't deliver?" gets comments that already carry qualification data before the conversation moves to direct message.

Five prompt types by what they surface: experience ("worst mistake this year"), aspiration ("what would success look like"), identification ("are you the one who keeps trying or gave up"), recommendation ("what would you tell a friend"), confession ("what habit are you still doing despite knowing better").

The three-stage direct message sequence

Stage 1 delivers the resource and references something specific from their comment. Sequences that pitch in the first message break the implicit agreement. Commenters came for the resource, not a sales conversation.

Stage 2 (4 to 8 hours later) qualifies. Prospects who reply with specifics advance to Stage 3. Minimal replies get a lighter approach. No reply means they exit the active sequence. Target: 25% to 40% of Stage 1 recipients respond.

Stage 3 (12 to 24 hours later) presents the offer tied to their specific situation, with a graceful exit baked in. "Otherwise no worries, the routine should still help." Prospects who weren't ready here often buy a future offer because the conversational trust persists. Pressure at Stage 3 kills both. Target: 25% to 40% for low-ticket, 10% to 20% for high-ticket.

Personalization at scale

At 30 slideshows per day per client, writing each direct message by hand is not an option. Claude reads each commenter's substantive response, pulls the qualification signals, and generates personalized elements across all three stages. After 60 days of qualification data, you know which pain points drive the highest capture rates and build the next batch of slideshows from that.

Third-year CS at an NIT here. Goldman Sachs came to campus for their Summer Analyst Trainee internship and I went through the full process. I already had a Visa offer locked in so there was no pressure, which made it easier to observe everything clearly. Sharing this for anyone who has this coming up.

Nearly 800 students sat the OA across all branches. By the HR round, 8 were left. The funnel is steep and the cuts happen fast.

Online Assessment

Taken from home, open to all branches. Around 1.5-2 hours total: three DSA problems, 15-20 aptitude questions, and one descriptive self-story question all in the same window. Minimum bar was solving at least 2 DSA problems with a solid aptitude score. Managing time between coding and aptitude in one sitting was the hardest part. 44 students cleared it. I was one of them.

First Interview

Face to face on campus, 25-30 minutes. Opened with a self-intro, then the interviewer went into my project, covering design decisions and specific optimizations. DSA section covered Merge Sort vs Quick Sort differences, writing Merge Sort on paper with a full recursion diagram, time complexity across all three cases, and Floyd's Cycle Detection for linked lists. A few SQL queries and basic OS questions at the end. 20 students advanced. I cleared it.

Second Interview

Same format, 25-30 minutes. First half went deeper into my project, focusing on architecture and design tradeoffs. Then Authentication vs Authorization, how JWT works internally, and a real-world scenario around Access Tokens and Refresh Tokens: if a token gets stolen, how do you protect the user without forcing a full re-login? For coding, I said Linked List was my strongest topic. The problem was reversing a linked list in groups of K, on paper, in 10 minutes. I didn't finish in time and didn't clear this round. Some friends who advanced were also given logic puzzles, so prepare those separately.

HR

I didn't sit this round, eliminated in the second interview. From the 13 who reached it: relaxed and conversation-based, covering behavioral questions, interests, teamwork scenarios, and a few short logic puzzles. No heavy technical content. 8 students got the offer.

Closing Thoughts

Topics that came up across all three rounds:

• Merge Sort and Quick Sort: differences, on-paper implementation, all three time complexity cases
• Linked list problems, especially group reversal (K groups)
• Floyd's Cycle Detection
• JWT internals, Access Tokens, Refresh Tokens, and token theft scenarios
• Authentication vs Authorization
• Basic SQL queries
• Logic puzzles (GeeksforGeeks is a solid source)