How to implement account-based attribution

1. Choose the outcome and account scope

Start with a question such as which campaigns contribute to qualified pipeline among target accounts. Agree on the opportunity definition, value, and period so the team can inspect the source of any difference in the report.

Pick outcomes that match how leadership runs the business

• Choose a reliable outcome, such as qualified opportunity creation or closed-won.

• Select the measures needed for the decision: attributed pipeline or won-deal value, relevant costs, account quality, and time to outcome.

• Write down exclusions: renewals, self-serve, partner-sourced, expansions, regions, or segments you don’t want in v1.

• Record the account group, selected stage, dates, and filters. Save changes that affect comparisons.

Keep contacts, companies, and opportunities distinct

Account-based attribution connects activity from people at the same company and assigns credit for a selected pipeline or revenue outcome. Contact detail helps describe the people involved, while the account view helps connect their interactions to the purchase.

For example, a marketing manager attends a webinar, an operations colleague reads a product page, and a director requests a product demo. Those events can all belong to one buying journey when the company relationships support the connection.

• Use the company view to understand the buying group and inspect the opportunity relationships behind pipeline or revenue.

• Use contact-level journeys for contact-scoped program outcomes (for example, webinar attendance as a program KPI).

• When a company has several opportunities, define how activity relates to each outcome. A company domain alone does not establish which deal an interaction supported.

Write the question, outcome, value definition, company relationships, and filters in a short scope note. Keep it beside the report so a reviewer can understand what the numbers describe.

2. Check the source records

Before choosing weights, inspect the CRM dates and values, company relationships, and campaign sources. A model can allocate incorrect data consistently, so correct the records that could change the decision.

Review the essentials

• CRM objects and fields: Account/Company, Contact/Lead, Opportunity; opportunity amount; created date; close date; stage field; and stage change timestamps (or field history).

• Stage definitions: written definitions for Opportunity Created and Closed-Won, including which opportunity types count (new business vs expansion) and which pipeline stages map to your reporting.

• Account mapping rules: company domain mapping, duplicate handling, and how subsidiaries/parent accounts are treated.

• Ownership fields: account owner and opportunity owner populated where you expect Sales follow-up and reporting cuts.

Connect the systems you want included in the journey

Dreamdata's account-based data model is the foundation for automated collection and attribution. It connects recorded activity from people, companies, and go-to-market sources with pipeline and revenue.

• Connect the supported CRM, campaign, event, and website sources relevant to the outcome. Check available objects and historical coverage.

• Install/verify tracking: deploy the tracking script across all web properties used for demand capture (main site, blog, docs, landing pages).

• UTM hygiene: standardize source/medium/campaign naming so channel and campaign reporting is usable.

Start with the milestone that answers your business question. Earlier contact outcomes can provide context, but keep them separate from qualified pipeline and won-deal value.

3. Inspect the account journeys

Review familiar accounts with sales and RevOps. Confirm the recorded activity, people, and outcomes fit together before interpreting a campaign's allocation.

Check the expected activity

Dreamdata's Customer Journeys show recorded account and contact timelines. Use a known campaign or deal to verify which sources are represented and which gaps need attention.

A short journey is not automatically a tracking failure. Compare it with known records and campaign activity. Missing event attendance and genuinely limited engagement call for different responses.

Review a varied sample

1. Include won, lost, and open opportunities, plus a parent/subsidiary example and a company with multiple deals.

2. Check touch types: web activity (anonymous and known), paid clicks, email engagement, form fills, and sales activities where available.

3. Confirm roll-up: contacts associate to the correct account (domain mapping, merged duplicates, subsidiaries if relevant).

4. Confirm timestamps: first touch, opportunity created, key stage changes, close date.

5. Log gaps as fixable items: missing integration, missing UTMs, missing field history, duplicate accounts, mis-mapped domains.

Record each material issue, affected account, owner, and proposed fix. Recheck the same account after the correction so you can see whether the relationship or missing event is resolved.

4. Configure stages and attribution

In the Data Hub, define the records representing each outcome and preview them against the CRM. Then apply an attribution model to eligible activity before the selected outcome.

Define the outcome records

1. Stage model: qualified opportunity
      ◦ Object: Opportunity
      ◦ Filter: the agreed opportunity type and qualification criteria
      ◦ Timestamp: when the defined qualification event occurred; opportunity creation and later stage entry may be different dates
      ◦ Value: the agreed pipeline amount and currency

2. Stage model: Closed-Won
      ◦ Object: Opportunity
      ◦ Filter: closed-won (and new business if that’s your scope)
      ◦ Timestamp: close date
      ◦ Value: the agreed won-deal field, with contract value or recurring value clearly labeled

3. Journey relationship: verify the company, contact, and opportunity connections for the selected stage, especially where several deals share an account.

Compare credit models

Dreamdata supports single-touch models such as first-touch and last-touch, alongside multi-touch options such as linear, U-shaped, W-shaped, and data-driven.

Account-based measurement and multi-touch allocation describe different aspects of the setup.

• Linear can provide an equal-credit baseline across eligible interactions.

• A milestone-weighted or data-driven model can provide another perspective. Keep the outcome, account group, dates, and filters consistent during the comparison.

Illustrative example: a $100,000 deal with four eligible touches assigns $25,000 to each under linear attribution. If those touches involve three contacts at the company, they still explain one $100,000 deal. Combining the buying group does not multiply the deal value.

Optional: review LinkedIn impression attribution

Dreamdata supports LinkedIn impression attribution using account-level ad exposure. It combines that exposure with a multi-touch model under an explicit importance setting. Account-level exposure does not identify which individual saw the ad.

• Compare the report with impression credit disabled and enabled, using the same accounts and outcome. Choose an importance assumption your team can explain rather than applying a default because the channel is large.

• With impression attribution disabled, the records can still appear in journeys and influenced metrics, while receiving no attributed credit.

• Record the setting and investigate meaningful allocation changes before using them for a budget recommendation.

Keep the stage, model, eligibility rules, impression setting where used, and filters in the configuration note. Attribution assigns recorded contribution; experiments help test additional impact.