FAQs
This FAQ page provides quick answers to common questions about Bonsai’s platform integrations, data onboarding requirements, and measurement capabilities.
Platform Integrations
Data Onboarding
What types of data does Bonsai need to build Business Reporting? Bonsai requires digital marketing data (ad platform performance and spend) and business / point-of-sale (POS) data (orders, revenue, customers). These datasets form the foundation of the Business Reporting product and enable accurate measurement of business outcomes alongside marketing activity.
What types of data does Bonsai need to build Multi-Touch Attribution (MTA)? Bonsai requires digital marketing data, analytics data (e.g., GA4 or Adobe Analytics), and business / point-of-sale (POS) data. Together, these sources enable Bonsai to construct customer journeys and assign fractional credit to marketing touchpoints that contributed to business outcomes.
What types of data does Bonsai need to build Incrementality Modeling? Bonsai requires digital marketing data, offline marketing data (e.g., TV, radio, OOH), business / POS data, and Google Search Console data. Incrementality modeling relies on a combination of marketing exposure signals and business outcomes to estimate causal impact beyond what can be observed through tracking-based attribution alone.
What types of data does Bonsai need to build Algorithms? Bonsai’s bidding algorithms require the same data inputs as Multi-Touch Attribution because algorithm training depends on attributed buyer behavior and outcomes. Bonsai requires digital marketing data, analytics data, and business / POS data in order to build the training audience and model the features associated with valuable customers.
Do I have to pay per connector? Pricing depends on your Bonsai plan and the integrations required to support your use case. Some plans include a standard set of connectors, while others may vary based on the number of platforms, data volume, refresh cadence, and optional integrations. Your Bonsai account team can confirm the pricing model for your deployment.
Measurement
Business Reporting
Can I see both offline and digital sales on this page? Yes. Bonsai can support visibility into both online (digital) and offline (in-store / POS) sales, based on what is included in your business source data. Sales are reflected according to the transaction records provided in your point-of-sale or ecommerce systems.
Would you consider this a C-suite level report? Yes. Business Reporting is designed to provide an executive-ready view of core business performance, including revenue, customer growth, and high-level marketing efficiency. It is intended to support leadership reporting and decision-making with trusted, business-outcome-based metrics.
Are my analytics metrics shown on this page? No. Business Reporting focuses on point-of-sale/order outcomes and marketing platform performance. Analytics data is primarily used for customer journey development, attribution, and other measurement products rather than being a core component of the Business Reporting view.
Why are “new customers” at a business level more important than “new users” in analytics or ad platforms? Business-defined new customers are based on first-party purchase behavior and represent the most reliable source of truth. Analytics and ad platforms rely heavily on cookie/device identifiers that can reset or expire (often within 30–90 days), which makes long-term identity and new customer measurement less accurate—especially as privacy constraints continue to limit tracking.
Can I export this data? Yes. Bonsai supports export functionality and allows you to download reporting data as a CSV file for offline analysis or internal reporting workflows.
How long does it take to see business data populated once I onboard my data? Business data is typically the first dataset configured because it is the foundation for Bonsai measurement products and often requires the most customization. In many cases, Business Reporting can be live within approximately five business days after required access and data delivery are in place.
Can I see lifetime value (LTV) and purchase frequency? Yes. Bonsai supports configurable business metrics, including lifetime value, repeat purchase rate, and purchase frequency. These metrics can be added and configured through the Business Metrics Configuration settings based on the fields available in your business data.
Is there any data Bonsai cannot or will not configure? Yes. Bonsai does not populate critical business KPIs using third-party conversion tracking when it conflicts with validated first-party outcomes. Business KPIs should be derived from authoritative business systems (POS, ecommerce, ERP, CRM) rather than ad platform conversion estimates.
Can my team own the data and query it for our own reporting? Yes. The Bonsai platform is powered by a data warehouse in Google Cloud Platform (GCP). Clients can either allow Bonsai to own and manage the warehouse while granting query access, or ownership can be transferred to the client at any time.
Multi-Touch Attribution (MTA)
What do I use MTA for? Multi-Touch Attribution is best used to evaluate the effectiveness of digital marketing channels. Bonsai builds customer journeys and assigns fractional attribution across marketing touchpoints that contributed to outcomes, allowing teams to understand the relative contribution of channels beyond last-click reporting.
How do I configure MTA KPIs? MTA KPIs are configured using the business metrics defined in Business Reporting. Any business metric configured in the platform (e.g., orders, revenue, new customers) can be selected as an attribution KPI.
Can I still use Bonsai if I don’t get 100% traffic consent to analytics?
Yes. Like all modern analytics and attribution platforms, Bonsai can only directly measure and attribute activity from users who have provided the required consent. In practice, most businesses see less than 100% consent rates, which means a portion of traffic will not be available for user-level attribution.
Bonsai’s measurement products are built to maximize data quality and matching within the consented and observable traffic across channels and brands. While the non-consented portion of traffic cannot be directly attributed due to privacy requirements—and there is no technical workaround for this across the industry—Bonsai enables full-funnel measurement through complementary statistical methods such as incrementality testing.
This approach allows teams to understand true marketing impact across their entire business, even when direct attribution is limited by consent.
How does Bonsai join advertising, analytics, and business/point-of-sale data to build attribution? Bonsai uses analytics data as a linking layer between advertising platforms and business or POS systems. Ad identifiers such as click IDs are first matched to analytics events, and a separate unique identifier is then used to connect analytics data to downstream revenue or POS records. Bonsai does not join advertising data directly to point-of-sale systems — analytics serves as the intermediary that enables accurate attribution and measurement.
Why is my attributed sales number lower than my actual sales reported by finance? Not every sale can be attributed back to a measurable digital marketing touchpoint. This is expected and typically results from customers purchasing without clicking an ad, limited tracking coverage, incomplete analytics history, or privacy-related loss of identity signals.
How can I view more granular results? You can drill down into attribution using customizable categories based on your campaign mapping and grouping configuration. Bonsai supports grouping campaigns into meaningful buckets so that performance can be interpreted at a level aligned with your internal reporting structure.
How do I read attributed sales? Attributed sales represent the outcomes credited back to ad clicks that occurred on a specific date. For example, if the platform shows $338K attributed sales on August 25, 2025, this means users who clicked ads on that date eventually generated purchases totaling $338K in fractional credit.
How long is the attribution window? Bonsai MTA is windowless and looks back across all available history. The primary constraint is how far back analytics data is available, because Bonsai relies on first-party analytics data to build customer journeys.
Is this the same as Google’s data-driven attribution (DDA)? No. Bonsai MTA differs from Google DDA because Bonsai supports true business / POS outcomes, is not biased toward any one advertising platform, and is designed to unify attribution across multiple channels. Google DDA is constrained to the Google ecosystem and does not provide the same cross-channel, business-outcome-driven view.
Why does it look like attribution is going down while business cost is increasing? This can occur when incremental efficiency declines due to audience saturation, competition, spend shifting into lower-performing tactics, or tracking coverage changes. It can also indicate an increasing share of spend going to placements that do not produce measurable click journeys. In these scenarios, Incrementality Modeling is often the best method to validate true causal impact.
How do I use Detailed Order Attribution? Detailed Order Attribution is used to view attribution based on when an actual sale occurred rather than when a click occurred. This is useful for answering questions such as which marketing channels contributed to the sales that occurred in a specific reporting month.
How granular can I view MTA data? Granularity depends on the attribution view. In the Attribution page, you can typically view results at the campaign level. In Detailed Order Attribution, you can view campaign-level performance and further drill down by landing page, creative, or any other analytics tag available in the dataset.
Incrementality
Testing
How do you measure success? Bonsai measures test success using lift percentage derived from a Difference-in-Differences (DiD) method. This compares changes in test markets pre/post against changes in control markets pre/post, helping isolate the impact of a strategy even when markets are not perfectly matched and external factors influence performance.
Modeling
What do I use Incrementality Modeling for, and how is it different from MTA? Incrementality Modeling estimates causal impact at an aggregate level, answering what would have happened if a marketing channel were turned off. MTA is based on measurable touchpoint journeys and explains what happened through attributed clicks. Incrementality is especially important for offline media and upper-funnel channels where click-based tracking is incomplete.
What questions can I answer with Bonsai’s Incrementality Model? Bonsai’s Incrementality Model helps identify which channels truly drove revenue, how much lift each channel generated, and what the true ROI is by tactic. It also supports questions around seasonality, diminishing returns, spend optimization, budget shifting scenarios, undervalued or overinvested channels, and forecasting outcomes under different spend levels.
What is a feature? A feature is a configurable subset of marketing activity. Features can represent a channel subset, tactic type, campaign grouping, or a highly granular campaign-level segment depending on how your taxonomy is configured.
What does “incremental” mean? Incremental outcomes are results that would not have occurred without the marketing activity. This represents the true lift driven by marketing beyond baseline demand.
What does “base” mean? Base represents the expected outcomes with no marketing investment, driven by underlying demand such as brand equity, seasonality, category demand, and external macro factors. Over time, base can decay if marketing remains off for an extended period.
What does “base+” mean? Base+ represents lift from drivers that influence demand but are not scalable with spend, such as Brand Search or Email. These drivers can elevate results beyond baseline, but they cannot be increased linearly through budget increases.
Why do my results on MTA look different from Incrementality? Differences are expected because the methodologies measure different concepts. MTA attributes results based on observed click journeys, while Incrementality predicts causal lift and estimates what would have happened without marketing. It is common for results to differ at campaign or feature level.
Activation
Algorithms
How do I view the Algorithm page? The Algorithm page provides visibility into how the model has been tuned for a specific channel, including how different features influence value and how traffic is scored for bidding decisions.
What is a feature? A feature is a measurable attribute of a click or user interaction. Examples include device type, search query characteristics, hour of day, geography, landing page behavior, operating system version, and other signals available through your marketing and analytics datasets.
What does fit score mean? Fit score represents the relative predicted value of a feature segment. In general, a higher fit score indicates that traffic with that feature profile is expected to be more valuable based on historical buyer patterns.
How does the algorithm work? Bonsai trains a model using attributed buyer outcomes to learn what signals correlate with high-value customers. The algorithm then adjusts bids higher or lower in ad auctions based on whether a new click resembles historically valuable users across features such as query intent, landing behavior, time, and geography.
How do I read the scatter plots for each feature? Scatter plots show how expected value changes across feature values. They are used to interpret which segments of a feature correlate with stronger outcomes and where performance varies across distributions.
Is this a replacement for conversion data? Bonsai algorithm conversion data is intended for ad buying optimization rather than serving as official purchase conversion reporting. In many cases, teams can reduce reliance on platform conversion tracking for bidding while still using Bonsai measurement products (MTA and Incrementality) to validate business lift and channel impact.
Can I test the algorithm before scaling across the whole channel? Yes. Bonsai validates new algorithms using Incrementality Testing (Matched Market Testing). A subset of markets are selected as test markets where the algorithm runs, while matched control markets remain unchanged, and lift is measured using a Difference-in-Differences method.
What is
bds_pcv_conversionand why is it higher than official purchase conversions in Google Ads?bds_pcv_conversionis a conversion goal used for ad buying purposes to support Bonsai’s algorithm training and optimization. It identifies an attributed buyer audience and trains a model to score new clicks (GCLIDs) based on similarity to historical valuable buyers using signals such as search queries, landing page views, time-of-day patterns, and geography. The score represents predicted downstream value (often modeled as predicted LTV), which is why it may not align with official purchase conversion counts and values.Why are we seeing reduced spending on listing group placements in PMax? This can occur when Performance Max reallocates spend toward placements and inventory it predicts will meet the optimization goal more efficiently. Common drivers include feed eligibility changes, asset performance shifts, competitive auction dynamics, or algorithm learning that prioritizes other placement types based on predicted conversion likelihood.
Why do we see such a high proportion of brand traffic in Google campaigns like PMax? Google’s systems naturally optimize toward high-intent users, which frequently includes people searching for brand terms. Since brand traffic is more likely to convert, PMax often prioritizes these auctions, increasing the share of brand-driven traffic. Bonsai’s modeling and campaign structure are designed to rebalance this behavior and drive more incremental non-brand growth over time.
Budget Planner
What can I view on the Budget Planner page? The Budget Planner allows you to forecast performance and outcomes under different spend scenarios. It can be used to maximize profit, estimate expected revenue, and understand how changing budget allocation may impact next-month performance.
Can I use the budget planner for a channel that I just turned on? Bonsai recommends collecting at least three months of performance data before including a new channel in Incrementality Modeling to establish reliable spend and response trends. For Budget Planner forecasting, twelve months of data is ideal in order to capture seasonality and long-term patterns. If you would like to evaluate a new channel before sufficient history is available, Bonsai’s Incrementality Testing can be used to measure incremental lift and early performance impact.
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