Fragmented data to a unified workflow

AI-Enabled Customer Data Strategy

Fortune 500 enterprise for a business-case modeling, architecture, vendor evaluation, MVP definition, and a measurement framework designed to govern scale.

Request field mapping schema Request architecture diagram

CRM / Core: Salesforce (Sales & Service Cloud)
Data Foundation: Salesforce Data Cloud
Autonomous AI / Execution: Agentforce
Marketing Automation: Adobe Marketo Engage
Enterprise Data Warehouse: Snowflake

What I led

Translated fragmented customer data and manual activation into a scoped, governed MVP with decision gates for data quality, adoption, and business value.

Current and future state architecture

Documented the current state and future state architecture diagrams so the data flow, system boundaries, and gaps were explicit before build.

Object and field mapping

Determined which objects and fields should be mapped from Salesforce, Marketo, and Marketo Engage into the unified customer profile.

Partnered with Marketing

Worked with marketing leaders and campaign operators to define the priority use cases, audience needs, and what success would look like in real campaigns.

Partnered with stakeholders across the business

Aligned sales, data, IT, and security stakeholders on scope, data governance, phasing, and the decision gates before scaling beyond the MVP.

MVP Scope

Three MVP use cases

Scope was limited to the three use cases that had to be true for the platform to be worth scaling. Everything else was deferred to full rollout.

Leads

Lead records ingested across paid media, organic, web forms, and campaign sources to prove identity resolution and source attribution end to end.

AI-assisted lead prioritization

AI evaluates available lead and account signals — such as source, engagement, fit, and buying intent — to help marketers identify the leads most ready for action.

Instead of treating every record the same, teams can prioritize follow-up, route leads to the appropriate owner, and focus campaigns on the accounts showing the strongest potential.

Validates · Ingestion, identity resolution, source attribution

Segmentation

Sample deliberately spanned key segments — industry, region, account type, and persona — to validate dynamic segment creation and maintenance.

AI-assisted audience creation

Marketers can describe the audience they need in plain language — for example, “show me accounts in healthcare with multiple locations, recent engagement, and an open opportunity.”

The AI translates that request into segment criteria using the available customer data, so users can create and refine audiences without needing technical expertise, complex queries, or manual list pulls.

Validates · Dynamic audience building and refresh

Buying Grids

Behavioral data, buying-intent signals, and service preferences imported to align opportunity progression with marketing touch.

AI-assisted buying-group intelligence

AI brings together account, contact, engagement, and intent signals to help reveal buying-group coverage across a target account.

It can surface where stakeholder coverage is incomplete, highlight engaged contacts and relevant roles, and help marketers build campaigns that reach more of the decision-making group — not just one known contact.

Validates · Intent signals and buying-group coverage

MVP success criteria

Identity match: >90%; Profile reliability
Audience build: >50% faster; Activation speed
Routing: >30% faster; Sales follow-up

586K

Total lead records in scope

Industry groups

Regions per industry group

MVP Deployed

Phase 3 — BNI, all 7 regions

Rollout Model

Prove it small, then repeat the pattern

Each phase expanded exactly one dimension — sub-region, then region, then all regions of the industry group — so every increase in scope tested one new variable before the phased rollout began. The MVP is now deployed across all seven BNI regions.

Phase 1POCSubset of 25,784 NE leadsDelivered
BNI — New England
Smallest credible slice: one industry group, one sub-region. Validated ingestion, identity resolution, and the minimal schema for all three use cases.
Phase 2Pilot25,784 leadsDelivered
BNI — full Northeast region
Widened to the complete NE region to test data volume, segment refresh behavior, routing accuracy, and lead-latency SLAs with real campaign activity.
Phase 3MVP128,920 leadsDeployed
BNI — all seven regions
Full industry-group coverage. Proved the operating model, governance, and reporting patterns that every subsequent group would reuse.

Measurement

How success would be measured

The MVP establishes a measurable path from trusted customer data to faster campaign activation and commercial impact. Metrics are reviewed at launch, 30 days, and 90 days — not presented as results until validated in production.

Outcome KPI	Baseline	MVP target	Why it matters
Addressable unified profiles	Establish at launch	≥ 80% of priority records	Shows whether the CDP has enough usable identity coverage to activate audiences
Time to create and activate an audience	Manual list-pull process	Reduce by 50%	Measures whether marketing can move from request to campaign faster
Duplicate / unresolved record rate	Establish at launch	Reduce by 25%	Proves that data quality is improving — not simply being centralized
Campaigns activated from CDP audiences	0 at launch	3–5 priority campaigns in MVP	Confirms the platform is being used in real work
MQL-to-opportunity conversion lift	Existing conversion rate	Test against control group	Connects segmentation quality to commercial performance
Pipeline influenced	Existing attribution baseline	Track by CDP audience	Gives leadership a credible value-realization measure

Measurement rulesBaseline before launch·Targets validated with Marketing and Sales·No ROI claim without attributable pipeline or conversion evidence·Data-quality metrics monitored weekly during MVP

Coverage Model

Seven industry groups across seven regions

The same regional structure repeats inside every industry group, which is what makes the rollout sequence predictable.

Industry groups

1Business & Industry (BNI)Commercial office, corporate campuses
2Manufacturing & Distribution (MND)Industrial plants, warehouses, logistics hubs
3Education (EDU)K-12 and higher-ed campuses
4Aviation (AVI)Airports, airlines, ground operations
5Healthcare (HC)Hospitals, medical office buildings
6Technical Solutions (ATS)Data centers, engineering-heavy facilities
7Government (GOV)Federal, state, municipal facilities

Regions

1Northeast (NE)New England, NY, NJ, PA
2Southeast (SE)FL, GA, Carolinas, TN, AL
3Southwest (SW)TX, AZ, NM, OK
4Midwest / CentralIL, OH, MI, IN, WI, MN
5Mid-AtlanticDC, VA, MD
6MountainCO, UT, NV, ID, MT, WY
7West / West CoastCA, OR, WA

Business & Industry was selected as the entry group because it carries the largest lead volume and the widest range of account types — the hardest test for segmentation.

Lead Distribution

Full lead distribution matrix (586K total)

Each row sums to its industry-group total and each column to its region total; both reconcile to 586,000. Minor variances are rounding.

Industry Group	NE	SE	SW	Midwest	Mid-Atl	Mountain	West	Total
Business & Industry (BNI)	25,784	20,627	18,049	20,627	15,470	12,892	15,470	128,920
Manufacturing & Distribution (MND)	21,096	16,877	14,767	16,877	12,658	10,548	12,658	105,480
Education (EDU)	17,580	14,064	12,306	14,064	10,548	8,790	10,548	87,900
Aviation (AVI)	14,064	11,251	9,845	11,251	8,438	7,032	8,438	70,320
Healthcare (HC)	14,064	11,251	9,845	11,251	8,438	7,032	8,438	70,320
Technical Solutions (ATS)	12,892	10,314	9,024	10,314	7,735	6,446	7,735	64,460
Government (GOV)	11,720	9,376	8,204	9,376	7,032	5,860	7,032	58,600
Region Total	117,200	93,760	82,040	93,760	70,320	58,600	70,320	586,000

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