Two Workstreams
The immediate need is reducing non-value consumption. The longer-term need is preserving visibility into authentic customers, improving data quality, and avoiding disruption to procurement and purchasing workflows. Both matter — and they map to two distinct workstreams.
Reduce unnecessary analytics consumption
Cut the automated traffic that consumes resources without representing real engagement or purchasing intent.
- Identify scraping sources
- Review authenticated vs. unauthenticated traffic
- Improve bot detection
- Evaluate edge-level controls & pre-collection suppression
- Protect procurement & approved partner integrations
Build a sustainable analytics foundation
Separate from the immediate overage challenge — capabilities that support longer-term, account-centric objectives.
- Visibility into procurement-driven buying behavior
- Account-centric customer analysis
- Consistent bot governance & enterprise data integration
- Repeat-purchase & share-of-wallet growth
- Continuous evolution of bot-classification strategies
Non-value consumption from scraping and automated traffic.
A clear, authentic view of real customer behavior and intent.
Procurement and approved partner workflows that drive real revenue.
How Adobe Analytics handles bots today
Adobe Analytics provides several established methods for identifying and filtering bot traffic. These are effective for improving reporting quality — but they have limits worth understanding.
Industry bot lists
Industry-standard identification through maintained crawler and bot lists — effective for many known crawlers and automated agents.
- Known crawlers
- Maintained lists
Custom bot rules
Organizations define their own logic to sharpen reporting accuracy — though rules require ongoing maintenance as bot behavior evolves.
- User agents
- IP addresses
- IP ranges
- Traffic patterns
Segmentation & reporting controls
Segments, Virtual Report Suites, and custom reporting views remove bot traffic from the environments teams analyze.
- Segments
- Virtual Report Suites
- Custom views
What this solves
- Removes known automated traffic from analysis
- Reporting quality improves
- Data quality improves
What this doesn't solve
- Much filtering happens after data has already entered the analytics environment
- Reporting and data quality improve — but consumption may not decrease proportionally
- Significant scraping often needs controls closer to the point of collection
Why bot management is becoming harder
Historically, bot management was straightforward: identify a crawler, block it, remove it from reporting. Today's environment is far more complex — and some automation now carries real business value.
A single decision — remove the crawler — covered nearly every case.
AI-generated and AI-assisted traffic often represents real customer intent — even when the interaction originates from software automation.
Customer Journey Analytics takes a different philosophy
Instead of treating bot traffic primarily as something to remove, CJA treats bot signals as part of the underlying data model — so classifications can evolve without rebuilding reporting environments.
Bot logic ends up embedded across dozens of reports and segments.
Decisions are centralized and governed — not scattered across reports.
Bot signals become data
Classifications live alongside behavioral data, detection methods evolve over time, and definitions can be refined without rebuilding reporting.
Consistent definitions across teams
Apply bot logic at the Data View level so analytics, marketing, merchandising, and leadership share one consistent view of "clean traffic."
Machine learning & behavioral detection
Go beyond known bot lists and user-agent matching to evaluate traffic velocity, navigation behavior, event patterns, and automation signals.
Future-proofing for an AI-driven world
Understand AI-driven discovery, measure AI-generated engagement, and connect AI-influenced journeys to business outcomes.
Where CJA B2B may create value for HD Supply
HD Supply runs a highly account-centric business — authenticated customers, procurement systems, and enterprise integrations drive purchasing. These are the strongest areas to explore, drawn directly from discovery discussions.
Customer and transaction data often lives across ERP, procurement systems, web analytics, customer service tools, data warehouses, and account platforms. CJA B2B can unify it into a single analytical view.
- Account-level customer behavior analysis
- Visibility into customer relationships across systems
- Reduced manual data preparation & faster insights
- Consistent measurement across teams
- What differentiates our highest-value accounts?
- How do buying patterns vary by customer segment?
- Which accounts show signs of growth or decline?
- Which behaviors are most predictive of repeat purchasing?
HD Supply supports extensive procurement and punchout integrations across major ecosystems. Many organizations can measure transactions but struggle to understand the digital behavior influencing them.
- Understand product discovery before procurement purchases
- Visibility into procurement-driven revenue
- Analysis of procurement customer journeys
- Identify friction points before purchases occur
- Which content influences procurement purchases?
- What actions occur before punchout transactions?
- Which procurement channels drive the greatest customer value?
- Where are opportunities to increase procurement adoption?
For many B2B distributors, growth depends on repeat purchases, retention, and expansion within existing accounts. CJA B2B can help HD Supply understand what drives them.
- Customer retention & repeat-purchase patterns
- Account expansion & cross-sell opportunities
- Share-of-wallet trends
- Customer health indicators
- What behaviors lead to repeat purchases?
- Which customers may be at risk of decreasing spend?
- What product categories are commonly purchased together?
- Where can we expand revenue within existing accounts?
HD Supply manages a large, complex catalog spanning multiple product categories. That scale creates rich opportunities for product, search, and recommendation analysis.
- Product affinity analysis
- Search effectiveness & recommendation performance
- Product adoption patterns
- Category growth & product journey analysis
- Which product journeys generate larger orders?
- Which products contribute most to repeat purchasing?
- How effective are recommendation strategies?
- Which products see strong interest but low conversion?
Many organizations struggle to combine customer, transaction, digital, and operational data into a single analytical framework. This is foundational to everything above.
- Unified reporting across systems
- Faster business-user access to insights
- Reduced dependence on manual data preparation
- Consistent measurement standards & better decisions
- How much effort is spent manually preparing data?
- Which business questions are difficult to answer today?
- Where do reporting gaps exist across systems?
- Which analyses require significant analyst involvement?
Traditional web analytics vs. Customer Journey Analytics B2B
| Traditional web analytics | Customer Journey Analytics B2B |
|---|---|
| Focused primarily on website visits and sessions | Focused on people, accounts, customers, and transactions |
| Measures digital interactions in isolation | Connects data across multiple business systems |
| Designed around web analytics data collection | Designed to analyze customer journeys across channels and sources |
| Server-call consumption model | Data-row based analytical model |
| Primarily digital-behavior focused | Supports online, offline, and enterprise data sources |
Suggested focus areas for further exploration
Based on HD Supply's business model and digital ecosystem, these are the strongest areas to explore next. This framework is intended to facilitate discovery — not to prescribe a solution.
How can HD Supply leverage its authenticated customer data, procurement ecosystem, and enterprise data assets to better understand customer behavior, improve retention, increase share of wallet, and make faster business decisions?
Areas of Discussion for Customer Journey Analytics
Twenty-three questions to guide the conversation, grouped by theme. Expand any group to explore.
01 · Business priorities
- What are the top business outcomes HD Supply is focused on over the next 12–24 months?
- What metrics are most important to leadership teams today?
- Which business decisions would benefit from better visibility and faster access to insights?
02 · Customer & account analytics
- Can teams easily view customer activity across all relevant systems today?
- How is account-level behavior currently measured?
- How are parent–child account relationships analyzed?
- What customer insights are difficult to obtain today?
03 · E-procurement & punchout
- What percentage of business now flows through procurement systems?
- Which procurement platforms are most strategic to future growth?
- What visibility exists into customer activity before procurement transactions occur?
- What procurement-related questions remain unanswered today?
04 · Repeat purchase & customer growth
- How is customer retention measured today?
- What indicators are most predictive of account growth?
- How is share-of-wallet expansion tracked?
- What opportunities exist to drive additional category adoption?
05 · Product analytics
- What product performance questions are most difficult to answer today?
- How is search effectiveness measured?
- How are recommendations evaluated?
- Which product categories represent the greatest opportunity for growth?
06 · Data & enterprise architecture
- What systems contain the most important customer and transaction data?
- How much manual effort is required to combine data sources?
- Which business questions require analyst support today?
- What reporting challenges would you most like to eliminate?