There is a fundamental disconnect in how most agencies operate. On one side, you have the strategy team analyzing cross-channel data, building attribution models, and recommending budget allocations across Facebook, Google, TikTok, email, and Instagram. On the other side, you have the execution team actually running Instagram accounts, managing DM conversations, scheduling content, and closing deals through direct messages. The strategy team works in spreadsheets and dashboards. The execution team works in Instagram and CRM tools. Data flows from the top down as recommendations, but ground-level execution data rarely flows back up to inform the models. This gap between data-driven strategy and on-the-ground execution is where agencies lose efficiency, miss opportunities, and misallocate budgets.
Northbeam and Inflowave solve opposite sides of this problem and, when used together, close the loop entirely. Northbeam is a data science platform for marketing attribution that uses machine learning models, media mix modeling, and incrementality testing to tell you exactly where your ad budget should go across every channel you advertise on. It takes a rigorous, statistical approach to attribution that appeals to data-driven teams who do not trust platform self-reported numbers. Inflowave is the execution platform for Instagram. It lets you schedule content, automate DM responses with AI, manage lead pipelines, and track revenue from DM conversations to closed deals. Where Northbeam answers "where should we spend," Inflowave answers "how do we execute on Instagram and prove it works."
The combination is particularly powerful for agencies because it creates a feedback loop between strategy and execution. Northbeam's models tell you that Instagram deserves more budget based on its incremental contribution to revenue. Inflowave confirms that finding by showing you the DM pipeline revenue that Instagram generates, revenue that standard attribution often undercounts because it happens through conversations rather than tracked clicks. The data flows both ways: Northbeam's macro-level insights guide Instagram budget decisions, and Inflowave's micro-level engagement data validates and enriches those insights with information that attribution models cannot capture on their own.
What Northbeam actually does (and why data-driven teams choose it)
Northbeam approaches marketing attribution the way a data scientist would, not the way an ad platform would. Instead of taking Facebook's word for it that a campaign drove 200 conversions, Northbeam builds its own statistical models from your raw data and arrives at its own attribution numbers. This independence from platform self-reporting is the core reason data-driven agencies and brands choose Northbeam over simpler attribution tools. The platform is built for teams that spend across multiple channels and need to make allocation decisions worth thousands or tens of thousands of dollars per month. Getting those decisions right by even a few percentage points can translate to massive revenue differences over a quarter.
Cross-channel attribution modeling - Understanding the real customer journey
Northbeam's cross-channel attribution modeling is the foundation of the entire platform. It connects to all your advertising platforms, including Meta (Facebook and Instagram), Google Ads, TikTok, Snapchat, Pinterest, and your email platform, and ingests the raw event data from each. Instead of treating each channel's self-reported conversions as truth, Northbeam's machine learning models analyze the complete customer journey across all channels simultaneously. The models learn patterns from your actual customer behavior: how many touchpoints a typical customer has before purchasing, which channels tend to appear early in the journey versus late, how much time passes between first exposure and conversion, and how different channels interact with each other.
The practical impact of this is significant. Facebook might claim credit for a conversion that actually started with a Google Search ad three days earlier, followed by a TikTok video the next day, and then a Facebook retargeting ad on the day of purchase. In Facebook's attribution, it gets 100% credit. In Northbeam's multi-touch model, credit is distributed proportionally across all three channels based on their actual influence on the conversion. This more accurate attribution changes budget allocation decisions dramatically. Agencies consistently find that some channels they thought were underperforming were actually doing heavy lifting in the upper funnel, and some channels they thought were their best performers were primarily capturing conversions that would have happened anyway. These insights prevent the common mistake of over-investing in last-click channels and under-investing in channels that drive discovery and consideration.
Incrementality testing - Measuring the true causal impact of your ads
Incrementality testing is considered the gold standard of marketing measurement because it answers the question that attribution models cannot: "Would this revenue have happened anyway without this ad spend?" Northbeam helps you design and run lift tests where you compare a test group that sees your ads against a holdout group that does not. The difference in conversion rates between the two groups represents the true incremental impact of your advertising. This is particularly important for channels like Facebook and Instagram where organic discovery and word-of-mouth also drive purchases, making it difficult to separate ad-driven conversions from conversions that would have happened regardless.
For agencies managing client budgets, incrementality testing provides the most defensible answer to the question every client asks: "Is my ad spend actually generating additional revenue, or am I just paying to reach people who would have bought anyway?" Northbeam's incrementality testing framework lets you run these tests at the channel level (testing the incremental value of all Instagram ads) or at the campaign level (testing whether a specific campaign is driving incremental sales or just cannibalizing other campaigns). The results often surprise agencies. A channel that looks great in attribution models might show low incrementality, meaning many of its attributed conversions would have happened organically. Conversely, a channel that looks mediocre in last-click attribution might show high incrementality, meaning every conversion it drives is genuinely additional revenue. These findings reshape budget allocation in ways that improve actual business outcomes, not just dashboard metrics.
Media mix modeling (MMM) - Optimal budget allocation across channels
Northbeam's media mix modeling takes a macro-level view of your marketing mix. It analyzes historical spend and revenue data across all your channels to model the relationship between marketing investment and business outcomes. Unlike user-level attribution which tracks individual customer journeys, MMM works at the aggregate level, looking at how changes in spend across channels correlate with changes in total revenue over time. This makes MMM particularly resilient to privacy restrictions because it does not depend on individual user tracking. It works with the same statistical rigor whether 100% of users are trackable or only 50% are.
The practical output of Northbeam's MMM is a set of budget allocation recommendations that tell you exactly how to distribute your total marketing budget across channels for maximum return. It answers questions like "What happens if I shift 20% of my Google budget to Instagram?" and "What is the point of diminishing returns for Facebook spend?" and "Am I underinvesting in TikTok relative to its actual revenue contribution?" For agencies managing multiple clients, MMM provides channel-level guidance that can be applied across the portfolio. If the model consistently shows that Instagram is underinvested relative to its contribution across multiple clients in similar verticals, the agency can proactively recommend Instagram budget increases to new clients in those verticals, backed by statistical evidence rather than opinion. This is the kind of data-driven strategic advice that differentiates premium agencies from order-takers.
Custom dashboards and cohort analysis - Data your team can actually use
Northbeam provides customizable dashboards that let you build views tailored to different stakeholders. The media buying team gets campaign-level attribution with creative breakdowns. The strategy team gets channel-level MMM recommendations with incrementality data. The client gets a clean executive dashboard showing overall ROAS, revenue trends, and channel contributions. You can compare different attribution models side by side, seeing how the same campaign performs under first-click, last-click, linear, and Northbeam's ML-based attribution. This comparison is invaluable for educating clients about why platform-reported numbers differ from independent attribution.
The cohort analysis capabilities let you track how different customer acquisition cohorts perform over time. You can see how customers acquired through Instagram ads in January perform in terms of repeat purchases, average order value, and lifetime revenue compared to customers acquired through Google ads in the same period. This is the data that helps you understand not just which channels drive the cheapest first purchase, but which channels drive the most valuable customers over the long term. An agency might find that Instagram-acquired customers have a 40% higher lifetime value than Google-acquired customers, even though Google campaigns show a lower CPA on the first order. That finding alone could justify a significant budget reallocation, and it can only come from the type of longitudinal cohort analysis that Northbeam makes accessible without requiring a dedicated data team.
What Inflowave brings to the equation
Northbeam tells you where to spend. But knowing that Instagram deserves more budget does not automatically translate into Instagram success. You still need to execute: manage DM conversations at scale, schedule content that supports paid campaigns, qualify leads that ads generate, and track pipeline revenue from first message to closed deal. This is where Inflowave fills the gap that analytics tools cannot. It is the operational layer that turns Northbeam's strategic insights into on-the-ground results, and it generates execution data that feeds back into Northbeam's models to make them more accurate over time.
DM automation and AI chatbot - Handle Instagram at agency scale
When Northbeam's data shows that Instagram is a high-value channel and you increase your Instagram ad budget, the immediate consequence is more DM conversations. More ads reaching more people means more questions, more inquiries, and more potential customers reaching out through direct messages. For agencies managing 10, 20, or 50 client accounts, this increase in DM volume can quickly overwhelm human teams. Inflowave's AI chatbot handles this scale by automatically responding to inbound DMs within seconds, answering common questions in the client's brand voice, and qualifying leads with custom question flows before handing qualified prospects to human team members.
The chatbot is configured per client account, so each brand has its own AI trained on its specific products, pricing, policies, and voice. A luxury fashion brand gets responses that feel premium and personalized. A fitness supplement brand gets responses that are direct and benefit-focused. A B2B service provider gets responses that are professional and consultative. This per-client customization is critical for agencies because the DM experience is an extension of the brand experience. A generic, robotic chatbot response can damage a premium brand's perception, while a well-trained AI that matches the brand's tone converts at rates comparable to human agents. For agencies, this means scaling Instagram execution across the entire client portfolio without proportionally scaling headcount, which is the leverage that makes agency growth sustainable.
Lead pipeline and revenue tracking - Prove Instagram ROI with deal data
Instagram often gets undercredited in attribution models because the conversion path is non-standard. A customer sees an Instagram ad, visits the brand's profile, sends a DM asking a question, gets an answer, and then either buys through a link shared in the DM or visits the website directly to purchase. In Northbeam's attribution model, this conversion might be credited to "direct" traffic or "organic" search because the customer typed the URL directly after the DM conversation. The Instagram ad that started the journey gets undercredited or ignored entirely. Inflowave solves this by tracking the DM conversation as a pipeline deal with explicit attribution to the campaign that generated the initial interaction.
Inflowave's pipeline tracks each lead through stages: New, Qualified, Negotiating, Won, Lost. Revenue is recorded at the deal level and attributed to the source campaign, ad set, and even the specific ad creative. This gives agencies a parallel revenue dataset to cross-reference with Northbeam's attribution. If Northbeam's ML model says Instagram drove $50,000 in attributed revenue this month, and Inflowave shows $18,000 in additional DM pipeline revenue that did not flow through a tracked conversion path, the true Instagram contribution is $68,000, not $50,000. This correction often changes the optimal budget allocation significantly. For agencies presenting attribution reports to clients, being able to show both Northbeam's modeled attribution and Inflowave's deal-level attribution builds a more complete and more credible picture of Instagram's value.
Content scheduling and multi-account management - Organic supports paid
Northbeam's models capture the interaction between paid and organic channels, and one of the most common findings is that organic Instagram activity amplifies paid ad performance. Accounts with consistent organic posting schedules tend to see higher ad engagement rates, lower CPMs, and better conversion rates compared to accounts that only run ads without maintaining an organic presence. Inflowave makes it operationally feasible to maintain organic posting schedules across dozens of client accounts by providing a unified content calendar where you can schedule posts, Reels, and Stories for every account from a single dashboard.
For agencies managing 20 or more Instagram accounts, the multi-account management capabilities are essential. Each client's account has its own content calendar, DM inbox, AI chatbot configuration, and lead pipeline, all accessible from the same interface. This eliminates the need to log in and out of different Instagram accounts throughout the day, which is both a time-saver and a risk reducer (no more accidentally posting client A's content on client B's account). When Northbeam's data shows that a specific client's Instagram channel is underperforming, you can quickly check their Inflowave dashboard to see if the organic posting schedule has gaps, if DM response times have increased, or if the chatbot needs updated training data, all without switching tools.
Workflow automation - Convert Northbeam insights into automated action
Northbeam gives you data. Inflowave lets you act on it automatically. When Northbeam's models identify that a specific customer segment responds well to Instagram engagement, you can build Inflowave workflows that automatically trigger when leads from that segment enter your pipeline. For example, if Northbeam's cohort analysis shows that customers who engage with your Instagram content within 48 hours of seeing an ad have 3x higher lifetime value, you can set up an Inflowave workflow that prioritizes instant follow-up for leads who show that pattern. The workflow can send a personalized welcome message, ask qualifying questions, and escalate high-potential leads to a human closer, all within minutes of the initial interaction.
You can also use workflow automation to implement the budget allocation changes that Northbeam recommends. If Northbeam's MMM shows that Instagram is underinvested, you increase Instagram ad spend. The resulting increase in DM volume is handled automatically by Inflowave's chatbot and workflows. If a specific campaign drives an unusually high volume of unqualified leads (people who DM but have no buying intent), the Inflowave pipeline data reveals this pattern, and you can feed that information back into Northbeam's models to refine the attribution for that campaign type. This closed loop between strategic analytics and operational execution is what separates agencies that scale profitably from agencies that grow revenue but erode margins through operational inefficiency.
The five-step workflow: from attribution data to Instagram revenue
Here is how data-driven agencies structure the workflow between Northbeam's strategic insights and Inflowave's Instagram execution. This is not a one-time setup but an ongoing cycle that runs monthly or quarterly, with each cycle producing better data and more accurate budget allocation. The key principle is that data flows in both directions: Northbeam informs Instagram strategy, and Inflowave's execution data refines Northbeam's models.
Step 1: Build the attribution baseline with Northbeam (Week 1-2)
Start by connecting all your advertising platforms to Northbeam and letting the ML models learn your customer journey patterns. This includes Meta (for both Facebook and Instagram ads), Google Ads, TikTok, email platforms, and any other channels you use. Northbeam needs at least two weeks of data to build meaningful attribution models, though the models continue to improve with more historical data. During this setup period, configure your attribution windows, set up your comparison views (first-click vs. last-click vs. ML-based), and build dashboards for each client showing channel-level performance with the metrics that matter most: ROAS, CPA, and incremental revenue contribution.
While Northbeam is building its baseline, simultaneously set up Inflowave for all the Instagram accounts you manage. Connect each client's Instagram accounts, configure their AI chatbot with product and FAQ data, set up pipeline stages, and begin tracking DM conversations. The goal during this initial period is to establish baseline metrics for both tools: Northbeam's attribution baseline for each channel, and Inflowave's engagement baseline for DM volume, response times, qualification rates, and pipeline conversion rates. These baselines become the reference points against which you measure the impact of every optimization you make going forward.
Step 2: Identify Instagram's true contribution with cross-channel data (Week 3-4)
Once Northbeam's models have enough data, pull the cross-channel attribution report and focus on Instagram's position. Look at how Instagram performs under different attribution models. In last-click attribution, Instagram often appears weak because many Instagram-initiated journeys end with a direct visit or a Google search, giving credit to "direct" or "search" instead of Instagram. In Northbeam's ML-based model, Instagram usually gets more credit because the model recognizes its role in the discovery and consideration phases of the customer journey. Run incrementality tests if your budget allows, as these provide the most definitive answer about Instagram's true causal impact on revenue.
Now layer in Inflowave's DM pipeline data. For each client, pull the total DM pipeline revenue attributed to Instagram campaigns over the same period. Add this to Northbeam's attributed revenue for Instagram to get a "total Instagram contribution" number that includes both website conversions and DM-sourced deals. For agencies managing multiple clients, create a summary view that shows each client's Instagram attribution gap, which is the difference between what Northbeam attributes to Instagram and the total including DM pipeline revenue. Clients with large attribution gaps are the ones where Instagram is most undervalued by standard analytics, and they are the best candidates for Instagram budget increases. This analysis typically takes an afternoon per client but can reshape the entire media strategy.
Step 3: Reallocate budgets based on combined data (Monthly)
With Northbeam's MMM recommendations and the adjusted attribution numbers from Inflowave, make informed budget allocation decisions. If Northbeam's MMM shows that Instagram is at 80% of its optimal spend level, and Inflowave data confirms that Instagram DM pipeline revenue adds another 20-30% on top of Northbeam's attributed revenue, the case for increasing Instagram budget is very strong. Present this combined data to clients during budget review meetings, showing both the modeled attribution from Northbeam and the actual deal-level data from Inflowave. Clients respond better to seeing specific deals and conversations rather than just statistical models, so the combination is more persuasive than either alone.
As you increase Instagram ad spend, ensure that Inflowave is configured to handle the resulting increase in DM volume. Scale the AI chatbot's training data to cover new product lines or campaign themes that the increased budget will promote. Add team members to the Inflowave workspace if needed. Set up workflows that automatically flag high-value leads for immediate human follow-up. The worst outcome is increasing Instagram ad spend and then losing the resulting DM leads because your team could not respond fast enough. Inflowave prevents this by ensuring that every DM gets an instant AI response, buying your team time to prioritize and follow up with the highest-potential prospects.
Step 4: Execute and track with Inflowave (Ongoing)
With budget allocated and the strategy set by Northbeam's data, Inflowave becomes the daily execution platform. Schedule organic content that supports the paid campaign themes. Monitor the DM inbox for conversations generated by ads. Track each conversation through the pipeline from initial inquiry to closed deal. Use the AI chatbot to handle the volume and the qualification, while human team members focus on closing the highest-value deals. Record revenue against each deal so pipeline data stays current and accurate for the next budget review cycle.
Track key execution metrics in Inflowave that serve as leading indicators for Northbeam's lagging revenue metrics. DM response time is a leading indicator: if response times increase, conversion rates will drop within days, and Northbeam's attribution for Instagram will decline within weeks. Pipeline velocity (how quickly leads move through stages) is another leading indicator: if leads are getting stuck in the "Qualified" stage for longer than usual, it suggests either the qualification criteria need adjustment or the sales team needs additional support. These operational metrics are invisible to Northbeam but directly impact the revenue numbers that Northbeam models. By monitoring them in Inflowave, you can catch and fix execution problems before they show up as declining ROAS in Northbeam's dashboards.
Step 5: Refine the models and scale what works (Quarterly)
Every quarter, review the combined data from Northbeam and Inflowave to refine your attribution models and scale successful patterns. Compare Northbeam's ML-attributed revenue for Instagram against Inflowave's pipeline revenue over the same period. If the gap is narrowing (Northbeam's attribution is catching up to the real number), the models are getting more accurate. If the gap is widening, it means Instagram's DM-driven revenue is growing faster than what attribution models can capture, which is a strong signal to increase investment further.
Apply cross-client learnings from both tools. If Northbeam's data shows that Instagram incrementality is consistently high for clients in the health and wellness vertical, and Inflowave's data shows that those same clients have the highest DM-to-deal conversion rates, you have a data-backed thesis for recommending aggressive Instagram investment to new health and wellness clients. If incrementality testing reveals that retargeting campaigns have low true incrementality (the customers would have purchased anyway), but Inflowave shows that DM-based retargeting recovers genuinely lost deals, the recommendation shifts from standard display retargeting to DM-based re-engagement. These refined strategies, informed by both macro-level attribution and micro-level execution data, are what separate top-tier agencies from the rest of the market.
Real agency use cases
Performance marketing agency managing six-figure monthly budgets
A performance agency managing $500,000 per month in ad spend across 12 clients uses Northbeam as their single source of truth for attribution. The agency's media buying team reviews Northbeam dashboards daily for campaign-level optimizations and monthly for channel-level budget reallocation. Before adding Inflowave, the agency consistently found that Instagram's attributed ROAS in Northbeam was lower than other channels, which led them to systematically underinvest in Instagram relative to Facebook and Google. The assumption was that Instagram was a weaker channel.
After implementing Inflowave across all 12 client accounts, the agency discovered that Instagram campaigns were generating an average of 22% more revenue than Northbeam attributed to them, because a significant portion of conversions happened through DM conversations that did not follow a trackable click path. For three clients in the premium skincare and jewelry verticals, the gap was even larger, with DM pipeline revenue exceeding Northbeam-attributed revenue by 35% to 40%. Armed with this data, the agency increased Instagram budgets for those clients by 25%, and total revenue (Northbeam-attributed plus Inflowave pipeline) grew proportionally. The agency now presents dual-source ROAS reports to every client, which has improved client satisfaction and reduced churn because clients finally see the full impact of the agency's work on Instagram.
Full-service digital agency balancing paid and organic
A full-service agency that handles both paid advertising and organic social media management for 30 clients uses Northbeam to allocate budgets across channels and Inflowave to manage the organic and engagement side of Instagram. The agency's unique challenge was proving the combined value of their services: clients who paid for both paid ads and organic management expected to see how the two worked together, but the agency had no tool that connected attribution data with organic engagement metrics. Northbeam showed them channel-level ROAS, but it could not distinguish between the contribution of paid ads alone versus paid ads supported by consistent organic content and active DM management.
The solution was combining Northbeam's channel attribution with Inflowave's engagement and pipeline data to create a "total Instagram impact" report for each client. The report showed Northbeam-attributed Instagram revenue, Inflowave pipeline revenue from DM conversations, organic engagement metrics (likes, comments, shares, saves) from Inflowave's analytics, and a correlation analysis showing how organic posting consistency affects paid ad performance. Clients in the luxury goods vertical, where purchase decisions heavily involve conversations, showed the strongest correlation: clients with consistent daily organic posting had 18% higher Instagram ad ROAS in Northbeam compared to periods without organic support. This data justified the agency's combined service offering and helped close larger retainer contracts because the ROI case included both the attribution data from Northbeam and the operational engagement data from Inflowave.
Brand scaling from one channel to omnichannel
A direct-to-consumer fashion brand that had been advertising exclusively on Facebook decided to expand into Instagram, TikTok, and Google Ads. They needed Northbeam to understand how these new channels interacted with their existing Facebook campaigns and to make data-informed budget allocation decisions. The initial Northbeam data showed that Instagram cannibalized some Facebook conversions (the same customers were being reached on both platforms), but it also drove genuinely incremental revenue from audiences that Facebook was not reaching. The incrementality test showed that Instagram ads drove a 15% lift in total revenue when measured against a holdout group.
What Northbeam could not capture was the brand's growing Instagram DM sales channel. As they invested more in Instagram ads, their DM volume tripled. Customers asked about sizing, styling advice, and exclusive collections. Without Inflowave, these conversations were managed in the native Instagram app by a single team member who could not keep up with the volume, resulting in average response times of 4 to 6 hours. After implementing Inflowave with its AI chatbot, response times dropped to under 30 seconds, and the DM pipeline grew from $8,000 to $32,000 per month within 60 days. Northbeam's incrementality test had shown a 15% revenue lift from Instagram. When including Inflowave's DM pipeline data, the actual lift was 23%. The brand used this combined data to justify doubling their Instagram budget over the following quarter, confident that the investment was driving genuinely incremental revenue through both direct purchases and DM-sourced sales.
Frequently Asked Questions
How does Northbeam attribution work differently from Facebook Ads Manager?
Northbeam uses machine learning models to analyze your customer journey data across all channels simultaneously and assign conversion credit based on actual influence, not self-reported platform data. Facebook Ads Manager uses its own attribution model that naturally credits Facebook for as many conversions as possible because it can only see the touchpoints that happen within its ecosystem. Northbeam provides an independent, cross-platform view that distributes credit proportionally across every touchpoint, including channels that Facebook cannot see like Google, email, and organic search. This independence from platform self-reporting is why agencies trust Northbeam for budget allocation decisions.
What is media mix modeling and why does it matter?
Media mix modeling (MMM) is a statistical approach that analyzes historical marketing spend and revenue data at the aggregate level to model the relationship between your investments and outcomes. Unlike user-level attribution, MMM does not track individual customers, which makes it privacy-resilient and effective even when individual tracking is limited by iOS restrictions or ad blockers. Northbeam's MMM answers channel-level questions like "What is the optimal budget split between Instagram, Facebook, Google, and TikTok?" and "At what point does increasing spend on Instagram produce diminishing returns?" It complements user-level attribution by providing a macro view that is less susceptible to the tracking gaps that affect individual journey data.
How much does Northbeam cost?
Northbeam is positioned as a premium attribution platform and pricing typically starts in the range of several hundred dollars per month for smaller brands. Enterprise plans with advanced features like incrementality testing, full MMM capabilities, and custom dashboards cost more and are usually priced based on ad spend volume and feature needs. Northbeam generally targets brands and agencies with significant monthly ad spend where accurate attribution directly impacts budget allocation decisions worth thousands of dollars. The return on investment is clear: if Northbeam helps you reallocate even 5% of a six-figure monthly ad budget to higher-performing channels, the subscription pays for itself many times over.
Is Northbeam better than Triple Whale?
They serve different strengths and different audiences. Northbeam excels at cross-channel modeling, incrementality testing, and data science-driven attribution for brands and agencies that advertise across many channels and need advanced statistical analysis. Triple Whale excels at Shopify-specific analytics, creative-level reporting, LTV analysis, and AI-powered recommendations with its Moby and Sonar products. Brands that need sophisticated cross-channel modeling and incrementality testing tend to prefer Northbeam, while Shopify-first DTC brands that want deep e-commerce-specific features often choose Triple Whale. Both are excellent tools, and the choice depends on whether your priority is advanced statistical rigor or deep e-commerce platform integration.
Does Northbeam track Instagram DM sales?
Northbeam tracks Instagram ads as part of its cross-channel attribution modeling through its Meta Ads integration. However, it tracks conversions that happen on your website, not conversations that happen in Instagram DMs. This is precisely why pairing Northbeam with Inflowave is valuable for brands where Instagram DM sales are a meaningful revenue channel. Northbeam captures the standard conversion path (ad click to website purchase), while Inflowave captures the conversation path (ad view to DM conversation to deal close). Together, they provide complete visibility into Instagram's total revenue contribution across both conversion paths.
What is incrementality testing and when should I use it?
Incrementality testing measures the true causal impact of your advertising by comparing a test group that sees your ads against a holdout group that does not. The difference in conversion rates represents genuinely incremental revenue, revenue that would not have happened without the ad spend. You should use incrementality testing when you need definitive proof that a channel is driving real additional revenue, not just capturing conversions that would have happened anyway. This is particularly valuable for justifying budget increases, defending channel investments during budget cuts, and resolving internal debates about which channels actually drive growth versus which channels just appear to in attribution reports.
Can small businesses use Northbeam?
Northbeam is best suited for brands and agencies with at least moderate ad spend across multiple channels. Very small businesses spending a few hundred dollars per month on a single channel may not get enough value from advanced attribution modeling to justify the subscription cost. The tool becomes most valuable when you are making meaningful budget allocation decisions across at least two or three paid channels, and when a small improvement in allocation accuracy translates to thousands of dollars in additional revenue. If you are in the early stages with limited ad spend, starting with platform-native analytics and upgrading to Northbeam as you scale is a reasonable approach.
How does Northbeam handle iOS privacy restrictions?
Northbeam uses a multi-layered approach to maintain attribution accuracy despite iOS privacy changes. First-party data collection captures events on your own domain without relying on third-party cookies. Statistical modeling fills gaps where individual user tracking is incomplete. Machine learning identifies patterns and assigns credit even when some user journeys cannot be fully tracked. Media mix modeling works at the aggregate level and is entirely unaffected by individual tracking restrictions. The combination of these approaches means Northbeam's attribution remains more reliable than platform-reported metrics that depend heavily on individual user tracking, which is increasingly restricted.
What platforms does Northbeam integrate with?
Northbeam integrates with all major advertising platforms including Meta (Facebook and Instagram), Google Ads, TikTok Ads, Snapchat Ads, Pinterest Ads, and more. It also connects with e-commerce platforms like Shopify for revenue data and email marketing platforms for email attribution. The platform pulls both cost data and conversion data from each source to build its cross-channel attribution models. For channels without native integrations, Northbeam supports custom data imports through its API. The breadth of integrations is important because the value of cross-channel attribution comes from seeing all your channels in one unified view.
Do I need both Northbeam and Inflowave, or can I use just one?
You can absolutely use each tool independently. Northbeam is valuable on its own for any brand or agency that needs accurate cross-channel attribution, even if you do not use Instagram DMs as a sales channel. Inflowave is valuable on its own for any business that uses Instagram for engagement and sales, even if you do not need advanced attribution modeling. The combination becomes especially powerful for businesses where Instagram is both an advertising channel and a conversation-driven sales channel. If your customers regularly DM before purchasing, or if you run ads that drive DM engagement, the combination reveals significant revenue that each tool misses on its own. The right approach depends on your business model and how much of your revenue involves Instagram conversations.