Most agencies run the same four moves: Google Ads, cold email, LinkedIn spray, referrals. These 20 ideas go deeper into public data, predictive signals, and competitive displacement. Ranked by compound impact. Hover any row for full context.
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| # | Spirit | Idea | Impact | Effort | Detail |
|---|---|---|---|---|---|
| 1 | Graph | LinkedIn + Obsidian contact graph Map warm intro paths across your business network |
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Med |
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LinkedIn + Obsidian Contact Graph
Map every 1st and 2nd-degree LinkedIn connection across your business footprint into a graph. Identify shared nodes (brokers, installers, investors) that can broker warm intros into target decision-makers. Obsidian's graph view makes the overlap visible in minutes. Highest-ROI move because a single warm intro from the right person converts 5-10x better than cold outreach. |
| 2 | Graph | Podcast guest network Co-appearance mapping across industry events |
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Low |
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Podcast Guest Network
Pull guest lists from 20-30 podcasts in your industry. Build a co-appearance frequency map: who keeps showing up alongside your ideal buyer profile? Those co-guests are warm-ish channels to the same audience. Takes 2-3 hours and surfaces names you'd never find via LinkedIn search. |
| 3 | Graph | Co-investor / board overlap map SEC + state registry filings |
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Med |
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Co-investor / Board Overlap Map
SEC Form D filings and state LLC/corp registries expose who co-invested or co-founded alongside a target. If your ideal referral partner appears on 3 boards that share an investor, that investor is the highest-leverage introduction. Fully public, almost no one uses it for lead development. |
| 4 | Graph | Data coverage vs competitor density heat map Geographic gap analysis |
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High |
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Data Coverage vs Competitor Density Heat Map
Overlay your data platform's geographic coverage against competitor density by zip. Gaps where coverage is high but competitors are thin represent the fastest-to-close territories. This is a direct multiplier on your existing data asset, not a net-new data source. |
| 5 | Graph | Event co-attendance graph Industry conference sponsor and speaker lists |
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Low |
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Event Co-attendance Graph
Conference sponsor and speaker lists for major industry events are publicly available. Cross-reference against LinkedIn profiles to build a "who goes where" map. People who attend the same events have an existing social contract, making cold outreach look like a warm peer touch. |
| 6 | Public | Solar permit + no-completion cross-reference County records; distressed pipeline flag |
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Med |
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Solar Permit + No-Completion Cross-reference
County permit portals show every solar installation permit pulled. Cross-reference against completion filings. Permits with no corresponding completion certificate = homeowner who started the process, hit a problem (contractor bailed, financing fell through, contractor failed inspection), and is now highly motivated. This is the most predictive intent signal available in public data. |
| 7 | Public | PACER / mechanic's lien mining Distressed sellers + motivated vendors |
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High |
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PACER / Mechanic's Lien Mining
PACER (federal court records) and county recorder lien filings surface contractors, developers, and business operators under financial stress. A motivated seller or a contractor with cash-flow issues is dramatically more likely to accept creative deal structures or referral arrangements than a stable business. Effort is high due to data normalization, but the signal quality is exceptional. |
| 8 | Public | Corporate officer change filings New C-suite = new vendor review cycle |
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Low |
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Corporate Officer Change Filings
State secretary of state filings record officer and director changes. A new CEO, CMO, or VP Sales at a target company triggers a 60-day vendor review window. New leadership wants to find their own providers before the previous team's relationships cement. File monitoring tools (like EDGAR for public companies, state portals for private) can be set to alert on target company names. |
| 9 | Public | FHA/VA origination data by zip HUD HMDA public data; new-mortgage density |
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Low |
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FHA/VA Origination Data by Zip
The HUD HMDA dataset (Home Mortgage Disclosure Act) is public and updated annually. It shows exactly where government-backed mortgage originations are occurring by zip code. High FHA/VA activity = high new homeowner density = high home-services demand. Cross-reference against your data platform for a compound in-market signal. |
| 10 | Public | FEMA flood zone + solar adjacency High-ground homeowners worried about outages |
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Low |
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FEMA Flood Zone + Solar Adjacency
FEMA flood maps are public and shapefile-exportable. Properties on the edge of flood zones (high ground) experience chronic power disruptions during weather events without flooding risk themselves. Those homeowners want backup power. Overlay FEMA zone boundaries against your zip-level data to surface the highest-anxiety, highest-intent segment for related services. |
| 11 | Signal | Job board demand pulse Sales hires = market expanding ahead of public data |
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Low |
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Job Board Demand Pulse
When companies in your target vertical post high volumes of inside sales or field sales roles, they are signaling planned expansion 30-90 days ahead of revenue. LinkedIn Jobs and Indeed both expose this via keyword + company-size filters. Companies that are actively hiring sales staff are the best partnership targets: they have capacity and they need leads. |
| 12 | Signal | Google Trends zip-level demand forecast 4-6 weeks before peak season |
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Low |
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Google Trends Zip-level Demand Forecast
Google Trends data at the DMA level shows search demand curves for key category terms. Trend inflection typically precedes peak season by 4-6 weeks. Use it to front-run content and outreach calendar in markets where your data has strong coverage, start the campaign before everyone else floods the channel. |
| 13 | Signal | Electricity rate hike tracker State PUC filings; rate increase = solar ROI improves instantly |
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Low |
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Electricity Rate Hike Tracker
Every state Public Utility Commission publishes rate change filings. A 12% rate increase in a Florida county means every homeowner in that county just got a better solar ROI calculation overnight - without knowing it. Trigger outreach campaigns within 72 hours of an approved rate hike filing. The homeowner gets outreach that solves a problem they just discovered. Near-zero competition for this signal. |
| 14 | Signal | New homeowner propensity match Zillow recent sales + your data in same zip |
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Med |
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New Homeowner Propensity Match
Zillow and Redfin both expose recent sale data. New homeowners in the first 90 days are in a major purchase window: insurance, upgrades, energy, home services. Match recent sales zips against your data coverage to identify records with both high in-market propensity and confirmed new ownership. This is a compound signal with a time limit: the window closes at about 120 days post-close. |
| 15 | Signal | Weather event + roof damage correlation NOAA hail data triggers windows/roofing wave |
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Low |
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Weather Event + Roof Damage Correlation
NOAA's Storm Events Database is public and updated near-daily. Significant hail events (1"+) in a county produce a wave of roof replacements 2-6 weeks later. A homeowner replacing a roof is an ideal candidate for roofing, solar, or gutter services. Cross-reference NOAA hail data with your zip-level data to time outreach perfectly. |
| 16 | Conquest | 1-3 star review mining Yelp + Google; competitor pain points for targeted outreach |
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Low |
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1-3 Star Review Mining
Systematically read every 1-3 star review of target competitors on Google Maps and Yelp. Cluster complaints by theme: "didn't show up," "wrong paperwork," "pushy sales," "no follow-up." Each cluster is a promise you can make in outreach. "We won't do X" is more persuasive than "We're great at Y." Low effort, high message precision, replicable across every vertical. |
| 17 | Conquest | Competitor GBP staleness audit Stale Google Business = customers already looking elsewhere |
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Low |
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Competitor GBP Staleness Audit
Google Business Profiles that haven't posted in 90+ days signal an inactive business or a distracted one. Their customers are being bounced to "find similar places." Mapping every competitor GBP in target markets and ranking by recency of posts, photo updates, and Q&A responses identifies where organic demand is unserviced. These are the best zip codes to target with any outreach campaign. |
| 18 | Conquest | YouTube commenter intent harvest Recent category videos in target metros |
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Med |
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YouTube Commenter Intent Harvest
Comments on category review videos and "best provider near me" comparisons in target metros contain named buyers with expressed intent. "Does anyone know a good provider in Tampa?" is a publicly visible sales lead. Harvest these at scale using YouTube Data API keyword + geography filters and respond or redirect to branded content. High impact because these are self-identified, in-market, and unpursued. |
| 19 | Conquest | Distressed competitor fallout harvest Bankruptcies, shutdowns, warranty orphans |
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Low |
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Distressed Competitor Fallout Harvest
When a category competitor files Chapter 11 or shuts down, their customers are left with orphaned products, warranties, or service contracts. Those customers are actively searching for a new service relationship. They have existing products, existing trust in the category, and existing pain. They are the warmest possible cold prospect. An outreach campaign targeting zip codes with high competitor-failure density is a near-term winner with minimal creative lift needed. |
| 20 | Conquest | Facebook group buyer-intent monitor Local homeowner groups; real-time keyword alerts |
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Med |
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Facebook Group Buyer-Intent Monitor
Local Facebook homeowner groups (neighbourhood associations, city sub groups) regularly contain members asking for installer, lender, or provider recommendations. Set up keyword alert monitoring for category-relevant terms using tools like Phantombuster or manual keyword alert stacks. Respond within the first 30 minutes of a post and conversion rates are significantly higher than any outbound approach. |
These three were selected for a combination of speed-to-signal, direct relevance, and low lift to first result.