Use Case Intelligence RSS Search use cases Search
PwC Commercial and Professional Services Global

SHRM

Marketing Personalization

SHRM needed to replace a fragmented, transactional member experience with a connected digital platform that could personalize journeys, modernize commerce, and support growth for a global membership base.

Read the original story Explore all use cases
43% faster page load times
52% increase in site performance
65% improvement in checkout speed
more than 325,000 members in 180+ countries

Reported impact

Business outcome

SHRM gained a unified view of members, faster site and checkout performance, better conversion and retention potential, and a digital platform that can deliver personalized journeys to a global membership community.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

SHRM replaced a fragmented, one-size-fits-all digital experience with an Adobe-based engagement and commerce platform designed around personalization. PwC helped unify data across SHRM’s enterprise systems and implemented Adobe Experience Manager, Adobe Commerce, and Adobe Real-Time CDP so member interactions, content delivery, product recommendations, and checkout all operate from a connected profile and analytics foundation. Personalization AI uses real-time engagement behavior to guide what resources, experiences, and offers members see, while the commerce stack supports fast, low-friction purchase flows for learning, certification, and other services. The program was built with a member-first operating model, mapping journeys and pain points before implementation and then feeding live usage data back into continuous improvement. The result is a digital platform that can orchestrate tailored journeys globally while giving teams a unified view of how and why members engage.

Data flow

Member, commerce, and engagement data from SHRM’s enterprise systems flows into Adobe Real-Time CDP, where profiles are unified and activation signals are shared with Adobe Experience Manager and Adobe Commerce. Personalization AI uses real-time behavior to tailor content, recommendations, and checkout experiences, while analytics feed continuous improvement of member journeys.

Solution architecture

4 components · 3 layers
  1. Compute
    • advanced personalization AI Uses real-time behavior to tailor content, recommendations, and next-best experiences for members.
  2. Storage
    • Adobe Real-Time CDP Unifies member data and engagement signals into connected profiles that support personalization.
  3. Serving
    • Adobe Experience Manager Delivers the digital content and member-facing experience layer for SHRM’s new platform.
    • Adobe Commerce Runs the digital commerce flow for certification, learning, and other member transactions.

Architecture clues

  • Adobe Commerce supports the fast, low-friction checkout experience that reduced abandonment.
  • Adobe Real-Time CDP creates unified profiles from SHRM’s enterprise data.
  • Member journeys are refined continuously using real-time engagement analytics and feedback.
  • The platform supports both digital content delivery and commerce from one connected foundation.

Evidence from the source

By unifying data across enterprise systems and leveraging advanced personalization AI, PwC helped SHRM to deliver the required resources, the right experiences, and the appropriate product recommendations through a seamless, lightning-fast checkout experience that ultimately enabled us to create a more connected, engaging member journey.
Page load times dropped by 43%. End-to-end checkout speed also improved by 65%.
SHRM’s Lighthouse Performance Score jumped 52 points, reflecting major gains in speed, accessibility, and user experience.
We now have a unified view of our members and thorough analytics on when, how, and why they choose SHRM.
With the new, enriched data, our teams can continuously curate and orchestrate personalized journeys based on real-time engagement activity.
Transparency Evidence & methodology 100/100 reference value · 85% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence85%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency10/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. PwC

    Modernizing member engagement through digital platforms: PwC

    First-party
    https://www.pwc.com/us/en/library/case-studies/shrm-transforming-member-engagement.html
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Region
    • Company size
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • Business outcome
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Mar 4, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Aug 4, 2026 · PwC · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry. Treat these normalized attributes as lower-confidence until a dedicated source is added.

Related

Similar use cases

See all like this
PwC Aug 31, 2026 Seattle Mariners Media and Entertainment United States The Mariners wanted to grow fan engagement and ticket sales, but their marketing still relied on six broad segments, annual survey responses from about 50,000 fans, and disconnected CRM, ticketing, and engagement… 1.15 million fans tracked across disparate data sources Marketing PersonalizationReal-time Analytics Anthropic · Mariners Intelligence · Microsoft Azure Databricks Jul 8, 2026 MiQ Commercial and Professional Services Global MiQ needed a better way to support agent-assisted media planning and campaign optimization because massive audience and campaign datasets were spread across separate tools, forcing planners to manually stitch together… Number of campaigns for more than 2,300 advertisers powered by Sigma (since launch) Enterprise Knowledge SearchMarketing Personalization AI Search · Agent Bricks · Databricks Data + AI Platform Google Cloud Modified · Jun 19, 2026 Genscom Commercial and Professional Services Belgium Genscom wanted to automate highly personalized print campaigns at scale, but it needed to digitize legacy print assets, combine live customer data with physical production, and handle order spikes without sacrificing… Marketing PersonalizationSales Automation BigQuery · Cloud Run functions · Firestore