<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;500;600;700&display=swap" rel="stylesheet">
<style>
section.hero--3way { padding: 40px 20px; background: linear-gradient(180deg, #ffffff 0%, #f8f9fd 100%) !important; }
.hero__container { max-width: 1200px; margin: 0 auto; }
.hero__eyebrow { font-weight: 600; font-size: 14px; color: #4763F5; text-transform: uppercase; letter-spacing: 1px; margin-bottom: 16px; text-align: center; }
.hero__title { font-weight: 600; font-size: 46px; line-height: 1.1; color: #130417; margin-bottom: 24px; text-align: center; }
.hero__scores { display: grid; grid-template-columns: repeat(3, 1fr); gap: 32px; margin-bottom: 32px; }
.score-card { background: #ffffff; border-radius: 12px; padding: 24px; box-shadow: 0 4px 16px rgba(0,0,0,0.08); text-align: center; }
.score-card--scoop { border: 2px solid #4763F5; background: linear-gradient(135deg, #f0f3ff 0%, #f8f9ff 100%); }
.score-card__name { font-weight: 600; font-size: 18px; color: #130417; margin-bottom: 12px; }
.score-card__score { font-weight: 700; font-size: 42px; margin-bottom: 8px; }
.score-card--scoop .score-card__score { color: #4763F5; }
.score-card--competitor .score-card__score { color: #E3165B; }
.score-card__label { font-size: 14px; color: #666; }
.score-card__badge { display: inline-block; background: #4763F5; color: white; padding: 4px 12px; border-radius: 20px; font-size: 12px; font-weight: 600; margin-top: 8px; }
section.content-section { padding: 32px 20px; background: #ffffff !important; }
section.content-section--alt { background: #f8f9fd !important; }
.content-section__container { max-width: 1000px; margin: 0 auto; }
.content-section__title { font-weight: 600; font-size: 36px; color: #130417; margin-bottom: 20px; }
.content-section__subsection { margin-bottom: 32px; }
.content-section__subtitle { font-weight: 600; font-size: 24px; color: #130417; margin-bottom: 12px; }
.content-section__paragraph { font-weight: 400; font-size: 16px; line-height: 1.6; color: #333333; margin-bottom: 12px; }
.content-section__list { margin: 16px 0 16px 20px; padding-left: 0; list-style-type: disc; color: #333333; }
.content-section__list-item { font-weight: 400; font-size: 16px; line-height: 1.6; margin-bottom: 8px; }
.content-section__table { width: 100%; border-collapse: collapse; margin: 24px 0; background: #ffffff; border-radius: 8px; overflow: hidden; box-shadow: 0 2px 8px rgba(0,0,0,0.06); }
.content-section__table th { background: #4763F5; color: #ffffff; font-weight: 600; font-size: 14px; text-align: left; padding: 16px; }
.content-section__table td { padding: 14px 16px; border-bottom: 1px solid #e5e5e5; font-size: 14px; color: #333333; }
.content-section__table tr:last-child td { border-bottom: none; }
.content-section__table tr:hover { background: #f8f9fd; }
.comparison-table--3way th:first-child { width: 30%; }
.comparison-table--3way th:nth-child(2),
.comparison-table--3way th:nth-child(3),
.comparison-table--3way th:nth-child(4) { width: 23.33%; }
.winner-cell { background: #f0fff4; font-weight: 600; color: #333333; }
.loser-cell { background: #fff5f5; color: #333333; }
.scoop-cell { background: #f0f3ff; font-weight: 600; color: #333333; }
section.cta-section { padding: 100px 20px; background: linear-gradient(135deg, #4763F5 0%, #3651D4 100%) !important; text-align: center; color: #ffffff; }
.cta-section__title { font-weight: 600; font-size: 36px; margin-bottom: 16px; }
.cta-section__subtitle { font-weight: 400; font-size: 18px; margin-bottom: 32px; opacity: 0.9; }
.btn--primary { font-weight: 500; font-size: 16px; padding: 14px 28px; background: #ffffff; color: #4763F5; text-decoration: none; border-radius: 8px; display: inline-block; box-shadow: 0 2px 8px rgba(255,255,255,0.3); transition: all 0.2s ease; }
.btn--primary:hover { transform: translateY(-2px); box-shadow: 0 4px 16px rgba(255,255,255,0.4); }
.faq-section { padding: 60px 20px; background: #f8f9fd; }
.faq-section__container { max-width: 900px; margin: 0 auto; }
.faq-section__title { font-weight: 600; font-size: 36px; text-align: center; color: #130417; margin-bottom: 40px; }
.faq-item { background: #ffffff; border-radius: 12px; padding: 28px; margin-bottom: 16px; box-shadow: 0 2px 8px rgba(0,0,0,0.06); }
.faq-item__question { font-weight: 600; font-size: 18px; color: #130417; margin-bottom: 12px; }
.faq-item__answer { font-weight: 400; font-size: 16px; color: #666666; line-height: 1.6; }
</style>
<section class="hero--3way">
<div class="hero__container">
<div class="hero__eyebrow">AI-POWERED COMPARISON</div>
<h1 class="hero__title">Domo vs Zenlytic vs Scoop: Complete Comparison</h1>
<div class="hero__scores">
<div class="score-card score-card--competitor">
<div class="score-card__name">Domo</div>
<div class="score-card__score">62/100</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 business autonomy through multi-pass investigation, while Domo (62/100) and Zenlytic (42/100) trap users in dashboard paradigms. Both competitors require IT support for anything beyond pre-built views, creating bottlenecks Scoop eliminates entirely. Choose Scoop for immediate 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, get answers with charts instantly. Works natively in Excel and Slack where business users already work. No SQL, no training, no semantic layer maintenance—just conversation with data.</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 (3-10 follow-up questions) not just dashboards</li>
<li class="content-section__list-item">Business users want immediate answers without waiting for IT ticket queues</li>
<li class="content-section__list-item">Your team lives in Excel/Slack and needs analytics there, not another portal</li>
<li class="content-section__list-item">You're tired of paying for training, consultants, and maintenance alongside licenses</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Consider Domo If</h3>
<ul class="content-section__list">
<li class="content-section__list-item">You're already deep in the Domo ecosystem with significant sunk costs</li>
<li class="content-section__list-item">Your use cases are purely operational dashboards with no investigation needs</li>
<li class="content-section__list-item">You have dedicated BI team resources to maintain semantic layers</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 SQL-based semantic layer control despite the overhead</li>
<li class="content-section__list-item">Your organization prefers technical tools over business user empowerment</li>
</ul>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Bottom Line</h3>
<p class="content-section__paragraph">The BUA scores reveal the truth: Scoop's 82/100 represents genuine business autonomy, while Domo's 62/100 and Zenlytic's 42/100 reflect continued IT dependency . Business users gain immediate independence, not another IT-managed portal prison.</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>Domo</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class=""><strong>BUA Score</strong></td><td class="">62/100</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>Domo</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Question Complexity</td><td class="">2/8</td><td class="">3/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Follow-up Capability</td><td class="">1/8</td><td class="">2/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Error Recovery</td><td class="">0/8</td><td class="">1/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Setup Requirements</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Learning Curve</td><td class="">1/8</td><td class="">2/8</td><td class="">8/8</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 versus near-zero for Domo and Zenlytic. While Domo requires IT-built dashboards and Zenlytic needs semantic layer setup, Scoop lets business users ask complex questions directly in plain English with unlimited follow-ups and no IT involvement.</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>Domo</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="">7/8</td>
</tr>
<tr>
<td class="">Context Preservation</td><td class="">0/8</td><td class="">0/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Response Delivery</td><td class="">0/8</td><td class="">0/8</td><td class="">4/8</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 enabling analytics directly in Slack, while Domo and Zenlytic score 0/20, requiring users to leave their workflow for separate portals. Scoop eliminates the constant app-switching that wastes 40% of analyst time.</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>Domo</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Natural Language Quality</td><td class="">2/8</td><td class="">3/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Business Terminology</td><td class="">1/8</td><td class="">2/8</td><td class="">5/8</td>
</tr>
<tr>
<td class="">Error Handling</td><td class="">0/8</td><td class="">1/8</td><td class="">4/8</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 unscored competitors Domo and Zenlytic. Scoop eliminates semantic layer requirements, understands business vocabulary naturally, and explains errors in plain English. Business users ask questions conversationally without learning technical schemas or SQL syntax.</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>Domo</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Automatic Visualization Selection</td><td class="">0/8</td><td class="">0/8</td><td class="">8/8</td>
</tr>
<tr>
<td class="">Natural Language Explanations</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Context-Aware Formatting</td><td class="">0/8</td><td class="">0/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Export and Sharing Quality</td><td class="">0/8</td><td class="">0/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Multi-Step Story Building</td><td class="">0/8</td><td class="">0/8</td><td class="">5/8</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 Domo and Zenlytic. Scoop automatically selects visualizations, writes plain English explanations, and adapts formatting to context. Domo and Zenlytic require manual chart selection, lack narrative generation, and export static views. Scoop delivers business-ready answers while competitors deliver raw visualizations requiring interpretation.</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>Domo</th><th>Zenlytic</th><th>Scoop</th>
</tr>
</thead>
<tbody>
<tr>
<td class="">Direct Database Access</td><td class="">2/8</td><td class="">3/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Data Preparation Requirements</td><td class="">1/8</td><td class="">2/8</td><td class="">6/8</td>
</tr>
<tr>
<td class="">Real-time Data Access</td><td class="">3/8</td><td class="">2/8</td><td class="">7/8</td>
</tr>
<tr>
<td class="">Governance and Security</td><td class="">4/8</td><td class="">3/8</td><td class="">4/8</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 by eliminating semantic layers entirely, while Domo and Zenlytic require extensive IT setup before business users can access information. Scoop connects directly to databases and understands schemas automatically, reducing data access time from weeks to minutes.</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 finding the root cause in 3 minutes versus 3 hours can determine competitive survival. Traditional BI shows you what happened through dashboards. Investigation platforms help you understand why. This capability separates single-query tools from true analytical thinking systems. Most platforms force users to manually construct each hypothesis, test it, then repeat. True investigation means the system automatically explores multiple paths, tests correlations, and surfaces hidden patterns without explicit instruction.</p>
<p class="content-section__paragraph">Domo's architecture fundamentally limits investigation to dashboard drill-downs. Users navigate pre-built views but cannot ask follow-up questions. When investigating revenue drops, you click through dashboards hoping someone anticipated your question path. Zenlytic offers more flexibility through SQL generation but requires technical knowledge. Users must understand table structures and field names. Each investigation step needs manual query construction. Scoop operates like a data analyst conducting an investigation. Ask 'Why did sales drop?' and it automatically checks seasonality, segments, product mix, and customer behavior. The system remembers context between questions. Follow-ups like 'What about enterprise customers?' build on previous analysis. This multi-pass architecture means 3-10 connected queries happen automatically. Traditional platforms require users to manually construct each hypothesis. Scoop tests multiple theories simultaneously. The difference shows in time-to-insight metrics. Domo users average 45 minutes for root cause analysis. Zenlytic reduces this to 20 minutes for SQL-capable users. Scoop delivers answers in under 5 minutes without technical skills.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations manager notices unusual inventory patterns on Monday morning. With Scoop, she types: 'Why are inventory levels spiking in Northeast stores?' Scoop automatically investigates: comparing regions, checking seasonal patterns, analyzing SKU movements, correlating with promotions, and identifying that a new competitor closure drove unexpected demand. Total investigation: 3 minutes, 7 automatic sub-queries. With Domo, she navigates to inventory dashboards, manually filters by region, exports data to Excel for comparison, schedules a meeting with the data team for deeper analysis. Time to insight: 2 days. Zenlytic requires writing SQL joins between inventory, sales, and store tables, manually testing each hypothesis. Even with SQL knowledge, the investigation takes 45 minutes of query writing.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Investigation capability determines whether business users can answer 'why' questions independently. Domo's dashboard-centric approach handles 'what' but fails on 'why'. Zenlytic enables investigation for technical users only. Scoop's multi-pass architecture delivers root cause analysis in minutes without technical skills, fundamentally changing how businesses respond to problems.</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 data from BI tools into Excel to create the reports executives actually use. This workflow reveals a fundamental truth: business users trust Excel. They know its formulas, love its flexibility, and rely on its familiarity. The question isn't whether your analytics platform works with Excel—it's how many clicks, exports, and manual steps stand between your data and that Monday morning report. Modern platforms approach this challenge differently: some treat Excel as a second-class citizen requiring constant exports, while others embed directly into Excel's interface.</p>
<p class="content-section__paragraph">The Excel integration battle reveals competing philosophies about where analytics should happen. Domo offers a comprehensive Excel plugin that maintains live connections to datasets, allowing real-time refresh without leaving Excel. Users can pull Domo cards and datasets directly into spreadsheets. However, creating new analyses still requires the Domo web interface. Zenlytic takes a traditional export approach—users must log into the web platform, run queries, then export results to Excel. This creates a disconnect between where analysis happens and where presentation occurs. Scoop embeds natural language directly into Excel, letting users type questions in plain English within their spreadsheets. This eliminates the training barrier completely. A financial analyst can type 'What were our top 10 customers last quarter?' directly in Excel and get results instantly. The architectural difference is profound: Domo and Zenlytic expect users to learn their platforms then export to Excel, while Scoop brings analytics capabilities into Excel itself. For the 750 million Excel users worldwide, this difference determines adoption success.</p>
<p class="content-section__paragraph"><strong>Example</strong>: Sarah, a financial analyst, needs to create the monthly board report combining data from multiple sources. With Domo, she opens Excel, uses the Domo plugin to pull three different cards, waits for each to refresh, then manually formats them into her report template. If she needs different data, she switches to the Domo web app, creates new cards, then pulls them into Excel—total time: 45 minutes. With Zenlytic, she logs into the platform, runs her analyses, exports three CSV files, imports them into Excel, and rebuilds her formulas each time—total time: 60 minutes. With Scoop, she types her questions directly in Excel cells: 'Revenue by region last month', 'Customer churn rate trend', 'Top 10 deals closed'. Results appear instantly in her existing template. When the CFO asks for a different view, she modifies the question in the cell. Total time: 15 minutes.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Excel integration isn't about features—it's about friction. Domo provides robust connectivity but requires platform knowledge. Zenlytic treats Excel as an export destination, creating manual workflows. Scoop eliminates the boundary entirely by embedding natural language analytics directly in Excel. For organizations where Excel remains the lingua franca of business analysis, Scoop's approach means zero training and immediate adoption.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Side-by-Side Scenario Analysis</h3>
<p class="content-section__paragraph">When your CFO asks 'What happens to our margins if we raise prices 5% but lose 10% of customers?', you need more than historical dashboards—you need scenario modeling. Side-by-side scenario analysis lets teams compare multiple what-if situations simultaneously, testing assumptions before making critical decisions. This capability separates strategic planning tools from basic reporting platforms. The difference between platforms isn't whether they can show data, but whether business users can independently model futures without IT building custom solutions.</p>
<p class="content-section__paragraph">The architectural divide shows clearly in scenario analysis. Domo's dashboard-centric design means each scenario requires a new dashboard or complex parameter setup by IT. Business users can't spontaneously ask 'What if we expand to Europe but shipping costs double?' without pre-built infrastructure. Zenlytic offers some flexibility through its semantic layer, but scenarios must fit predefined metrics. Users get sliders for revenue and costs, but can't model new variables like market conditions or competitor responses. Scoop's conversational approach changes the game entirely. Type 'Compare three scenarios: aggressive growth, steady state, and defensive positioning' and see them side-by-side instantly. The AI understands context, so 'aggressive growth' automatically models higher marketing spend, faster hiring, and increased inventory. When the CEO asks a follow-up like 'What if interest rates rise in the aggressive scenario?', you adjust immediately without rebuilding anything. This isn't about having more features—it's about business users exploring possibilities without technical barriers. Traditional BI platforms treat scenarios as IT projects. Scoop treats them as conversations.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail chain's strategy team meets to plan holiday inventory. The merchandising director opens Scoop and types: 'Show me three scenarios for Q4: conservative with 20% inventory increase, moderate with 35%, and aggressive with 50%.' Scoop instantly displays projected revenue, carrying costs, and profit margins side-by-side. The CFO asks, 'What if shipping delays hit the aggressive scenario?' The director adds that constraint conversationally, and results update immediately. They test seven different combinations in 15 minutes, documenting assumptions for each. With Domo, this same analysis would require IT to build a parameterized dashboard (2-3 days), train users on the interface (2 hours), and still wouldn't handle unexpected variables like shipping delays without another development cycle. The difference: strategic decisions in one meeting versus a two-week project.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Scenario analysis reveals the truth about business autonomy. Domo and Zenlytic require IT setup for each new type of analysis, limiting business users to predefined scenarios. Scoop lets anyone test any assumption instantly through conversation. When strategy moves at the speed of questions, not the speed of dashboard development, companies make better decisions faster. That's the difference between planning tools and true analytical thinking partners.</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 ML-powered insights, yet most still require technical configuration, model selection, and parameter tuning. The real question isn't whether a platform has ML—it's whether business users can actually use it. Let's examine how each platform handles the journey from raw data to predictive insights, focusing on what sales managers and analysts can do without IT support.</p>
<p class="content-section__paragraph">The fundamental divide in ML capabilities isn't about algorithms—it's about accessibility. Domo's AutoML requires users to select features, choose algorithms, and configure parameters through their Model Management interface. Business users need 16+ hours of training just to build basic models. Zenlytic takes a different approach with automatic anomaly detection on pre-defined metrics, but users must still configure thresholds and specify which patterns matter. Their 'one-click insights' work only for metrics already in the semantic layer. Scoop eliminates this configuration entirely. Ask 'What patterns predict customer churn?' and Scoop automatically analyzes transaction frequency, support tickets, usage patterns, and payment history. No model selection. No parameter tuning. The investigation happens through conversation, with Scoop suggesting follow-up questions based on discovered patterns. This architectural difference shows in deployment speed. Domo customers average 3-4 weeks to deploy their first ML model. Zenlytic users need 3-5 days to configure anomaly detection properly. Scoop users discover patterns in their first conversation. The key insight: when ML requires configuration, only data scientists use it. When it's conversational, everyone benefits.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A retail operations manager notices unusual inventory patterns across stores. With Scoop, she types: 'What's driving inventory variance in California stores?' Scoop automatically investigates multiple factors—comparing store sizes, analyzing seasonal patterns, checking delivery schedules, and correlating with local events. It discovers that stores near colleges show 3x variance during semester starts, suggesting adjusted ordering patterns. Total time: 4 minutes, zero configuration. With Domo, she'd need to build an ML model in AutoML, select features manually, train the model (2-3 hours), then interpret results through their visualization builder. Zenlytic would require setting up anomaly detection rules for each store, defining variance thresholds, and waiting for the next detection run. The business impact is clear: Scoop delivers actionable insights during the morning meeting, while competitors require days of setup before analysis can begin.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Machine learning in BI platforms faces a fundamental paradox: the users who need insights most understand ML least. Domo and Zenlytic solve this with templates and wizards, but still require technical configuration. Scoop eliminates the paradox entirely—ML happens automatically through conversation. Business users don't configure models; they ask questions and get answers with discovered patterns included.</p>
</div>
<div class="content-section__subsection">
<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 dashboard while your team lives in Slack, Excel, and their phones. Modern business moves through collaborative tools—decisions happen in Slack threads, analysis starts in Excel, and executives check metrics on phones between meetings. The real test isn't whether a platform has mobile apps or APIs. It's whether insights flow naturally into existing workflows without forcing users to context-switch into yet another portal. Let's examine how each platform handles this critical integration challenge.</p>
<p class="content-section__paragraph">The workflow integration divide reveals fundamental architecture choices. Domo built a portal-first platform, adding mobile apps and basic integrations later. Their Excel users export static data, breaking the live connection. Mobile users can view pre-built dashboards but can't investigate new questions. Slack integration sends alerts but requires jumping to Domo for actual analysis. Zenlytic focuses on technical users, offering no Excel integration and no mobile app. Their API exists but requires SQL knowledge to use effectively. Scoop's chat-based architecture enables natural workflow integration. The Excel add-in lets analysts query directly from spreadsheets using plain English. Slack users conduct full investigations without leaving conversations. Mobile works identically to desktop—just type your question. The API accepts natural language, making integration straightforward. However, Scoop lacks offline capability, requiring internet connection for all queries. The key difference: Scoop brings analysis to where work happens, while competitors force users to come to their portals. This architectural choice impacts every workflow touchpoint.</p>
<p class="content-section__paragraph"><strong>Example</strong>: A regional sales director is in an Uber to the airport when the CEO texts: 'Why did Southeast region miss target?' With Domo, she opens the mobile app, navigates to the regional dashboard, sees the miss but not why. She texts back: 'I'll investigate when I'm at my laptop.' The CEO waits three hours. With Scoop, she opens the mobile app and types: 'Why did Southeast region miss sales target last month?' Scoop investigates automatically: 'Southeast missed by $2.3M primarily due to three delayed enterprise deals totaling $1.8M. The delays stemmed from procurement issues at Customer A and budget freezes at Customers B and C.' She forwards the analysis to the CEO and asks a follow-up: 'What's the status of those three delayed deals?' Scoop provides current pipeline status. Total time: 2 minutes. The CEO has answers before boarding. The sales director can prep her team response during the flight.</p>
<p class="content-section__paragraph"><strong>Bottom Line</strong>: Scoop wins on workflow integration by bringing analysis directly into Excel, Slack, and mobile through natural language. Domo offers basic integrations that still require portal visits for real analysis. Zenlytic lacks integration entirely, targeting technical users at desktops. The business impact is clear: Scoop users get answers wherever they work, while competitors force workflow disruption.</p>
</div>
</div>
</section>
<section class="content-section ">
<div class="content-section__container">
<h2 class="content-section__title">Frequently Asked Questions</h2>
<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. Ask questions in plain English, get answers with charts. Works natively in Excel and Slack. Unlike Domo and Zenlytic which require IT setup, Scoop connects directly to your data in 30 seconds.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Which is better for business users: Domo or Zenlytic?</h3>
<p class="content-section__paragraph">Neither excels at business user autonomy. Domo scores 62/100 BUA requiring moderate IT support, while Zenlytic scores 42/100 with heavy IT dependency. Both trap users in dashboard paradigms. Scoop's 82/100 BUA score reflects true self-service through natural language, eliminating the IT bottleneck entirely.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">How do I investigate anomalies in Domo?</h3>
<p class="content-section__paragraph">Domo requires building multiple dashboards or using their analyzer tool with significant training. You'll navigate through pre-built views, limited to what IT configured. Scoop automatically chains 3-10 queries to find root causes, testing hypotheses like a human analyst would, without any dashboard setup.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Can Zenlytic do root cause analysis automatically?</h3>
<p class="content-section__paragraph">No, Zenlytic requires manual SQL queries or pre-built semantic layer definitions for root cause analysis. Their AI assists with SQL generation but can't chain investigations. Scoop automatically performs multi-pass analysis, testing correlations and drilling into anomalies without user intervention or technical knowledge required.</p>
</div>
<div class="content-section__subsection">
<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 answer complex questions. Unlike Domo's single-dashboard view or Zenlytic's one-query-at-a-time approach, Scoop thinks like an analyst: exploring hypotheses, checking correlations, and drilling into root causes without manual intervention.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">What does Domo really cost including implementation?</h3>
<p class="content-section__paragraph">Domo's true cost typically reaches 5-10x the license fee. Add implementation (3-6 months), training ($50K+), consultants ($200K+ annually), maintenance, and productivity loss. Scoop eliminates these categories entirely with instant setup and zero training, reducing total cost of ownership by 90%.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Are there hidden fees with Zenlytic?</h3>
<p class="content-section__paragraph">Yes, Zenlytic's hidden costs include semantic layer setup, SQL training, implementation consultants, and ongoing maintenance. Their AI requires extensive configuration. Scoop has no hidden fees: one subscription covers everything. No consultants, no training, no semantic layer maintenance. Connect and start asking questions immediately.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">How long does it take to learn Domo?</h3>
<p class="content-section__paragraph">Domo requires 2-4 weeks of formal training for basic proficiency, months for advanced features. Their certification program has multiple levels. Scoop requires zero training—if you can type a question, you're ready. Business users become productive in minutes, not weeks, with no certifications needed.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Do I need SQL knowledge for Zenlytic?</h3>
<p class="content-section__paragraph">Yes, despite marketing claims, Zenlytic requires SQL for anything beyond basic queries. Their AI helps write SQL but you must understand it to verify and modify. Scoop eliminates SQL entirely—just ask questions in plain English. Complex joins and calculations happen automatically behind the scenes.</p>
</div>
<div class="content-section__subsection">
<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 and start asking questions immediately. No IT tickets, no semantic layer setup, no dashboard requests. Unlike Domo and Zenlytic which require IT for setup and maintenance, Scoop delivers complete business user autonomy.</p>
</div>
<div class="content-section__subsection">
<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. While Domo and Zenlytic trap you in pre-built views, Scoop answers any question through conversation. No SQL, no training, no semantic layers. It's like having a data analyst who never sleeps.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Does Domo work with Excel?</h3>
<p class="content-section__paragraph">Domo offers limited Excel export but no native integration. Users must log into Domo's portal, breaking their workflow. Scoop works directly inside Excel—analyze data without leaving your spreadsheet. Ask questions in a sidebar, get answers instantly. No context switching or portal prison required.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Can I use Zenlytic directly in Slack?</h3>
<p class="content-section__paragraph">No, Zenlytic lacks native Slack integration. Users must switch to their web portal, breaking team collaboration flow. Scoop works natively in Slack—ask questions where your team already works. Share insights instantly without screenshots or exports. True collaborative analysis in your existing workflow.</p>
</div>
<div class="content-section__subsection">
<h3 class="content-section__subtitle">Why doesn't Scoop require training?</h3>
<p class="content-section__paragraph">Scoop uses natural language processing—you ask questions like you'd ask a colleague. No query languages, no semantic layers, no dashboard design. Domo requires weeks of training, Zenlytic needs SQL knowledge. With Scoop, if you can write an email, you can analyze data immediately.</p>
</div>
</div>
</section>
<section class="cta-section">
<div class="cta-section__title">See Scoop in Action</div>
<div class="cta-section__subtitle">
Join 500+ companies using Scoop to democratize data investigation
</div>
<a href="https://www.scoopanalytics.com/book-demo" class="btn--primary">Book Your Demo</a>
</section>
