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BA1: User Adoption Analytics
Current State
Paying for seats nobody uses. Most SF orgs log in fewer than 60% of licensed seats daily — yet pay full per-seat cost at renewal. Without visibility into daily active user rates vs licensed seats, the renewal conversation stalls on anecdote rather than data, and the CFO questions every dollar.
With Solution
SF ValueAnalytics surfaces DAU/seat ratio in real time across every profile and team. When adoption climbs from 60% to 85%, cost-per-outcome drops 30% and the renewal business case writes itself from live data rather than slides.
Total Salesforce licenses your org is paying for
Fully loaded annual cost per Salesforce license
% of licensed seats that log in on an average business day
Realistic adoption rate after visibility and coaching program
% of seats likely challenged at next renewal without proof
Real-time DAU/seat ratio eliminates anecdote at renewal
Adoption coaching targets lowest-engagement teams first
30% cost-per-outcome improvement at 85% DAU vs 60%
⚡
BA2: Sales Productivity Analytics
Current State
Pipeline value is sitting stale. Opportunities not updated in 7+ days signal rep disengagement, forecast inaccuracy, and competitive exposure. A $10M pipeline with 35% stale records is really a $6.5M pipeline — but nobody knows until the quarter closes badly.
With Solution
SF ValueAnalytics flags stale opportunities in real time and surfaces pipeline velocity metrics by rep, team, and stage. Reducing stale pipeline from 35% to 10% recovers meaningful close rate and compresses average deal cycle — directly measurable in EventLogFile data.
Current total open opportunity value in Salesforce
% of pipeline opportunities with no SF activity in 7 days
Realistic stale rate after velocity monitoring is live
% of pipeline that closes to won
Average closed-won deal value
Real-time stale pipeline alerts drive rep re-engagement
Velocity metrics by stage expose hidden bottlenecks
Forecast accuracy improves when stale records drop below 10%
🔒
BA3: Customer Value Analytics
Current State
Accounts with no SF activity in 90 days are churn signals hiding in plain sight. Without automated engagement tracking, CSMs only discover at-risk accounts when the customer has already decided to leave — too late to recover the revenue.
With Solution
SF ValueAnalytics monitors account engagement cadence in real time and surfaces 90-day dormancy alerts before churn crystallizes. Every at-risk account identified and recovered with a standard save play returns its full ARR — measurable directly from SF activity data.
Total active customer accounts in Salesforce
% of accounts with zero SF touchpoints in last 90 days
Average annual recurring revenue per account
% of 90-day dormant accounts that churn at renewal
% of at-risk accounts recovered when flagged early
90-day dormancy alerts fire before the customer decides to leave
Save plays are triggered by data, not gut feel
Every recovered account returns full ARR to the renewal base
🎯
BA4: Lead Value Analytics
Current State
Leads that go untouched for 24 hours convert at half the rate of leads contacted within the hour. With hundreds of inbound leads per month and no visibility into response time by rep, marketing spend is being wasted on leads that ops never follows up on in time.
With Solution
SF ValueAnalytics measures lead response time in real time from EventLogFile data — no manual reporting. When average response drops from 6 hours to under 1 hour, close rate on inbound leads measurably improves, and marketing ROI is finally attributable to sales execution speed.
Average inbound leads entering Salesforce per month
Average hours from lead creation to first SF activity
Target response time with real-time alerting live
% of leads that close when responded to in 6+ hours
Industry benchmark: <1hr response doubles close rate
Average closed-won value from inbound lead source
Real-time response time alerts eliminate slow-follow-up blind spots
Sub-1-hour response rate measurably doubles inbound close rate
Marketing spend attribution finally connects to sales execution data
🛠️
BA5: Service Value Analytics
Current State
Every hour a customer case sits open is an hour of customer frustration and agent cost. With average resolution times above 48 hours and no visibility into which case types drag longest, service ops cannot target the improvements that actually move CSAT and cost simultaneously.
With Solution
SF ValueAnalytics measures case resolution time by type, team, and complexity from live Service Cloud data. When average resolution drops by 30%, agent cost per case drops in parallel and CSAT scores recover — all with evidence that ties directly to the SF data record.
Average new service cases per month across all channels
Average hours from case open to case closed
Total cost per agent hour including benefits and overhead
Total agent touch time per case (not clock time)
Realistic improvement from case routing and knowledge visibility
Case resolution bottlenecks identified by type and team in real time
28% resolution improvement drives parallel CSAT and cost improvement
Evidence ties directly to SF Service Cloud data — no spreadsheet needed
⚙️
BA6: Apex Performance Analysis
Current State
Slow Apex execution means users wait — and waiting users adopt less. When governor limit violations and long-running queries are invisible to the business, developers get blamed for symptoms rather than root causes, and remediation cycles drag on for quarters.
With Solution
SF ValueAnalytics exposes Apex execution time by class, user, and trigger from EventLogFile data. When the slowest 20% of Apex is remediated first, developer hours land on the highest-impact fixes and user wait time measurably drops — with before/after evidence from live SF logs.
Full-time SF developers including contractors
Fully burdened developer hourly cost
% of developer hours currently spent diagnosing and fixing slow Apex
Realistic reduction in performance-related dev work with visibility
Apex execution hotspots ranked by impact — fix the 20% that cause 80% of waits
Developer time shifts from diagnosis to delivery
Governor limit violations caught before they hit users
🚨
BA7: Apex Exception Analysis
Current State
Apex exceptions firing in production are silent revenue killers. Each exception may mean a failed automation, a missing record, or a broken workflow — but without exception volume and trend data, the business has no idea how many transactions are silently failing every week.
With Solution
SF ValueAnalytics aggregates Apex exception logs by class, frequency, and user impact from EventLogFile. When exception volume is visible, the highest-frequency failures are prioritized and resolved — directly reducing rework, data cleanup, and the developer hours spent on recurring fires.
Average Apex exceptions firing in your org per week
Average developer + admin hours to investigate and resolve each exception
Blended developer and admin hourly cost
Realistic reduction when top exception classes are prioritized and fixed
Exception volume by class exposes the recurring fires consuming dev capacity
50% exception reduction cuts rework hours in half
Automated workflows stop failing silently
⚡
BA8: Lightning Page Performance
Current State
Every extra second of Lightning page load time costs user attention and adoption. A page that takes 8 seconds to load gets avoided — reps work around SF instead of in it, and the data quality that management needs for forecasting silently degrades.
With Solution
SF ValueAnalytics surfaces Lightning page load times by page, profile, and network from EventLogFile data. When the slowest pages are identified and optimized, rep time-in-SF increases and the data quality that drives accurate forecasting follows — with load time before/after measured in the same system.
Average users in Salesforce on a typical business day
Average Lightning page loads per active user per day
% of page loads currently taking more than 5 seconds
Average extra wait time above acceptable 1-second baseline
Fully burdened hourly cost of users spending time waiting for SF
Realistic improvement after top slow pages are identified and fixed
Slowest Lightning pages ranked by user impact — fix the worst first
Every second removed from page load recovers measurable productivity
Faster pages increase voluntary SF usage and data quality downstream
🏗️
BA9: Tech Debt Counter
Current State
Visualforce pages block Lightning adoption and accumulate maintenance cost every quarter they remain. Without a precise count of remaining VF page views vs Lightning, tech debt remediation is impossible to prioritize — and Salesforce's own roadmap makes VF a progressively larger liability.
With Solution
SF ValueAnalytics measures VF vs Lightning page view ratio from EventLogFile in real time. When remaining VF surface is quantified, remediation can be sequenced by usage volume — highest-traffic VF pages migrated first — and the annual developer cost avoidance of each migration is calculable in advance.
Total Lightning + Visualforce page views per month in your org
% of monthly page views served by Visualforce (not Lightning)
Annual maintenance hours per active Visualforce page
Total distinct Visualforce pages still in production use
Fully burdened SF developer hourly cost
Realistic % of VF pages that can be migrated to Lightning in year 1
VF vs Lightning ratio measured from live EventLogFile — no survey needed
Highest-traffic VF pages migrated first for maximum impact
Each migration permanently removes ongoing maintenance cost
Your Enterprise Value Gap
$0
Current Enterprise Value$0
Positioned Enterprise Value$0
Value Gap (Annual)$0
3-Year Impact$0
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