System Study: Recent Client
Hypergrowth Blueprint
Goal
Help achieve a sustainable 40% YoY growth trajectory by transforming manual, disconnected operations into a unified, intelligent ecosystem that empowers employees, consultants, and customers.
Path to Success
Transform Recent Client into a digitally-enabled organization through parallel improvements in operations and field/customer engagement.
Operations: Unify fragmented data across Exigo, Sage, and HubSpot into a single source of truth. Automate manual workflows consuming XX-XX% of employee time. Enable real-time decision-making with predictive analytics.
Engagement: Deploy AI coaching systems for both consultants and customers. Consultants receive real-time guidance on next best actions and personalized training. Customers get 24/7 proactive wellness coaching, intelligent autoship management, and frictionless mobile commerce.
Operations Track
to full operational transformation
Single source of truth consolidating Exigo, Sage, HubSpot data
Real-time visibility replacing fragmented, manual reporting
Predictive analytics enabling proactive decision-making
Engagement Track
to full engagement transformation
Health goals tracking monitoring progress and outcomes
Consumption patterns understanding usage and preferences
Engagement analytics optimizing touchpoints and messaging
investment in engagement transformation
System Study Findings
Recent Client has a powerful product and has demonstrated impressive growth, achieving a XX% increase last year by re-engaging top leaders. However, the operational foundation is brittle, characterized by manual processes and fragmented data that cannot support a sustained XX% YoY trajectory.
No standardized processes. Every team has its own way of working. Processes exist only in people's heads. Tool fragmentation is rampant. Automation is impossible.
Critical business functions run on Excel and email. Employees spend XX-XX% of their time on repetitive, low-value tasks. Month-long cycles for critical processes. High risk of human error.
No single source of truth. Data is fragmented across Exigo, Sage, Core E, HubSpot, Amazon. Executives spend days gathering data instead of analyzing it. Conflicting reports and inconsistent metrics.
Leadership duplication model is broken. Training is static, "boring," and untracked. No proactive guidance for consultants. One-size-fits-all approach fails across 18 markets.
Product customers forced into consultant journey. "Starter pack" model creates X-month churn cycle. No systematic follow-up or consumption tracking. Only XX% of sales from retail customers.
Growth Constraint #1: Siloed Operations
This must be a Recent Client-led initiative—Revencrease cannot and will not dictate internal processes. Recent Client teams will lead the work to systematically capture existing workflows, establish standardized processes with clear ownership, align on core tools with defined usage guidelines, and implement data-driven process intelligence to measure and optimize effectiveness. Revencrease's role is to facilitate, provide tooling and frameworks, and support implementation—but all process decisions remain with Recent Client leadership and operational teams.
Maps current-state processes across all departments, identifying who does what, how they do it, and what tools they use. This creates a foundational blueprint of the organization's real-world operations.
PMO Director: A new project owner has to navigate a complex financial analysis Excel file. 'Many people don't know exactly how to fill this out... I spend a lot of hours with everyone... to go over and over this, work with them, kind of like little workshops on how to fill it in.' This undocumented, person-dependent process takes months.
A new project owner accesses a documented, step-by-step guide for the project approval process, complete with field definitions, examples, and approval workflows. They are productive immediately without needing weeks of hand-holding.
Defines standard operating procedures (SOPs) for key cross-departmental processes like budgeting, recruiting, and project approvals. It assigns clear ownership and establishes a governance model for how processes are changed and improved.
Finance Director: The budget process involves emailing Excel templates to departments, who often return them in their own formats. 'In some cases, they use their own template... the business unit needs to take that file and translating this standardized file, and this is taking a lot of time.'
All departments use a single, standardized budget submission process, either through a unified portal or a locked-down template. Data flows directly into the financial system, eliminating manual translation and rework. Process ownership is clear.
Establishes clear rules of engagement: Wrike for project tracking, Teams for internal chat, SharePoint for document collaboration. This isn't about forcing everyone onto one tool, but standardizing how and when each tool is used to create a predictable system.
Marketing Manager: A single product launch involves tracking tasks and approvals across 'duplicated spaces' in Wrike, emails, and multiple Teams chats. 'I'm having to go in every single person's space and make sure that my content now is accurate... my biggest pain point.'
A single, cross-departmental project space is created for the product launch. All communications, approvals, and assets are centralized. The marketing manager has one place to track everything, saving hours of chasing information.
Uses data to track process performance (e.g., cycle time, error rate). This allows for the identification of bottlenecks and measures the impact of changes, creating a virtuous cycle of improvement.
HR Director: The recruiting process takes 'X or X months.' The bottleneck is known to be approvals, but there is no data to pinpoint where the delays are or to justify a change in the process. Customer Service Manager: The manual account reactivation process is known to be costly, but the full financial impact ($XXX,XXX/year) was not quantified until this analysis, preventing it from being prioritized.
A process dashboard shows that offer letter approvals average XX days, with XX of those days spent waiting for the final C-level signature. This data justifies a new policy allowing pre-approval for roles within budget, cutting the cycle time by XX%.
Growth Constraint #2: Manual Work & Internal Inefficiency
Employees spend XX-XX% of their time on manual data entry, report building, and cross-system reconciliation, with critical business functions running on disconnected Excel files shared via email. This includes commissions, budgeting, inventory planning, and financial consolidation. Staff work extended hours to compensate for process inefficiencies, while the manual data handling creates high risk of costly errors in sensitive areas like payroll, intercompany transfers, and promotion setup.
Eliminate manual work through intelligent automation by deploying conversational AI agents for natural language queries and actions, implementing guided wizards for complex multi-system processes, establishing background automation for routine tasks, and creating self-service portals that democratize access to real-time data and insights.
Empowering the Recent Client IT team to become automation architects for the organization.
Rather than depending on external consultants for every process improvement, we will transform the internal team into a self-sufficient automation powerhouse by providing:
01. Platforms
Modern, AI-powered development platforms that enable rapid creation of automation solutions, custom tools, and integrations without extensive coding.
02. Tools
Pre-built templates, reusable components, and automation frameworks specifically designed for common Recent Client business processes.
03. Guidance
Training, documentation, and ongoing support to help the team identify automation opportunities, design solutions, and implement them following best practices.
04. Autonomy
The capability to independently automate company processes, from simple data integrations to complex multi-system workflows, reducing dependency on external vendors and accelerating time-to-value.
Users ask questions in plain English ('Show me the top 5 promotions in Canada last month'). The agent queries multiple systems, analyzes the data, provides a direct answer, and can take follow-up actions with approval.
Marketing Manager: 'I just launched on Friday, and I couldn't see the sales, like, for today, because the cube didn't... give me the right answer, so I have to ask someone to give me the year-to-day sales.' She has to wait for another person to manually run a report.
The marketing manager asks an AI agent in Teams: 'What are the sales for the new Product XYZ in Europe since launch?' She gets an instant, real-time sales number without waiting for anyone.
What are the sales for the new Product XYZ in Europe since launch?
Product XYZ launched on March 15, 2025.
Total Sales: €127,450
Units Sold: 3,186
Top Markets:
Growth trend: +23% week-over-week
Would you like to see a breakdown by consultant or compare to the original K21 performance?
The internal team builds ad-hoc tools with AI assistance to solve specific problems (e.g., promotion creation, SKU management). These tools provide real-time calculations, pull data from multiple systems, offer AI-powered insights and recommendations, and dramatically reduce manual work for complex processes.
Promotions Manager: Spends 'one week' every month creating promotions. The process involves manually filling out three different, complex Excel files (Margin Template, SKU Sheet, Weight Calculator) for each promotion in each country, then emailing them to different departments for manual setup.
The promotions manager uses the AI-powered Promotion Builder tool created by the internal team. She selects products and discount, the tool instantly calculates margins, weights, shows sales insights, and provides AI recommendations. Promotion is created in all systems in under an hour.
These processes run on a schedule or are triggered by an event (e.g., a new invoice arriving). They handle routine tasks like data extraction, system reconciliation, and report generation, alerting humans only when an exception occurs.
Finance Team: The intercompany transfer and financial consolidation process requires manually preparing over 300 Excel 'working papers' per month, downloading data from Sage and Tagetic, and performing manual reconciliations. The process is a massive bottleneck. A finance team member receives ~25 emails with Excel files at month-end for intercompany allocations. She must manually convert, consolidate, and upload this data into Sage, a process fraught with error risk.
The month-end close process runs automatically. A background agent extracts data from all systems, performs intercompany allocations, generates the 300+ working papers, and flags only the 5 reconciliation exceptions that require human review. The finance team's time shifts from data wrangling to strategic analysis.
Replaces static, manually-created Excel reports with live, role-based dashboards. Executives, managers, and analysts can explore data, drill down into details, and answer their own questions without needing to submit a ticket to the BI team.
VP Finance: 'I desperately need is having, I would say, two dashboards... We have an excellent business tool for all the sales... But we don't have that for our financials... A GM... If'
The VP of Finance uses a self-service portal to access real-time financial data and create customized dashboards.
Growth Constraint #3: Inability to Make Fast, Data-Driven Decisions
Data is fragmented across Exigo (sales), Sage (finance), Core E (inventory), HubSpot (marketing), Amazon, and hundreds of individual Excel files. Employees spend days manually gathering and reconciling data instead of analyzing it, while poor system connectivity makes critical analyses—like correlating marketing campaigns to sales—impossible or require heroic manual effort. Manual data handling, sync failures, and differing definitions between systems lead to unreliable, inconsistent data that undermines trust in the numbers.
Create a unified data foundation by consolidating all business data into a centralized platform, establishing automated real-time pipelines that eliminate manual extraction, and deploying predictive analytics powered by machine learning to forecast trends, identify opportunities, and enable proactive decision-making across the organization.
Consolidates data from all source systems into a centralized data lakehouse (Microsoft Fabric). It provides consistent, reliable, and governed access to all historical and current data, eliminating silos.
GM: Cannot get a full year of historical data from the system. 'If I need to get one report of one year... I need to pull out one month. And one month, and one month... It's just a waste of time.' Marketing: To analyze promotion performance, they pull sales data from reporting tools, but must cross-reference it with a separate, manual Excel file from another department to understand what products were in each promotion.
The GM runs a multi-year trend analysis on consultant performance in seconds by querying the unified platform. Marketing opens a dashboard where promotion composition is automatically joined with sales data, allowing them to instantly see which bundles are driving performance.
Unified Data Architecture
Establishes automated pipelines that continuously sync data from source systems to the unified platform. These pipelines run validation rules, cleanse data, and update dashboards in near real-time, with alerts for quality issues.
E-commerce Team: Spend 80% of their time manually downloading ~15 reports from Amazon, cleaning currency symbols and codes in Excel, and pasting the data to refresh a Power BI dashboard. Finance: Manually runs SQL queries against Sage, exports to CSV, moves the file to their computer, and then refreshes the Power BI dashboard for financial reviews.
The Amazon sales dashboard is connected directly to the Amazon API via an automated pipeline. It refreshes hourly with no manual intervention, freeing up the e-commerce team to focus on strategy and decision-making.
Uses machine learning trained on historical data to predict future outcomes like consultant churn risk, product demand, and promotion effectiveness. It provides early warnings and proactively identifies high-value opportunities.
VP Europe: Uses his own complex Excel model for sales forecasting, relying on historical data and manual adjustments. 'It's in my head, but of course it means that we have info from the past.' It's effective but not scalable or automated. Marketing: Runs multiple promotions simultaneously, making it impossible to determine which one actually drove results. 'The compounding effect of all of those things was, hey, we re-qualified more leaders... but I don't think there's a concrete way for us to confirm which was the effective metric.'
An ML model predicts a potential stockout of a key product in a major market 6 weeks in advance based on sales velocity and seasonal trends. Operations proactively places a purchase order, preventing lost sales. Before launching a promotion, Marketing uses a predictive model to simulate the impact of different discount levels, identifying the optimal offer that maximizes revenue without excessive margin erosion.
Unoptimized Customer Support Leading to Low Retention & Poor LTV
After an initial purchase (often a X-month supply), customers receive little to no systematic follow-up, leading to high churn. Product customers are miscategorized as "consultants" to secure better discounts but then receive an unwanted business-builder journey. The company has no visibility into whether customers are using products correctly or consistently—a key driver of results and retention. High-friction checkout and lack of modern, mobile-first e-commerce options create poor buying experiences and high cart abandonment. With only XX% of sales coming from genuine retail customers, this prevents sustainable, compounding growth.
Seeking Relief
Chronic health condition
Wants Results
Needs guidance to succeed
Abandoned
No support after purchase
Churns
Eventually stops reordering
Transform customer retention and lifetime value by deploying an AI wellness coach that provides 24/7 personalized support, implementing intelligent autoship management that adapts to actual consumption patterns, creating an AI shopping assistant for conversational product guidance, optimizing the mobile commerce experience with shared carts and frictionless checkout, and building a customer data intelligence platform to power personalization and product innovation.
Proof that personalized care transforms retention
Industry research shows that top-performing consultants who provide deep, personalized support and consistent care for their customers achieve an extraordinary XX% retention rate—dramatically higher than average performers.
This success demonstrates the transformative power of personalized customer care. However, this level of individual attention is impossible to scale manually across thousands of customers. Our AI-powered approach makes this exceptional care accessible to every customer, democratizing the practices that drive outstanding retention.
An AI assistant provides personalized wellness guidance, helps customers stay consistent with product usage, reminds them when to reorder, and celebrates their progress toward health goals, building a supportive relationship.
Consultant Experience Manager: 'The typical starter pack has 6 boxes of the product. So if I buy 6 boxes of product, you're not going to know anything about me like at least 6 months.' The customer is forgotten after the sale.
An AI coach checks in with the customer: 'Hi Jane, I see you've been taking Product X for 30 days straight! That's great for building consistency. How are you feeling?' The customer feels supported, stays engaged, and is more likely to see results and reorder.
AI Wellness Coach in Action
AI monitors product usage and automatically adjusts delivery schedules to prevent inventory buildup. It sends proactive alerts for payment issues and suggests optimal shipment timing, reducing churn from logistical friction.
A customer signs up for a monthly autoship but only takes the product every few days. They accumulate excess product and eventually cancel the subscription because they have too much. A customer's credit card expires. The autoship fails silently, they run out of product, and the habit is broken. They never restart their subscription.
The AI detects a customer is consuming their 30-day supply in about 45 days. It sends a message: 'It looks like you have some extra product. Would you like to move your next shipment back by two weeks?' The customer agrees, preventing overstock and churn.
Intelligent Autoship Management in Action
An AI assistant engages customers in natural conversation to understand their health goals, recommends personalized product selections, and suggests complementary items that enhance results.
A potential new customer browses the website, confused by the different product versions. With no guidance available, they either choose randomly or abandon their purchase. A customer buys a single product, unaware of complementary items that could improve their experience and results. Average order value remains low.
A customer asks the AI assistant, 'Which product is best for athletic performance?' The AI provides a personalized recommendation with a clear explanation, giving the customer confidence in their purchase. The AI then suggests, 'Many athletes also add our supplemental product for better taste and extra nutrients,' increasing the order value.
AI Shopping Assistant in Action
Consultants can share pre-filled shopping carts via a simple mobile link. The checkout experience is optimized for mobile, with saved payment info and one-tap reordering, dramatically reducing friction.
A consultant shares a link to the website. The customer opens it on their phone, struggles with a clunky, non-optimized interface, and has to manually find products and enter payment information. Cart abandonment on mobile is over XX%.
A consultant sends a pre-filled cart link. The customer taps it, the mobile app opens with the products ready, and they complete the purchase in XX seconds using Face ID. The transaction is frictionless.
Mobile Shopping Experience
AI-assisted category browsing
Product selection with pricing
Detailed product information
Frictionless checkout experience
Captures customer health goals, consumption patterns, and behavioral data. This data powers personalized AI recommendations and, when aggregated and anonymized, provides invaluable insights for product innovation and market trend analysis.
Discussed separately.
Recent Client launches new products based on market research and assumptions, with no direct feedback loop on what customers truly need or how they use existing products. A customer churns after three months. The company has no data to understand why—Was it inconsistent usage? The wrong product for their goals? A bad experience?
The platform captures customer goals and tracks usage patterns. Anonymized data reveals a growing trend of customers seeking solutions for athletic recovery. This insight directly informs the next product development cycle, leading to a highly successful launch. The AI detects a customer's consumption is declining. It proactively reaches out to understand the issue, offers alternative usage tips, and successfully prevents churn, while the anonymized data helps improve the onboarding journey for future customers.
Consultant Management & Training Limiting Sales Potential
Top performers have not been able to develop the next tier of leaders, while training remains a static, "boring" library with no personalization, interactivity, or tracking. The company cannot connect training data with business data, provides no proactive guidance to consultants who must interpret complex reports on their own, and uses a one-size-fits-all approach that ignores the different needs of new consultants versus established leaders across 18 different markets.
Not Sales Pros
Part-time, passion-driven
Limited Time
Juggling multiple priorities
Training Gap
Unsure how to develop others
Not Tech-Savvy
Struggle with complex systems
Scale consultant success through intelligent enablement by deploying AI-powered coaching that provides proactive, personalized guidance, implementing adaptive learning systems that deliver the right training at the right time, using predictive intelligence to identify at-risk consultants before they quit, and unifying all consultant tools into a single mobile-first platform that integrates performance data, training, and e-commerce.
Industry Validation: Tupperware increased consultant retention by 80% using AI-powered coaching and gamification.
AI analyzes individual and team performance to surface high-impact opportunities with specific, pre-scripted recommendations. It turns complex data into simple, actionable tasks.
Consultant Experience Manager: Leaders must manually 'download a report, analyze that report and and say, Okay, this is how I need to grow my business,' a process most consultants find too difficult.
A new consultant opens their app and sees a notification: 'AI suggests: Call Maria—her autoship failed yesterday. Tap here for a pre-written message to send her.' They act immediately with confidence, saving a customer. A leader receives an alert: 'AI found 3 at-risk consultants in John's team. Recommended action: schedule a product training call to re-engage them.'
AI-Powered Coaching in Action
AI Notifications
Maria's autoship just failed
Reach out today to help her resolve the payment issue. I've prepared a message you can send.
4 of your team members are so close!
Sarah needs just $XXX more to rank up. Want me to send personalized messages?
John's team needs your help
I noticed 3 consultants haven't been active lately. A training call might re-engage them.
Perfect timing for an upsell
I found 5 customers who are ready for the Product X Platinum bundle.
AI proactively identifies opportunities
A dynamic learning system that delivers the right content to the right person at the right time. It tracks completion, measures business impact (correlating training with sales), and creates a scalable onboarding and development engine.
Head of Training: Describes the training portal as 'totally boring... It's just seeing videos and seeing PDFs, and that's it.' She laments her inability to track results from a XXX-person event: 'I don't know what happened with them.'
A new consultant follows a personalized XX-day learning path with interactive quizzes and role-playing exercises. The AI tracks their progress and adjusts the content. They make their first sale within XX days. The system shows the head of training that this path has a XX% higher activation rate.
Adaptive Learning System in Action
Uses machine learning to predict who is likely to quit based on their behavior patterns (e.g., declining activity, low engagement). The AI can send automated motivational messages and alert sponsors with specific intervention recommendations.
A sponsor only discovers their new recruit has quit weeks or months after the fact, when they no longer appear on reports. The opportunity to intervene has been lost. Corporate identifies success patterns through slow, manual analysis, with insights arriving too late to impact the current cohort of consultants.
The AI flags a new recruit as a high churn risk within two weeks due to low engagement with training. It automatically sends an encouraging message and alerts the sponsor with a recommendation to schedule a check-in call.
Replaces the disconnected ecosystem of the consultant app, back office, training portal, and manual reports with a single, unified mobile platform. It integrates performance data, training, AI coaching, and e-commerce into one seamless experience.
A consultant uses the app for basic stats but must log into the 'horrible,' 'Windows 95'-like back office to download resources. They get training from a separate portal and manage their customers in a notebook or Excel.
The consultant opens a single app that provides everything they need: AI-powered 'next best actions,' personalized training modules, pre-filled shopping carts to share, and real-time team performance data—all with proactive guidance.
Investment — Timeline
Data Platform & Reporting Foundation
Build unified data foundation consolidating all systems into single source of truth.
- — All business data in one platform • Real-time dashboards
- — Consistent KPIs across departments • Self-service analytics
- — Critical insights for both direct selling and e-commerce strategies
Predictive & Prescriptive Analytics
Deploy AI models for forecasting and proactive decision-making.
- — Predict consultant churn 60-90 days advance • Forecast product demand
- — AI-recommended actions • Automated business alerts
Automation/Agent Platform
Create foundation for AI agents to handle repetitive tasks across systems.
- — AI agents for routine questions • Automated data reconciliation
- — Transform Excel-over-email workflows (days → minutes)
- — Recent Client IT trained to build and deploy agents independently
Workflow Automation & Work Management
Automate manual approval processes and interdepartmental workflows.
- — Unified work management • XX%+ fewer manual handoffs
- — Automated promotions and SKU launches
- — Recent Client IT enabled to automate future workflows independently
Pricing Structure
One-time implementation, customization, integration with core systems
Ongoing platform access, AI capabilities, support, and continuous improvements
$XXK setup + $XXK × XX months = $XXK
Customer Experience (AI Lifestyle Coach)
AI lifestyle coach transforming product consumption into daily habits with personalized guidance.
- — Months X-X: Discovery, design, Exigo integration
- — Months X-X: MVP pilot (XX-XXX customers)
- — Months X-XX: Full rollout to entire customer base
- — Product usage tracking • Personalized recommendations
- — Smart upsells • Lifestyle integration
- — Intelligent autoship management • In-app checkout
Consultant Enablement (AI Business Assistant)
AI assistant replicating top performer strategies and automating routine tasks.
- — Months X-X: Discovery, design, data integration
- — Months X-X: Pilot (XX-XXX consultants)
- — Months XX-XX: Full network rollout
- — Adaptive training • AI-powered role play
- — Conversational business intelligence • Automated tasks
- — AI mentoring • Digital playbooks
- — Predictive analytics • At-risk identification
Redesign and standardize processes before automation to maximize ROI.
- — Eliminate redundant workflows
- — Document standardized SOPs
- — Identify automation opportunities
- — Enable 2-3x faster deployment
- — Recent Client leads redesign
- — Revencrease provides workshops
- — Templates and guidance
- — Data platform setup and system connections
- — First AI agents handling routine questions
- — Excel workflow elimination begins
- — Engagement platform integration and customization
- — Unified reporting with real-time dashboards
- — AI models predicting churn and demand
- — Workflow automation eliminating manual handoffs
- — Phased engagement platform deployment for consultants and customers
- — First measurable improvements in retention and productivity
- — Complete data integration with AI recommendations
- — Process automation eliminating repetitive work
- — Full engagement platform rollout to entire network
- — Target outcomes achieved: XX% growth trajectory confirmed