Marketing automation and conversion improvement
Advertising → Data analysis → Automated actions → Better results
Advertising campaigns generate clicks, visits, enquiries and sales. These data often sit in different systems: advertising accounts, web analytics, CRM, ecommerce services and internal databases.
Marketing automation brings these sources into a single pipeline, tracks conversion at each stage of the customer journey, evaluates advertising spend and automatically acts according to predefined business rules.
The purpose of this system goes beyond collecting marketing statistics: it continuously monitors campaign results and reduces ineffective spending.
Automated pipeline
From data to action and verification
Three sources feed a single pipeline. Choose a scenario and follow one cycle from data collection to an authorised action and feedback.
Illustrative demo figures, not actual BPFlow AI data or universal benchmarks. In this example, CAC includes advertising costs only.
- Spend
- —
- Clicks
- —
- Leads
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- Paying customers
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- Click → lead
- —
- Lead → sale
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- CPL
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- CAC
- —
- CPL limit
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All stages and branches are shown below. JavaScript is required to switch scenarios and run the demonstration.
↻ After verification, data return to the pipeline. The demonstration shows one cycle; use the button to replay it.
Choose a scenario and click “Start”. The demonstration does not change campaigns or budgets.
Sources
- Google Ads / other advertising platforms — spend, impressions, clicks and campaign metrics.
- Website / Web Analytics — visits, agreed events and leads.
- CRM — leads, qualification, deal statuses and confirmed sales.
- PostgreSQL — reference data, history, rules and prepared data.
Core sequence
Advertising / Website / CRM → API → Python-script → PostgreSQL → Conversion analysis → KPI check → Decision → Automated action → Ongoing monitoring
Possible decisions
A. High cost per lead
The system detects that the cost of acquiring a lead exceeds the defined limit.
After checking that enough reliable data are available, the pipeline identifies the problematic campaign and takes an authorised action: notifying the marketer, creating an ad review task or suggesting an A/B test. A clearly ineffective ad may be paused automatically if this procedure has been approved in advance.
B. Many leads, but few sales
Advertising attracts visitors successfully, but a large share of leads do not become paid orders.
The system compares marketing data with CRM statuses, identifies the problematic funnel stage and creates a task to investigate lead quality, the landing page, the offer or the enquiry handling process.
Without sufficient evidence, the system does not automatically assign a cause to low conversion. The pipeline presents facts, possible explanations and the checks needed.
C. The campaign is effective
Customer acquisition cost is within the defined range, and CRM data confirm the results.
The system keeps the current route, continues monitoring and may suggest scaling the campaign. Automatic budget adjustments remain within the established limits.
Example: marketing automation for a service company
Consider a company that installs and services air conditioning equipment.
It attracts customers through Google Ads, other advertising platforms, its website and landing pages. Visitors click ads, review offers, submit enquiries or call sales managers.
Enquiry details enter CRM. A manager contacts the customer, clarifies requirements, prepares an offer and records the deal outcome.
An advertising click alone does not create a customer. Even a submitted enquiry does not guarantee a sale.
One campaign may generate many inexpensive leads that rarely become paid orders. Another may deliver fewer enquiries but attract customers who are much more likely to buy.
Effective marketing automation therefore accounts for the entire available journey:
Advertisement → Website visit → Enquiry → CRM → Confirmed customer → Paid order
Once the automated pipeline is implemented, it regularly collects advertising account metrics, website visit data and enquiry processing results from CRM.
The information is combined, validated and analysed against agreed criteria. The system calculates cost per click, lead and acquired customer, determines conversion between funnel stages and detects deviations.
If advertising no longer meets the performance targets, the pipeline can automatically send a warning, create a marketing task, suggest an A/B test or adjust campaign settings within previously authorised limits.
If results are good, the system continues monitoring and may recommend a budget increase. Changes beyond established limits go to a person for approval.
The company gains a continuously operating system that connects marketing analytics to actual business results.
How marketing automation works
1. Collecting data from advertising platforms and CRM
The pipeline starts on a schedule or when a new event arrives. Advertising platform APIs provide the available metrics: spend, impressions, clicks, campaign IDs and ad IDs.
In parallel, the system collects permitted website events and CRM data on enquiries, qualified leads and sales.
python-script converts this information to a common structure and checks identifiers, time periods and value formats.
With valid tracking tags and identifiers, the system connects advertising sources to subsequent user actions. Events that cannot be matched reliably are tracked separately rather than attributed to an arbitrary campaign.
2. Building the marketing funnel
Prepared data are stored in PostgreSQL and used to build a conversion funnel from ad impressions and website visits to enquiries, deals and paid orders.
The system can calculate CTR, CPC, CPL, CAC and conversion between individual stages.
For example, cost per lead (CPL) is advertising spend divided by the number of leads. Customer acquisition cost (CAC) uses the number of customers actually acquired and an agreed set of costs.
A cheap lead is different from a profitable customer. The pipeline therefore considers both advertising account metrics and the available CRM outcomes of handling enquiries.
3. Analysing user behaviour and finding losses
Using permitted website events, the system can analyse where visitors most often stop interacting.
For example, users click an ad, open a service page and start completing a form, but do not submit it.
This identifies a problematic funnel stage but does not, by itself, prove the cause. An experiment can test it by changing the form, offer wording or button placement.
Behavioural analysis can use aggregated events without creating individual user profiles. Personal data collection and marketing identifiers must comply with applicable privacy and consent requirements.
4. Automated KPI checks and decisions
python-script compares metrics with the established business rules.
Before taking action, the pipeline checks statistical completeness, the observation period, data delays and the minimum sample size.
When a condition is met, the system selects the appropriate route: keep observing, send a notification, create a task, prepare an experiment or make a previously authorised adjustment.
This automates repetitive marketing decisions and reduces the need to continually open multiple advertising accounts and manually check the same metrics.
5. A/B testing and advertising optimisation
When needed, the system creates a task for an A/B test of an ad or landing page.
text-LLM-model can help prepare alternative advertising copy and offers. Image-LLM-model — generate alternative advertising visuals.
Using an AI model does not mean every asset should be published without review. Advertising creatives, legally significant claims and strategy changes may require prior approval.
Experiment results are assessed using predefined metrics and sufficient data, rather than random short-term fluctuations.
6. Automated actions and result monitoring
After validation, the system performs an authorised action through an API, creates a CRM task or notifies the marketer.
Every operation is logged. If an advertising platform API is temporarily unavailable, the pipeline records the error and uses the configured retry mechanism.
Campaign changes start a new monitoring cycle. The system checks subsequent results against the defined criteria.
The main principle is to take a detected problem through to a controlled action and then verify the outcome.
What the business gains
Automating advertising campaigns and marketing analytics helps to:
- combine advertising, website and CRM metrics;
- analyse conversion across the sales funnel;
- distinguish lead volume from actual sales;
- identify ineffective campaigns and problematic stages;
- initiate A/B tests and optimisation tasks;
- automatically monitor cost per lead and customer;
- make authorised campaign adjustments;
- reduce the effort needed for regular marketing monitoring;
- notify responsible people about deviations promptly.
Where this approach applies
Marketing automation is useful for ecommerce stores, service companies, online services, education providers, B2B sales and other businesses that attract customers through online advertising.
A similar pipeline can assess channel performance, analyse the sales funnel, control spend, regularly test offers and manage marketing experiments.
From marketing statistics to automated actions
Conventional analytics answers: “What happened?”
An automated marketing pipeline goes further: “What happened, does it comply with our rules, and what action is needed?”
The system should not replace proper marketing analysis with arbitrary AI decisions. Calculations, limits and actions follow verifiable rules, while non-standard decisions remain with people.
Marketing automation starts with data
Do your advertising accounts show clicks and leads, while making it hard to see which campaigns actually generate customers and sales?
Show us your advertising sources, analytics system and CRM structure. We will determine how to combine the data and automate monitoring, conversion analysis and regular marketing operations.