The fastest way to understand how email and SMS marketing actually works in practice is to walk through a real rebuild from start to finish. What follows is a composite, illustrative walkthrough drawn from the pattern we see repeatedly with local service clients at Salterra Digital Services — not a single named account, but a representative sequence showing exactly how a stalled program gets diagnosed and rebuilt, step by step.
The scenario: a regional home services company with an email list of several thousand past customers and leads, a Mailchimp account nobody had touched meaningfully in over a year, and no SMS program at all. The list existed. It just was not doing anything.
Before sending a single campaign, the first move is always a full audit — not a guess. That means pulling the platform’s engagement report and segmenting the list by activity: who opened or clicked in the last 90 days, who has been silent for six months to a year, and who has been dead for over a year. In a stalled account like this, it is common to find that 60-70% of the list falls into the “dead” bucket — addresses that will never open again and are actively hurting deliverability just by sitting there.
The audit also checks technical fundamentals that non-specialists routinely skip: is SPF, DKIM, and DMARC properly configured on the sending domain? What does the current sender reputation look like? Is the list double opt-in or single opt-in, and how was it originally collected? In this walkthrough, DKIM was missing entirely — a technical gap that alone was suppressing inbox placement regardless of how good the content was.
The instinct to “just start emailing again” is the single most common mistake at this stage. Sending a fresh campaign to a list that is 60% dead weight tanks the open rate immediately, which damages sender reputation with mailbox providers and makes every subsequent send harder to deliver — a downward spiral.
The correct sequence is:
Rather than deleting the dormant segment outright — which throws away contacts who may simply have been ignored rather than uninterested — the walkthrough used a short three-touch re-engagement sequence sent only to that segment, isolated from the healthy list:
Anyone who did not engage across all three touches was suppressed from future sends entirely. This is the step agencies most often skip because it feels counterintuitive to shrink the list further. But a smaller, engaged list consistently outperforms a larger, stale one on every downstream metric, and it protects deliverability for every future campaign.
With the list cleaned, attention shifted to the automations that should have existed from day one:
SMS was introduced only after the email foundation was stable, and only for use cases where speed mattered: appointment confirmations, day-of reminders, and a two-way line for rescheduling. The business registered its sending number properly (10DLC registration in the U.S.), built an explicit opt-in checkbox into the booking form with clear consent language, and kept SMS volume deliberately low — a handful of transactional-style messages per customer relationship, never promotional blasts to the full list.
This restraint mattered. SMS earns trust or destroys it fast. A customer who opted in for appointment reminders and instead receives frequent promotional texts will opt out immediately and may report the number as spam, which can get it blocked by carriers. The walkthrough treated SMS as a precision tool, not a volume channel.
Results were tracked against the pre-rebuild baseline, not against generic industry benchmarks, because the starting point (a neglected, largely dead list) made outside benchmarks meaningless. The metrics that mattered:
The pattern held true to what we see across most stalled-list rebuilds: engagement on the cleaned segment improved substantially versus the blended pre-rebuild average, no-shows dropped meaningfully once SMS reminders were in place, and review volume increased as the automated follow-up removed the dependency on staff remembering to ask.
Nothing in this walkthrough involved a clever subject line trick or a growth hack. The turnaround came from sequencing: fix the technical foundation first, clean before sending, re-engage before deleting, automate the predictable touchpoints, and introduce SMS only where speed genuinely mattered. Skipping any one of those steps — especially the list cleaning — is the most common reason similar rebuilds fail to stick.
Technical fixes (authentication records) can improve deliverability within days. List cleaning and re-engagement sequencing usually take two to four weeks to fully play out. Automation impact, like reduced no-shows or increased reviews, typically becomes measurable within one to two months as enough customers move through the new flows.
Rarely, if the list was collected with proper consent. A structured re-engagement sequence usually recovers a meaningful portion of dormant contacts at very low cost, and only after that sequence fails to engage someone should they be suppressed or removed.
Text messages interrupt in a way email does not — most people carry their phone on them constantly. Overusing SMS for promotional content burns opt-ins fast and risks carrier filtering or complaint reports, so it works best reserved for time-sensitive, expected communication like reminders and confirmations.
Verify SPF, DKIM, and DMARC records are correctly configured on the sending domain, check current sender reputation through the platform's deliverability dashboard, and confirm how the list was originally collected and whether consent records exist.
A well-built re-engagement sequence is designed to catch exactly those customers before removal — giving them multiple chances across different angles and a final explicit opt-back-in step, rather than silently dropping anyone who has not opened recently.
Terry has 30+ years in software and SEO. He’s the founder of Salterra Digital Services and SEO Spring Training, host of the Roundtable SEO Mastermind, and lead instructor at SEO University — teaching the exact tactics his team uses on client work.
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