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

Customer Lifetime Value

A retailer used cardholder transaction data to calculate Customer Lifetime Value across more than 530,000 unique customers, creating six value-based segments and a three-dashboard MVP for planning and activation.

A cashier hands an unbranded store card to a customer at a department store checkout, with merchandise and shoppers in the background.

530K+

Unique customers with CLV calculated

CLV-based cardholder segments

MVP dashboards in production

2023

CLV projection available for planning

A retail leader overlooks an active department store as an associate assists a customer at a service table.

From cardholder transactions to customer lifetime value

Customer value was visible only after the fact.

目的

Measure the Customer Lifetime Value of the retailer's cardholders to create a basis for increasing their value.

機会

Use customer-level value and six distinct CLV groups to shape mechanics intended to increase cardholder value.

Turning transaction history into a forward view of customer value

The initiative needed to move from retrospective transaction data to a consistent, customer-level view that planners could use.

主な課題

A retail commercial lead and data specialist arrange transaction printouts and color samples beside folded garments.

1: A value definition the business could use
The retailer needed agreed business rules for calculating CLV, rather than leaving value visible only in hindsight.

2: Customer-level data at meaningful scale
The initiative required cardholder and transaction data to be extracted and connected closely enough to calculate value for each customer.

Two retail professionals review an analytics display overlooking an active department store.

3: Planning visibility, not another dataset
Projected CLV, cardholder volumes and transaction volumes needed to be available in a form the business could use.

Six red shopping baskets contain different mixes of groceries, apparel and home products.

4: A path from measurement to activation
The retailer had no CLV-based grouping of cardholders or value-increasing mechanics anchored in that segmentation.

解決策

A customer lifetime value MVP designed for business action

NTT DATA combined calculation rules, data processing, visualization and segmentation in a Tableau MVP that makes expected customer value visible and usable.

Business rules that anchor customer value

NTT DATA defined the business rules for the CLV calculation, establishing a consistent basis for customer-level measurement.

Customer-level calculation at scale

Cardholder and transaction data were extracted and used to calculate CLV for more than 530,000 unique customers.

A decision layer in Tableau

Three MVP dashboards brought together the 2023 CLV projection, cardholder volumes and transaction volumes for business planning.

Six segments for value-based activation

The MVP identified six cardholder groups based on CLV, giving the retailer a structured view for value-increasing mechanics.

影響

Customer value becomes visible and actionable

The retailer can now prioritize investment by expected customer value. CLV has been calculated for more than 530,000 unique customers, six value-based segments have been defined and three MVP dashboards are in production. The available 2023 projection provides a planning view, while the segmentation creates a basis for mechanics intended to increase cardholder value.

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