All Foundries

Commodity Management & Trading

Transforming trading ecosystems through data-driven insights and secure, agile platforms that empower global commerce.

Customer & Marketing Strategy

Reimagining customer engagement with personalized, connected experiences that build trust and long-term value.

Digital Engineering

Turning ideas into scalable digital solutions—accelerating transformation through design, technology, and engineering excellence.

Station Studio

Redefining mobility hubs with digital innovation to create smarter, sustainable experiences for people and businesses on the move.

Supply Chain & Industry 5.0

Building resilient, intelligent supply networks that anticipate change and deliver efficiency with purpose.

Sustainability

Driving responsible innovation to help organizations achieve net-zero goals and create a positive impact for future generations.

Physical AI

Physical AI gives machines, robots and physical operations the ability to perceive their environment, decide what matters and act in real time. Factories, plants, stores and infrastructure become systems able to sense conditions, learn from them and improve on their own, closing the gap between the physical and the digital world.

Next-Gen Infrastructure

From networks as infrastructure to connected platforms that enable critical, autonomous and intelligent operations.

Profit per square meter, now on the sales floor of 13 department stores in Mexico

Every square meter in a department store has a cost, and the sales team decides what fills each one. A Mexican department store retailer built a report with NTT DATA linking profitability to floor space across 13 stores and 11 divisions. Sales teams use the report to decide on space and follow each action on the floor.

Profit per square meter, now on the sales floor of 13 department stores in Mexico

Four business units, one technology roadmap after the merger

After an acquisition, a retail group ran four business units on four technology stacks. NTT DATA mapped processes, applications, ERP, CRM and infrastructure across all of them and designed a low-risk transition roadmap. The group now works from a prioritized portfolio of initiatives, with synergies identified for each unit.

Four business units, one technology roadmap after the merger

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Marketing Mix Modeling for Energy Sales

Energy retail growth depends on knowing what truly moves demand. For this utility, the priority was to turn fragmented commercial, customer and contextual data into a repeatable analytical view that could separate marketing effects from climate, seasonality, promotions and other sales drivers. The initiative created a clearer basis for channel investment, campaign planning and cross-sell decision-making.

Sales Attribution

Marketing and non-marketing drivers separated

Investment Decisions

Channel and campaign choices supported

Cross-Sell Visibility

Contribution made easier to understand

ROI Framework

Portfolio comparison made more consistent

Energy retail leaders reviewing campaign investment options with utility customer context in the background.

Marketing Mix Modeling for Smarter Energy Retail Growth

A data-led approach to understanding sales contribution across campaigns, channels and external factors in a complex utilities market.

Energy retailers need to know which commercial actions create incremental sales, not just which activities happened before a conversion. The challenge is to read marketing performance alongside weather, seasonality, promotions and other demand signals, so investment decisions can be made with greater confidence.

Objectives

The initiative aimed to build a sales-decomposition capability for an energy retailer, covering climate, seasonality, promotions, advertising and other explanatory factors. Its objective was to estimate the contribution of channels and campaigns to sales outcomes, measure cross-sell effects and channel or campaign ROI, and provide a repeatable analytical view for marketing budget and campaign decisions.

Opportunity

The same analytical foundation can support a more disciplined commercial operating model across energy retail marketing. By extending the approach across campaign planning, customer prioritization and portfolio comparison, the company can continue moving from partial attribution toward a more consistent view of how investment, customer behavior and external conditions interact.

Making commercial performance explainable

The work required more than a technical model. It needed a trusted analytical structure that could reflect the realities of energy retail sales and be used by marketing, sales and customer-care teams when decisions had to be made.

Key Challenges

Utility district and campaign planning materials illustrating the need to separate weather, seasonality and marketing effects in energy sales.

1: Separating sales drivers
Commercial teams needed to distinguish the contribution of marketing activity from climate, seasonality, promotions and other external factors.

Energy retailer campaign planning environment showing the challenge of comparing marketing channels and campaigns on a common basis.

2: Comparing channels consistently
Budget allocation depended on a common basis for comparing channels and campaigns, rather than relying on partial attribution.

Utility service setting with smart meter and customer materials representing fragmented customer, interaction and consumption data.

3: Connecting fragmented signals
Customer, interaction and consumption data had to be brought together in a form that analytical models and operational teams could use consistently.

Energy retail commercial and service teams discussing campaign actions based on analytical outputs.

4: Turning insight into action
Model outputs needed to be understandable, traceable and usable within existing commercial, campaign and service processes.

Solution

An end-to-end analytical workflow for energy retail decisions

NTT DATA structured the capability from data preparation and feature definition through modelling, scoring and operational consumption, creating a repeatable view of marketing contribution and commercial performance.

Sales-decomposition model

The model covered climate, seasonality, promotions, advertising and other explanatory factors to help isolate the drivers behind energy sales outcomes.

Channel contribution analysis

The approach estimated the contribution of channels and campaigns, supporting more comparable decisions across the marketing portfolio.

Integrated analytical workflow

Data preparation, feature definition, modelling and scoring were organized as a coherent workflow for consistent analytical use.

Operational decision outputs

The outputs were designed so business users could prioritize customers, cases or actions and incorporate recommendations into campaign decisions.

Impact

A clearer basis for marketing investment

The initiative provided clearer attribution of sales to marketing and non-marketing drivers, improved the basis for channel and campaign investment decisions, increased visibility of cross-sell contribution and created a consistent framework for comparing marketing ROI across the commercial portfolio.

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