Connect Taboola to ChatGPT

Native Ads Portfolio Health Scan

Using the Catchr MCP, pull Taboola data for [List of Client Accounts] over [Period] and compare it with [Comparison Period]. For each account, summarize Total Spent, Impressions, Visible Impressions, Clicks, CTR, CPC, CPA Actions Number, CPA, Conversions Value, and ROAS. Use Campaign Status and Campaign Learning State to add operational context, then label each account Healthy, Watch, or Critical against [Client Targets or Historical Baseline]. Rank the accounts needing attention, explain the evidence behind every Watch or Critical label, and recommend one concrete next action for the consultant.

Client Native Performance Brief

Using the Catchr MCP, analyze Taboola performance for [Client Account Name/ID] over [Reporting Period] versus [Previous Period]. Break down Total Spent, Impressions, Visible Impressions, Clicks, CTR, VCTR, CPC, VCPM, CPA Actions Number, CPA, Conversions Value, and ROAS by Campaign Name. Identify which campaigns are expanding efficient content discovery, which are losing engagement or viewability, and which are driving the account-level change. Produce a client-ready brief with an executive summary, three wins, three risks, and a prioritized action for each underperforming campaign.

Cross-Account Budget and Learning Watchlist

Using the Catchr MCP, review [List of Client Accounts] for [Period] and inspect Campaign Name, Campaign Status, Campaign Learning State, Campaign CPC, Campaign Daily Cap, Campaign Spending Limit, Campaign Spending Limit Model, Total Spent, Clicks, CPC, CPA, and ROAS. Compare current spend pacing and efficiency with [Pacing Targets] and [Previous Period]. Flag campaigns that are active but barely spending, approaching a cap too quickly, inefficient despite stable delivery, or still constrained by their learning state. Return a cross-account watchlist ordered by urgency with the client, campaign, evidence, likely operational cause, and the exact budget, bid, or learning-phase review to perform.

Morning Native Ads Client Queue

Using the Catchr MCP, pull Taboola data for [List of Client Accounts] for [Today or Recent Period] and compare it with [Baseline Period]. For each client, review Total Spent, Impressions, Visible Impressions, Clicks, CTR, CPC, CPA Actions Number, CPA, ROAS, Campaign Status, and Campaign Learning State. Flag abrupt delivery or efficiency changes against [Client KPI Targets], distinguish a budget or status issue from an engagement or conversion issue, and create a ranked morning queue with what changed, why it matters, and the first action I should take for each client.

Monthly Taboola Client Report

Using the Catchr MCP, pull Taboola data for [Client Account Name/ID] for [Reporting Period] and [Previous Period]. Summarize Total Spent, Impressions, Visible Impressions, Clicks, CTR, VCTR, CPC, VCPM, CPA Actions Number, CPA, Conversions Value, and ROAS, then explain the largest changes by Campaign Name. Write a concise client-ready report in plain English covering results against [Client Goal], what improved, what declined, which campaigns drove each change, and three priorities for [Next Period], with every conclusion tied to a reported metric.

Campaign Optimization Backlog

Using the Catchr MCP, analyze Taboola campaigns for [Client Account Name/ID] over [Period]. For every Campaign Name with at least [Minimum Volume], compare Campaign CPC, Campaign Daily Cap, Total Spent, Impressions, Visible Impressions, CTR, VCTR, CPC, CPA Conversion Rate, CPA, and ROAS against [Performance Targets]. Diagnose whether the main constraint is budget pacing, viewability, click engagement, traffic cost, or conversion efficiency. Create a freelancer-friendly backlog ranked by expected impact and effort, with one specific change, success metric, guardrail, and review date for each recommended task.

Click-Through vs View-Through Revenue Check

Using the Catchr MCP, pull Taboola data for [Account Name/ID] over [Period] and compare it with [Previous Period]. Report Total Spent, Conversions Value, CPA Actions Number, CPA Actions Number from Clicks, CPA Actions Number from Views, ROAS, ROAS Clicks, and ROAS Views by Campaign Name. Calculate the share of actions attributed to clicks versus views, identify which campaigns create profitable direct response and which rely heavily on view-through attribution, and flag any material attribution-mix shift. Recommend where to scale, hold, or validate tracking based on [Target ROAS], [Target CPA], and the evidence.

Native Audience Profitability Map

Using the Catchr MCP, analyze Taboola performance for [Account Name/ID] over [Period] by Audience Name and, where useful, Country Name and OS Family. Compare Total Spent, Impressions, Impressions Percentage, Clicks, CTR, CPC, CPA Actions Number, CPA, Conversions Value, and ROAS for segments with at least [Minimum Spend or Click Volume]. Rank scalable profitable audiences, expensive non-converters, and promising low-volume segments. Deliver a budget reallocation table with the segment, supporting metrics, proposed increase or decrease, and the success threshold to review after [Test Window].

Content Discovery Viewability Diagnostic

Using the Catchr MCP, evaluate the quality of Taboola content discovery for [Account Name/ID] over [Period] versus [Comparison Period]. By Campaign Name, report Impressions, Visible Impressions, Clicks, CTR, VCTR, CPM, VCPM, CPC, CPA Conversion Rate, CPA, and ROAS; calculate visible-impression rate as Visible Impressions divided by Impressions. Separate campaigns with an attention problem, a click-efficiency problem, or a post-click conversion problem, and identify the strongest campaign benchmark for each stage. Recommend one specific campaign, targeting, bid, or landing-page test for every material bottleneck without claiming access to creative-level fields.

Taboola Reporting Integrity Audit

Using the Catchr MCP, audit Taboola data for [Account Name/ID or All Connected Accounts] over [Period] at Date and Campaign Name level. Check completeness and consistency for Campaign ID, Campaign Name, Campaign Status, Currency, Total Spent, Impressions, Visible Impressions, Clicks, CTR, CPC, CPM, CPA Actions Number, Conversions Value, and ROAS. Flag missing identifiers or dates, duplicate campaign-date rows, negative values, Visible Impressions above Impressions, Clicks above Impressions, percentage values outside their valid range, and mismatches between supplied ratios and ratios recalculated from their components. Return an exception table with account, date, campaign, failed rule, observed values, severity, and validation step.

Attribution Mix Consistency Monitor

Using the Catchr MCP, pull Taboola data for [Account Name/ID or All Connected Accounts] over [Lookback Period], grouped by Date and Campaign Name. Monitor CPA Actions Number, CPA Actions Number from Clicks, CPA Actions Number from Views, CPA Clicks, CPA Views, CPA Conversion Rate Clicks, CPA Conversion Rate Views, ROAS Clicks, ROAS Views, Total Spent, and Conversions Value. Check whether click-through and view-through action counts reconcile with total actions, track each attribution share over time, and flag missing values, discontinuities, or shifts beyond [Threshold]. Classify each exception as likely tracking, attribution-window, campaign-mix, or genuine performance change and provide the evidence and next validation query.

Daily Native Performance Outlier Monitor

Using the Catchr MCP, pull at least [Lookback Period, e.g., 90 days] of Taboola data for [Account Name/ID or All Connected Accounts], grouped by Date and Campaign Name. Monitor Total Spent, Impressions, Visible Impressions, Clicks, CTR, VCTR, CPC, VCPM, CPA Actions Number, CPA, and ROAS. Compare each daily value with its trailing [Baseline Window, e.g., 30-day] mean and standard deviation, flag deviations beyond [Z-score Threshold], and use Campaign Status, Campaign Learning State, Campaign Daily Cap, and Traffic Allocation Mode as context. Return an alert table that classifies each outlier as likely data quality, delivery configuration, learning-phase behavior, or genuine performance change, with supporting values and the next investigation step.

Replace [variables] Facts ≠ hypotheses Action + follow-up metric

Connect Taboola to ChatGPT

Trusted by marketing teams that talk to data everyday

How to connect Taboola to ChatGPT ?

Authorize the Taboola account in Catchr

Add the Catchr MCP from official ChatGPT store

Choose, adapt, and run a prompt.

A simple client question should not trigger an hour of data prep.

Pull another CSV

Rebuild the context

Summarize it by hand

Ask the account. Get to the explanation.

Prepare data before every question.

Connect once. Keep asking.

Twelve Taboola prompts for analyzing real account data.

Taboola to ChatGPT FAQs

What Taboola data can ChatGPT analyze through Catchr?

How granular can Taboola analysis be in ChatGPT?

Can ChatGPT find efficiency and budget opportunities in Taboola?

Can ChatGPT diagnose targeting, creative, or conversion issues in Taboola?

What types of prompts work best for Taboola analysis?

Can I combine Taboola with other data sources in ChatGPT?

Can I analyze multiple accounts or campaigns together?

What do I need to connect Taboola to ChatGPT with Catchr?

How recent is the Taboola data, and how much history can I analyze?

Can ChatGPT change anything in Taboola through Catchr?

How much does it cost to connect marketing data to ChatGPT with Catchr?

‍Can I try the ChatGPT integration before choosing a plan?

Still have a question ?

Ask the account question before building another export.