The salon was posting maybe twice a month, mostly stock photos and price flyers with no captions, no location tags, and no consistent posting time. There was no way to tell who was actually seeing the content or whether it was driving anyone through the door.
We reviewed competitor accounts in the same zip code, looked at when local searches for the salon's services spiked, and found the account had decent follower count but almost zero engagement — a sign of bought followers and stale content, not real local reach.
We rebuilt the content calendar around real client transformations, added consistent geo-tagging and local hashtags, scheduled posts around peak local browsing hours, and layered in Stories polls to rebuild genuine engagement before touching any paid spend.
Within two months, Story replies and saves climbed substantially, and the front desk started tracking new clients who mentioned finding them through a specific Reel — something that had never happened before.
The shop was boosting whatever post happened to be up that week with no targeting logic — sometimes a product photo, sometimes a meme — and couldn't tell us which boosted post had ever brought someone into the store.
We mapped out their actual sales calendar (restock days, weekend rushes, slow weekdays) against what was being posted, and found zero alignment. The content had no relationship to what was actually happening in the business.
We built a content and paid calendar tied directly to restock days and slow periods, used local radius targeting instead of broad boosts, and coordinated in-store signage with what was being promoted online so the two channels reinforced each other.
Promo weekends started showing a visible bump in foot traffic that the owner could track at the register, and ad spend dropped because targeting was tighter instead of just throwing budget at everything.
The page had long gaps between posts, captions were copy-pasted across locations with no localization, and there was no system for answering DMs — several legitimate repair inquiries sat unanswered for days.
We found that most engagement was actually happening in the comments and DMs already, but nobody was responding fast enough to convert it. The opportunity wasn't more content — it was follow-through on what was already coming in.
We set up automated first-response replies for common questions (pricing, turnaround time, walk-in hours), built a simple weekly content rhythm around real repairs and before/afters, and made sure every location's content felt locally specific instead of copy-pasted.
Response time on DMs dropped from days to minutes, and the front counter started seeing customers reference a specific post or fast DM reply as the reason they came in instead of a competitor.
A SaaS client had a large segment of customers who had gone inactive for 90+ days but were still on paid plans — a clear churn risk that wasn't being addressed by any existing email program.
We segmented the dormant base by last-active feature and usage pattern instead of treating them as one group, since a customer who stopped using one feature needed a different message than one who'd gone fully silent.
We built a 4-touch re-engagement sequence triggered by inactivity thresholds, with content tailored to the specific feature each segment had stopped using, rather than a generic 'we miss you' email.
About 22% of the dormant segment showed renewed activity within the campaign window — not a dramatic turnaround, but a meaningful recovery of accounts that were otherwise trending toward cancellation.
The client's customer success team only found out an account was at risk when it was already too late — usually at renewal time, with no earlier warning system in place.
We mapped existing customer behavior data (login frequency, support ticket sentiment, feature usage decay) against historical churn to find which signals actually predicted risk months in advance.
We built automated internal alerts that flagged accounts crossing risk thresholds and routed them to customer success with context, plus a parallel nurture track that kept lower-risk accounts engaged automatically.
The success team started catching at-risk accounts noticeably earlier, giving them time to intervene before renewal conversations instead of during them, which contributed to a real, if modest, drop in churn.
The client had a sizable list of accounts that hadn't ordered in 6+ months. The default approach had been blanket discount emails, which weren't moving the needle and were eroding margin on the accounts that did respond.
We segmented lapsed accounts by what they used to order and why they likely stopped — price sensitivity, a switched vendor, or just an internal process gap — instead of treating every account the same.
We built a tiered win-back sequence: a no-discount relationship touch first, then relevant product updates, and only a targeted offer for the segment that showed price sensitivity in their order history.
Just under a fifth of lapsed accounts placed a new order within the campaign window, and average order value held steady rather than dropping — meaning the wins weren't just discount-driven.
All of the client's marketing effort was pointed at new lead generation. Their existing customer base — a sizable, already-paying group — received almost no targeted communication beyond invoices and support emails.
We pulled segmentation directly from CRM data: contract type, services used, and renewal date, to identify which existing customers were good candidates for additional services they weren't yet using.
We built an ongoing nurture track specifically for existing customers — separate from new lead campaigns — surfacing relevant case studies and service expansions based on what each segment had and hadn't purchased.
Over a quarter of the existing base engaged with at least one nurture touch, and the sales team reported more inbound cross-sell conversations starting from customers who referenced the content directly.
The client's CRM had thousands of contacts that hadn't been touched in over a year — old leads, past quote requests, and former points of contact at active accounts that nobody was actively marketing to.
Rather than purge the list, we audited it for signals worth acting on: contacts at accounts still active in other parts of the business, and former quote requests for products still in the current catalog.
We built a low-pressure reactivation sequence — relevant product updates and a simple check-in — aimed at re-establishing contact rather than pushing for an immediate sale, with sales only looped in once a contact engaged.
About 19% of the stale list re-engaged in some form — opening, clicking, or replying — and over 240 contacts were moved back to active status in the CRM for sales to follow up on directly.