The Gap Between
Deal and Delivery
"A deal closes in Salesforce on Friday. Delivery stalls in SAP on Monday. The customer churns by next quarter — and nobody saw it coming."
Deal2Delivery was built to close that gap. Permanently.
11 Problems. 11 Databricks Solutions.
Every problem we identified has a direct Databricks feature behind its solution.
01
Siloed CRM & ERP Data
The Problem
Sales teams live in Salesforce. Operations live in SAP. Neither system sees the other — causing mis-forecasted demand, stock shortages, and revenue leakage from deals that never get fulfilled.
Databricks Solution
Databricks Lakehouse ingests and joins both sources into a single Delta Lake. Unity Catalog governs access across dev, staging, and prod with fine-grained permissions.
02
Reactive Churn Management
The Problem
Sales reps discover a customer has churned only after they've gone silent for months. There's no early warning system — churn signals from support cases, sentiment, and order behaviour are invisible.
Databricks Solution
XGBoost churn model trained on a composite behavioral label (inactivity + case burden + sentiment + revenue decline). Optuna-tuned with 5-fold CV. Predictions stored in Unity Catalog for governed consumption.
03
Gut-feel Demand Planning
The Problem
Demand plans are built on intuition and last month's spreadsheet. SKU-level trends, seasonality, and customer buying cycles are buried in SAP tables nobody queries.
Databricks Solution
XGBoost demand forecast model trained on 24 months of SAP order data with lag features (prev month, rolling 3 & 6 month avg). 6-month forward predictions written to a gold table and surfaced in the app.
04
AI Inference is a Black Box
The Problem
When an ML model flags a customer as high-risk, nobody can explain why. Trust in AI predictions is low because the chain from raw data to decision is invisible.
Databricks Solution
MLflow Tracing logs every customer scoring as a trace with nested spans — model input, churn probability, and a Databricks Foundation Model LLM explanation. The Traces tab shows the full chain per customer.
05
Insights Locked Inside Databricks
The Problem
Business stakeholders — sales managers, account execs, finance — don't have Databricks access. Insights stay in internal dashboards that only data engineers can open.
Databricks Solution
Next.js app on Vercel queries Databricks SQL REST API from server-side API routes. Stakeholders get a public URL with curated KPIs, ML predictions, and OpenAI GPT-4o explanations — no Databricks login required.
06
Non-Technical Users Can't Query Data
The Problem
Business questions like 'Which healthcare customers haven't ordered in 60 days?' require a data analyst to write SQL, schedule a Jira ticket, and wait a week for an answer.
Databricks Solution
Databricks Genie AI/BI space backed by all 8 gold views. Users type natural language — Genie generates and runs SQL instantly. A self-improving LLM-as-a-Judge evaluation loop keeps answer quality high using Claude Opus.
07
Every Query Hits the Warehouse
The Problem
Without caching, every page load or tab switch sends a new query to the SQL warehouse — adding seconds of latency and burning unnecessary DBUs on repeated identical queries.
Databricks Solution
Two-layer cache: Next.js ISR (5-min TTL at Vercel CDN) + Databricks SQL Warehouse Result Cache (24h TTL). Tab switching is instant after first load. Repeated identical queries cost zero additional DBUs.
08
No Model Governance or Lineage
The Problem
ML models trained in ad-hoc notebooks are deployed without version control. Nobody knows which model version is in production, what data it was trained on, or how its accuracy has changed over time.
Databricks Solution
Every training run is tracked in MLflow with full metrics, parameters, and artifacts. Models are registered in Unity Catalog with the @champion alias pattern — production always uses the best validated version.
09
No Inventory Visibility Against Forecast
The Problem
Demand is forecasted in isolation — nobody can see which SKUs are critically understocked relative to the 6-month ML-predicted demand until purchase orders are already overdue.
Databricks Solution
SAP MARD stock data is ingested to a bronze Delta table, joined with ML forecast predictions in a new gold view, and classified as Critical / Warning / OK per SKU. Surfaced in a dedicated Inventory page.
10
Static Demand Plans — No Scenario Modelling
The Problem
Sales managers can't answer 'what if we run a 20% promo on Cloud products next quarter?' Demand planning is a fixed output with no interactivity — scenarios exist only in spreadsheets.
Databricks Solution
Scenario Simulator page lets users pick a product category and apply a % adjustment (−50% to +100%) against the XGBoost forecast in real time. Shows unit delta and estimated revenue impact instantly via SQL multiplier.
11
All Customers Look the Same
The Problem
High/Medium/Low churn tiers treat customers as a binary risk signal. Upsell opportunities, win-back plays, and loyalty investment decisions are invisible because there's no richer segmentation.
Databricks Solution
K-Means RFM clustering groups customers into Champions, Loyal, At-Risk, Hibernating, and Prospects. Silhouette score and inertia tracked in MLflow. Segments appear as filters and colour-coded badges in the Customer Risk view.
Meet NovaTech Electronics
A company that looks successful on paper — and is quietly losing customers it never noticed leaving.
70
Enterprise clients
$9.2M
Annual revenue
12
Electronics SKUs
24
Months of history
~18%
Silent churn rate
3
Systems, 0 bridges
NovaTech Electronics sells computing devices, mobile hardware, and accessories to 70 enterprise clients across tech, finance, and professional services. Their account managers live in Salesforce CRM. Warehouse stock, order fulfilment, and procurement run in SAP HANA. These two systems have never spoken to each other — and that silence is costing the business in ways nobody can even measure, because the data is split across systems that refuse to meet.
The Silence Between Systems
Each system knows half the truth. Neither knows enough to act.
Salesforce CRM
The customer-facing half
Knows
Blind to
SAP HANA ERP
The operations half
Knows
Blind to
The result: stock shortages appear only after deals fail. Churn is discovered only after customers go quiet. Demand plans are built on intuition. And when the AI flags a risk, nobody can explain why — so nobody acts. This is the deal-to-delivery gap.
What the Gap Costs, Week by Week
Four real scenarios from NovaTech's operations — none of them visible without unified data.
The Invisible Stockout
Monday, 9 AM. A sales rep closes a $200K laptop order in Salesforce.
The warehouse in SAP has 12 units. The order is for 80. Nobody knows this until the customer calls three weeks later asking where their devices are.
Fulfilment failure, expediting fees, damaged relationship.
The Silent Churn
A top-tier client hasn't placed an order in 94 days.
Their SAP interaction log shows 4 unresolved support tickets and a billing dispute. Their Salesforce account shows 'Active'. The account manager has no idea.
Full account lost. $180K ARR gone before anyone noticed.
The Gut-Feel Forecast
Q4 demand planning meeting. The ops team asks: what do we order?
SKU-level trend data sits in SAP tables. The analyst who knows the queries is on leave. The plan is based on last year's spreadsheet and instinct.
Over-stock on slow-moving accessories. Stockout on flagship laptops.
The Unexplained Flag
The ML model flags a customer as 87% churn risk.
The sales manager asks: why? The data scientist says: it's a combination of 12 features across inactivity, sentiment, and case burden. The manager doesn't act.
High-risk customer churns. The model was right. No one trusted it.
How Deal2Delivery Solves It
One lakehouse. Both systems. Every insight. No excuses.
Unified data
SAP HANA + Salesforce CRM ingested into a single Delta Lakehouse — one source of truth, governed by Unity Catalog.
Proactive churn detection
XGBoost model trained on 30+ behavioural signals flags at-risk customers weeks before they go silent.
Inventory intelligence
SAP MARD stock joined with 6-month ML demand forecast. Critical gaps visible before the sales call is even made.
Explainable AI
Every churn flag comes with an GPT-4o explanation in plain English. Sales reps trust it because they understand it.
Ready to see the live data?
Every chart and ML prediction below is live from Databricks.