The Consistency Bet: Forecasting 90-Day Reach Under Three Posting Strategies

Marketing Academy · Field Work●Social Media Marketing

All projects·From: How Social Algorithms Work

CoreForecast· 55 minutes

Jyoti CNC Automation

Objective: Using the lesson's consistency-builds-predictable-history mechanic, LinkedIn's -34% YoY organic reach decline, and its 15x comment-weighting, forecast 90-day cumulative reach for a B2B company page under three posting strategies (pre-event burst, sparse, steady 3x/week) and recommend one with numbers.

You run organic social for Jyoti CNC Automation, the Rajkot-founded, NSE-listed CNC and precision-engineering manufacturer (JYOTICNC), ahead of a decision on next quarter's LinkedIn posting cadence.

Model reach under 3 strategies using the lesson's first-60-minutes velocity mechanic and consistency-beats-volume principle, then recommend one with a projected number, not a vibe.

Before you start

What you'll need

Free path (everything below is enough to finish)

Google Sheets

FreeBuild the 12-week reach model and weighted engagement score

Free, sufficient for a spreadsheet-based forecast with no live API needed

The process

2 steps

Step 01 of 02

Consistency beats volume for distribution

The lesson explains that predictable posting histories make the algorithm's engagement-probability estimates more accurate, so it distributes more willingly. Three posts/week every week beats seven one week and none the next.

Strategy A posts 12x in the 2 weeks before a trade show then goes silent for 10 weeks. Strategy B posts 3x/week for all 12 weeks. Both total roughly the same post count. Which builds more reach?

Google Sheets— Build a 12-week grid with columns for posts/week and a 'history predictability' multiplier.

Procedure

List all 3 strategies across 12 weeks: burst, sparse (1x every 2 weeks), steady (3x/week)
Apply a 1.0x baseline reach-per-post multiplier to steady, 0.6x to burst (post-history reset after the silent gap), 0.75x to sparse
Sum total projected reach per strategy across 12 weeks using LinkedIn's current 5-7% organic reach-per-follower baseline
Flag which strategy the lesson's mechanic favors even before adding real numbers

Sample output

12-WEEK REACH FORECAST (10,000 follower base)
  Burst (12 posts, 2 weeks): 12 x 500 x 0.6 = 3,600 total reach
  Sparse (6 posts, spread): 6 x 500 x 0.75 = 2,250 total reach
  Steady (36 posts, 3x/week): 36 x 500 x 1.0 = 18,000 total reach

Healthy

Steady posting projects the highest cumulative reach despite the lowest per-post ceiling.

Unhealthy

Leadership approves a 12-post pre-show burst expecting it to outperform a quieter, steady cadence.

What this means

Total post count matters less than the shape of the posting history the algorithm has learned to predict.

So what do I do about it?

Symptom	Action	Effort
Marketing wants to save posts for a pre-trade-show burst	Redistribute the same post budget into a steady 3x/week cadence instead	30 min

YouYou can do this yourself, no engineering access required.

Step 02 of 02

Early engagement velocity determines whether a post's initial test distribution expands

LinkedIn's initial test distribution is small; if engagement in the first 60 minutes is flat, the post rarely recovers. Comments carry 15x the weight of likes.

A steady-cadence post gets 40 likes and 0 comments in the first hour. A less-liked post gets 8 likes and 6 comments. Which one is more likely to expand past the test audience?

Google Sheets— Add a weighted-engagement column: comments x 15 + likes x 1.

Procedure

Compute weighted engagement score for both posts using the 15x comment multiplier
Compare against a same-account historical median weighted score
Flag which post clears the expansion threshold

Sample output

Post A: (0 comments x 15) + (40 likes x 1) = 40
Post B: (6 comments x 15) + (8 likes x 1) = 98

Post B clears the account's median weighted score (75); Post A does not.

Healthy

The team seeds a question in the first comment on every steady post to manufacture early comment velocity.

Unhealthy

The team judges the first-hour success of a post by like count alone and stops promoting a comment-rich but like-poor post.

What this means

A quieter-looking post with real comment threads is outperforming algorithmically, even if it looks weaker on a dashboard sorted by likes.

So what do I do about it?

Symptom	Action	Effort
Weekly reporting sorts posts by like count	Re-sort the reporting view by the weighted comment-first score before making cadence decisions	5 min

YouYou can do this yourself, no engineering access required.

Final deliverable

A 90-day (12-week) reach forecast comparing burst, sparse, and steady posting strategies, plus a first-60-minutes weighted-engagement scoring method, ending in a single cadence recommendation.

See a reference example

Sample output

TBO Tek, LinkedIn Q3 cadence recommendation (excerpt)

RECOMMENDATION: Steady 3x/week cadence
Projected 12-week reach: 18,400 vs. 3,900 for a pre-conference burst

FIRST-HOUR RULE: seed a question in comment #1 on every post; team replies to every comment within 60 minutes.

Success criteria

You're done when you can:

Correctly applies a higher multiplier to steady posting than burst posting, tied to the lesson's mechanic
Computes a weighted engagement score using the 15x comment multiplier, not raw likes
Delivers one clear cadence recommendation backed by the forecast numbers