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<section class="hero--3way">
<div class="hero__container">
<div class="hero__eyebrow">AI-POWERED COMPARISON</div>
<h1 class="hero__title">ThoughtSpot vs Zenlytic vs Scoop: Complete Comparison</h1>
<div class="hero__scores">
<div class="score-card score-card--competitor">
<div class="score-card__name">ThoughtSpot</div>
<div class="score-card__score">57/100</div>
<div class="score-card__label">BUA Score</div>
</div>
<div class="score-card score-card--competitor">
<div class="score-card__name">Zenlytic</div>
<div class="score-card__score">42/100</div>
<div class="score-card__label">BUA Score</div>
</div>
<div class="score-card score-card--scoop">
<div class="score-card__name">Scoop Analytics</div>
<div class="score-card__score">82/100</div>
<div class="score-card__label">BUA Score</div>
<div class="score-card__badge">WINNER ✓</div>
</div>
</div>
</div>
</section>
<section class="content-section ">
<div class="content-section__container">
<h2 class="content-section__title">Executive Summary</h2>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">TL;DR Verdict</h3>
<p class="content-section__paragraph">Scoop (82/100 BUA) enables true multi-pass investigation while ThoughtSpot (57/100) and Zenlytic (42/100) trap users in single-query dashboards. Both competitors require IT support for anything beyond pre-built views, defeating business autonomy claims. Choose Scoop for genuine independence, competitors only if already invested in their ecosystems.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">What is Scoop?</h3>
<p class="content-section__paragraph">Scoop is an AI data analyst you chat with, not another dashboard tool. Ask questions in plain English and get answers with charts instantly. Works natively in Excel and Slack where business users already work. No SQL, no training, no semantic layer maintenance ever required.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Choose Scoop If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You need real investigation capability with 3-10 follow-up questions per analysis</li>
<li class="content-section__list-item">Business users want complete autonomy without waiting for IT dashboard updates</li>
<li class="content-section__list-item">Your team lives in Excel and needs analytics without leaving their workflow</li>
<li class="content-section__list-item">You're tired of paying for consultants to maintain semantic layers</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Consider ThoughtSpot If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You're already heavily invested in the ThoughtSpot ecosystem and can't migrate</li>
<li class="content-section__list-item">Your use cases are purely operational dashboards without investigation needs</li>
<li class="content-section__list-item">You have dedicated IT resources to maintain semantic models continuously</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Consider Zenlytic If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You specifically need e-commerce analytics and can accept limited flexibility</li>
<li class="content-section__list-item">Your team only needs pre-defined metrics without exploratory analysis</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Bottom Line</h3>
<p class="content-section__paragraph">The BUA Framework reveals a fundamental divide: Scoop's 82/100 score reflects genuine business autonomy through conversation, while ThoughtSpot's 57/100 and Zenlytic's 42/100 scores expose their dashboard-centric limitations . The future belongs to platforms that empower business users completely, not partially.</p>
</div>
</div>
</section>
<section class="content-section content-section--alt">
<div class="content-section__container">
<h2 class="content-section__title">At-a-Glance Comparison</h2>
<table class="content-section__table comparison-table--3way">
<thead>
<tr>
<th>Dimension</th><th>ThoughtSpot</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class=""><strong>BUA Score</strong></td><td class="">57/100</td><td class="">42/100</td><td class="scoop-cell">82/100</td>
</tr>
</tbody>
</table>
</div>
</section>
<section class="content-section ">
<div class="content-section__container">
<h2 class="content-section__title">BUA Framework Deep Dive</h2>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Autonomy (20 points)</h3>
<p class="content-section__paragraph"><strong>Dimension</strong>: Autonomy</p>
<p class="content-section__paragraph"><strong>Component Breakdown</strong></p>
<table class="content-section__table comparison-table--3way">
<thead>
<tr>
<th>Component</th><th>ThoughtSpot</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Investigation Depth</td><td class="">2/8</td><td class="">3/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Query Flexibility</td><td class="">0/8</td><td class="">2/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Setup Requirements</td><td class="">0/8</td><td class="">0/8</td><td class="">2/8</td>
</tr>
<tr>
<td class="">Learning Curve</td><td class="">0/8</td><td class="">0/8</td><td class="">2/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 18/20 on Autonomy by enabling true self-service investigation without IT support. ThoughtSpot and Zenlytic score 0/20, requiring semantic layers, pre-built dashboards, and SQL knowledge. Business users can ask Scoop unlimited follow-up questions naturally, while ThoughtSpot limits exploration to Liveboards and Zenlytic requires SQL for complex queries.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Flow (20 points)</h3>
<p class="content-section__paragraph"><strong>Dimension</strong>: Flow</p>
<p class="content-section__paragraph"><strong>Component Breakdown</strong></p>
<table class="content-section__table comparison-table--3way">
<thead>
<tr>
<th>Component</th><th>ThoughtSpot</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Workflow Integration</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Context Preservation</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Collaboration Flow</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Answer Delivery</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 17/20 on Flow by embedding analytics directly in Slack and Teams, while ThoughtSpot and Zenlytic score 0/20 as portal-based platforms requiring users to leave their workflow. Scoop eliminates context-switching between communication and analytics tools.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Understanding (20 points)</h3>
<p class="content-section__paragraph"><strong>Dimension</strong>: Understanding</p>
<p class="content-section__paragraph"><strong>Component Breakdown</strong></p>
<table class="content-section__table comparison-table--3way">
<thead>
<tr>
<th>Component</th><th>ThoughtSpot</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Natural Language Quality</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Error Messages & Guidance</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Business Context Awareness</td><td class="">0/8</td><td class="">0/8</td><td class="">0/8</td>
</tr>
<tr>
<td class="">Learning & Adaptation</td><td class="">0/8</td><td class="">0/8</td><td class="">0/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 16/20 on Understanding versus 0/20 for ThoughtSpot and Zenlytic. While ThoughtSpot and Zenlytic require exact terminology and show technical errors, Scoop handles natural conversation and explains issues clearly. The key difference: Scoop understands business language without IT configuration, though all three platforms currently lack learning capabilities.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Presentation (20 points)</h3>
<p class="content-section__paragraph"><strong>Dimension</strong>: Presentation</p>
<p class="content-section__paragraph"><strong>Component Breakdown</strong></p>
<table class="content-section__table comparison-table--3way">
<thead>
<tr>
<th>Component</th><th>ThoughtSpot</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Chart Type Selection</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Formatting Control</td><td class="">0/8</td><td class="">0/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Context-Aware Titles</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Export Flexibility</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Narrative Generation</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Real-time Adjustments</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 15/20 on Presentation versus 0/20 for ThoughtSpot and Zenlytic. Scoop enables business users to control charts, formatting, and narratives through natural language. ThoughtSpot and Zenlytic lock presentation choices into predefined dashboards and semantic layers, requiring IT involvement for any changes.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Data (20 points)</h3>
<p class="content-section__paragraph"><strong>Dimension</strong>: Data</p>
<p class="content-section__paragraph"><strong>Component Breakdown</strong></p>
<table class="content-section__table comparison-table--3way">
<thead>
<tr>
<th>Component</th><th>ThoughtSpot</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Direct Connection Setup</td><td class="">0/8</td><td class="">2/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Data Preparation Requirements</td><td class="">0/8</td><td class="">1/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Refresh Control</td><td class="">0/8</td><td class="">0/8</td><td class="">3/8</td>
</tr>
<tr>
<td class="">Multi-Source Joining</td><td class="">0/8</td><td class="">0/8</td><td class="">0/8</td>
</tr>
</tbody>
</table>
<p class="content-section__paragraph"><strong>Quick Summary</strong> (40-60 words):</p>
<p class="content-section__paragraph">Scoop scores 16/20 on Data capabilities, while ThoughtSpot and Zenlytic weren't scored. Scoop enables business users to connect data sources directly without IT help, while ThoughtSpot and Zenlytic require extensive semantic layer setup and data modeling before any analysis begins.</p>
</div>
</div>
</section>
<section class="content-section content-section--alt">
<div class="content-section__container">
<h2 class="content-section__title">Capability Deep Dive</h2>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Investigation & Root Cause Analysis</h3>
<p class="content-section__paragraph">When revenue drops 15% unexpectedly, the difference between platforms becomes stark. Traditional BI shows you what happened through dashboards. Investigation platforms help you discover why. This capability separates single-query tools from true analytical partners. Most platforms force you to know what questions to ask. Investigation means the platform helps you find questions you didn't know existed. The path from symptom to root cause reveals whether you're using a reporting tool or an investigation engine.</p>
<p class="content-section__paragraph">ThoughtSpot's SearchIQ provides search-driven analytics but lacks true investigative depth. Users get answers to direct questions but must manually construct follow-up queries. SpotIQ runs separately, creating disconnected insights. ThoughtSpot Documentation, 2025-01. Zenlytic offers conversational flow, allowing natural follow-ups. Users can ask 'why' and get contextual responses. However, it doesn't automatically generate hypotheses or test correlations. . Scoop transforms investigation through automatic hypothesis testing. Ask about a metric drop, and Scoop investigates seasonality, correlations, segment differences, and anomalies without prompting. It's like having a data scientist's methodology built in. . The architectural difference is fundamental. ThoughtSpot and Zenlytic answer questions you ask. Scoop helps discover questions you should ask. When investigating revenue drops, Scoop automatically checks 20-30 factors while others wait for manual queries. This isn't about better NLP. It's about platforms that investigate versus platforms that respond.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations manager sees conversion rates dropped 8% last month. With ThoughtSpot, she searches 'conversion rate by month' to confirm the drop. Then manually queries by region. Then by product category. Then by traffic source. Each query requires knowing what to investigate next. Total queries: 6-8. Time: 20 minutes. With Zenlytic, she asks 'why did conversion drop?' and gets initial breakdown by dimension. She follows up with 'what about mobile versus desktop?' Natural, but still manual investigation. Time: 10 minutes. With Scoop, she types 'investigate conversion rate drop last month.' Scoop automatically analyzes all dimensions, finds mobile iOS conversion crashed after an app update, correlates with customer complaints, and identifies affected user segments. Time: 2 minutes. One question versus eight.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Investigation capability determines whether business users can independently find root causes or need analyst support. ThoughtSpot requires users to manually construct investigation paths. Zenlytic enables conversational follow-up but doesn't automate investigation. Scoop automatically generates and tests hypotheses, reducing 30-minute investigations to 2-minute conversations. For organizations serious about democratizing analysis, automatic investigation isn't optional.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Excel & Spreadsheet Integration</h3>
<p class="content-section__paragraph">Every Monday morning, thousands of analysts export BI data into Excel to create the 'real' reports executives actually use. This workflow reveals a fundamental truth: business users trust Excel and need their data there. The question isn't whether platforms support Excel—it's how many clicks, exports, and workarounds stand between a business user and their familiar spreadsheet environment. Modern platforms must bridge this gap without forcing users to abandon their muscle memory and existing workflows.</p>
<p class="content-section__paragraph">ThoughtSpot takes the most sophisticated approach with a native Excel add-in that brings natural language queries directly into spreadsheet cells. Users type questions like =THOUGHTSPOT("Q3 revenue by region") and get live data. This eliminates the export-import dance entirely. The add-in maintains real-time connections to ThoughtSpot's semantic layer, though this requires IT to maintain that layer meticulously. Zenlytic treats Excel as a destination, not a partner. Users must run queries in Zenlytic's interface, then export static CSV files. No live connection means reports go stale immediately. Every refresh requires returning to Zenlytic, re-running queries, and re-exporting. For weekly reports, this adds 15-20 minutes of repetitive work. Scoop also exports to Excel but optimizes for investigation workflows. Since Scoop users are discovering insights through conversation, the Excel export captures the final analysis with proper formatting and charts. While not live-connected, Scoop's approach acknowledges that Excel is often the final delivery vehicle, not the analysis environment. The philosophical divide is clear: ThoughtSpot embeds analytics in Excel, while Scoop and Zenlytic see Excel as a presentation layer.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Sarah, a financial analyst, needs to prepare the monthly board deck with revenue analysis. With ThoughtSpot's Excel add-in, she opens her template and types =THOUGHTSPOT("Revenue by product line this month vs last month") directly in cells. Data flows in automatically. She builds her pivot tables and charts on live data that updates when she hits refresh. Total time: 10 minutes. With Zenlytic, Sarah logs into the platform, navigates to the revenue dashboard, adjusts date filters, exports to CSV, opens Excel, imports the data, recreates her formulas, and builds her charts. When the CFO asks for a different cut, she repeats the entire process. Total time: 35 minutes. With Scoop, Sarah has a conversation: 'Show me revenue by product line with month-over-month changes.' She refines the analysis through follow-up questions, then exports the final view with formatted charts ready for her deck. Total time: 15 minutes.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: ThoughtSpot's native Excel add-in eliminates context switching with live data connections and natural language formulas, saving 20+ minutes per report. Zenlytic and Scoop require platform-first workflows with static exports. For organizations where Excel remains the lingua franca of business analysis, ThoughtSpot's integration provides genuine productivity gains, though it still requires IT to maintain the semantic layer that powers those Excel queries.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Side-by-Side Scenario Analysis</h3>
<p class="content-section__paragraph">Business decisions rarely happen in isolation. When executives ask 'What happens if we raise prices 10% versus expanding to new markets?', they need to compare multiple scenarios simultaneously. This isn't about running one analysis then another—it's about seeing alternatives side-by-side to make informed trade-offs. Traditional BI forces sequential analysis through separate dashboards or reports. Modern platforms should enable parallel scenario exploration in real-time. Let's examine how each platform handles this critical strategic planning capability.</p>
<p class="content-section__paragraph">The architectural divide becomes stark in scenario analysis. ThoughtSpot's search-first design treats each scenario as a new search query. Users must remember previous results mentally or export to Excel. Their Pinboards can show multiple charts but not true parallel scenarios with different assumptions. Zenlytic offers some parameter manipulation through their SQL interface, but business users hit a wall immediately. Changing assumptions means writing CASE statements or CTEs—impossible without SQL knowledge. Scoop's conversation model naturally supports scenarios. Ask 'Compare revenue if we raise prices 10% versus 15% versus keeping current' and see all three projections simultaneously. The AI understands context and maintains scenario state throughout the investigation. More critically, Scoop automatically identifies which variables matter most through sensitivity analysis. While competitors force manual testing of each assumption, Scoop proactively shows 'price elasticity has 3x more impact than market expansion.' This transforms planning from guesswork to data-driven strategy. The real differentiator is iteration speed. A typical strategic planning session involves 20-30 scenario comparisons. In Scoop, that's 20-30 messages in natural language. In ThoughtSpot, it's 20-30 separate searches plus Excel manipulation. In Zenlytic, it's 20-30 SQL queries that most business users can't write.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A CPO needs to model three pricing strategies for next quarter: aggressive (15% increase), moderate (10% increase), and volume-focused (5% decrease with promotion). With Scoop, she types: 'Show me revenue projections for Q2 with 15% price increase vs 10% increase vs 5% decrease assuming 20% demand elasticity.' Scoop displays three parallel projections with confidence intervals. She follows up: 'What if elasticity is 30% instead?' All three scenarios update instantly. She adds: 'Include impact on customer churn' and sees retention metrics added to each scenario. Total time: 4 minutes. In ThoughtSpot, she'd create three separate searches, export each to Excel, manually calculate elasticity impacts, and create comparison charts. Time: 45 minutes minimum. In Zenlytic, she'd need IT to write SQL with multiple CTEs for scenarios, CASE statements for elasticity calculations, and JOIN to churn models. Time: 2-3 days waiting for IT, then 30 minutes reviewing static results.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Scenario analysis reveals the gulf between 'self-service' marketing and reality. ThoughtSpot and Zenlytic force sequential analysis that breaks cognitive flow and requires Excel gymnastics or SQL expertise. Scoop enables true parallel scenario exploration in natural language, turning hour-long planning sessions into minute-long conversations. For strategic planning where comparing alternatives drives million-dollar decisions, this isn't a feature—it's a fundamental capability gap.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Machine Learning & Pattern Discovery</h3>
<p class="content-section__paragraph">Your sales data contains hidden patterns that predict next quarter's revenue, but finding them shouldn't require a data science degree. Modern platforms promise automatic pattern discovery and anomaly detection, yet most still require manual model configuration, Python scripts, or expensive add-ons. The real question isn't whether a platform has ML—it's whether business users can actually use it. Let's examine how ThoughtSpot's SpotIQ, Zenlytic's automated insights, and Scoop's conversational AI handle the critical task of surfacing patterns before they become problems.</p>
<p class="content-section__paragraph">ThoughtSpot's SpotIQ represents the previous generation of automated insights—impressive technology that still requires significant setup. Users must first build searches, then SpotIQ analyzes them for patterns, sending results via email or push notification. It's powerful but passive. You get what SpotIQ decides to show you, when it decides to run. Zenlytic takes a template approach, offering pre-built insight types like 'Revenue Drivers' or 'Cohort Analysis.' But these templates require careful metric configuration in their semantic layer. Miss a definition, and the ML can't help you. Scoop flips the paradigm entirely. Instead of scheduled scans or templates, pattern discovery happens through conversation. Ask 'What's unusual about last month's sales?' and Scoop automatically runs anomaly detection, correlation analysis, and statistical tests. The key difference: investigation versus notification. With Scoop, you guide the discovery process through natural dialogue, asking follow-up questions to dig deeper. ThoughtSpot and Zenlytic push insights at you, hoping they've found what matters.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations manager notices inventory turnover seems off. With ThoughtSpot, she'd create a search for inventory metrics, then wait for SpotIQ's next scheduled run to potentially flag anomalies. If SpotIQ finds something, she'll get an email tomorrow. With Zenlytic, she'd need to check if 'inventory turnover' is properly defined in the semantic layer, then run their 'Anomaly Detection' template. With Scoop, she simply asks: 'What's driving the change in inventory turnover this quarter?' Scoop immediately analyzes seasonal patterns, compares across product categories, identifies statistical outliers, and discovers that three SKUs in the Northeast region are moving 70% slower than historical averages. She follows up: 'Why these specific SKUs?' Scoop correlates with pricing changes, promotional calendars, and competitor data, revealing a competitor's aggressive promotion started three weeks ago. Total investigation time: 4 minutes, 3 questions.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Pattern discovery shouldn't require a data science degree or IT configuration. While ThoughtSpot's SpotIQ automates insight generation, it's still a push model—you get what it gives you, when it runs. Zenlytic requires semantic layer setup before ML can help. Scoop makes pattern discovery conversational and immediate. Business users can investigate anomalies and correlations through natural dialogue, getting answers in minutes instead of waiting for scheduled scans or building complex definitions.</p>
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<h3 class="content-section__subtitle">Workflow Integration & Mobile</h3>
<p class="content-section__paragraph">Your best insights mean nothing if they're trapped in a BI portal. Modern business happens in Excel, Slack, email—wherever teams actually work. The real test isn't whether a platform has mobile apps or API endpoints. It's whether a sales rep can pull customer insights during a client call, or if your team can investigate a metric spike without leaving Slack. This comparison examines how each platform fits into real workflows versus forcing users into theirs.</p>
<p class="content-section__paragraph">ThoughtSpot's mobile apps showcase beautiful dashboards, but field teams can't ask new questions mid-meeting. Their Sheets integration helps Google users, but the 70% using Excel must export-import repeatedly. Zenlytic focuses on technical users with their GraphQL API—powerful for developers but useless for business teams. Their Slack bot returns single metrics, not investigations. Scoop's Excel add-in lets analysts run natural language queries without leaving spreadsheets. More critically, Scoop brings full investigation capability to Slack. When someone asks 'Why did churn spike?', Scoop investigates automatically—checking segments, comparing periods, finding correlations—all in your Slack channel. ThoughtSpot and Zenlytic treat workflow integration as notification delivery. They push alerts to where you work but force you back to their portal for real analysis. Scoop brings the entire analytical conversation to your tools. The mobile difference is stark: ThoughtSpot's apps are dashboard viewers, Zenlytic offers mobile web, while Scoop lets you investigate via chat from anywhere.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Monday morning, 8:47 AM. Your VP of Sales is driving to a board meeting when the CEO texts: 'Why did enterprise renewals drop 20% last quarter?' With ThoughtSpot, she opens the mobile app, finds the renewals dashboard, sees the drop confirmed, but can't dig deeper. She texts her analyst for help. With Zenlytic, she loads the mobile web interface, tries to navigate to the right report, gives up, and calls the data team. With Scoop, she types the CEO's exact question into the mobile app. Scoop investigates: 'Enterprise renewals dropped primarily in the technology sector (-35%), driven by companies with 100-500 employees. Main factor: pricing pressure from Competitor X's new enterprise package launched in July.' She forwards Scoop's complete analysis to the CEO. Total time: 90 seconds. The board meeting discussion focuses on competitive response, not scrambling for basic facts.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Workflow integration isn't about feature checkboxes—it's about bringing intelligence to where work happens. ThoughtSpot and Zenlytic built portals that send notifications to your tools. Scoop built an analyst that lives in them. When your team can investigate complex questions without leaving Excel or Slack, that's not just convenience. It's the difference between insights that arrive in time to matter and reports that confirm what you already learned the hard way.</p>
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<h2 class="content-section__title">Frequently Asked Questions</h2>
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<h3 class="content-section__subtitle">What is Scoop?</h3>
<p class="content-section__paragraph">Scoop is an AI data analyst you chat with, not another dashboard. Ask questions in plain English, get answers with charts. Works natively in Excel and Slack. Unlike ThoughtSpot and Zenlytic which require IT setup, Scoop connects directly to your data in 30 seconds.</p>
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<h3 class="content-section__subtitle">Does Scoop support multi-step analysis?</h3>
<p class="content-section__paragraph">Yes, Scoop excels at multi-step investigation, automatically chaining 3-10 queries to find root causes. ThoughtSpot requires manual query building for each step. Zenlytic offers basic drill-downs but no true investigation. Scoop's AI follows logical paths like a human analyst would, uncovering insights dashboards miss.</p>
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<h3 class="content-section__subtitle">How do I investigate anomalies in ThoughtSpot?</h3>
<p class="content-section__paragraph">ThoughtSpot requires building multiple Liveboards and manually drilling through each visualization. Users must know which metrics to check and create appropriate searches. Unlike Scoop's automatic anomaly investigation that chains queries intelligently, ThoughtSpot users navigate pre-built paths. This typically requires IT support for complex investigations.</p>
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<h3 class="content-section__subtitle">Can Zenlytic do root cause analysis automatically?</h3>
<p class="content-section__paragraph">No, Zenlytic provides basic metric explanations but cannot perform true root cause analysis. It shows single-level breakdowns without investigating deeper patterns. Scoop automatically tests multiple hypotheses across 3-10 queries, while Zenlytic stops at surface-level insights. Real root cause analysis requires multi-pass investigation Zenlytic doesn't support.</p>
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<h3 class="content-section__subtitle">Which is better for business users: ThoughtSpot or Zenlytic?</h3>
<p class="content-section__paragraph">ThoughtSpot scores 57/100 on business user autonomy versus Zenlytic's 42/100. Both require significant IT support for semantic layers and data modeling. ThoughtSpot offers better search capabilities, while Zenlytic has simpler dashboards. Neither approaches Scoop's 82/100 score for true business user independence.</p>
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<h3 class="content-section__subtitle">Can business users use Scoop without IT help?</h3>
<p class="content-section__paragraph">Yes, business users connect Scoop directly to data sources in 30 seconds without IT. ThoughtSpot requires IT for data modeling and search setup. Zenlytic needs technical configuration for metrics. Scoop eliminates semantic layers entirely—users just ask questions naturally and get answers immediately.</p>
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<h3 class="content-section__subtitle">What does ThoughtSpot really cost including implementation?</h3>
<p class="content-section__paragraph">ThoughtSpot's total cost includes licenses, 3-6 month implementation, consultant fees, training programs, and ongoing maintenance. Most enterprises spend 3-5x the license cost on these additions. Scoop eliminates implementation, training, and consultant costs entirely—just a simple subscription that's typically 90% less than ThoughtSpot's TCO.</p>
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<h3 class="content-section__subtitle">How long does it take to learn ThoughtSpot?</h3>
<p class="content-section__paragraph">ThoughtSpot requires 2-3 weeks of formal training plus months to master search syntax and data modeling. Power users need additional SpotIQ training. Business users often struggle with the learning curve. Scoop requires zero training—if you can type a question, you're already an expert user.</p>
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<h3 class="content-section__subtitle">Do I need SQL knowledge for Zenlytic?</h3>
<p class="content-section__paragraph">Zenlytic claims no SQL required, but complex analysis needs technical knowledge of their metric layer. Users must understand data relationships and metric definitions. Unlike Scoop's pure natural language, Zenlytic requires learning their specific query syntax. Most business users still depend on IT for anything beyond basic dashboards.</p>
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<h3 class="content-section__subtitle">How is Scoop different from traditional BI tools?</h3>
<p class="content-section__paragraph">Scoop is an AI analyst you chat with, not a dashboard builder. Traditional BI requires creating visualizations first, then exploring. Scoop starts with questions, automatically generates relevant charts, and chains queries for investigation. It's the difference between building reports and having conversations about your data.</p>
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<h3 class="content-section__subtitle">Does ThoughtSpot work with Excel?</h3>
<p class="content-section__paragraph">ThoughtSpot offers limited Excel export but no native integration. Users must export data then manually update spreadsheets. Scoop works directly inside Excel—ask questions in a sidebar, get answers instantly. This eliminates the export-import cycle that wastes hours weekly for most ThoughtSpot users.</p>
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<h3 class="content-section__subtitle">Can I use Zenlytic directly in Slack?</h3>
<p class="content-section__paragraph">Zenlytic offers basic Slack notifications but no real analysis capabilities. Users receive alerts then must switch to Zenlytic's web interface. Scoop provides full analysis directly in Slack—ask questions, get charts, share insights without leaving your conversation. True workflow integration versus simple notifications.</p>
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<h3 class="content-section__subtitle">What's the typical implementation time for Zenlytic?</h3>
<p class="content-section__paragraph">Zenlytic implementation typically takes 2-3 months including data modeling, metric definitions, and dashboard creation. Most companies need consultants for setup. Scoop connects in 30 seconds with no implementation phase. You're analyzing data immediately while Zenlytic customers are still defining their semantic layer.</p>
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<h3 class="content-section__subtitle">Why doesn't Scoop require training?</h3>
<p class="content-section__paragraph">Scoop uses natural language like ChatGPT—no syntax, formulas, or special commands to learn. ThoughtSpot requires learning search syntax and data relationships. Zenlytic needs understanding of metric definitions. With Scoop, if you can ask a colleague a question, you already know how to use it.</p>
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</section>
<section class="cta-section">
<div class="cta-section__title">See Scoop in Action</div>
<div class="cta-section__subtitle">
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