WFS Increases Sales Efficiency by 200% Through Unified Data Strategy and Real-Time Visibility

Challenge

1. Churn and Blind Spots

• WFS had no unified insight into sales performance across teams.

• Bottlenecks in the sales process went undetected, hurting win rates.

• Clients couldn’t view their teams’ performance transparently — leading to rising churn.

2. Fragmented Data Landscape

• Each delivery team ran its own Pipedrive or HubSpot instance.

• Dozens of disconnected CRMs and marketing tools made consistent reporting impossible.

• Leadership had no single source of truth for forecasting or rep performance.

3. Massive Manual Overhead

• 2 analysts + 3 managers spent 70+ hours each week manually reconciling spreadsheets.

• Over 500 hours/week were lost on reporting that still produced unreliable numbers.

• Despite investing in BI tools, “the data never matched.”

4. Inconsistent KPIs

• Metrics were defined differently by every team.

• Performance comparisons across clients and regions were meaningless.

• There was no version control or data governance in place.

5. Unscalable Infrastructure

• 10+ systems (CRMs, dialers, scheduling tools, and marketing platforms) operated in silos.

• Forecasting was reactive and lagged behind growth.

• Analysts couldn’t keep up with requests, slowing decisions across the org.

“We were scaling fast, but our data wasn’t. Every manager had their own numbers, and none of them were right.”
— Director of Operations, WFS Group

Solution

Aztela partnered with WFS Group to implement a modern data foundation — delivering unified visibility, trust, and self-service insights across 150+ sales reps and multiple client portfolios.

1. Unified Data Infrastructure

• Built a central data warehouse on Google BigQuery as the single source of truth for all revenue data.

• Integrated 10+ Pipedrive and HubSpot instances, along with OnceHub and other tools.

• Designed automated ETL pipelines using Python and Airflow for ingestion, transformation, and validation.

• Reduced manual reporting time to near-zero through full automation.

2. Analytics Semantic Layer

• Developed standardized data models covering pipeline, conversion, and activity metrics.

• Created 20+ interactive Looker dashboards for every level of the organization:

  • Executive dashboards: revenue visibility, forecasting accuracy, client portfolio KPIs.

  • Manager dashboards: funnel leakage, time-in-stage, rep productivity, call-to-meeting ratios.

  • Team dashboards: daily targets, conversion tracking, and quota attainment.

• Implemented lead source attribution and campaign ROI analytics to connect marketing spend to sales outcomes.

3. Data Strategy & Governance

• Conducted a data strategy sprint to define company-wide KPI standards and data ownership rules.

• Authored a governance playbook and SOPs to guide analysts and managers.

• Established a data dictionary and version control process to maintain metric consistency.

• Enabled self-service analytics, empowering managers to answer questions independently.

4. Scalable Architecture for Future Growth

• Designed a modular architecture — new data sources, clients, or business units can now be added in hours, not weeks.

• Infrastructure built to support future AI-driven forecasting and performance optimization.

• Architecture designed for multi-client, multi-instance scalability as WFS expands globally.

Business Impact

• 200% improvement in sales efficiency across 150+ reps.

• 20% increase in funnel conversion rate after identifying leakage points.

• 30% boost in forecast accuracy, enabling precise capacity and resource planning.

• 25% improvement in rep performance through data-driven coaching.

• 80+ hours/month saved in manual data prep and reconciliation.

• Avoided 4 full-time hires, achieving scale with the same analytics team.

• New clients now onboarded in <1 week, thanks to plug-and-play data architecture.

• Full trust and adoption of data across leadership and client teams.

Technology Stack

Key Takeaways

• A single, governed data foundation replaced chaos with clarity.

• Standardized KPIs allowed apples-to-apples performance comparison.

• Managers became data self-sufficient, reducing dependence on analysts.

• Forecasting accuracy and rep efficiency both surged.

• WFS now runs its operations and client reporting from a unified, automated analytics platform.