This discovery framework is designed to help facilitate a collaborative conversation regarding Tableau. By exploring these operational scenarios, Retirement Plan Administrators can accurately assess internal workflows and identify how specific Tableau capabilities can support their unique clinical and administrative needs, starting with areas of highest impact to business revenue and institutional client care.
How does the executive team monitor asset flight and revenue leakage across regional business units during periods of market volatility?
Scenario A: Delayed Risk Reporting
"We lose millions in assets under management because our executive reports on participant rollover trends arrive weeks after a market event occurs."
Scenario B: Static Spreadsheet Consolidation
"Our regional directors send weekly Excel files that my finance team spends three days consolidating before I can see the macro picture of our business health."
What systems provide visibility into operational bottlenecks as the firm scales administration services across new regional offices?
Scenario A: Blind Operational Expansion
"Expanding our footprint has created massive blind spots. Service levels dropped sharply in our newest region and we lost a major institutional client before we even knew there was a problem."
Scenario B: Fragmented Leadership Metrics
"We track regional performance, but every office uses a different reporting standard. It is impossible to compare processing efficiency across our locations accurately."
How are account managers currently identifying institutional plan sponsors at risk of terminating their contracts?
Scenario A: Unanticipated Client Churn
"We only discover an institutional sponsor is unhappy when they issue a termination notice, costing us stable revenue streams with zero chance to intervene."
Scenario B: Disconnected Health Scores
"Our account managers log touchpoints, but they cannot easily view a client's overall plan performance alongside those service metrics to gauge true account health."
How do you visually aggregate participant contribution data to identify potential ERISA non-discrimination testing failures before the regulatory deadline?
Scenario A: Costly Compliance Penalties
"We frequently miss top-heavy plan indicators hidden in raw data tables, forcing our plan sponsors to make reactive, expensive corrective distributions to satisfy the IRS."
Scenario B: Manual Testing Workarounds
"Our compliance analysts spend weeks downloading CSV files and running complex Excel formulas to ensure each plan passes annual compliance testing."
How does the investment committee analyze fund performance against participant demographic trends to ensure optimal plan health?
Scenario A: Disconnected Investment Strategy
"We recommend default fund lineups based purely on external market data, ignoring our own participants' specific age demographics and actual risk tolerance profiles."
Scenario B: Generic Vendor Reporting
"We rely on standard reports from our fund providers, but those documents are static and prevent us from interactively filtering asset allocations by plan size or industry."
What is the process for analyzing historical RFP data to determine which institutional plan profiles yield the highest lifetime value?
Scenario A: Unprofitable Client Acquisition
"We aggressively bid on large 401(k) plans without realizing the complexity of their payroll systems will result in negative profit margins for the first three years of administration."
Scenario B: Tedious Pipeline Analysis
"Our sales operations team spends hours calculating win rates by industry segment, which delays our ability to pivot strategy when a specific sector becomes highly competitive."
How are contact center leaders forecasting call volume spikes related to tax season or regulatory changes?
Scenario A: Severe Understaffing Events
"When quarterly statements go out during market downturns, we are completely unprepared for the volume spike, resulting in angry participants abandoning calls."
Scenario B: Reactive Resource Allocation
"We look at last year's raw call logs to guess staffing needs, but this method fails to account for how recent plan design changes impact the types of questions participants ask."
How do you track the end-to-end processing times for participant hardship withdrawals across different administrative teams?
Scenario A: Missed Processing Deadlines
"Withdrawal requests frequently miss critical SLA deadlines because we have no visual way to see when a request gets stuck between the document review and fund dispersal teams."
Scenario B: Limited Queue Visibility
"Managers pull static queue reports twice a day, but this isolated view fails to show the cumulative delay a participant experiences across the entire lifecycle of their request."
How is the data team cleansing and preparing legacy recordkeeping data for analysis by non-technical business units?
Scenario A: Decisions Based on Dirty Data
"Business leaders are making strategic pricing decisions based on duplicated or malformed census data extracted directly from our aging mainframe systems."
Scenario B: Overburdened Engineering Resources
"Our data engineers spend eighty percent of their week writing custom SQL scripts just to format basic reporting data for the sales and operations departments."
What methods are used to measure the impact of financial wellness campaigns on overall plan enrollment rates?
Scenario A: Unproven Marketing Spend
"We spend heavily on retirement readiness campaigns, but we have absolutely zero visibility into whether those emails actually convince participants to increase their contribution rates."
Scenario B: Surface-Level Engagement Metrics
"We track open rates and webinar attendance, but because we cannot visually map that data against actual fund deposits, our reporting lacks true business value."
