Creating a 360-Degree Customer View with Retail Data Management

The retail landscape is more competitive than ever before. With consumers able to shop anywhere, anytime online, retailers need to focus on delivering personalized, seamless shopping experiences across channels to thrive. A critical component of this is having a 360-degree view of each customer.

When retailers understand who their customers are, what they want, and how they behave, they can provide targeted, relevant experiences. They can anticipate needs, solve problems proactively, and foster brand loyalty.

But constructing this unified customer profile is easier said than done. Retailers have data spread across disjointed systems and teams. Bringing it together into a single version of the truth is a major challenge.

In this post, we’ll explore how retailers can create a 360-degree customer view by effectively managing their data. We’ll cover:

• The value of a 360-degree customer view
• Key data sources for building customer profiles
• Challenges with retail data management
• Best practices for unifying customer data
• Leveraging the customer view to deliver personalized experiences

The Power of a 360-Degree Customer View

A 360-degree customer view provides a complete, unified profile of each customer by stitching together data from all business systems and touchpoints. This includes:

• Demographic data: Details like name, age, location, job title, income level, and family status.
• Transactional data: Information on past purchases, items bought, prices paid, purchase frequency, channel preferences, returns, service cases, and more.
• Behavioral data: Web browsing history, search terms used, clicks, downloads, response to promotions, communication preferences, satisfaction scores, and other actions.
• Engagement data: Notes from contact center interactions, loyalty program activity, support tickets, emails, chat conversations, and other engagements.

With all this information connected, retailers can gain unique insights like:

• What line of products does a customer frequently purchase?
• What price range are they comfortable with?
• Do they prefer shopping online or in-store?
• How do they respond to different marketing offers?
• When did they last interact with the brand?

These insights help retailers hyper-personalize across the customer journey:

• Marketing: Send customized promotions and offers based on interests and behaviors
• Merchandising: Craft relevant product recommendations and customize services
• Customer service: Resolve issues faster by accessing purchase history and prior interactions
• Loyalty: Provide personalized rewards, gifts, and experiences
• Sales: Understand needs and preferences to provide tailored guidance

According to a survey by EY, 63% of retailers say a unified customer view is extremely or very important. Those with a superior view reported a 10-15% increase in sales. It’s a competitive differentiator that boosts revenue, engagement, satisfaction, and loyalty.

Key Data Sources for Building Customer Profiles

Constructing a 360-degree customer view requires consolidating data from systems across the retail technology stack:

Point of Sale (POS) Systems

POS systems record transactions made in physical stores. This provides purchase history, items bought, prices paid, payment methods, and other transactional data tied to a customer. Expanding POS capabilities to tie purchases to customer profiles is key.

Ecommerce Platforms

Ecommerce platforms like Shopify and Magento track all online purchases, browsing history, discounts used, cart abandonment, and more. They’re a rich source of behavioral data for online customers.

Customer Relationship Management (CRM) Systems

CRM systems contain details of past customer service interactions, support tickets, engagement history, and communication preferences. This provides a view into service and engagement behaviors.

Loyalty Programs

Loyalty programs track attributes like member tier level, points earned, benefits redeemed, and participation across partners. This data reveals loyalty behavior patterns.

Marketing Automation Platforms

Tools like Adobe Campaign and HubSpot house granular data on email campaigns, landing pages visited, CTAs clicked, and response to different promotions. This shows marketing effectiveness.

Master Data Management (MDM) Systems

MDM systems are the “golden record” for core master data like customer contact information, demographics, segmentation, and other domains. This provides a base identity.

Analytics Platforms

Analytics platforms aggregate and analyze behavioral event data across channels. They enable granular tracking of engagement, conversion, churn risk, product affinities, and more.

Unifying insights from these systems provides a multifaceted view of each customer. But major challenges exist in bringing this disparate data together...

Challenges with Retail Data Management

While having many specialized systems allows retailers to deliver robust capabilities across channels, the fragmented data silos also create substantial pain points:

• Data inconsistencies: With data residing in separate systems, there are often mismatches and discrepancies across sources. For example, two systems having different email addresses or birthdates for the same customer.
• Integration headaches: Moving data between systems with complex APIs and ETL processes is cumbersome. IT teams must build brittle point-to-point connections.
• Limited single customer view: Disjointed systems make it hard to connect insights about a customer from different channels. Information exists in isolation.
• Security and privacy risks: More data spread across more systems increases vulnerability to cyberthreats. It also makes managing privacy permissions complex.
• ** Analytics constrained:** With data fragmented, it’s nearly impossible to perform analytics across the whole business. Insights are limited to individual data silos.
• Inefficient use of data: Duplicated and disconnected data spreads resources thin. Creating a unified layer on top with unified semantics is key to driving more value.

Solving these challenges requires a new approach to managing data across retail systems...

Best Practices for Unified Retail Data

To create a 360-degree customer view, leading retailers adopt new ways to manage data across their technology stack:

1. Implement a Retail Data Hub

A retail data hub (or retail data platform) provides a centralized semantic layer to connect and govern data from source systems. With well-defined schemas and unified language, it eliminates siloed data spread across specialized tools.

Key capabilities include:

• Connectors to ingest data from POS, ecommerce, CRM, and other systems
• Pre-built data models for customer, order, cart, inventory, pricing, and other domains
• Metadata management for consistent data definitions and rules
• Identity resolution to stitch customer records into golden profiles
• Data quality to cleanse, enrich, and maintain reliable information
• Security and access controls for managing privacy and permissions

With a hub underpinning systems, data is harmonized under common standards. This makes it easily accessible for enterprise use cases.

2. Use a Data Mesh Architecture

Data mesh pushes control of data domains to self-serving teams rather than IT. For retailers, this means merchant, marketing, and customer service teams can autonomously manage the data assets they need.

The retail data hub serves as an interoperability layer between domains. Technology teams provide an architectural framework, tools, and standards for teams to build domain-oriented data products. They empower teams to curate trustworthy data for business use cases.

This decentralizes data management and puts ownership in the hands of domain experts. Coordinating through a hub retains consistency.

3. Make Customer Data Accessible with a CDP

A customer data platform (CDP) provides marketer-friendly access to customer data for activation. It segments audiences, builds profiles, activates campaigns, and measures results.

The CDP connects natively to the retail data hub. This gives marketers a ready-to-use 360-degree customer view for executing hyper-personalization without IT help.

With data democratized through the hub, CDPs make customer data readily accessible to frontline business users.

4. Embrace Developer Self-Service with APIs

As teams across the business look to innovate with data, IT can unlock flexibility and time-to-value with self-service.

Using modern API gateways, they can expose retail data as reusable APIs. Developers can tap into these APIs to easily build new applications and customer experiences.

This “API economy” approach accelerates data leverage and innovation. It lets developers self-serve data access without disrupting underlying systems.

With these best practices - a retail data hub for harmonizing data, data mesh for decentralized ownership, CDP for activation, and APIs for self-service access - retailers can overcome data fragmentation. They gain a trusted 360-degree customer view to engage shoppers intelligently.

Delivering Personalized Shopping with a 360-Degree View

Constructing this unified profile is just the first step. The real business value comes from activating it across the shopping journey:

Website Personalization

Leverage behavioral, transactional, and preference data to customize product recommendations, promotions, search results, and website content for each visitor.

Omnichannel Fulfillment

Use channel preferences to enable buy online, ship-to-store or buy online, pickup in-store. Stock stores based on local demand.

Loyalty and Promotions

Offer personalized rewards, gift suggestions, and bonus promotions based on purchase history and activity profile.

Customer Service

Equip agents with full interaction history and purchase information to resolve issues quickly and deliver personalized care.

Store Experience

Recognize loyal customers in-store via mobile apps. Offer VIP services and tailored offers based on preferences.

Marketing Campaigns

Orchestrate consistent messaging across channels tailored to customer interests and behaviors.

With a 360-degree view fueling these use cases, retailers can shift from broad-based to hyper-personalized shopping. This drives revenue, loyalty, and differentiated customer experiences - key competitive advantages in today's digital retail landscape.

A 360-degree customer view unlocks immense value, but it relies on connecting disparate data across complex retail technology stacks. With the right data management approach - unified retail data platforms, decentralized data ownership, and self-service access - retailers can conquer data fragmentation.

They can bring a complete picture of every customer journey into focus and use it to engage shoppers in more meaningful ways. This data foundation is key to providing the personalized omnichannel experiences that today's consumers expect.

1. What are the key benefits of a 360-degree customer view?

A 360-degree view provides a single, complete picture of every customer by unifying data across all systems and touchpoints. This enables hyper-personalization across channels, drives higher conversion, improves customer satisfaction through tailored experiences, increases wallet share through personalized recommendations, and provides a competitive advantage. With a 360-degree view, retailers can anticipate customer needs, solve problems proactively, and foster brand loyalty.

2. What types of data are required to build a 360-degree customer view?

Key data types needed include:

• Demographic data like name, age, location, and household details
• Behavioral data such as web browsing history, search terms, clicks, and in-store interactions
• Transactional data including purchase history, items bought, prices, purchase frequency, returns
• Communication data like marketing response across channels
• Loyalty data including tier level, partner activity, and benefits redeemed
• Engagement data including service interactions, cases, feedback, and communication history

A wide variety of data across these categories from systems like POS, ecommerce, CRM, marketing automation, and analytics are all required to construct a robust unified profile.

3. How can retailers connect siloed data from across their technology stack?

Retailers need to implement a retail data platform or hub that provides a centralized semantic layer to ingest and harmonize data from all source systems. This hub manages consistent data definitions, schemas, metadata, access controls, and identity linking to create golden customer records. It eliminates disjointed data spread across specialized tools into a single version of the truth.

4. How does a customer data platform differ from a retail data hub?

A retail data hub consolidates data from all systems for use across the enterprise. A customer data platform (CDP) specifically focuses on unifying customer data for marketing and customer experience use cases. The CDP is tailored for marketers to segment, analyze, and activate customer data. It can sit alongside and connect to a broader retail data hub to enable marketing use cases.

5. How can retailers shift from IT-owned data management to self-service?

Retailers can embrace a "data mesh" architecture where domain-oriented data management is decentralized to business teams rather than IT. This puts customer data curation in the hands of frontline marketers and customer service teams. IT provides standards, tools, and a retail data hub for interoperability. APIs also allow self-service access to data.

6. What data governance considerations are important?

With larger volumes of connected data, having strong data security, access controls, and privacy protections is crucial. Rules for data usage must be clear. Compliance with regulations like GDPR is also key as personalization intensifies. Ongoing consent from consumers on data usage needs to be communicated and maintained.

7. How can data quality be maintained at scale?

The retail data hub layer needs capabilities like automated data quality monitoring, outlier detection, missing value imputation, and robust master data management. Reference data and data dictionaries ensure consistency. Data stewardship processes that continuously monitor and improve data veracity are essential.

8. How can retailers future-proof their 360-degree view?

Adopting flexible, cloud-native platforms provides the agility to continuously adapt to new sources and use cases. APIs, microservices and modular architecture allow painless extension. Using machine learning and AI on top of the 360-degree view enables more predictive insights over time. Focusing on semantics and architecture over fixed schemas also sustains evolution.

9. What risks does fragmented customer data create for retailers?

Fragmented data leads to inconsistent decision-making, poor visibility into customer behavior, duplication of efforts, security vulnerabilities from information spread across systems, constrained analytics, and ineffective marketing. Without a unified view, retailers operate with an incomplete picture of their customers.

10. How can the value of a 360-degree view be measured?

Key metrics to track include:

• Data consistency rates
• Degree of customer profile completeness
• Time to generate insights and action campaigns
• Uplift in customer retention and share of wallet
• Reduction in redundant data sourcing efforts
• Improvement in personalization accuracy
• Increased cross-sell and up-sell revenue
• Higher customer satisfaction and NPS scores

Continuously measuring these quantifiable outcomes is crucial to demonstrate the business impact of a 360-degree customer view.

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