Marketing Automation That
Converts More, With Less Manual Work.
Most organisations that invest in marketing automation platforms — HubSpot, Salesforce Marketing Cloud, Marketo Engage, or Adobe Campaign — capture only a fraction of the value the platform is capable of delivering. The tool is live, emails are going out, and some workflows are running. But the lead nurturing sequences are not converting because the content is generic rather than behaviour-triggered. The email open rates are declining because the segmentation is based on list membership rather than real-time behavioural signals. The CRM is not seeing what marketing is doing because the integration was never properly configured.
And the marketing leadership cannot answer whether the automation programme is generating pipeline or merely generating activity metrics that look impressive in a weekly report. SourceMash's marketing automation practice builds programmes that are designed from the revenue outcome backwards — what does qualified pipeline look like, what journey gets a prospect to that qualification threshold, and what automation makes that journey happen at scale with consistent quality and measurable ROI.
Every Major Platform. One Strategy Partner.
Marketing automation platforms are not interchangeable — HubSpot's strength is the unified CRM-Marketing-Sales-Service hub that gives revenue teams a single customer record without a separate CRM integration; Salesforce Marketing Cloud's strength is enterprise-scale journey orchestration with the depth of personalisation that large B2C programmes require; Marketo Engage's strength is B2B lead management with the most configurable lead scoring and account-based marketing capability in the market; and Adobe Campaign's strength is enterprise multi-channel campaign execution with the Adobe Experience Cloud data layer. Choosing the right platform for your programme — and implementing it to extract the capability that justifies the licence cost — requires genuine platform expertise rather than a generalist "we support all tools" approach.
SourceMash is certified across all four major marketing automation platforms and brings the strategic depth to design the customer journey and content architecture before the platform configuration begins — because a well-configured platform executing a poorly designed journey still produces poor results. Every engagement starts with journey design and audience strategy before we touch a single workflow builder or email template.
Marketing Automation Platforms We Implement
Certifications Held
Marketing Automation Platform Implementation & Configuration
A marketing automation platform implementation done well is not a tool deployment — it is a revenue operations infrastructure build. The difference between a marketing automation implementation that delivers measurable pipeline contribution and one that produces email volume metrics without business impact comes down to a handful of foundational decisions made in the first four weeks of the programme: how the contact database is structured and segmented, how leads are defined and qualified, how the scoring model maps to actual buying signals rather than demographic proxies, how the CRM integration is designed so that sales can see what marketing is doing without duplicate data and without manual syncing, and how the content architecture is mapped to the buyer journey before a single email template is built. Organisations that skip this architecture phase and go straight to email template design and workflow building in the platform produce programmes that are superficially active but structurally misaligned with how their customers actually make buying decisions.
SourceMash implements all four major marketing automation platforms — HubSpot Marketing Hub, Salesforce Marketing Cloud, Marketo Engage, and Adobe Campaign — with a consistent methodology: strategy and architecture before configuration, CRM integration as a first-class deliverable rather than a post-implementation activity, and a content and nurture sequence design that is grounded in the actual buyer journey for your specific audience and offer rather than adapted from a generic template. We also audit and rescue existing marketing automation implementations that are underperforming — where the platform is live but the programme is not delivering qualified pipeline.
Duration 8–16 weeks (scope-dependent)
Supported HubSpot, SFMC, Marketo, Adobe
Integration First-class, in-scope deliverable
Definition Included — not optional
Optimisation 90-day performance programme
Platform-Specific Implementation Scope
What we configure and build on each of the four major marketing automation platforms
HubSpot Marketing Hub Implementation
Full HubSpot Marketing Hub setup — contact property taxonomy and lifecycle stage architecture; landing page and form design with progressive profiling configuration; workflow automation for lead nurturing, lifecycle transitions, and internal notifications; email template design system; lead scoring using HubSpot's predictive and manual scoring tools; blog and SEO tool configuration; social media publishing integration; and Sales Hub CRM integration with deal stage-to-lifecycle-stage mapping and bidirectional contact sync.
Salesforce Marketing Cloud Configuration
Salesforce Marketing Cloud implementation — Business Unit architecture for multi-brand or multi-regional programmes; Journey Builder customer journey design and configuration; Email Studio template development with AMPscript personalisation; Audience Studio for behavioural segmentation and lookalike modelling; Mobile Studio for SMS and push notification automation; Data Extensions and SQL query design for complex audience segmentation; Marketing Cloud Connect for bidirectional Salesforce CRM synchronisation; and Marketing Intelligence reporting setup.
Marketo Engage Implementation
Marketo Engage setup for B2B demand generation — workspace and partition architecture for multi-product or multi-region programmes; Smart Campaign design with trigger and batch logic for the full lead lifecycle; lead scoring programme with demographic and behavioural score configuration; engagement programme (nurture stream) design; landing page and form design with Marketo progressive profiling; Salesforce CRM or Dynamics 365 bidirectional integration with field mapping and sync rules; Revenue Cycle Analytics for pipeline influence reporting; and Marketo Sales Insight configuration for sales-facing intelligence.
Adobe Campaign Implementation
Adobe Campaign Classic or Standard implementation — data model design for the Adobe Campaign operational schema; targeting and segmentation workflow design; delivery (email, SMS, direct mail) template design with personalisation blocks; Campaign automation workflow for multi-step, multi-channel journeys; Adobe Experience Cloud integration (Adobe Analytics for behavioural triggers, Adobe Audience Manager for segment import); typology rule configuration for frequency capping, fatigue management, and regulatory compliance; and campaign reporting with dashboards for campaign performance, deliverability, and audience reach.
Platform Audit & Rescue
Structured audit of underperforming marketing automation implementations — assessing database health (bounce rate, unsubscribe rate, contact age and completeness), workflow logic for errors and gaps, email deliverability configuration (SPF, DKIM, DMARC, IP warm-up status), CRM integration reliability and data divergence, lead scoring model accuracy against actual conversion data, and content architecture alignment with the buyer journey. Produces a prioritised remediation roadmap with the changes most likely to deliver immediate performance improvement.
Platform Migration & Data Transfer
Migration from legacy ESP or marketing automation platform (Mailchimp, ActiveCampaign, Eloqua, Pardot, Constant Contact) to a new platform — covering contact database export, cleansing, and import with engagement history preservation where supported; email template redesign for the new platform's template language; workflow recreation; form and landing page migration; suppression list and unsubscribe history migration to maintain compliance with DPDP Act, GDPR, and CAN-SPAM obligations; and parallel-run testing before decommissioning the old platform.
Customer Journey Design & Automation Architecture
A customer journey is not a marketing funnel diagram on a slide — it is the sequence of touchpoints, content, and automated interventions that move a specific audience segment from initial awareness through consideration to purchase decision, mapped to the actual signals that indicate where in the decision process each individual prospect is at any given moment. The gap between a journey that is designed on a whiteboard and a journey that is configured correctly in a marketing automation platform and performing as designed is where most marketing automation programmes lose their ROI: the journey was designed for a hypothetical average prospect rather than for the actual behavioural signals that the platform can observe, the content for each stage was never produced, the handoff from marketing automation to sales was never operationally defined, and the journey was never tested with real audience data before going live.
SourceMash's journey design practice builds customer journeys from the audience and objective first — who is this journey for, what decision are we trying to move them towards, what content actually addresses the questions they have at each stage, and what behavioural signals indicate readiness to advance. The journey architecture is then configured in the platform to observe exactly those signals and deliver exactly the right content at the right time — not on a fixed schedule, but triggered by the prospect's own engagement behaviour. We design and build journeys for B2B demand generation (awareness → MQL → SQL → opportunity), B2C customer acquisition and retention, post-purchase onboarding, renewal and upsell, and win-back for churned customers.
Built Acquisition, nurture, onboard, renew
Architecture Persona-based segment design
Design Behavioural — not fixed schedule
Definition MQL → SQL SLA and process
The Customer Journey — Five Stages We Design and Automate
Every automated journey we build covers the full prospect lifecycle — from first touch to sales-ready, and from customer to advocate
Journey Types We Design & Build
Specific journey architectures for each stage of the customer relationship — each with distinct trigger logic, content architecture, and success metrics
B2B Demand Generation Journey (Awareness → MQL)
Journey architecture for B2B organisations targeting specific ICP (Ideal Customer Profile) segments — gated content capture, progressive profiling to build demographic completeness, behavioural nurture stream with industry-specific case studies and thought leadership, lead scoring model calibrated to actual MQL conversion data, and MQL-to-sales handoff automation with context delivery (pages visited, content consumed, score breakdown) surfaced in the CRM for the sales rep's first call preparation.
B2C Acquisition & Retention Journey
Multi-channel B2C customer journey — welcome series for new subscribers, abandoned browse and cart recovery, post-purchase onboarding sequence for new customers, replenishment reminders timed to actual purchase cycle data, win-back sequence for lapsing customers with personalised incentive logic based on customer lifetime value tier, and loyalty programme trigger automation for milestone recognition and tier upgrade communications that drive repeat purchase behaviour.
Customer Onboarding Journey (Post-Purchase)
Structured onboarding automation that reduces churn in the critical first 30–90 days — feature adoption prompts triggered by product usage signals (or lack of them), milestone celebration emails when key adoption events occur, human check-in triggers for customers showing low engagement, training resource delivery sequenced to the customer's actual product usage stage, and CSM (Customer Success Manager) alert automation for accounts showing early churn risk indicators before the renewal conversation becomes difficult.
Renewal & Upsell Journey
Revenue expansion automation — renewal reminder sequences beginning 90 days before contract expiry with escalating urgency and personalised renewal benefit summary; upsell trigger automation based on product usage signals that indicate readiness for the next tier or adjacent product; NPS-triggered advocacy programmes that identify promoters and route them to case study and referral requests; and cross-sell recommendation journeys built on purchase history and behavioural data that surface the right adjacent offer at the right moment.
Win-Back & Re-Engagement Journey
Systematic re-engagement of churned or dormant contacts — engagement decline triggers that initiate re-engagement sequences before a contact goes fully dark, personalised win-back offers calibrated to the contact's historical purchase value and lapse duration, sunset sequences for contacts who do not re-engage (removing non-responders from active lists before they damage deliverability scores), and post-churn recovery programmes for cancelled customers with time-delayed incentive sequences based on churn reason data.
Account-Based Marketing (ABM) Journeys
Account-level journey orchestration for ABM programmes — target account list management in the MAP with account scoring that aggregates individual contact engagement signals to the account level; personalised multi-touch journey design for each buying committee role (economic buyer, technical evaluator, end user champion, procurement); account engagement heat-mapping that surfaces which target accounts are showing active buying signals; and sales-assist automation that alerts account executives when a target account crosses an engagement threshold that indicates active buying intent.
Lead Nurturing, Scoring & MQL Definition
Lead scoring is one of the most frequently implemented and least accurately calibrated capabilities in marketing automation. Most lead scoring models are built once during implementation, assign positive points to activities that intuitively feel like buying signals (downloaded a whitepaper –5 points, visited pricing page –10 points, attended webinar –8 points), and are never validated against actual conversion data. The result is a scoring model that fires MQL alerts based on accumulated engagement points rather than on demonstrated buying intent — sales receives MQLs that represent curious content downloaders rather than actively evaluating prospects, the MQL-to-SQL conversion rate is low, sales stops trusting marketing-qualified leads, and the alignment breakdown between marketing and sales that the scoring model was supposed to solve gets worse.
SourceMash builds lead scoring models from conversion data rather than from intuition — analysing the historical CRM records of closed-won deals to identify which activities, firmographic attributes, and behavioural signals were actually present on accounts that converted, and weighting the scoring model to reflect those proven signals. We also define the MQL threshold with sales leadership — not as a marketing decision but as a revenue alignment decision — and build the MQL-to-sales handoff workflow with the specific context delivery (what the prospect looked at, when, in what sequence) that makes the first sales conversation worth having. Lead scoring models we build are reviewed and recalibrated quarterly against conversion data so they improve continuously rather than degrading as market conditions and buyer behaviour evolve.
Improvement 2–4x vs. intuition-based model
Velocity 30–50% shorter time-to-SQL
Recalibration Quarterly against conversion data
Rate Avg. 3.2x vs. broadcast email
Configuration ✓ Prevents stale lead inflation
Alignment Defined SLA at MQL threshold
Lead Scoring & Nurturing — What We Build
From scoring model design through nurture sequence architecture to MQL-sales handoff automation
Behavioural Lead Scoring Model
Lead scoring model design grounded in conversion analysis — mining closed-won CRM records to identify the behavioural signals (specific page visits, content consumption patterns, session frequency, feature-specific interest signals) that have the highest predictive correlation with actual purchase. Scoring model built with positive signals (high-intent actions), negative signals (competitor employment, student classification, unsubscribes that indicate poor fit), and score decay rules that prevent stale engagement from inflating scores of contacts who disengaged 6 months ago.
Demographic & Firmographic Scoring
Two-dimensional scoring combining behavioural engagement score with a fit score based on firmographic attributes — company size, industry, job title, geography, technology stack (from intent data) — so MQL thresholds are reached only when both high engagement AND good ICP fit are present. A contact who has consumed significant content but works at a company outside the ICP is routed to a different workflow than a contact with the same engagement score who matches the ICP exactly — preventing sales capacity being spent on high-engagement but low-fit leads.
Nurture Sequence Design & Copywriting
Behaviour-triggered nurture sequences — email series designed for each audience segment and buyer stage, with each email in the sequence triggered by the prospect's actual engagement with the previous email and website rather than on a fixed schedule. Content mapping to identify the specific questions a prospect at each stage needs answered, and creative brief development for the nurture copy and assets. Includes the A/B testing framework that continuously identifies the highest-performing subject lines, send times, and content formats for each segment.
MQL-to-Sales Handoff Automation
MQL handoff workflow design and configuration — when a lead crosses the MQL threshold, the automated handoff creates or updates the CRM lead/contact record with full engagement history, assigns to the correct sales rep or SDR based on territory and round-robin logic, sends a context-rich notification (not just "new MQL" but "Priya at TechCorp, Head of Engineering, visited pricing page 3x this week, downloaded infrastructure whitepaper") and sets an SLA-governed follow-up task that escalates to management if the MQL is not contacted within the agreed window.
Recycled Lead Nurturing
Nurture programme for leads that sales has returned to marketing — SQL → Recycle → back to nurture — with stage-aware content that picks up where the prospect left off rather than restarting them at the top of the funnel. Segmented by recycle reason (timing, budget, competitor, no decision) so the nurture content addresses the specific objection that prevented conversion the first time, with re-engagement triggers that surface recycled leads to sales when they show renewed buying signals.
Intent Data Integration & Prioritisation
Integration of third-party intent data (Bombora, G2 Buyer Intent, 6sense, Demandbase) into the lead scoring model — surfacing which target accounts are actively researching your category on third-party sites, even before they have visited your website. Intent signals layered on top of first-party engagement data produce significantly earlier and more accurate MQL identification. Includes the workflow that automatically routes high-intent accounts to targeted ABM campaigns and BDR outreach before inbound interest is expressed.
CDP Implementation & Audience Data Management
A Customer Data Platform (CDP) solves the most persistent problem in marketing automation — the fragmented customer record. Most organisations accumulate customer data across five to fifteen touchpoints: the e-commerce platform, the CRM, the support system, the mobile app, the marketing automation platform, the loyalty system, the in-store POS, and the offline transactional records. Each system has a partial view of the customer, and the customer profiles in the marketing automation platform are populated from a subset of these sources with varying degrees of completeness and recency. The result is personalisation based on incomplete data, segmentation based on stale attributes, and journey triggers that fire on email engagement alone rather than on the full picture of how the customer is actually behaving across every touchpoint. A CDP creates the unified customer profile that makes truly personalised, behavioural automation possible.
SourceMash implements Segment (Twilio Segment), Salesforce Data Cloud (formerly Marketing Cloud Customer Data Platform), Adobe Real-Time CDP, and mParticle — selecting the right CDP for your organisation's tech stack, data volume, and activation requirements. We design the data collection architecture (event tracking, server-side data pipelines, identity resolution rules), the identity graph that merges anonymous web sessions with known contacts across devices and channels, and the audience definitions that feed the marketing automation platform with real-time behavioural segments rather than static list exports. For organisations not yet ready for a full CDP, we also implement lightweight unified customer data solutions using GA4, BigQuery, and marketing automation native connectors as a stepping stone.
Supported Segment, Salesforce Data Cloud, Adobe
Resolution Cross-device, cross-channel merge
Activation ✓ Live segments to MAP and ad
Architecture Server-side + client-side
Compliance DPDP Act, GDPR, consent management
Unified CRM, e-com, app, support, POS
CDP & Audience Data Capabilities We Build
From unified customer profile to real-time audience activation in your marketing automation platform and paid media channels
Unified Customer Profile Design & Implementation
Data source inventory and connection architecture — identifying every system that holds customer data, designing the Segment Connections or Salesforce Data Cloud Data Streams to ingest each source, and configuring the identity resolution rules that merge anonymous web sessions with known contact records, resolve duplicate customer identities from multiple CRM records, and maintain a consistent customer ID across every downstream activation destination. The output is a golden customer record with complete behavioural, transactional, and demographic data from every touchpoint.
Event Tracking Architecture & Data Collection
Server-side and client-side event tracking design — Segment Analytics.js or server-side Track/Identify/Page calls for website and web app events; mobile SDK integration for iOS and Android apps; server-side e-commerce event streaming for order, cart, and product interaction events; and CRM change data capture for contact lifecycle and deal stage events. Includes the event schema design that standardises event naming, property structure, and data types across every collection source so downstream audiences and triggers are based on consistent, clean data.
Real-Time Audience Segmentation & Activation
Audience definitions built on the unified customer profile — behavioural segments (viewed product category 3x in 7 days but not purchased), lifecycle segments (active customer, at-risk, lapsed, churned), value segments (LTV decile, RFM tier), and predictive segments (churn propensity, next-best-offer model output). Real-time audience activation to marketing automation platforms (HubSpot, SFMC, Marketo), paid media destinations (Google Ads, Meta, LinkedIn), and data warehouses for offline analysis — so every channel is operating from the same real-time customer profile.
Consent Management & Data Privacy
Consent collection and enforcement architecture — OneTrust or Cookiebot integration for DPDP Act (Digital Personal Data Protection Act) and GDPR consent collection; consent preference storage in the CDP; downstream consent enforcement that suppresses contacts from marketing automation sends and ad audiences when consent is withdrawn; data retention policy configuration for automatic data deletion at consent expiry; and audit trail generation for consent records that demonstrate regulatory compliance to data protection authorities and auditors.
Marketing Automation CRM Integration & Revenue Ops
The integration between marketing automation and CRM is the most operationally critical interface in the revenue technology stack — and the one most frequently implemented incorrectly. A poorly designed MAP-CRM integration produces: duplicate contact records where the same person appears in both systems with different data; sync conflicts where CRM updates overwrite marketing automation data and vice versa; MQL handoff notifications that do not include the engagement context that makes the sales follow-up call worth making; contact opt-out statuses that are not propagated from CRM to MAP and result in emailing people who have already asked to be removed; and pipeline reporting that cannot answer the question "which marketing campaign influenced this deal?" because the attribution data is not flowing correctly between systems.
SourceMash designs MAP-CRM integrations that treat the bidirectional data relationship as a first-class architecture problem — defining the master-of-record for each field type, the sync direction and conflict resolution rules, the contact and lead lifecycle mapping between MAP stages and CRM lifecycle stages, and the campaign influence data model that enables marketing attribution reporting. We have integrated HubSpot, Salesforce Marketing Cloud, Marketo, and Adobe Campaign with Salesforce CRM, Microsoft Dynamics 365, HubSpot CRM, and custom CRM systems — and we build integrations with the operational monitoring and error alerting that detect silent data divergence before it becomes an operational problem.
Integrated HubSpot, SFMC, Marketo, Adobe
Integrated Salesforce, D365, HubSpot CRM
Architecture Field-level master-of-record rules
Model First touch, last touch, linear, W
Monitoring Error alerting + divergence checks
MAP ↔ CRM Integration — What We Build and Connect
Every integration interface, data model decision, and monitoring requirement in the MAP-CRM relationship
Bidirectional Contact Sync Architecture
Contact and lead sync design between MAP and CRM — defining the master-of-record for each field (CRM is master for job title, company, phone; MAP is master for email engagement history and lead score), sync frequency (real-time for opt-outs and MQL status changes, batch for lower-priority field updates), conflict resolution rules for concurrent updates, and deduplication logic that prevents the same person existing as both a Lead and a Contact in Salesforce while also appearing in the MAP as a single unified record.
Lead Lifecycle & Status Mapping
Mapping of MAP lifecycle stages (subscriber → lead → MQL → SAL → SQL → opportunity) to CRM lead status and contact stage values — including the automations that transitions contacts through lifecycle stages based on behavioural triggers in the MAP and deal stage changes in the CRM. Prevents the common problem of lifecycle stages in MAP and CRM diverging because the bidirectional mapping was never defined, leaving marketing reporting on one set of stage definitions while sales reports on another.
Campaign Attribution Data Model
Campaign influence tracking architecture — configuring the MAP to write campaign membership and engagement data to CRM Campaigns and Campaign Members (Salesforce) or Campaign Response records (D365) so that every marketing touchpoint on a prospect's journey to pipeline is recorded. Attribution model configuration (first touch, last touch, linear, U-shaped, W-shaped) that enables the marketing team to answer "which campaigns influenced the deals closed this quarter" with pipeline-level data rather than with lead-level proxies that do not reach closed revenue.
Opt-Out & Suppression Sync
Real-time propagation of email opt-outs, unsubscribes, and suppression list updates between MAP and CRM — ensuring that a contact who unsubscribes from a marketing email is immediately suppressed in the MAP, has their opt-out status updated in the CRM, and is excluded from any active journey automations. Includes DPDP Act and GDPR compliance architecture: consent withdrawal triggers CRM update, MAP suppression, and audit log entry within the regulatory-required timeframe.
MQL Alert & Sales Intelligence Delivery
MQL notification workflow that surfaces marketing engagement context in the CRM for the receiving sales rep — Slack, email, or CRM task notification with the prospect's engagement timeline (pages visited, content downloaded, emails clicked, webinars attended) rather than just a score number. Sales Intelligence page within the CRM contact record (Marketo Sales Insight, HubSpot Sales extension) that gives sales reps real-time visibility into the prospect's current MAP engagement without leaving the CRM.
Revenue Operations Reporting
Marketing-to-revenue reporting architecture — connecting MAP engagement data with CRM pipeline and revenue data to enable end-to-end funnel reporting: leads → MQL → SQL → opportunity → closed revenue, with conversion rates and average time at each stage. Campaign ROI reporting that shows the pipeline value and closed revenue influenced by each campaign. Built on the CRM reporting layer (Salesforce Reports and Dashboards, D365 Power BI) or on a dedicated BI tool (Looker, Metabase, Power BI) connected to both MAP and CRM data exports.
Platforms We Connect to Marketing Automation
Pre-built connectors and proven integration patterns for the most common MAP-to-ecosystem connections
Marketing Analytics, Attribution & Performance Reporting
Marketing analytics is the difference between a marketing team that can demonstrate the revenue contribution of its automation programme and one that reports on vanity metrics — email open rates, click rates, and MQL volume — that look positive in a dashboard but do not answer the question that the CFO and CEO are actually asking: what is the return on the marketing technology investment, which channels and campaigns are generating pipeline and closed revenue, and what should be invested more in and what should be cut. The challenge is that answering these questions requires connecting the marketing engagement data that lives in the MAP with the pipeline and revenue data that lives in the CRM — a connection that most marketing automation implementations never fully establish.
SourceMash builds marketing analytics infrastructure that connects the MAP, CRM, website analytics (GA4), and paid media platforms into a unified view of the funnel from first touch to closed revenue. We configure multi-touch attribution models that distribute revenue credit across the marketing touchpoints that influenced each deal, build the marketing performance dashboards that surface the leading indicators (MQL volume, lead velocity, stage conversion rates) alongside the lagging indicators (pipeline influence, closed revenue attribution) that give marketing leadership the data to make investment decisions rather than to report activity. We also implement the A/B testing and experimentation framework that makes marketing automation programmes improve continuously rather than remaining static after implementation.
Models First, last, linear, U, W, data-driven
Reporting Leads → MQL → SQL → revenue
Framework Email, landing page, journey variants
Monitoring Sender reputation + inbox rate
Tools Power BI, Looker, Metabase, native
Reporting Pipeline influence + closed revenue
Marketing Analytics Capabilities We Deliver
From funnel analytics through attribution modelling to email deliverability and A/B experimentation
Marketing Automation for Your Industry.
Buyer journeys, content requirements, compliance obligations, and channel mix differ significantly across industries. We design and implement marketing automation programmes tuned to the specific purchase dynamics and regulatory context of each sector.
- Free trial and freemium onboarding journey with feature adoption triggers
- ICP-targeted ABM programme with intent data and LinkedIn integration
- Multi-stakeholder nurture for technical buyer and economic buyer
- Product usage data triggers for upsell and churn prevention journeys
- SDR sequencing integration (Outreach, Salesloft) with MAP handoff
- MQL scoring calibrated to SaaS buying signals (pricing page, ROI calculator)
- Welcome series for new subscribers with progressive offer sequencing
- Abandoned cart and browse abandonment recovery (email + SMS + push)
- Post-purchase onboarding and cross-sell recommendation journeys
- Replenishment reminders timed to actual purchase cycle data per SKU
- Loyalty tier upgrade and milestone celebration automation
- Winback sequence with incentive logic calibrated to customer LTV tier
- Lead nurturing for home loan, personal loan, and investment product enquiries
- KYC completion journey with document upload nudge automation
- Onboarding journey for new account holders with product adoption triggers
- Renewal and portfolio review reminder automation for investment clients
- SEBI and RBI compliant communication — mandatory disclosure automation
- NPS-triggered promoter identification and referral programme automation
- Pharmaceutical MR (medical representative) digital detailing automation
- Healthcare provider (HCP) education programme with CME content journeys
- Patient appointment reminder and pre-visit preparation sequences
- Post-discharge follow-up and care plan adherence nudge automation
- Clinical trial patient recruitment journey with eligibility pre-screening
- CDSCO and IRDAI compliant promotional content workflow and approval
- Site visit request follow-up and appointment scheduling automation
- Long-cycle nurture for residential buyers (6–24 month decision window)
- RERA-compliant project update communication to registered enquiries
- Channel partner (broker) communication and co-marketing automation
- Home loan pre-qualification journey with banking partner handoff
- Post-booking customer communication and handover milestone updates
- Enquiry-to-application nurture with programme-specific content tracks
- Application completion nudge sequence with document checklist automation
- Offer acceptance to enrolment journey with pre-joining community engagement
- Student onboarding and early engagement monitoring for retention risk
- Alumni engagement and continuing education programme promotion
- B2B corporate training lead nurturing for L&D buyer personas
Marketing Technology Platforms We Work With
Certified across the major marketing automation, CDP, analytics, and integration platforms — enabling us to implement and connect the full marketing technology stack for your organisation.
What Our Clients Say
We had HubSpot in place for 18 months and were generating thousands of MQLs per quarter — but our sales team had effectively stopped working them because 70% were people who downloaded a whitepaper and had zero intent to buy. SourceMash rebuilt our scoring model from the ground up using 18 months of closed-won deal data — identifying that pricing page visits, ROI calculator completions, and third consecutive session within 14 days were the three signals that actually correlated with purchase intent rather than accumulated engagement points. MQL volume went down by 60%, MQL-to-SQL conversion went up 3.4x, and marketing-sourced pipeline nearly tripled within 9 months. Our sales team is now calling every MQL we send them the same day because they trust that the score means something.
Our email programme before the Salesforce Marketing Cloud and Segment CDP implementation was essentially a broadcast list — the same promotional email to the entire subscriber base every two weeks, watched our unsubscribe rate climb, and kept lowering our send frequency to avoid losing more people. SourceMash built the unified customer profile in Segment that connected our Shopify store, app, and customer service history for the first time, then designed the Journey Builder automation to send behaviour-triggered emails rather than broadcast campaigns. Abandoned cart recovery alone is generating revenue we were previously losing completely. Overall email revenue is up 38% and our unsubscribe rate is down 62% because people are receiving content that is relevant to what they were actually looking at rather than whatever we wanted to promote that week.
Our previous marketing automation approach was essentially calling every lead the call centre generated within 48 hours — no nurturing, no qualification, just volume. Our conversion rate was 2% and the call centre cost was unsustainable. SourceMash implemented Marketo Engage with a multi-product nurture programme that educated loan product enquiries on the specific product they were interested in, asked progressive profiling questions that identified employment status and income band before we called, and scored leads so our relationship managers were calling only the contacts who had completed the education sequence and shown active decision-making intent. Lead-to-application conversion went from 2% to 2.9% — a 45% improvement — and cost per application fell 33% because we were making fewer, higher-quality calls.
Latest from SourceMash
Perspectives, research, and practical guidance from our enterprise technology experts.
