All Foundries

商品管理と取引

データに基づくインサイトと、セキュアで機動性の高いプラットフォームにより、グローバルな商取引を支えるトレーディングエコシステムを変革します。

顧客とマーケティング戦略

パーソナライズされたつながりある体験を通じて顧客エンゲージメントを再構築し、信頼と長期的な価値を築きます。

デジタルエンジニアリング

アイデアをスケーラブルなデジタルソリューションへと転換し、デザイン・テクノロジー・エンジニアリングの力で加速させます。

ステーション・スタジオ

デジタルイノベーションでモビリティハブを再定義し、移動中の人々と企業に、よりスマートでサステナブルな体験を創出します。

サプライチェーン＆インダストリー5.0

変化を予測し、目的を持って効率性を実現する、強靭で知的な供給ネットワークを構築します。

サステナビリティ

責任あるイノベーションを推進し、組織がネットゼロ目標を達成し、将来の世代に良い影響をもたらすよう応援します。

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.

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

小売

A pharmacy retailer grows average ticket by 8% and lab services sales by 18%

Pharmacy and health retailers compete on the visit itself: the counter, the advice, the lab test. This retailer measured its digital maturity with NTT DATA, organized more than 16 initiatives into cross-functional work cells and tracked each result. Average ticket rose 8% and lab and diagnostic services sales grew 18%.

A pharmacy retailer grows average ticket by 8% and lab services sales by 18%

お問い合わせ

Predictive NPS & Customer Detractor Intelligence

For an energy retailer / utility, customer dissatisfaction could no longer be treated only as a post-experience signal. The initiative turned NPS and customer interaction data into a predictive intelligence capability, helping the business identify likely detractors earlier, understand the drivers behind dissatisfaction and focus retention, service and commercial actions on the customers most at risk.

Energy customer operations leaders review anonymized customer experience signals in a modern utility environment connected to electricity infrastructure.

Detractor Precision

More than 80% precision

Earlier Signals

At-risk customers identified sooner

Driver Clarity

Main detractor factors analyzed

Targeted Actions

Loyalty and service actions focused

Utility customer care and marketing teams align targeted retention and service actions using predictive customer intelligence.

Predictive customer intelligence for energy-retail detractor management

The utility needed to move from observing NPS after the experience to anticipating which customers were likely to become detractors before launching service, loyalty or commercial actions.

目的

The objective was to anticipate customers at risk of dissatisfaction and understand the factors associated with detractor behavior, so marketing, sales and customer-care teams could prioritize interventions with greater relevance and consistency.

機会

The documented approach creates an opportunity to extend predictive customer intelligence across retention and loyalty workflows, embedding reusable scoring, driver analysis and decision support into customer operations without treating analytics as a stand-alone exercise.

Turning customer experience signals into operational foresight

The challenge was not only to build an analytical model, but to make its outputs consistent, trusted and usable inside the commercial and service processes of an energy retail organization.

主な課題

Customer experience leader reviews late-arriving satisfaction signals in an energy retail operations setting.

1: Reactive NPS visibility: NPS was often observed only after the customer experience had already occurred, limiting the ability to act before dissatisfaction translated into detractor behavior.

Analysts organize customer, interaction and energy consumption information from fragmented sources for predictive modeling.

2: Fragmented customer signals: The initiative required heterogeneous customer, interaction and consumption data to be brought together in a form that analytical models and operational teams could use consistently.

A utility business lead and data specialist discuss explainable detractor insights for trusted customer decisions.

3: Business trust in predictions: The solution needed to make detractor classification and driver analysis understandable and traceable for the teams expected to use the outputs in real decisions.

Customer care and marketing operations teams prepare prioritized outreach actions from predictive insights in an energy utility hub.

4: Operational consumption: Predictive insight had to be translated into practical prioritization for existing commercial, loyalty and service workflows, rather than remaining an isolated analytics exercise.

解決策

A predictive NPS intelligence workflow connected to customer operations

The delivery approach connected customer data preparation, driver analysis, predictive classification and business-facing outputs into an end-to-end decision process.

Predictive detractor classification

Built a model to classify customers as likely detractors or non-detractors, enabling earlier identification of customers at risk of dissatisfaction.

Customer profiling and feature definition

Prepared and profiled customer information, defining the variables needed to support model development and consistent operational scoring.

Detractor driver analysis

Analyzed the variables associated with detractor behavior and customer dissatisfaction, giving business teams clearer insight into the factors behind the prediction.

Dashboards and inference outputs

Created dashboards and inference outputs so business users could prioritize customers, cases or actions and incorporate recommendations into existing decision and campaign processes.

影響

From broad response to focused customer action

The documented result shows more than 80% precision in identifying customers classified as detractors. The initiative also supports earlier identification of customers at risk of dissatisfaction, a better understanding of key NPS and detractor drivers, more targeted loyalty, service and commercial interventions, and a repeatable predictive-customer-intelligence capability for customer operations.

注目の成功事例

デジタル化されたサービス管理による現場業務の強化と、コネクテッドワーカー技術による安全性の向上

すべての成功事例を見る