Marketing Automation Software Development

Behavioural lead nurturing sequences triggered by real prospect actions rather than fixed time delays -- because a lead who just visited your pricing page twice in one week is a different priority from a lead who downloaded a top-of-funnel guide six weeks ago and hasn't engaged since. Trigger events pulled from your full stack: website behaviour (Google Analytics 4 events, Segment track events, or custom pixel events for specific page visits and content downloads), email platform engagement (SendGrid, Klaviyo, or Mailchimp open and click events via webhook), CRM stage changes (HubSpot contact property changes, Salesforce lead status updates), and product activity (trial sign-ups, feature activation events, usage milestones). Sequence logic branches based on what each lead does: a lead who clicks the pricing page link in email #1 enters a bottom-of-funnel sequence; a lead who doesn't engage gets a different topic sequence on email #3. Exit conditions: when a lead converts (creates an account, books a call, submits a form), they exit the nurturing sequence and transition to the post-conversion flow. Score-based escalation: when a lead's cumulative engagement score crosses a configured threshold, the sequence pauses, a CRM task is created for the assigned sales representative, and the lead is flagged as "high intent" in the CRM view. No lead goes cold because an SDR missed a follow-up -- the system tracks every lead's engagement state and routes them appropriately regardless of human attention.

Campaign deployment triggered by schedule, segment condition change, or an external event (a new product launch in your CMS, a pricing change in your billing system, a new case study published) -- not by a marketer manually clicking send at the correct time across multiple platforms. Dynamic audience segmentation built from your CRM fields and behavioural data: segments recalculate automatically before each send so a segment defined as "enterprise accounts without a paid subscription for 60+ days" always reflects the current state of your CRM, not the state when someone manually exported the list. Personalisation at the field level using contact data (company name, job title, industry, prior product usage, account manager name) and account data (plan tier, renewal date, number of seats) pulled from your CRM API at send time -- the personalisation that goes beyond {first_name} to content that reflects the recipient's actual relationship with your product. A/B test variant assignment with automatic winner selection: variant performance monitored for a configurable period, the winning variant deployed to the remaining audience automatically without requiring manual review of results. Send time optimisation per contact: delivery time calculated from the individual's historical open pattern (the SendGrid or Klaviyo STO feature, or a custom model built from your historical engagement data) rather than a single send time for the entire list. Campaign send logs written to your analytics data store for attribution and performance reporting.

Lead scoring models built from your actual engagement and conversion data -- calibrated to what drives conversions in your specific funnel, not a generic points framework borrowed from a platform template. Score components: implicit behavioural signals (website page visits weighted by intent signal strength -- pricing page worth 20 points, blog post worth 3), email engagement (open worth 2, click worth 8, reply worth 25), content downloads weighted by funnel stage (top-of-funnel content worth less than bottom-of-funnel collateral like case studies and ROI calculators), product usage signals (trial activation worth 30, specific feature adoption worth 15), and direct engagement (booked a call worth 100, requested a demo worth 80). Firmographic fit scoring as a separate dimension: company size, industry, technology stack, and geographic market scored against your ICP definition and multiplied with the behavioural score to produce a combined MQL score. Score decay applied to inactive leads: a contact who was highly engaged 90 days ago and has not opened an email since has their score reduced automatically, preventing stale leads from appearing in the high-intent queue. Automatic CRM actions triggered at score thresholds: lead assigned to an SDR at 75 points, lead status updated to MQL at 100 points, high-intent alert sent to Slack channel at 150+ points. Score history tracked per lead so sales can see what the lead engaged with before making the outreach call -- context that turns a cold call into a warm one.

Content distribution automation triggered by publication events in your CMS (Sanity, Contentful, WordPress webhook on post publish), content calendar schedules, or campaign launch conditions -- so publishing a new case study, blog post, or product update initiates its distribution across every relevant channel without a team member manually copying it to four different platforms at different times. Channel-specific formatting applied automatically: a LinkedIn post gets a professional framing with appropriate character limits; a Twitter/X thread gets the content condensed to the required length; an email digest gets the content formatted in the newsletter template; a Slack channel notification gets a brief summary with a link. Social platform APIs used for scheduling: LinkedIn Marketing API, Twitter API v2, Facebook Pages API, Instagram Graph API. Buffer or Hootsuite as an intermediate scheduling layer where multi-approval workflows or content calendar visualisation are required. UTM parameter generation per channel and campaign: every distributed link carries a UTM tag (source, medium, campaign, content) generated from the campaign metadata, so attribution reporting can trace traffic back to the specific piece of content and the specific distribution channel. Distribution log stored per content piece: which channels, when, which URL, UTM parameters used, and performance metrics pulled back from each platform after 24 and 72 hours. Performance data feeds into the attribution reporting system automatically.

Campaign performance data pulled automatically from every platform in your marketing stack on a daily schedule -- no analyst manually exporting from five different dashboards and pasting into a Google Sheet. Data sources connected via API: Google Ads API for paid search and display performance (impressions, clicks, conversions, ROAS by campaign and ad group), Meta Marketing API for Facebook and Instagram campaigns, LinkedIn Marketing API for B2B paid campaigns, Google Analytics 4 Reporting API for website traffic and conversion events, email platform APIs (SendGrid, Klaviyo, Mailchimp) for send volume, open rate, click rate, and unsubscribe rate. Metrics normalised into a unified campaign taxonomy (campaign name, channel, spend, clicks, conversions, revenue attributed) and loaded into a central analytics data store (BigQuery, Snowflake, or Redshift). Automated performance report generated and delivered to configured recipients on a daily, weekly, and monthly schedule: the daily report covers the previous day's spend and performance, the weekly report covers the 7-day period with week-over-week comparison, and the monthly report covers the full period with prior month and prior year comparisons. Variance alerting: when a campaign's click-through rate drops more than 20% week-over-week, or when cost per conversion exceeds the configured ceiling, an automated alert is sent to the campaign manager with the specific campaign, the metric, and the variance -- catching underperforming campaigns before they drain budget through an entire cycle.

Multi-touch attribution calculated automatically from the full touchpoint history for every converted account -- eliminating the last-click attribution problem where Google paid search gets 100% of revenue credit for a customer who first discovered you through a LinkedIn post, downloaded a case study from an email, attended a webinar, and then searched your brand name before converting. Touchpoint data collected across all channels: UTM-tagged web sessions, email clicks with campaign identifiers, ad platform conversion events, CRM activity (demo calls, sales emails, event attendance), and product trial activations. Each touchpoint stored against the contact's journey timeline in the attribution data model. Attribution models applied and compared simultaneously: first-touch (credit to the first touchpoint), last-touch (credit to the converting touchpoint), linear (equal credit split across all touchpoints), time-decay (credit weighted toward touchpoints closer to conversion), and position-based (40% first, 40% last, 20% split across middle touches). The model your team chooses as the primary model determines reported channel contribution; secondary model views available for comparison. CRM integration: when a deal closes in Salesforce or HubSpot, the revenue value is attributed back to the touchpoints from that account and the channel performance report updates automatically. Budget decisions made from attribution reports showing which channels generate pipeline (MQLs created) and which generate revenue (deals closed) -- because they are often different channels, and conflating them produces the wrong budget allocation.