HD Supply’s buyers research products online, authenticate, move across devices, and often complete the purchase through an e-procurement workflow.
Customer Journey Analytics (CJA) extends Adobe Analytics into a more flexible environment for understanding those connected journeys, so HD Supply can see authenticated buyer behavior, e-procurement, customer and account segments, journey friction, and unusual changes in performance in one place.
From digital activity to connected customer journeys.
Adobe Analytics is a strong foundation for understanding digital behavior. CJA expands it by bringing digital and other customer and business data together for journey-level analysis. For HD Supply, that means answering a broader question: what happens between product research, authenticated buyer activity, e-procurement, and the eventual transaction?
| Capability | Adobe Analytics | Customer Journey Analytics |
|---|---|---|
| Primary focus | Digital behavior | Connected customer journeys |
| Data | Primarily web and app interactions | Digital, customer, procurement, transaction and other event data |
| Data model | eVars, props, events and report suites | Flexible schema-based dimensions and metrics |
| Journey analysis | Primarily digital interactions | Analysis across connected datasets and channels |
| Identity | Primarily visitor and device analysis | Person-centric analysis across connected identity |
| Reporting logic | Many definitions established through implementation | Greater flexibility in the analytical layer |
| Sessions | Traditional visit/session processing | Configurable session definitions |
| Historical analysis | Many changes are point-forward | Many analytical rules can be applied to historical data |
| Attribution | Digital attribution | Attribution across connected journey data |
| Insights | Digital reporting and analysis | Journey analysis, anomaly investigation and AI-assisted insights |
Connected data, consistent identity, governed Connections and Data Views, and continuous data-quality validation. Connecting web research to procurement outcomes depends on a reliable customer, person, account, transaction or other common identifier. The recommended approach is to begin with priority journeys and business questions rather than connecting every dataset at once.
When the necessary data and connecting identity are available, CJA can analyze product research, authentication, PDP engagement, e-procurement and the transaction as one broader journey.
E-procurement, repeat purchasing, share-of-wallet growth and authenticated buyer behavior are HD Supply priorities. CJA makes them easier to analyze together.
How do authenticated and unauthenticated journeys differ?
What digital behavior precedes an e-procurement transaction?
Where do buyers encounter friction?
How do journeys differ by device or customer segment?
What behaviors are associated with repeat purchasing?
Which interactions precede downstream transactions?
The opportunity is not simply more reporting. It is a more complete understanding of how customers research, engage and purchase.
A Data View separates the underlying data from many of the rules used to analyze it. Rather than embedding every analytical decision into data collection, HD Supply can configure how connected data behaves for different users and business questions.
Connections and Data Views can also be governed through ownership, access controls, documentation, monitoring and change management. The underlying event does not change. How HD Supply interprets it for analysis can.
Focused on how buyers move through the site experience, from first search to checkout.
Focused on what happens before a procurement-system order. A reliable connecting identifier remains essential to linking web research with procurement outcomes.
HD Supply’s B2B structure makes this view particularly relevant: known buyers, the business accounts they act for, and how both engage and purchase over time.
A curated view of trusted business metrics, built on governed business definitions.
The same connected data can support different analytical perspectives without creating a separate collection model for every business question.
Derived Fields create new analytical values from information that has already been captured. Rather than rewriting the original event, the analytical layer creates a more useful interpretation of the available data.
Different site releases represent the same page in different ways.
Especially relevant because HD Supply has described maintaining analytics tagging through releases as challenging.
Procurement-related traffic was historically classified as Referral, Direct or Partner.
When referring domain, source, URL parameters or campaign values contain enough information to recognize that traffic, HD Supply can analyze procurement-source activity with one common definition.
Existing fields become easier-to-use dimensions.
Definitions would reflect HD Supply’s available data and agreed business rules.
“We fixed it, but history still reflects the old definition.”
When tagging or reporting logic changes, analytics teams can traditionally end up saying exactly that. CJA gives more flexibility because many reporting definitions are managed in the analytical layer.
When the required source information exists, a tagging or classification issue does not necessarily have to remain a permanent historical reporting problem. A successful foundation still requires reliable identity, schemas, pipelines, quality controls and governance.
Native CJA provides the analytical capabilities to identify unusual behavior, monitor important KPIs, and investigate changes in Analysis Workspace.
CJA can identify when a metric behaves differently from its expected pattern. Instead of seeing “authenticated traffic declined,” the analyst can tell when that decline is unusual enough to warrant investigation.
Not “did the metric move?” but “did it move in a way that is meaningfully different from what we normally expect?”
Alerts monitor selected KPIs so analysts do not have to keep watching dashboards. Two complementary approaches:
Threshold alert: did the metric cross a boundary we defined? Anomaly detection: is it behaving differently from what history suggests is normal?
Example: authenticated traffic drops unexpectedly.
CJA identifies unusual behavior.
The appropriate users are notified.
Break the decline down by device, customer segment, product category, traffic source, journey stage and e-procurement activity.
Use Analysis Workspace dimensions, segments, filters and visualizations to see where the change occurred.
Intelligent Captions summarize notable patterns on supported Workspace visualizations.
Decide whether the cause points to customer behavior, site experience, measurement, a release, product availability or something else.
| HD Supply asks… | Native CJA capability |
|---|---|
| “Is traffic behaving unusually?” | Anomaly Detection |
| “Tell us if traffic drops more than X%.” | Threshold / percentage alert |
| “Notify us when a KPI behaves abnormally.” | Intelligent Alerts |
| “Where did the decline occur?” | Analysis Workspace |
| “Which device, segment, product or source contributed?” | Dimensions, segments, filters and breakdowns |
| “Explain what this visualization is showing.” | Intelligent Captions |
| “Build and share an analysis.” | Analysis Workspace projects |
Intelligent Captions explain the visualization already on screen. They are not a conversational agent that builds an analysis from a request. The broader requirement is not only detecting an anomaly, but understanding how it relates to authenticated buyers, segments, devices, friction and procurement journeys.
HD Supply asked whether natural language could be used to build reports or dashboards. The distinction matters, so here it is plainly.
The analytical environment, anomaly investigation and AI-assisted visualization summaries.
A conversational layer where users start with the business question instead of building the analysis first. It is not a prerequisite for anomaly detection, alerting or Workspace analysis.
Current Adobe positioning describes Coworker supporting natural-language analysis, KPI investigation, root-cause exploration, executive summaries and comparisons across journeys, channels and audiences. It can also move conversational analysis into live CJA Analysis Workspace projects while preserving relevant analytical components.
Note Adobe documentation currently labels the in-product experience for building and editing Workspace projects with Coworker as Limited Testing.
Start with the business question.
Use follow-up prompts to explore potential contributors.
Create a starting point for deeper Workspace analysis.
Translate analysis into concise explanations for stakeholders.
Validate and deepen the analysis using governed CJA data.
Coworker is an additional capability, not a prerequisite for CJA, and it is scoped separately from core CJA with consumption-based AI Credits. CJA stands on its own. Coworker can be evaluated separately if conversational analytics and assisted analysis creation become priorities.
Bring together relevant digital, customer, account, procurement and transaction data.
What happened across the journey?
Use Data Views to establish governed analytical definitions.
How should we analyze it?
Use Derived Fields when classifications or business definitions change.
Can we reinterpret without recollecting?
Watch priority metrics and surface unusual changes.
Did something important change?
Use Analysis Workspace to isolate the issue across customer, device, product, account and procurement dimensions.
Where did it change?
Add business context and determine the response.
What does the evidence tell us?
HD Supply can move toward CJA without abandoning Adobe Analytics overnight. Adobe Analytics can remain a reporting baseline while CJA is built, compared and validated.
The current CMS and PDP redesign is a natural moment to incorporate Web SDK, XDM, identity and CJA requirements while the experience is already being modernized. Web SDK provides a unified collection foundation across Adobe applications, but it is not auto-tagging: HD Supply still defines the business events and attributes it wants to collect.
Start with a priority journey.
Compare results and establish trust.
Enable analysts and business users.
Add datasets and use cases through governed patterns.
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