The apartment marketer has a peculiar job. A campaign can produce cheap clicks while a building still has empty units. A leasing call can sound promising but disappear inside somebody else's phone system. A property manager may need more leads in Phoenix, fewer in Denver and a fair-housing review everywhere - before lunch. Digible, founded in 2017 in Englewood, Colorado, built its company around that mess. It sells the media work, the organic work and the software that attempts to explain what should happen next.
This makes Digible an agency with a product habit, or a software company that refuses to leave execution to the customer. Both descriptions fit. Its managed services cover paid search, social, display, geofencing, audio, video, native ads, email, SEO, reputation and websites. Fiona, the proprietary technology layer launched in 2018, handles budget and media-mix planning, call tracking, fair-housing monitoring and automated Google Business Profile posts. Digible says more than 5,995 properties in over 575 cities trust the company, spread across 140-plus management companies.
The software learned the job from the agency
Vertical software gets interesting when its odd little fields come from real work. Fiona does not begin with a generic prompt box. A property marketer can enter current occupancy, target occupancy and close rate, then turn media channels on or off and adjust the total available budget. The system produces a recommended allocation and explains the reasoning. That is closer to a working spreadsheet with an institutional memory than to the resident-facing chatbots usually labeled “apartment AI.”
Campaign teams buy media, tune search, track calls and see where a leasing funnel actually breaks.
Comparable property data becomes budgets, monitoring, attribution and recommendations a marketer can override.
That override matters. A recommendation outside the available budget is not useful, and an algorithm cannot know that a regional manager has ruled out a channel next month. Fiona lets the operator constrain the answer, then reallocates. The pitch is not autonomy. It is a faster, more legible planning conversation between software, client and account team.
The proof is a budget, not a robot trick
Freeman Webb, a Nashville real-estate operator, offers the most concrete early example. Its marketing team had already been changing budgets every month, mostly by looking backward at campaign performance. In November 2018 it began testing Fiona, which added local-market, seasonal, occupancy and cross-channel information to that ritual. According to Digible's case study, 20 percent of Freeman Webb's multifamily portfolio eventually used the platform. Those properties recorded a 48 percent lift in paid-search lead volume and a five-percentage-point year-over-year increase in occupancy.
Fiona provides rare insight, allowing us to strategically utilize our advertising spend and resources.Erika Walker, Freeman Webb
The useful detail is not the victory percentage. It is what changed in the room. A budget recommendation arrived beside a natural-language explanation. The marketer could test email or display, switch a channel off, change the occupancy goal and rerun the allocation. The interface turned a monthly argument about instinct into a set of editable assumptions. That is a product a portfolio operator can use, even if the final decision remains human.
How much does the machine cost?
Digible does something refreshingly un-agency-like: it publishes prices. Its standard menu lists paid-search management at 25 percent of spend, paid social at a $250 management fee, review management at $249 a month, display and geofencing at $12 per thousand impressions, Performance Max at 35 percent, audio starting at $750 a month and native advertising at $1,000 a month. A standard SEO package was listed from $450 a month, with lower enterprise rates on several lines. The company emphasizes month-to-month flexibility rather than long contracts.
These are public menu prices, not a quote for a particular portfolio. Media spend, package scope, property count and enterprise terms still change the bill.
The business model is therefore a blend: recurring managed-service fees, percentage-of-spend arrangements, media pricing and software subscriptions. This matters because Digible can monetize both the recommendation and its execution. It also creates an obvious tension. A percentage fee grows when spend grows, while optimization sometimes says spend less. Transparent reporting and the ability to adjust or cancel are the checks the company puts against that conflict.
What failed first?
A 2021 experiment built campaigns around the sudden appeal of working from home. The idea sounded tidy: sell floor plans through “cloffice” culture and home-office language. On paid social, it lost. Compared with generic ads, the work-from-home creative produced lower click-through rate, shorter canvas viewing and a lower site-visit rate. Search delivered too little traffic to be a growth tactic, even though the small group that did arrive converted efficiently.
Rates are shown within channel, not on one shared performance scale. The test covered one account, so Digible explicitly called for more testing.
Display told the opposite story. The work-from-home version produced a 0.46 percent site-visit rate against 0.16 percent for generic prospecting, while cost per site visit fell from 41 cents to 23 cents. The lesson was not “work from home wins.” It was that a narrow message can create efficient awareness in one channel, fail to generate volume in another and simply bore people in a third. Conditions decide the tactic.
Digible found a cleaner win when it tested current rent prices in paid-search copy. Across portfolio data available in August 2020, ads with pricing produced an 8.1 percent conversion rate, versus 4.04 percent without pricing. Cost per lead fell from $42.79 to $21.90. The mechanism is delightfully ordinary: showing rent before the click helps the renter self-qualify. In 2026, Digible partnered with Engrain to bring SightMap's real-time all-in pricing and mandatory-fee data into ads, turning that old test into infrastructure.
A small check changed the pace
The founders had already bootstrapped through year one when they raised $250,000 from Axiom Multifamily Realty Advisors in April 2018. The announcement said Digible had customers in more than 50 markets and over $1 million in annual recurring revenue. CEO Reid Wicoff said he and co-founder David Staley struggled with the decision because agency revenue and personal capital were already funding the company. Meeting investor Steve Donohue changed their minds: his industry reputation and operating value mattered beyond the check.
That is the strategic hinge. Digible did not use a large venture round to search for a customer. It used a small, sector-connected round to accelerate a customer pattern it had already found. The company went on to appear on the Inc. 5000 in 2022, 2023, 2024, 2025 and 2026. Inc. reported 371 percent three-year growth for its 2025 ranking and 342 percent for 2026. LinkedIn listed 164 employees in August 2026, close to the 170 in company data supplied for this profile.
The narrowness is the advantage
Digible competes with specialist platforms and agencies including Conversion Logix, Respage, PERQ, Apartment SEO and the internal teams at large property managers. Some alternatives cover more of the leasing journey, from lead nurture to resident engagement. Others offer comparable search, social and website services. Digible's position is the seam between them: multifamily-only marketing execution joined to a proprietary planning and measurement layer.
Its culture and media work reinforce that position. The weekly Digible Dudes podcast turns founders and customers into an industry classroom. Digible Summit offers professional development in a sector the company says lacks enough of it. Internally, leaders have discussed salary ranges, promotion controls and career development in public episodes. In 2023 the company announced No. 8 rankings on both the Best Places to Work Multifamily list and its list for women. The culture story is not free snacks. It is the attempt to keep learning public while headcount and expectations rise.
What another operator can copy
- Choose a customer narrow enough that the same expensive decisions recur.
- Capture the business constraint - occupancy and close rate, not merely clicks.
- Give the recommendation an explanation and an override.
- Publish prices and results with enough detail to let buyers disagree.
- Report the losing channel, then state the sample-size limit.
When the formula does not travel
This model works best for portfolios with repeated property-level campaigns, comparable data, clear occupancy goals and enough media activity to learn. It is weaker for a single building with little historical data, a luxury lease-up whose brand problem overwhelms its media problem, or an operator unable to connect calls and leases back to campaigns. Automated fair-housing flags are useful monitoring, not legal judgment. Predictive allocations can optimize the channels they can see, not fix slow leasing follow-up, bad pricing or an uncompetitive property.
Nor should every test be generalized. Digible's work-from-home result came from one account. The Freeman Webb numbers are a company-published case study, not a controlled trial. Even the attractive dynamic-pricing result depends on accurate, eligible and timely rent feeds. Old prices in new ads would turn qualification into disappointment. The conditions are the product.
That final point explains Digible better than the AI vocabulary does. Apartment marketing is not one model producing one answer. It is a loop of rent, occupancy, search behavior, calls, regulations, budgets and humans. Digible has spent nine years making that loop visible enough to manage. The clever bit is not replacing the marketer. It is giving the marketer fewer places to hide a guess.