Foundation
Define audience, objective, message
Week 1
Strategy approved
EDRA Publishing / Paid Social & Growth Marketing
I have managed 300+ advertising campaigns across my career. This case study examines a selected EDRA Meta program, within a broader specialist catalog of 200+ books. Organic content builds specialist authority; paid campaigns test audience definitions, hooks, scripts, videos, graphics, copy, formats, and calls to action. Performance determines which combinations scale, which receive renewed creative or copy, and which are deactivated over time.
The nine-campaign figure in the historical operating snapshot means nine campaigns running concurrently, not my total experience. Career volume, program-period results, and catalog size are shown separately.
One funnel. Six connected decisions.
The story below follows the exact operating sequence. Each stage names the objective, the work produced, the signal measured, and the evidence used to decide what happens next. Stage four is the bridge: social intent becomes lifecycle data, and lifecycle behavior becomes a stronger Meta audience.
Define audience, objective, message
Week 1
Strategy approved
Design and A/B test stories, scripts, storyboards, Reels, graphics, and CTAs at scale
Weeks 2–6
Views · reach · retention
Turn attention into trust and intent
Always on
Saves · engagement · clicks
Build Custom Audiences and lookalikes from lifecycle, site, and cart intent
Event based
Custom · lookalike · exclusions
Convert proven stories into qualified leads and recovered carts
Campaign sprints
Leads · CPL · quality
Test, protect efficiency, and reallocate toward evidence
Weekly
CPL ceiling · ROAS · scale
Start with the audience, not the post.
The organic objective was to make Edra useful to a niche professional community. I translated the publishing calendar into practitioner problems, paired each topic with the right author, and defined audience and content hypotheses that could be A/B tested through hooks, formats, and response signals before production scaled.
Veterinarians and specialist practitioners
Solve a recognizable clinical problem
Useful education before product promotion
Author-led demonstration built for Meta

Earn attention by solving a clinical problem.
I design each Reel end to end: translate the clinical vision into a story, write the script, build the storyboard, direct the visual treatment and production, and shape the final call to action. I A/B test story angles, hooks, scripts, video cuts, graphics, formats, and CTAs with the defined specialist audience, then use views, retention, saves, shares, and comments to improve the next iteration and scale content production without lowering creative quality.
Translate the clinical problem into a clear narrative angle
Write the hook, specialist script, shot sequence, and payoff
Direct the Reel, graphics, visual treatment, edit, and final asset
Compare hooks, scripts, cuts, formats, and CTAs, then scale the winners
Turn attention into a recognizable content system.
A Reel is only the entry point. Strong topics are extended into carousels, static posts, author collaborations, and reusable campaign variants. Hooks, graphics, videos, copy, and calls to action are prepared for A/B testing so each specific audience reveals what builds familiarity, trust, and intent. Once that intent becomes identifiable through a lead form, site visit, product view, or cart action, the funnel moves into the audience handoff shown next.

Reels, posts, and carousels carry the same clinical story across the feed.
Decision signal: saves, shares, comments, profile actions, clicks, and repeated topic demand.

One core idea becomes reusable organic, author, and campaign assets.
Decision signal: saves, shares, comments, profile actions, clicks, and repeated topic demand.

Hooks, video scripts, graphics, formats, copy, and CTAs are prepared for A/B testing.
Decision signal: saves, shares, comments, profile actions, clicks, and repeated topic demand.
Meta captures intent. Lifecycle protects it. Behavior improves Meta.
Stage three created engagement and intent. Stage four makes that intent addressable: Meta leads enter segmented lifecycle nurturing, while website, email, product, and cart behavior returns to Meta as Custom Audiences, lookalikes, exclusions, and retargeting signals. That audience intelligence is what makes stage five acquisition more precise.
Meta lead forms and website actions move qualified intent into the permissioned lifecycle database. Profession, specialty, source, title interest, and engagement determine the next email sequence, so the 2.4K acquired leads do not disappear after conversion.
Segment the captured lead
Match education and offer to intent
Nurture toward the relevant title
Suppress irrelevant or completed paths
Permissioned email segments, website visitors, product viewers, checkout abandoners, and inactive buyers become Meta Custom Audiences. Their patterns inform lookalike acquisition, while purchasers are excluded from wasteful recovery ads.
Build behavior-based Custom Audiences
Create lookalikes from qualified customers
Retarget product and cart intent
A/B test creative, ad copy, and CTA
Professionals in the lifecycle database
Email campaigns in a captured 30-day report
Email sends in that 30-day period, not unique people
ROAS delivered by remarketing
Community creates the signal. Paid media converts it.
With the stage-four audiences now defined, the paid track can act precisely. I rebuild the strongest organic story around lead acquisition or recovery, match creative and copy to prospecting or retargeting intent, then A/B test audience, creative, and call-to-action variables to uncover what improves qualified volume, recovered carts, cost efficiency, and remarketing return.
Qualified lead acquisition and ecommerce recovery
Historical snapshot: nine Meta campaigns running concurrently; 200+ books in the wider catalog
Prospecting, qualified-customer lookalikes, website and email Custom Audiences
A/B test storyboards, video scripts, finished videos, graphics, hooks, formats, ad copy, offers, and CTAs
Scale winners, renew creative and copy when performance weakens, and deactivate combinations that stop working over time

Two hundred-plus products, governed by evidence.
The historical EDRA snapshot included nine Meta campaigns running concurrently, not nine campaigns over my career. Across a broader catalog of 200+ books, I use a continuous decision loop: establish efficiency guardrails, A/B test comparable variables, scale combinations that work, renew creative or copy when performance weakens, and deactivate what does not recover over time.
Define the acceptable CPL, lead-quality threshold, and remarketing ROAS before increasing delivery.
Give each audience, video, graphic, script, ad-copy, and CTA variation enough comparable delivery to produce a decision, not a guess.
Refresh creative, assets, or copy when results weaken, then judge the renewed combination against the same guardrails.
Scale winners progressively and deactivate combinations that fail to recover over time.
Observed qualified-lead CPL range
Remarketing ROAS delivered
Scale only while marginal efficiency and lead quality hold
A measurable growth loop, not a content calendar.
The cumulative total reflects my broader campaign-management experience. The EDRA lead-generation results cover a specific period, and the historical snapshot contained nine campaigns running simultaneously. I evaluate audiences, creative, lead quality, and downstream value to decide what to scale, refresh, or stop.
Advertising campaigns managed cumulatively across my career
Qualified Meta leads in a selected 3–4-month EDRA period
Observed CPL range in that lead-generation program
Products in the wider EDRA catalog managed
Social assets directed across the body of work
Videos produced across the body of work
Read CPL, lead quality, ROAS, frequency, and saturation together
Separate prospecting, Custom Audience, lookalike, and retargeting performance
Read A/B-test results and renew creative or copy where improvement remains possible
Scale winners progressively and deactivate combinations that no longer perform over time
The thinking behind this work