Immersion Analytics for CRM & Marketing
Effective Sales, Marketing and Customer Support each entail multiple columns of data, for example:
- For Opportunities in a CRM: pinpoint true pipeline risk.
- Marketing & Growth: justify budget shifts.
- Retail & E-commerce: optimize price and conversion.
- Customer Support / CX: balance experience with efficiency.
- Product Analytics: prioritize roadmap and grow retention.
Opportunities in a CRM
Here are 10 numeric, per-opportunity variables that are high-value in a CRM (such as Salesforce.com) and well suited to visualize using Immersion Analytics:
| # | Variable | What it is (numeric) | Why it matters | Good IA mapping (suggestion) |
|---|---|---|---|---|
| 1 | Deal Amount (ACV/TCV) | $ value | Core impact on forecast | Size |
| 2 | Win Probability (%) | 0–100 | Likelihood of closing | Y-axis |
| 3 | Expected Value ($) | Amount × Prob | Risk-adjusted revenue | Color (continuous scale) |
| 4 | Stage Age (days) | Days in current stage | Stalls/aging risk | X-axis |
| 5 | Days to Close | Close date − today | Urgency/pipe timing | Z-depth |
| 6 | Activity Intensity (last 14–30d) | Weighted touches (calls/emails/meetings) | Engagement health | Glow (more = brighter) |
| 7 | Response Latency (hrs) | Median rep reply time | Execution quality | Transparency (faster = more solid) |
| 8 | Stakeholder Coverage (%) | Engaged buying roles ÷ required roles | Deal completeness | Color hue |
| 9 | Competitive Pressure (count) | Active competitors on deal | Win headwinds | Satellites |
| 10 | Momentum (Δ Prob, 14d) | Change in win prob | Getting better or worse | Pulsation rate (up = faster) |
What revenue could you unlock or risks could you mitigate by gaining perspective on all these for your entire pipeline?
Campaigns in a Marketing Automation Platform
Here are 10 numeric, per-campaign (or per-channel) variables that are high-value in a MAPs (e.g., HubSpot Marketing Hub, Adobe Marketo Engage, Salesforce Marketing Cloud) and well suited to visualize using Immersion Analytics:
| # | Variable | What it is (numeric) | Why it matters | Good IA mapping (suggestion) |
|---|---|---|---|---|
| 1 | Attributed Revenue ($) | Revenue tied to the campaign via multi-touch | Core business impact | Size |
| 2 | Ad Spend / Cost ($) | Media + platform costs | Baseline for efficiency | X-axis |
| 3 | ROAS (×) | Revenue ÷ Cost | Efficiency across channels | Y-axis |
| 4 | CAC / CPA ($) | Cost per acquired customer (or action) | Unit economics | Z-depth (closer = lower) |
| 5 | Visit→Lead Conv. Rate (%) | Sessions that became leads | Top-funnel quality | Color (continuous scale) |
| 6 | Lead→MQL Conv. Rate (%) | Leads that reached MQL | Mid-funnel quality | Transparency (solid = higher) |
| 7 | Time-to-MQL (days) | First touch → MQL | Funnel latency / friction | Glow (shorter = brighter) |
| 8 | Lead Velocity Rate (%) | MoM growth in MQLs | Pipeline growth momentum | Pulsation (faster = stronger) |
| 9 | Incremental Lift (%) | A/B or geo-holdout lift | True causal impact | Shimmer (higher = more shimmer) |
| 10 | Predicted LTV ($) | Modeled lifetime value | Long-term payoff | Metallicity (richer = higher) |
What budget efficiency and growth could you unlock by seeing all of these at once across your entire campaign portfolio?
Retail & E-commerce
Here are 10 numeric, per-product/per-campaign variables that are high-value in retail & e-commerce (e.g., Shopify, BigCommerce, Adobe Commerce/Magento, Salesforce Commerce Cloud, Amazon Seller Central) and well suited to visualize using Immersion Analytics:
| # | Variable | What it is (numeric) | Why it matters | Good IA mapping (suggestion) |
|---|---|---|---|---|
| 1 | Net Sales Revenue ($) | Sales for SKU/category/channel/time window | Core topline impact | Size |
| 2 | Gross Margin (%) | (Revenue − COGS) ÷ Revenue | Profitability signal beyond sales | Color (higher = cooler/greener) |
| 3 | Conversion Rate (%) | Sessions that complete a purchase | Ties traffic to sales efficiency | Y-axis |
| 4 | Average Order Value ($) | Revenue ÷ orders | Basket value; drives unit economics | X-axis |
| 5 | Return Rate (%) | Returned orders ÷ fulfilled orders | Erodes profit; flags quality/fit issues | Transparency (higher = more hollow) |
| 6 | Days to Stockout (days) | Projected days until OOS at current run rate | Urgency for replenishment/markdowns | Z-depth (closer = fewer days) |
| 7 | Promotion Uplift (%) | Incremental lift vs baseline/holdout | Measures real promo effectiveness | Glow (brighter = higher lift) |
| 8 | Price Elasticity |ε| | Absolute demand elasticity (unitless): |%ΔQuantity| ÷ |%ΔPrice| over a recent window | Reveals price sensitivity to guide promos/markdowns and guardrails | Shimmer (stronger shimmer = higher |ε|) |
| 9 | Units per Transaction (#) | Items per order | Merchandising & bundle health | Pulsation (faster = higher UPT) |
| 10 | Customer LTV ($) | Modeled lifetime value from buyers | Long-term payoff by product/channel | Metallicity (richer sheen = higher LTV) |
What profit could you unlock by gaining perspective on all ten, simultaneously, across your entire catalog and channels?
Customer Support / CX
Here are 10 numeric, per-queue/per-channel (or per-issue-type) variables that are high-value in Customer Support & CX (e.g., Zendesk, Salesforce Service Cloud, ServiceNow, Genesys, Five9, NICE CXone) and well suited to visualize using Immersion Analytics:
| # | Variable | What it is (numeric) | Why it matters | Good IA mapping (suggestion) |
|---|---|---|---|---|
| 1 | Average Handle Time (AHT, min) | Avg talk + hold + wrap | Efficiency and staffing needs | X-axis (longer → right) |
| 2 | First Contact Resolution (FCR, %) | % solved on first touch | Reduces cost and churn drivers | Y-axis (higher → up) |
| 3 | Service Level (% within target) | % answered within SLA (e.g., 80/20) | Measures accessibility and speed | Glow (brighter = better) |
| 4 | Queue Wait Time (sec) | Median/95th wait to answer | Friction customers feel | Z-depth (closer = shorter) |
| 5 | Backlog (# open cases) | Open, unworked/active tickets | Work-in-progress risk & delay | Size (bigger = more) |
| 6 | SLA Breach Rate (%) | % cases violating SLA | Quality/risk exposure | Transparency (higher = more hollow) |
| 7 | CSAT (0–100 or 1–5) | Post-interaction satisfaction | Sentiment on outcomes | Color (cooler/greener = higher) |
| 8 | NPS (−100 to 100) | Promoters − Detractors | Loyalty and WOM impact | Metallicity (richer = higher) |
| 9 | Reopen Rate (%) | % cases reopened after “resolved” | Indicates fix quality gaps | Pulsation (faster = worse) |
| 10 | Escalation Rate (%) | % routed to Tier 2/3 | Complexity/knowledge gaps | Shimmer (more = higher) |
What customer loyalty and operational efficiency could you unlock by seeing all ten, simultaneously, for every queue, channel, and segment?
Product Analytics
Here are 10 numeric, per-feature/per-cohort (or per-release) variables that are high-value in product analytics (e.g., Amplitude, Mixpanel, Heap, Pendo, GA4) and well suited to visualize using Immersion Analytics:
| # | Variable | What it is (numeric) | Why it matters | Good IA mapping (suggestion) |
|---|---|---|---|---|
| 1 | Active Users (#) | Users engaging with the feature in the window | Shows real scale and reach | Size (bigger = more users) |
| 2 | Time-to-First-Value (TTFV) | Median time from sign-up to activation | Reveals onboarding friction | X-axis (left = faster) |
| 3 | Activation Rate (%) | New users hitting the activation milestone in X days | Strong predictor of retention | Y-axis (higher = better) |
| 4 | 30-Day Retention (%) | Cohort users active at day 30 | Core health of product value | Color (cooler = higher) |
| 5 | Feature Adoption Rate (%) | % of active users using the feature (≥N uses) | Prioritizes roadmap & GTM | Shimmer (stronger = higher) |
| 6 | Stickiness (DAU/MAU, %) | Frequency of use relative to monthly actives | Captures habit formation | Pulsation (faster = stickier) |
| 7 | Trial→Paid Conversion (%) | % of trials converting to paid (or free→paid) | Ties usage to revenue | Z-depth (closer = higher) |
| 8 | p95 Latency (ms) | 95th percentile end-to-end performance | Direct driver of drop-off | Outline thickness (thicker = slower) |
| 9 | Error/Crash Rate (%) | Failures per session or user | Signals quality risk | Transparency (more hollow = worse) |
| 10 | Expansion MRR / Account ($) | Upsell/cross-sell per account | Monetization beyond initial sale | Metallicity/Sheen (richer = higher) |
What adoption, retention, and revenue could you unlock by seeing all ten—simultaneously—across your features, cohorts, and releases?
