What else is affecting my business?
Advertising is one influence. NEXT90's Insights & Data Engine sees the rest.
Your ad platform only sees ads
Your ad platform knows your ads ran. It doesn't know the temperature hit 115 degrees in Phoenix the day your HVAC calls spiked. It doesn't know that corn planting season in Iowa drives agricultural equipment searches every April. It doesn't know the news cycle just ran a segment on home energy costs.
If you only look at advertising, you're looking at one influence in a world of thousands.
Every ad platform is structurally limited to its own data. It sees impressions, clicks, and conversions within its walls. It cannot see the external forces that shape consumer behavior — the weather events, the seasonal patterns, the demographic shifts, the economic conditions that drive demand independently of any advertising stimulus.
When your HVAC calls double on a Tuesday, your ad platform will take credit. It ran ads on Monday. The calls came on Tuesday. Attribution confirmed. But what if every HVAC company in the market saw the same spike — including the ones that weren't advertising? The platform cannot ask that question. The IDE can.
The reality is bigger than advertising
The IDE doesn't care where a signal comes from. If it influences behavior, it belongs in the picture.
Weather — Temperature, precipitation, severe weather, seasonal patterns. Home services calls correlate with weather as strongly as they correlate with advertising. The IDE ingests historical weather data and forecast data at the geographic level, matching weather conditions to response patterns within the same TV markets and zip codes where advertising is being traced.
Agriculture — USDA crop data, farm acreage, geo shapes, field boundaries, irrigation districts, crop usage by region. These are not modeled estimates. They are structured datasets from federal sources, mapped to the same geographic entities the IDE uses for everything else. When the IDE knows that a zip code contains 12,000 acres of corn requiring center-pivot irrigation, that data is as precise as any advertising signal.
Demographics and psychographics — Public US Census data on neighborhood population, income, and consumer profiles. The kind of population context that helps explain why the same ad performs differently in different neighborhoods. This data enriches every geographic entity in the IDE's geographic data layer — more than one million geographic data points — adding population context to every zip code where advertising delivery and response are being compared.
Current events and news — The news cycle affects consumer behavior. So do elections, economic reports, and viral trends. These signals are harder to structure, but the IDE correlates temporal patterns in response data with known events to identify when something outside advertising drove a change.
Search behavior — Organic and paid search trends, branded versus category search volume, geographic search patterns. Search is often the bridge between a stimulus and a response — someone sees something, then searches. The IDE traces search behavior alongside advertising and non-advertising signals to understand what prompted the search in the first place.
These signals are already part of the IDE. Real data, used in production.
Context in action
The Olympics effect
A nonprofit association promoting technology adoption ran donated broadcast airtime across stations nationwide. There was no media buy to optimize — the question was purely: where and when did the donated air work?
The IDE traced response across every market, every network, and every programming context. Two patterns stood out. News programming drove the strongest digital response by a wide margin — viewers in a learning mindset engaged with technology messaging at rates entertainment and reality programming couldn't match. And Minnesota spiked in February 2026, coinciding with the Winter Olympics on NBC. The Olympics didn't just drive viewership — they put audiences in a mindset where television technology mattered. Viewers paying attention to the broadcast experience itself were more receptive to ads about improving it.
No media plan produced that insight. The context did — and the IDE traced it.
Political squeeze-out
During a hard primary year, the IDE detected that direct response advertisers were being squeezed off the air by political buyers paying premium rates for available inventory. The pattern surfaced early — before response numbers dropped — because the IDE could see airing density shifting across markets.
Early detection gave advertisers time to shift spend to programmatic and CTV before the damage compounded. The same pattern repeats every election cycle, and it is especially acute for Medicare Advantage advertisers, who can only advertise from October through December — competing with both political spending and holiday advertising in the same window.
An ad platform sees that your response dropped. The IDE shows you why — and gives you time to act before it does.
Agriculture audience building with USDA data
Federal data meets geographic intelligence
USDA crop data, farm acreage, irrigation districts, and weather patterns — structured and mapped to the same geographic entities as every advertising signal.
An agriculture equipment company wants to reach farmers who are likely to need specific irrigation products this season. No ad platform can build this audience because no ad platform has the data.
The IDE combines structured federal datasets with its geographic intelligence. USDA crop data identifies which zip codes grow which crops. Farm acreage data shows the scale of operations in each area. Irrigation district boundaries and water availability records indicate where irrigation is needed. Weather data — historical precipitation patterns, current season rainfall, temperature trends — identifies where conditions are creating demand for irrigation equipment.
The result is a set of zip codes with a high probability of needing specific products this season. Not a modeled lookalike audience based on browsing behavior. A geographic audience built from agricultural reality — what is planted, how much water it needs, and whether nature is providing enough.
This audience activates through the same channels as any other: programmatic display, CTV, paid search geographic targeting. The difference is that the audience exists because the IDE has the data to build it. The signals are agricultural, not behavioral. The precision comes from federal data and weather patterns, not cookies or device graphs.
Separating ad influence from environmental influence
This is the core capability that non-advertising signals provide. Without them, every response gets attributed to the most recent stimulus. The ad ran, the customer called — the ad gets credit. But when you add weather data, demographic data, and seasonal patterns to the picture, you can see which responses were genuinely influenced by advertising and which would have happened anyway.
The methodology is the same three pillars applied to every signal type. Context: does the combination of conditions explain the magnitude of the response? Geography: did the environmental condition exist in the same location as the response? Time: did the response follow the environmental trigger in the expected pattern?
A heat wave in Phoenix and a TV ad in Phoenix can both drive calls in Phoenix. The IDE doesn't choose one and discard the other. It traces each influence through its own geographic, temporal, and contextual evidence and assigns proportional credit based on what the data supports. Overlapping influences get resolved, not averaged.
The result is not "your advertising doesn't work." The result is "your advertising works, and here is precisely how much of the demand it drove versus how much the environment drove." That distinction is the difference between accurate budget decisions and guessing.
Trigger-based recommendations
Observation is only the beginning. When conditions match historical high-response patterns, the IDE can trigger recommendations: adjust paid search bids, push specific creative, alert the agency. Pre-baked playbooks for when certain conditions exist.
Weather triggers
Temperature thresholds, precipitation patterns, severe weather events. When Phoenix crosses 115 degrees, the playbook executes before demand spikes.
Seasonal triggers
Planting season, back-to-school, holiday ramp-up. Historical patterns predict demand shifts weeks before they arrive.
Competitive triggers
Market share shifts, new entrant activity, competitive spend changes. Conditions that historically correlate with changes in consumer behavior.
The data tells you what's happening. The recommendations tell you what to do about it.
These triggers work across any signal type the IDE ingests. Weather thresholds, seasonal patterns, news events, competitive shifts — any condition that historically correlates with a change in consumer behavior can become a trigger. The playbook is defined once and executes when the conditions are met, turning observation into action without requiring manual monitoring.
Let's see the full picture
Your business is influenced by more than advertising. Let's see what the full picture looks like.