Data Analytics for Retail & eCommerce
Pricing and promotions set by habit leave margin on the table. We build demand and price-sensitivity models that recommend a price and show the expected effect.
Data Analytics for Pricing, Demand & Risk Decisions
Pricing set by whoever argues hardest, demand plans built on last year's numbers, risk reviewed by gut feel: most teams have the data to decide these better and aren't using it. We're a data analytics service: we build the models and dashboards that turn your data into a pricing call, a forecast, or a risk score, not just a chart.
We work hands-on with: Python, Power BI, Tableau, Looker, Metabase.
What Is This Service?
It's the business side of your data: turning it into a pricing call, a demand forecast, a risk score, or a dashboard an executive actually acts on, not a report someone still has to interpret.
Most teams have data sitting in a warehouse or a set of dashboards, but the actual decisions, what to price something at, how much inventory to hold, which accounts are at risk, still get made on gut feel or last year's numbers, because nobody's turned the data into a model built for that specific decision.
We fix that with decision support and optimization (pricing, allocation, routing, procurement, production planning), predictive analytics (demand forecasts, churn, inventory), and risk and credit scoring (scores and reason codes), all built into an executive dashboard with drill-downs and same-day exception alerts.
What we don't do is guess at the technical layer underneath, if your pipelines or metric definitions aren't trustworthy yet, that's Data Reliability, and we'll tell you if that needs fixing first.
What Problems Trigger This Engagement?
- A pricing decision gets made by whoever argues hardest in the meeting, not by a model
- Demand planning still runs on last year's numbers, and stockouts or overstock keep recurring
- A customer churns and nobody saw it coming, because nothing was scoring the risk
- A loan, claims, or account review queue is worked in arrival order instead of by risk
- An executive dashboard shows the numbers but nobody's built a recommendation into it
- A data science hire is being considered for one recurring decision, not a whole team's worth of work
Match Your Situation to the Fix
A quick lookup. Every row maps to one of the four things we do, listed below.
| Situation | Likely Solution |
|---|---|
| Pricing changes based on whoever argues hardest in the room | Decision Support & Optimization — a pricing recommendation, not a debate |
| You find out demand shifted after you're already overstocked or stocked out | Predictive Analytics — a demand forecast to plan against |
| Nobody knows which customers are about to churn until they've already left | Predictive Analytics — a churn score that ranks accounts before they leave |
| Loan, claims, or account review relies on manual judgment with no consistent score | Risk & Credit Scoring — a score and reason code for every case |
| Executives wait days for a report instead of opening a live dashboard | Business Performance Analytics — drill-downs and same-day exception alerts |
| Procurement or production planning still runs on last year's numbers | Decision Support & Optimization — planning against real demand signals |
What DharmOps Does
One service, delivered across these four areas depending on which decision you're trying to make.
Decision Support & Optimization
Optimum pricing, allocation, routing, procurement, and production planning, built as models that turn your data into a specific recommendation instead of a range to argue over.
A recommendation, not a range someone still has to decide between.
Predictive Analytics
Demand forecasts, churn prediction, and inventory planning built from your historical data, so you plan against a number instead of last quarter's guess.
Forecasts your team can plan against, not a black box.
Risk & Credit Scoring
Scores with reason codes and early-warning signals for credit, churn, or fraud risk, so a review queue gets ranked by what actually matters instead of worked in arrival order.
A score and a reason, not just a red flag.
Business Performance Analytics
Executive-ready dashboards with drill-downs by product, channel, and region, plus same-day alerts on exceptions, in Power BI, Tableau, Looker, or Metabase.
An alert the same day, not a surprise at month-end.
Specific Use Cases
Setting pricing across a catalog instead of gut-feel discounting or matching a competitor blindly
Forecasting demand by product or region to cut stockouts and overstock before they happen
Scoring which customers are likely to churn so retention outreach targets the right accounts first
Building a credit or fraud risk score with reason codes for loan, claims, or account review
Optimizing procurement or production planning against real demand signals instead of last year's numbers
An executive dashboard with drill-downs by product, channel, or region, and same-day alerts on exceptions
Routing or allocation optimization, like balancing inventory or capacity across locations
How the Engagement Works
- 1First callDescribe the decision you're trying to make and what data you already have. We look at whether it's answerable with what exists.
- 2ScopingA one-time project or an ongoing plan, priced to the decision and the data involved, not a generic package.
- 3The workWe build the model, score, or forecast, and the dashboard on top of it, in a project you own and keep afterward.
- 4Handoff or ongoing planOne-time work ends with a documented model and dashboard handed to your team. Ongoing work continues as the model gets retrained or the decision evolves.
Technology & Platforms
Python for the optimization, forecasting, and scoring models, feeding the executive dashboard in Power BI, Tableau, Looker, or Metabase. If you're already set up differently, or have an existing model or dashboard in place, we work inside that instead of replacing it.
How This Differs From Alternatives
| Basis | DharmOps | In-house analyst, part-time | BI-tool vendor prof. services |
|---|---|---|---|
| Output | A recommendation or score built into the dashboard, ready to act on | Whatever the one analyst has time to interpret and present | A generic dashboard template; the model behind any recommendation is usually an add-on |
| Ownership | One team scoping the decision, the model, and the dashboard together | Splits across whoever owns data, whoever owns the tool, and whoever owns the decision | Sold as a licensed platform; your team still builds the model logic inside it |
| Validation | Model accuracy and forecast error checked and reported before rollout | Depends on whether that person has time to validate before shipping | Rarely disclosed; you're trusting a vendor's benchmark, not your own data |
| What you keep | A documented model and dashboard your team owns and can retrain | Depends entirely on whether that person documents as they go | Locked into the vendor's platform and pricing |
Frequently Asked Questions
Data Analytics Use Cases by Industry
Analytics earns its cost when it changes a decision: a price, a forecast, a credit call. These are the decisions we build models and dashboards for.
Data Analytics for Manufacturing
Production plans built on last year's volumes miss demand shifts. We build demand and capacity forecasts that feed scheduling and purchasing.
Data Analytics for Banking & Insurance
Credit and underwriting decisions depend on consistent scoring. We build and validate risk models on your own data, with the inputs and outputs documented for review.
Data Analytics for Logistics
Lane pricing and capacity planning run on gut feel when volume swings. We build lane-level demand and cost models that support quoting and route decisions.
Data Analytics for Energy & Utilities
Load and consumption swing with weather and season. We build consumption forecasts that support supply planning and load balancing.
Data Analytics for SaaS & Software
Churn shows up in the revenue line after it is too late to act. We build churn and expansion models from usage data and surface accounts at risk to the team that owns them.
Data Infrastructure Engineering for 13 Industries
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Often Paired With
Data Reliability
This service builds the decision on top of your data; Data Reliability keeps the pipelines and metric definitions underneath it correct.
Data Engineering
Platform engineering builds the pipes that move data reliably; this service is what you build on top once the data lands.
Database Engineering
This service turns data into decisions; Database Engineering keeps the database it's often pulled from fast and reliable.
Tell Us Which Decision You're Still Making on Gut Feel
A pricing model, a demand forecast, a risk score, or an executive dashboard with recommendations built in: describe it and we'll scope the right fix, one-time or ongoing.
Stop Deciding on Gut Feel
One call, one diagnosis, and a scoped fix, whether that's a pricing model, a forecast, a risk score, or an ongoing plan.