Turn Your Data Into Decisions That Drive Revenue.
Most organisations have far more data than insight. Reports pile up, dashboards multiply, and yet the questions that matter most — why did revenue decline in the North region last quarter, which customer segments are most at risk of churning, which product SKUs should be discontinued — still require weeks of analyst effort to answer. SourceMash's BI & Advanced Analytics practice builds the analytical infrastructure, dashboards, and statistical models that give your leadership, finance, commercial, and operational teams direct access to the answers they need — reducing decision latency from weeks to hours and transforming analytics from a reporting function into a strategic business driver.
From Spreadsheets to Strategic Intelligence.
Most organisations live between Stage 1 and Stage 2 of the analytics maturity curve — reactive reporting and siloed spreadsheets that answer what happened after considerable analyst effort, but cannot answer why it happened or what will happen next without weeks of custom work.
SourceMash's BI & Advanced Analytics practice moves organisations rapidly through this curve — building the data model foundations, self-service tooling, and statistical intelligence that make data a genuine competitive advantage rather than a reporting obligation.
SourceMash builds the data models, dashboards, and analytical capabilities that take your organisation from Stage 1 reactive reporting to Stage 4 intelligence-driven decision-making — with pragmatic, incremental delivery that produces business value at every step of the journey.
Executive Dashboards & KPI Command Centres
The most expensive decision-making process in any organisation is the weekly executive review meeting where leadership spends the first 30 minutes waiting for someone to compile last week's numbers from five different spreadsheets. SourceMash builds executive dashboard and KPI command centre solutions that eliminate this preparation overhead entirely — giving your C-suite, board, and functional leaders a single, always-current view of the metrics that matter, accessible in seconds from any device.
We start with a structured discovery process that identifies the decisions each executive role actually makes and the specific data required to make those decisions confidently — then design the information hierarchy, drill-down structure, alert thresholds, and narrative framing that makes the dashboard a decision-support tool rather than a vanity metrics display. The result is dashboards that leadership actually uses, rather than dashboards that get built, presented once, and never opened again.
Dashboard Types We Build
Designed for the specific decision-making needs of each leadership audience
CEO / MD Command Centre
Revenue vs. target, P&L summary, growth drivers, risk signals, and strategic initiative progress — with one-click drill-down to any function
CFO Financial Dashboard
Cash flow, budget vs. actuals, working capital, margin analysis by BU, and real-time P&L across entities and currencies
CMO Revenue & Growth
CAC, LTV, campaign ROI, funnel conversion, cohort retention, and channel performance — in one unified revenue analytics view
COO Operations Centre
OEE, throughput, quality yield, supply chain KPIs, headcount productivity, and SLA achievement across all operational functions
How We Design Dashboards That Get Used
Our structured dashboard design process ensures every dashboard answers the right questions for the right audience
Decision Discovery
Structured interviews with each executive user to map the decisions they make, the questions those decisions require, and the data granularity each question needs — before any chart is drawn.
Metric Architecture
Agreed metric definitions, calculation logic, and hierarchy — ensuring every number on the dashboard means exactly what the business intends and is consistent across every report and system.
Visual Design & UX
Information hierarchy designed to surface exceptions and alerts first, with progressive drill-down from summary to detail. Wireframes reviewed by stakeholders before any development begins.
Data Modelling & Build
Underlying semantic layer and data models built with full test coverage — ensuring the dashboard numbers are trustworthy rather than requiring manual verification every time leadership reviews them.
Industry KPI Dashboards We Build
Pre-configured KPI frameworks for the metrics that matter in each industry
- NIM, CASA ratio, cost-to-income ratio
- Loan disbursement vs. target by product, region, channel
- Gross NPA, net NPA, PCR by portfolio segment
- RoA, RoE, capital adequacy ratio (CRAR)
- Collection efficiency, DPD bucket movement
- GMV, revenue, order volume by channel and category
- CAC, LTV, LTV:CAC ratio by acquisition channel
- Conversion rate, basket size, return rate
- Inventory turnover, days inventory outstanding (DIO)
- Net promoter score, CSAT by touchpoint
- OEE (availability x performance x quality) by line
- First pass yield, scrap rate, rework cost
- On-time delivery (OTD), OTIF by supplier
- Inventory turnover, days inventory outstanding
- Maintenance cost per unit, unplanned downtime %
- Bed occupancy rate, ALOS, bed turnaround time
- Revenue per available bed (RevPAB)
- Clinical outcome metrics by specialty and physician
- Revenue cycle: claim denial rate, days in AR
- Patient satisfaction (HCAHPS / KLAS) by unit
- ARR, MRR, expansion MRR, churn MRR
- NDR (net dollar retention), gross revenue retention
- CAC payback period, magic number
- DAU/MAU, feature adoption rate, time-to-value
- NPS, CSAT, support ticket CSAT by segment
- Generation capacity utilisation, availability factor
- Heat rate efficiency, specific fuel consumption
- Revenue per MWh, OPEX per MWh
- Transmission & distribution losses by zone
- Plant load factor (PLF), forced outage rate
Self-Service BI & Semantic Data Models
The promise of self-service BI — business users exploring data and answering their own questions without waiting for analyst resources — fails in most organisations because the underlying data is not trustworthy, the data model is not comprehensible to non-technical users, or the BI tool surface is too complex for the average business user. The result is a data team drowning in ad-hoc report requests while the self-service BI tool sits unused, and a business operating on instinct rather than data because getting data requires a two-week ticket queue.
SourceMash builds self-service BI implementations that actually work — combining a well-designed semantic layer (dbt metrics layer, LookML, Power BI dataset, or Tableau data source) that provides business users with trusted, pre-defined metrics and dimensions in business language, with a BI tool configuration optimised for the specific analytical tasks your business users actually need to perform. We also run structured data literacy training to ensure business users know how to extract insight rather than just how to use the tool.
The Semantic Layer — The Foundation of Self-Service Success
Without a semantic layer, every business user computes the same metric differently — destroying trust in data
BI Tools We Implement & Optimise
Tool-agnostic — implementing the platform that best fits your existing stack, team capability, and use case
Power BI
Best for Microsoft 365 organisations, Office integration, and broad user base self-service
Tableau
Best for data exploration, visual analytics, and organisations with a strong data culture
Looker / Looker Studio
Best for Google Cloud environments, embedded analytics, and LookML semantic layer governance
Metabase / Superset
Best for cost-sensitive organisations, open-source flexibility, and developer-led analytics
Embedded Analytics & Product Intelligence
The highest-value analytics is analytics embedded directly into the operational tools, customer-facing products, and workflow applications where decisions are actually made — rather than sitting in a separate BI portal that users need to context-switch to access. When a loan officer sees a customer's risk intelligence directly in the CRM during a credit assessment, when an operations manager sees real-time production KPIs in the MES without opening a separate dashboard, when a retail customer sees personalised purchase analytics in their account portal — the insight is consumed at the moment of maximum relevance and directly influences the next action.
SourceMash builds embedded analytics solutions using modern embedding frameworks (Looker Embed, Power BI Embedded, Tableau Embedded, Superset Embedded, or custom-built chart libraries like ECharts, D3.js, or Recharts) — designed to feel native to the host application rather than like a BI tool bolted on. We handle authentication, multi-tenancy, performance optimisation, and white-labelling requirements that make embedded analytics production-ready for customer-facing use cases at scale.
Embedded Analytics Use Cases
Where embedding analytics directly into operational or customer-facing applications creates the most value
Customer Portal Analytics
Personalised analytics in customer portals — account performance dashboards for banking customers, spend analytics for corporate card holders, usage analytics for SaaS users, and investment performance views for wealth management clients — increasing portal engagement and reducing support contact volume.
CRM Intelligence Layer
Customer intelligence — lifetime value, product penetration, propensity scores, recent behaviour signals, NPS history — embedded directly in Salesforce, HubSpot, or your CRM record view, so sales and relationship managers see the analytical context for every customer interaction without leaving the CRM.
MES & Operations Intelligence
Real-time production KPIs, quality metrics, and equipment status embedded in the manufacturing execution system — giving floor managers and supervisors the analytics they need without requiring them to navigate to a separate BI system during a production shift.
Vendor & Partner Portals
Performance dashboards for suppliers, channel partners, and franchise operators — embedded in partner portals showing each partner their own performance data (sell-through rates, order fulfilment metrics, quality scores, incentive attainment) with multi-tenancy ensuring each partner sees only their own data.
EdTech & LMS Analytics
Learner progress analytics, cohort performance comparisons, and outcome metrics embedded in learning management systems — giving students visibility into their own learning trajectory and giving instructors insight into where learners are struggling, without requiring access to a separate analytics platform.
ERP Financial Intelligence
Rich financial analytics — margin analysis by cost centre, budget vs. actuals trending, cashflow forecasting — embedded in ERP finance modules, eliminating the need for finance teams to export data to Excel for analysis and ensuring analytical conclusions are always based on the same underlying data as the operational system.
Statistical Modelling & Advanced Analytics
Standard BI dashboards tell you what happened. Advanced analytics — statistical modelling, causal inference, experimentation, and simulation — tells you why it happened, what the effect of a proposed intervention will be, and which factors are driving the outcomes your business cares about. SourceMash's advanced analytics practice applies statistical rigour to business questions that dashboard visualisations cannot answer: which marketing channels genuinely cause incremental revenue rather than just correlating with it, what the price elasticity of your product is across different customer segments, whether the uplift in conversion observed after a UX change is statistically significant or just noise, and which operational factors most strongly predict quality defects.
We bridge the gap between data science and business decision-making — translating statistical concepts and model outputs into the language of business impact, confidence intervals, and recommended actions that decision-makers can act on without a statistics degree. Every analytical engagement produces a business intelligence report alongside any technical deliverable — ensuring the insight is accessible and actionable.
Analytics Capabilities We Apply
A rigorous statistical toolkit applied to the business questions that descriptive dashboards cannot answer
A/B Testing & Experimentation Design
Rigorous experimental design for product, pricing, marketing, and UX tests — including sample size calculation, randomisation strategy, guardrail metric selection, and statistical significance testing with pre-registered hypotheses. We design experiments that produce definitive, actionable conclusions rather than ambiguous results that require subjective interpretation.
Causal Inference & Attribution
Difference-in-differences, instrumental variables, propensity score matching, and regression discontinuity designs — establishing genuine causal relationships from observational data where randomised experiments are not possible. Particularly valuable for marketing attribution, policy impact evaluation, and understanding the true drivers of business outcomes.
Time Series Analysis & Decomposition
Decomposition of business metrics into trend, seasonality, cyclicality, and residual components — separating signal from noise, identifying structural breaks, and isolating the true underlying growth rate from seasonal fluctuations. Essential for honest performance benchmarking and understanding whether observed changes represent genuine signal or normal variation.
Price Elasticity & Optimisation
Statistical estimation of price elasticity across product categories and customer segments — quantifying the revenue and volume impact of price changes and identifying the optimal price point that maximises contribution margin. Combined with simulation modelling that lets business teams test hypothetical pricing scenarios before committing to a price change.
Marketing Mix Modelling (MMM)
Bayesian MMM that decomposes historical revenue into contributions from each marketing channel — quantifying the incremental ROI of each channel, identifying diminishing returns thresholds, and optimising budget allocation to maximise total revenue from a given marketing spend envelope.
Customer Segmentation & Clustering
Unsupervised clustering (K-means, DBSCAN, hierarchical) combined with supervised validation to identify statistically distinct customer segments based on behaviour, value, and needs — producing segments that are genuinely different in their business characteristics rather than arbitrarily defined demographic buckets.
Customer Analytics & Lifetime Value Intelligence
Customer analytics is the analytical discipline that separates companies that grow efficiently from companies that grow expensively. Understanding which customers are most valuable, which are most at risk of leaving, which acquisition channels produce the highest-value customers, and which product or service experiences drive the behaviours that increase lifetime value — these are the questions that determine whether your marketing spend is an investment or a cost, and whether your customer base is a growing asset or a leaking bucket that requires ever-increasing acquisition spend to stay flat.
SourceMash builds customer analytics programmes spanning the full customer intelligence stack: cohort analysis and retention tracking, customer lifetime value modelling, churn prediction with early intervention scoring, RFM segmentation for personalisation and CRM targeting, acquisition channel attribution, and product usage analytics that identify the engagement patterns predictive of long-term retention. All delivered in dashboards and models integrated with your CRM and marketing automation platform so insights translate directly into action.
Customer Intelligence Capabilities
End-to-end customer analytics — from acquisition attribution through lifetime value to win-back
Cohort Analysis & Retention Tracking
Customer cohort visualisation by acquisition period, channel, product, and geography — tracking 30/60/90/180-day retention rates, identifying which cohorts retained best and why, and surfacing the early behavioural signals that predict long-term retention before they are visible in aggregate retention metrics.
Customer Lifetime Value (CLTV) Modelling
Probabilistic CLTV models (BG/NBD + Gamma-Gamma or ML-based) that estimate individual customer lifetime value — enabling LTV-based acquisition bidding, customer tier classification, investment allocation across retention programmes, and LTV-weighted reporting that gives a truer picture of business performance than revenue alone.
Churn Prediction & Early Warning
ML churn propensity models that score every active customer daily — identifying at-risk customers 30 to 90 days before they would churn, with SHAP-based reason codes that tell relationship managers and CX teams why each customer is flagged at risk, enabling targeted, relevant intervention rather than generic win-back campaigns.
RFM Segmentation & CRM Targeting
Recency-Frequency-Monetary segmentation that classifies customers into actionable behavioural segments (Champions, Loyal, At-Risk, Lost, New) — with CRM integration that automatically assigns customers to segments and triggers the appropriate engagement sequence in your marketing automation platform as segment membership changes.
Funnel & Acquisition Attribution
Full-funnel analytics from acquisition channel through activation, engagement, retention, and revenue — with data-driven attribution that distributes revenue credit across the touchpoints that genuinely contributed to conversion, replacing last-click attribution that over-credits retargeting and branded search while under-crediting upper-funnel channels.
Product Usage & Engagement Analytics
Feature-level usage analytics, user journey mapping, drop-off analysis, and engagement depth scoring — identifying the product features most strongly associated with long-term retention, the user journeys that lead to expansion revenue, and the engagement gaps that predict churn before it registers in satisfaction scores.
Financial Analytics & FP&A Intelligence
Financial planning and analysis is the function that should connect historical performance with forward-looking strategy — but in most organisations, FP&A teams spend 70–80% of their time in data collection, reconciliation, and report production rather than on the analysis and insight generation that justifies their existence. The quarterly budgeting process consumes weeks of analyst effort consolidating spreadsheets from across the business; variance analysis is retrospective rather than predictive; and the financial dashboards visible to business unit leaders show historical actuals with no forward-looking context.
SourceMash builds financial analytics and FP&A intelligence platforms that automate the data collection and consolidation burden — connecting GL systems, cost centre hierarchies, HR data, and operational KPIs into a unified financial data model — and redirect FP&A analyst capacity toward driver-based forecasting, scenario modelling, profitability analytics, and forward-looking financial intelligence that actually informs business decisions rather than merely documenting them after the fact.
Time 5–10 days → Same-day
Improvement 20–35% MAPE reduction
Reduction 6 weeks → 2 weeks
Granularity Cost centre → transaction level
Currency ✓ Full consolidation support
Financial Analytics Capabilities
From automated reporting to driver-based forecasting — the full FP&A intelligence stack
Automated Financial Consolidation
Automated multi-entity P&L, balance sheet, and cash flow consolidation — ingesting trial balances from ERP systems (SAP, Oracle, Tally, Zoho Books), applying intercompany eliminations, currency conversions, and group accounting adjustments to produce same-day consolidated financials rather than a multi-week manual process.
Driver-Based Revenue Forecasting
Statistical forecasting models that predict revenue from operational leading indicators (pipeline value, order backlog, sales activity metrics, macro variables) rather than extrapolating historical trends — with confidence intervals, scenario analysis (base / bull / bear), and rolling forecast updates that reflect current business reality rather than the static annual budget.
Profitability & Margin Analytics
Contribution margin analysis at the product, customer, channel, and geography level — allocating direct costs and activity-based overheads to identify which revenue streams are genuinely profitable and which are contributing negative margin obscured by blended reporting. Essential for pricing decisions, product portfolio rationalisation, and customer relationship management.
Budget vs. Actuals & Variance Analysis
Real-time budget vs. actuals dashboards with automated variance decomposition — identifying the specific volume, price, mix, and currency effects driving each variance, and flagging significant deviations with contextual commentary that tells the business what happened rather than requiring manual analysis to explain the numbers.
Cash Flow Analytics & Forecasting
13-week rolling cash flow forecasting that combines AR ageing, AP due dates, payroll, capex commitments, and revenue pipeline into a daily cash position model — with what-if scenario analysis for collection timing changes, discretionary spend decisions, and drawdown/repayment timing for working capital facilities.
Scenario Modelling & Stress Testing
Interactive financial scenario models where FP&A teams and business leaders can modify key business drivers (revenue growth, gross margin, headcount, capex) and immediately see the P&L, balance sheet, and cash flow implications — replacing static spreadsheet models with governed, auditable, live financial simulation.
Financial Systems We Integrate
We connect your financial analytics platform to every system that generates financially relevant data — from your GL and ERP to your CRM, HR system, and operational data sources — building the unified financial data model that makes real-time consolidation and driver-based forecasting possible.
BI & Analytics Technology Stack
We are tool-agnostic — selecting the BI platform, semantic layer, and analytical programming environment that best fits your existing stack, team capability, user base, and use case requirements, rather than prescribing a single platform.
What Our Clients Say
Our Monday morning leadership review used to start with 45 minutes of someone reading out numbers from a spreadsheet that took three days to compile. The commercial analytics platform SourceMash built replaced all of that — our CEO, CFO, and all functional heads now walk into the weekly review having already seen the key metrics on their phone, and we spend the entire meeting discussing what to do rather than what happened. The 78% reduction in analyst ad-hoc requests was a welcome bonus.
We were losing ₹3–4 crore of ARR annually to churn that we could not see coming until the customer had already decided to leave. The customer intelligence platform SourceMash built identifies at-risk accounts 60 days before they would churn, tells our customer success team exactly why they are at risk, and integrates directly with Salesforce so the CSM gets an automatic task. Churn is down 28% in the first year. The ROI calculation was embarrassingly straightforward.
We had six entities across three currencies and a group close process that took 12 working days — by which point the financial information was already stale for decision-making. SourceMash automated the entire consolidation, built a driver-based forecasting model that our FP&A team now runs as a live rolling forecast, and cut our budget cycle from six weeks to two. Our FP&A analysts now spend their time on genuine analysis rather than on data plumbing. Transformational for our finance function.
Latest from SourceMash
Perspectives, research, and practical guidance from our enterprise technology experts.
