A full strategic brief, researched and written, ready for a human to run the play.
Nobody has a free week. So the research, the stakeholder mapping, and the first draft of the plan happen here, and the human spends their time running the play instead of building the deck.
The account: every stakeholder researched, not just the champion.
Your config: profile, targeting theses, and the personas each stakeholder maps to.
The research library: competitor and partner intelligence docs, crawled from their actual sites and kept current.
A strategic brief with stakeholder angles matched to personas: what each person cares about and how to argue it.
The opportunities: rip-and-replace when they run your competitor, joint-pitch scenarios when they run your partner.
An intro email and a sales sheet, ready for a human to send and to carry into the room.
A real account brief, unabridged, generated for our customer Attribution about the account Honeylove. Every section below came out of the runtime: the research, the stakeholder strategy, the openers, all of it.
Honeylove is a fast-growing DTC shapewear and intimates brand on Shopify with an extremely sophisticated, multi-channel advertising stack spanning 12+ ad platforms (AppLovin, Criteo, Taboola, Meta, Google, TikTok, Pinterest, and more) plus TV attribution via Tatari. They already use Northbeam for attribution but their stack complexity - including Black Crow AI for predictive analytics, Snowplow for event-level data collection, GrowthBook for experimentation, and Global-e for international commerce - signals a data-mature team that likely needs deeper auditability and true cost reconciliation across this sprawling media mix.
Honeylove has one of the most complex and sophisticated DTC marketing stacks in the shapewear/apparel category. They run ads across 12+ platforms spanning programmatic, social, native, and TV, with Snowplow for first-party event collection, GrowthBook for experimentation, Black Crow AI for predictive analytics, and Northbeam for attribution. This signals a data-literate team that invests seriously in measurement.
However, the sheer complexity of their media mix is likely exposing Northbeam's limitations. With spend flowing through AppLovin, Beeswax, Criteo, Taboola, Meta, Google, TikTok, Pinterest, and Tatari (TV), they need user-level cost data reconciliation that matches what actually hits the bank account - not modeled estimates. Their Snowplow investment shows they value raw data, but there is no visible layer connecting that event data to true ad costs per user. The addition of Blotout and ID5 for privacy-first identity suggests they are actively trying to solve cross-device identity in a post-cookie world, which is exactly where Attribution's persistent user profiles and identity resolution shine.
Extremely diversified paid media mix across 12+ ad platforms including programmatic (Beeswax, Criteo, AppLovin), social (Meta, TikTok, Pinterest), native (Taboola), and TV (Tatari) - indicating sophisticated media buying
Snowplow event-level analytics pipeline shows investment in first-party data collection beyond standard platform pixels
GrowthBook for experimentation indicates a culture of testing and data-driven decision-making
Black Crow AI for predictive analytics suggests they value data science-driven marketing optimization
Blotout for privacy-first analytics shows awareness of cookie deprecation and data privacy challenges
ID5 universal ID adoption indicates proactive approach to identity resolution in a post-cookie world
Global-e for international expansion plus Shopify indicates scaling DTC operations globally
Comprehensive conversion tracking across Bing, Google, Facebook, Pinterest, and TikTok
GRIN for influencer management and Superfiliate for affiliate/creator commerce show multi-channel growth strategy
Sentry and New Relic for error monitoring and performance - engineering team cares about reliability
Northbeam is their current attribution solution, but with 12+ ad platforms, they likely face the classic Northbeam limitation: inability to trace reported ROAS back to individual user-level cost data for auditability
No visible CDP or identity resolution layer connecting Snowplow event data to ad platform costs at the user level
Tatari for TV attribution likely operates as a siloed measurement tool disconnected from their digital attribution in Northbeam
No visible MMM or incrementality testing capability to validate whether channels like AppLovin, Taboola, or Criteo are truly incremental
Gap between Snowplow raw data collection and actionable attribution - they have the ingredients but likely no unified model connecting costs to conversions
Klaviyo email/SMS data likely disconnected from attribution model, making it hard to measure true email contribution vs. last-click over-attribution
Global-e international orders may create attribution blind spots as cross-border transactions complicate conversion tracking
Using Northbeam already means they understand the need for multi-touch attribution and have budget allocated for it - this is a displacement opportunity
12+ ad platforms with separate conversion pixels suggests they are spending heavily and need to know which channels truly drive incremental revenue
Snowplow adoption indicates their data team wants raw, event-level data - exactly what Attribution provides via warehouse export
GrowthBook experimentation platform signals they value measurement rigor and would appreciate Attribution's incrementality testing
Black Crow AI for predictive scoring plus Northbeam for attribution suggests they are layering multiple tools to compensate for gaps in any single solution
Tatari for TV measurement as a separate tool indicates fragmented measurement across online and offline channels
Global-e international expansion creates new attribution complexity that their current stack may not handle well
Amazon API Gateway, Amazon SES, AppLovin, Axon AI, Beeswax, Bing Universal Event Tracking, Black Crow AI, Blotout, core-js, Criteo, DoubleClick.Net, Facebook Custom Audiences, Facebook for Websites, Facebook Pixel, Facebook SDK, Facebook Signal, Fancybox, Fastly, Firebase, FLoC, Forter, Global Site Tag, Global-e, Google Analytics, Google Apps for Business, Google Cloud Functions, Google Conversion Linker, Google Conversion Tracking, Google Remarketing, GRIN, GrowthBook, Hotjar, ID5, Impact, jQuery, Klaviyo, Loop Returns, Microsoft Azure DNS, Monocle, New Relic, Next.js, Northbeam, Pinterest Conversion Tracking, Pusher, React, Remix, Salesforce, Sendgrid, Sentry, Shopify, Snowplow, Superfiliate, Taboola, Tatari, TikTok Conversion Tracking Pixel, TikTok Embed, Wistia, Zendesk, Zendesk Guide, Zendesk Mail
Complete attribution model customization with full transparency - customers can audit all data and trace any reported number back to underlying touchpoints
Honeylove is already paying for Northbeam, but with 12+ ad platforms, their marketing team likely cannot trace any Northbeam-reported number back to the underlying touchpoints. Attribution's full auditability and model customization lets them verify every dollar of reported ROAS against actual bank account revenue. The '4 requirements for real attribution' framework (user-level cost data, full auditability, customizable models, raw data export) will expose where Northbeam falls short - particularly on auditability and raw data export, given that Honeylove already invests in Snowplow for event-level data.
→ With 12+ ad platforms feeding into Northbeam, ask whether their team can trace any single reported ROAS number back to the actual user touchpoints and costs that produced it.
Three measurement methodologies in one platform: MTA for granular daily spend decisions, MMM for forward-looking budget planning and iROAS forecasting, and incrementality for validating true campaign lift - all included
Honeylove uses Tatari separately for TV attribution and Northbeam for digital, creating fragmented measurement. They cannot see how TV spend interacts with digital channels in a single model. Attribution's three-methodology approach (MTA for daily granular decisions, MMM for forward-looking budget planning including TV, and incrementality testing for validating true lift) unifies their measurement in one platform - with MMM and incrementality included free, replacing what would otherwise require Tatari plus separate MMM vendors.
→ Their TV attribution via Tatari and digital attribution via Northbeam are two separate views of the same budget - position unified MTA plus MMM as the way to finally see cross-channel interaction effects.
Raw, un-modeled warehouse data export in relational schema, giving data teams actual event-level ingredients to build their own models
Honeylove has invested in Snowplow for raw event-level data collection, showing their data team values having the actual ingredients. But Snowplow captures user behavior without connecting it to ad platform costs at the individual user level. Attribution's raw, un-modeled warehouse data export in relational schema would complement their Snowplow investment by adding the cost dimension their data team needs to build custom models and validate marketing spend against actual revenue.
→ Their Snowplow investment captures what users do on-site but not what it cost to bring each user there - position Attribution's warehouse export as the missing cost layer.
Incrementality testing for validating true campaign lift
Honeylove runs significant spend through channels like AppLovin, Criteo, Beeswax, and Taboola that are notoriously difficult to measure for true incrementality. These retargeting and native platforms often claim credit for conversions that would have happened anyway. Attribution's incrementality testing can scientifically validate whether these channels are truly driving lift or just intercepting existing demand, potentially saving significant wasted spend.
→ Ask whether they have ever tested whether their Criteo and AppLovin retargeting spend is truly incremental or just claiming credit for conversions already in the funnel.
Full CDP functionality with persistent user profiles and identity resolution across devices and sessions, tracking the person not just the session
Honeylove runs on Shopify and has invested in identity solutions (ID5, Blotout) to handle cross-device tracking in a privacy-first world. Attribution is the only CDP connector that makes Segment work properly on Shopify with bidirectional integration. If they use or are considering Segment, this is a unique technical advantage. Even without Segment, Attribution's persistent user profiles and identity resolution across devices and sessions directly addresses the identity challenge they are clearly trying to solve with ID5 and Blotout.
→ Their ID5 and Blotout investments show they are solving for cross-device identity - position Attribution's persistent user profiles as the layer that ties identity to actual cost and revenue.
Cannot confidently allocate budget across 12+ ad platforms when Northbeam's numbers cannot be audited back to source data
TV spend via Tatari is measured separately from digital, making holistic budget optimization impossible
Board and leadership expect defensible ROAS numbers that reconcile to actual revenue, not modeled estimates
Scaling international via Global-e adds attribution complexity that current tools may not handle
With 12+ channels and international expansion, you need attribution that reconciles to what actually hits the bank account - not modeled estimates from Northbeam. Attribution gives you one platform with MTA, MMM, and incrementality so you can defend every budget decision to leadership with auditable data.
"Running 12+ ad platforms including programmatic, native, social, and TV is impressive scale - but I'm curious whether your team can actually trace Northbeam's reported ROAS on any given channel back to the individual touchpoints and costs that produced it."
Daily spend decisions across AppLovin, Criteo, Taboola, Meta, Google, TikTok, and Pinterest require granular, trustworthy channel-level data
Retargeting platforms (Criteo, AppLovin) likely over-claim credit, inflating their reported ROAS
No way to scientifically validate incrementality of any channel without running holdout tests
Black Crow AI predictions are only as good as the attribution data feeding them
Your programmatic and retargeting channels are almost certainly over-claiming credit. Attribution's incrementality testing lets you scientifically validate whether AppLovin, Criteo, and Taboola are driving true lift - and the MTA model gives you auditable daily data to make real-time spend decisions across all 12+ platforms.
"You're running a sophisticated mix of AppLovin, Criteo, Beeswax, and Taboola alongside your social channels - have you ever been able to isolate whether the retargeting spend is truly incremental or just intercepting conversions already in your funnel?"
Snowplow collects rich event data but lacks the cost-side connection to ad platforms at the user level
GrowthBook experiments need reliable attribution data to measure downstream revenue impact
Multiple disconnected tools (Northbeam, Tatari, Black Crow AI, Snowplow) create data reconciliation headaches
Wants raw, auditable data - not another black box on top of existing black boxes
You've built a serious data foundation with Snowplow and GrowthBook, but your attribution layer should match that rigor. Attribution exports raw, un-modeled data in a relational schema that your team can query directly - complementing your Snowplow events with user-level cost data you can actually audit and build custom models on.
"Your Snowplow and GrowthBook setup tells me your data team values raw, auditable data and rigorous experimentation - so I'm guessing Northbeam's black-box modeling is a source of frustration when your team tries to reconcile attribution numbers against actual revenue."
Honeylove runs ads across 12+ platforms (AppLovin, Beeswax, Criteo, Taboola, Meta, Google, TikTok, Pinterest, and TV via Tatari) but measures digital attribution in Northbeam and TV attribution in Tatari separately - two disconnected views of the same marketing budget
Their investment in Snowplow for raw event data and GrowthBook for experimentation signals a data team that values auditability and rigor - yet their attribution tool (Northbeam) is a black box that cannot be traced back to source touchpoints
With retargeting spend flowing through Criteo, AppLovin, and Beeswax, Honeylove likely has significant budget allocated to channels that may be over-claiming credit for conversions already in the funnel - incrementality testing could reveal material savings
Honeylove's adoption of ID5 and Blotout shows they are actively solving for cross-device identity in a post-cookie world - Attribution's persistent user profiles and identity resolution directly address this challenge while connecting identity to cost data
As a Shopify-based DTC brand expanding internationally via Global-e, Honeylove faces increasing attribution complexity from cross-border transactions that fragment the customer journey across domains and payment systems
Start with the Head of Growth or Performance Marketing lead, as they feel the daily pain of allocating spend across 12+ ad platforms without auditable attribution data - lead with the incrementality angle on their retargeting spend. Follow up with the Head of Data/Analytics, positioning Attribution's raw data export as the complement to their Snowplow investment that Northbeam cannot provide. The initial outreach should come from a senior account executive who can credibly discuss the 4 requirements framework, with a solutions engineer brought in for the technical deep-dive on Snowplow integration and warehouse data export.
Fatty15 is a DTC brand that, like Honeylove, needed to identify which channels were truly driving returns across a complex media mix. Honeylove's challenge of validating ROAS across 12+ ad platforms mirrors Fatty15's need to find 'sleeper' channels delivering outsized returns.
Identified channels delivering between 476.33% and 1,647.68% ROAS, giving the team confidence to double their 2025 marketing budget while maintaining positive cash flow
Honeylove's data team likely spends significant time manually reconciling attribution data across Northbeam, Tatari, Snowplow, and individual platform dashboards. Vendr's experience of saving 2-3 days per month in manual reporting time by consolidating into Attribution's customized dashboard directly addresses this pain.
Saved 2-3 days per month in manual reporting and increased ROAS by 8.6x
Honeylove is a DTC shapewear and intimates brand competing with Skims, ThirdLove, and other players in the bodycare/shapewear category. They run on Shopify with Global-e for international expansion. The tech stack suggests a sizable marketing and data team given the breadth of tools deployed (Snowplow, GrowthBook, Black Crow AI, multiple ad platforms). Exact team structure and employee count are unknown.
Limited recent information available. The company was reportedly preparing for Series B funding as of 2022. Their adoption of newer tools like Black Crow AI, Blotout, ID5, and GrowthBook suggests active investment in data infrastructure and privacy-first measurement in 2024-2025.
Currently uses Northbeam for multi-touch attribution and Tatari for TV attribution. Black Crow AI provides predictive analytics. Snowplow handles raw event collection. This multi-tool approach suggests they have not found a single platform that meets all their measurement needs - a classic signal of attribution tool fragmentation that Attribution's unified platform addresses.
Cookie deprecation and privacy changes make their current Northbeam MTA-only approach increasingly fragile. Their investments in Blotout and ID5 confirm they are actively responding to these shifts. International expansion via Global-e adds new attribution complexity. The combination of 12+ ad platforms with fragmented measurement tools creates an urgent need for a unified, auditable attribution platform. If they are post-Series B, they likely face increased pressure to demonstrate efficient marketing spend to investors.
A senior account executive or solutions engineer who can speak credibly to both the marketing measurement challenges (Northbeam displacement, incrementality testing) and the data infrastructure angle (Snowplow complement, warehouse data export). Given the technical sophistication of this account, the initial outreach should come from someone who can discuss the 4 requirements framework with technical credibility.
The presence of Axon AI in their ads stack is notable - this is a relatively niche tool suggesting they are experimenting with AI-driven ad optimization. Combined with Black Crow AI for predictive analytics, Honeylove appears to be a team that actively evaluates and adopts new martech. They may be receptive to a platform switch if the value proposition is clearly differentiated. The Shopify + Global-e combination is important context - Attribution's Shopify integration expertise and potential Segment connector are strong technical differentiators for this account.
Fed by outbound and social listening: the account that lights up becomes the account that gets a brief.