Neighborly

Neighborly Native App

01.-

Context

Retooling the App

The Neighborly app was already live in the App Store, but the first release fell short for both sides of the marketplace, customers couldn't easily browse or understand the 20+ (and growing) service brands under the Neighborly umbrella, and franchise owners weren't getting the booking volume or lead quality the platform promised.

A new release was scoped to fix that: clearer brand visibility and descriptions, personalized offers by zip code, a real booking flow instead of a lead form, and live status updates on scheduled work. Rather than treat this as a standalone app redesign, I built it on the same strategic foundation as Project OPUS, the jobs-to-be-done framework and personas developed for the web platform became the backbone of the app's information architecture.

02.-

From Strategy to IA

The Same Framework, Shipped Twice

Rather than starting fresh, the app's home screen structure is a direct application of the JTBD framework already validated on the web platform: Repair, Maintain, Enhance, the same three jobs (emergency, recurring, big-decision) that shaped the OPUS journey map became the primary navigation of the app itself.

This is worth calling out specifically because it's easy for strategy work and shipped screens to live in separate silos in a portfolio. Here, the connection is direct and visible: the same mental model that organized a 20-brand website migration is the thing a customer taps on day one of opening the app.

03.-

Research & Competitive Framing

Observing Beyond the Category

Before designing the app's flow, a competitive audit was run across two different sets of apps:

Direct competitors — Urban Company, Pinch, JOBOY, and other on-demand home services apps
Adjacent experience-based apps — Marriott Bonvoy, Airbnb, Hotels.com, Expedia, TripAdvisor

The second set was a deliberate choice, not a default benchmark. Travel and hospitality apps have spent years refining high-trust, high-stakes booking flows, date/time selection, service tiers, loyalty and rewards, real-time status. Home services shares more with "booking a stay" than with a typical e-commerce checkout: it's infrequent, higher-trust, and tied to a specific time and place. Borrowing those patterns (rather than copying direct competitors) shaped decisions like the multi-step booking flow and the "upcoming jobs" status screen.

04.-

Mapping the Flow

From Home to Booking Confirmation

The core IA for the app follows a defined path: Home → Brand Selection → Brand Details → Service Type → Leadflow (booking).

A specific decision worth calling out: the service type bumper, a lightweight modal that interrupts the flow between the Brand Details page and the Leadflow to confirm service type before booking begins. This exists because a single brand (e.g. Five Star Painting) can offer multiple distinct service types with different booking requirements, and skipping this step was producing mismatched leads for franchise owners.

05.-

The Booking Funnel in Detail

Designing for Two Very Different Users

The booking flow itself, service frequency, schedule selection, contact details, review, confirmation, was designed around the same two personas from OPUS:

Diane (Adapter) wants visibility and control: the step indicator at the top of each screen, the ability to review before confirming, and the option to specify exact preferences (bedrooms, bathrooms, square footage) all serve her "show me options" mindset.
Elizabeth (Optimizer) wants speed: guest checkout (no forced sign-in), sensible defaults, and a "call us directly" escape hatch on every screen for when she doesn't want to fill out a form at all.

Both personas get the same flow, the difference is in how much of it they're required to engage with before booking completes.

06.-

Personalization & Retention

Bringing Users Back Without Requiring Them to Remember

Two features specifically targeted retention and reduced repeat-effort: zip-code-based offers, and a persistent "upcoming jobs" view.

Offers are surfaced by zip code and brand, so a customer opening the app sees relevant savings rather than a generic promotions feed.
The "upcoming jobs" screen consolidates every scheduled service across every brand the customer has used, with a fallback for adding a phone/job number if a booking isn't showing, addressing the reality that customers might book through a call center, not just the app.

This second point matters for a multi-brand, multi-channel business: booking a Molly Maid cleaning and an Aire Serv repair shouldn't feel like two different companies to the same customer.