Digital Marketing: Lead Management & CRM

Analyze the core data entities (Contacts, Companies, Deals/Opportunities) and their relationships in a standard CRM data model.

Compare the architectural trade-offs of major CRM platforms (e.g., Salesforce, HubSpot, Pipedrive) regarding data model flexibility and API limits.

Define the stages of a lead lifecycle (e.g., MQL, SQL) and map them to a practical sales pipeline structure.

Design and implement a multi-stage sales pipeline in a CRM, defining clear entry/exit criteria and properties for each stage.

Model custom data requirements for a specific business by creating and deploying custom properties on CRM objects.

Map customer journey touchpoints for different business models (e.g., B2B SaaS vs. e-commerce) to specific pipeline stages and activities.

Configure core CRM automation rules for lead assignment, task creation, and deal stage progression.

Distinguish between demographic, firmographic, and behavioral lead scoring attributes.

Design a lead scoring model that combines explicit (profile-based) and implicit (behavior-based) rules.

Implement a lead scoring system within a CRM or marketing automation platform.

Create dynamic lead segments based on scoring thresholds, engagement levels, and funnel stage.

Use segmentation to prioritize sales outreach and trigger targeted marketing campaigns.

Analyze the performance of a lead scoring model by correlating scores with conversion rates.

Develop a strategy for lead score decay to maintain the relevance of engagement data over time.

Compare email, SMS, push notifications, and messenger platforms as communication channels by their technical capabilities, use cases, and limitations.

Analyze the legal and technical requirements for obtaining and managing user consent across different channels (e.g., GDPR, CAN-SPAM, TCPA).

Design a multi-channel contact strategy that maps appropriate channels and message types to different funnel stages.

Develop a contact frequency and suppression policy to prevent audience fatigue.

Evaluate channel suitability based on message type, audience preference, and geographic market.

Outline a notification preference center to allow users to control communication channels and frequency.

Explain the technical mechanisms of email deliverability: SPF, DKIM, DMARC, and sender reputation.

Analyze how inbox placement algorithms differentiate between spam, promotions, and primary inbox delivery.

Compare email marketing platforms (e.g., Mailchimp, ActiveCampaign, SendGrid) by their architecture, deliverability tools, and automation depth.

Design and build automated email sequences for key lifecycle stages (welcome, nurture, re-engagement).

Apply copywriting principles to write email subject lines and body copy optimized for engagement.

Set up, execute, and interpret A/B tests for email subject lines, content, and send times.

Analyze campaign metrics (open rate, CTR, conversion rate) to diagnose and optimize performance.

Explain the architecture of the Telegram Bot API and its capabilities for one-to-one and one-to-many communication.

Design a conversational flow for a chatbot to qualify leads or answer common questions.

Build and deploy a simple rule-based Telegram bot using a no-code platform or a basic script.

Integrate a Telegram bot with a CRM to create or update lead records based on user interactions.

Design a strategy for using Telegram channels and broadcasts for lead nurturing and announcements.

Compare the technical and strategic differences between marketing on Telegram, WhatsApp, and Facebook Messenger.

Implement lead capture from a Telegram bot directly into a marketing automation workflow.

Explain the event-driven architecture (triggers, conditions, actions) that powers marketing automation platforms.

Implement conditional branching ('if/then/else' and 'wait') in automation workflows to create adaptive user journeys.

Design a multi-stage lead nurturing workflow that dynamically adjusts content based on lead score and behavioral triggers.

Distinguish between rule-based and behavioral personalization, identifying the required data inputs for each.

Implement personalized content in campaigns using merge fields and dynamic content blocks based on CRM data.

Design a workflow for delivering behavior-based personalized offers, such as product recommendations or abandoned cart reminders.

Compare major marketing automation platforms (e.g., HubSpot, ActiveCampaign, Marketo) by their workflow logic and personalization capabilities.

Debug and troubleshoot complex automation workflows using logs and execution history.

Explain how retargeting works technically via pixel-based audiences and customer list matching.

Create custom audiences in Meta Ads and Google Ads based on website visitor activity (e.g., page visits, events, time on site).

Build and automatically sync customer list audiences from a CRM to ad platforms.

Use CRM data and segmentation to create highly targeted exclusion audiences (e.g., existing customers, unqualified leads).

Create lookalike (Meta) and similar (Google) audiences to scale prospecting campaigns.

Design an abandoned cart recovery sequence using both email automation and paid retargeting ads.

Develop a sequential messaging strategy for retargeting campaigns that aligns ad creative with the user's funnel stage.

Configure retargeting campaigns with appropriate audience targeting, exclusions, and frequency capping.

Compare attribution models (e.g., last-click, linear, data-driven) and their impact on channel performance evaluation.

Capture ad platform click identifiers (GCLID, FBCLID) in hidden form fields and pass them to the CRM.

Configure a CRM to store offline conversion data, including click identifiers and conversion timestamps.

Implement offline conversion uploads to ad platforms using both manual CSV imports and API-based automation.

Calculate and interpret funnel conversion rates at each stage of the lead lifecycle.

Calculate and interpret Return on Ad Spend (ROAS) and Customer Acquisition Cost (CAC).

Model Customer Lifetime Value (LTV) and analyze the LTV:CAC ratio to assess long-term profitability.

Connect disparate data sources (CRM, Google Analytics, Ad Platforms) to a dashboarding tool like Looker Studio.

Design a marketing dashboard that visualizes the full funnel from initial touchpoint to revenue.

Analyze a funnel report to identify and quantify the stages with the most significant drop-off.

Formulate and prioritize optimization hypotheses using a framework like ICE (Impact, Confidence, Ease).

Select the appropriate testing methodology (A/B, split URL, multivariate) for a given optimization scenario.

Establish a process for documenting, analyzing, and iterating on test results to inform future campaigns.

Perform a cohort analysis to track customer behavior and value over time.

Map the end-to-end data flow between a website, CRM, email platform, and ad platforms for a given business case.

Select the appropriate 'single source of truth' for core customer data and justify the choice.

Implement integrations between marketing tools using native connectors and no-code platforms (e.g., Zapier, Make).

Explain the difference between webhook-based and API polling-based integrations and when to use each.

Define data synchronization rules (one-way vs. two-way sync) and field mappings between connected platforms.

Outline an identity resolution strategy to merge customer profiles from different sources into a unified view.

Configure and validate end-to-end lead source tracking from ad click (UTM capture) to the CRM deal record.

Debug data discrepancies between platforms by inspecting API logs and webhook payloads.