Same data we built together.
Now AI collects, automates and analyzes it.
You’ve already trusted me with your marketing stack — the platform migrations, the Google Ads and Bing accounts, the blog, the newsletter, the CTA tests. I know where the manual work in your business actually lives. Now I run an AI workflow practice built to remove exactly that kind of work — and I’d like to point it at your business.
Why I’m writing again
Most consultants spend months learning how a business actually runs. We already did that part together — the audits, the ad accounts, the Monday-morning data cleanup. I know your systems, your reports, and where the manual work in your business is hiding before I open a single new file.
That’s why I’d like to tell you about the next step in marketing: DigiBlender AI, my new practice focused on one thing: turning the manual, repetitive parts of a marketing and operations workflow into automated systems — without the vague “AI transformation” talk, and without asking you to rip out what already works.
— Michael de Beer
Former Digital Analytics Lead • Disney & Universal Online
Your AI Marketing Analysis Agent
Turn your existing marketing data into an AI-powered business intelligence system
Your business already has the data. It sits across:
The opportunity is turning the data you already have into useful business intelligence.
Instead of producing another marketing report or dashboard, we build an AI Marketing Analysis Agent around your existing marketing ecosystem.
The Agent analyzes your marketing data across channels, identifies patterns and opportunities, and produces a daily (if you want), weekly, monthly, quarterly and annual strategic analysis of your marketing performance.
It will compare all data to the previous day, week, month, quarter or year.
Build Your AI Marketing Analysis Agent
Because your marketing data is already available, we start by creating an AI Analysis Agent designed specifically around your marketing ecosystem. Instead of examining each marketing platform separately, the Agent analyzes the relationships between them.
Which searches generate traffic — and which generate valuable visitors and conversions?
Where are you paying for traffic that could potentially be captured organically?
Which social campaigns create engagement versus meaningful website activity and conversions?
What happens after someone clicks an email, and which campaigns produce the most valuable customer behavior?
Which marketing channels, campaigns, audiences, and touchpoints are associated with your strongest business outcomes?
Let AI Do the Heavy Analytical Work
Your marketing ecosystem can contain hundreds of thousands or millions of individual data points. Manually examining every possible relationship is unrealistic. That’s where AI becomes valuable.
The AI Marketing Analysis Agent systematically analyzes the data looking for:
Behaviors repeatedly associated with conversions or stronger business outcomes.
Unexpected changes in traffic, costs, conversions, engagement, or customer behavior.
Groups of customers or visitors behaving differently from the overall audience.
Relationships between marketing activities and business outcomes that deserve further investigation.
Changes developing throughout the year.
Areas that may deserve additional investment, optimization, or experimentation.
Campaigns, audiences, landing pages, channels, or activities consuming resources without producing proportional results.
AI Does the Analysis. People Make the Decisions.
The objective isn’t to replace your marketing team. It’s to give them a much more powerful analytical tool.
AI becomes the analytical assistant behind those decisions.
Annual Marketing Intelligence Analysis
Each year, your AI Marketing Analysis Agent analyzes the latest 12 months of marketing performance. The result is a comprehensive Annual Marketing Intelligence Analysis that answers five fundamental questions:
WHAT HAPPENED?
A clear picture of marketing performance across every available channel.
WHY DID IT HAPPEN?
The campaigns, audiences, content, channels, and customer behaviors associated with those results.
WHAT CHANGED?
Year-over-year changes, emerging trends, and shifts in customer behavior.
WHAT DID AI DISCOVER?
Patterns, anomalies, correlations, segments, inefficiencies, and opportunities that deserve further investigation.
WHAT SHOULD WE INVESTIGATE OR TEST NEXT?
A prioritized roadmap covering measurement, SEO, paid media, social, email, CRO, content, automation, and experimentation.
The Agent Gets More Valuable Every Year
The first year establishes your baseline. But that’s only the beginning. Every additional year gives the Analysis Agent more historical context.
Establish the Baseline
Understand your current marketing ecosystem, customer behavior, channels, campaigns, and conversion patterns.
Compare
Identify year-over-year changes and determine which trends are strengthening, weakening, or emerging.
Learn
Analyze longer-term patterns and examine whether previous initiatives and recommendations produced measurable changes.
Build Marketing Intelligence
Develop a richer historical understanding of customers, channels, campaigns, seasonality, acquisition, retention, and business performance.
Predictive Analytics
From Understanding What Happened to Anticipating What Happens Next
Once the AI Marketing Analysis Agent understands what has happened across your marketing ecosystem, the next opportunity is Predictive Analytics.
This is a distinct operational discipline — not something buried under the generic label of AI.
Using predictive and prescriptive machine learning, we move beyond descriptive hindsight — “What happened?” — toward predictive foresight — “What is likely to happen next, and what actions should we consider?”
The objective is to help your business anticipate changes in revenue, customer demand, conversions, churn, campaign performance, and sales opportunities before committing additional time or capital.
DigiBlender 6-Model Predictive Framework
1. Revenue Forecasting
Use historical sales, seasonality, marketing, and customer data to forecast revenue, cash-flow trends, demand, and potential inventory requirements.
2. Conversion Prediction
Analyze session behavior, traffic sources, engagement, and clickstream depth to identify visitors and audiences showing stronger conversion intent.
3. Customer Lifetime Value
Use early customer behavior, transaction history, acquisition source, and purchasing patterns to estimate potential long-term customer value and identify higher-value segments.
4. Churn & Replenishment Prediction
Identify behavioral changes that may indicate a customer is becoming less engaged or is approaching a likely replenishment window, creating opportunities for earlier retention and reactivation efforts.
5. Campaign Performance Prediction
Use early campaign signals to estimate potential performance, identify underperforming campaigns sooner, and provide data to support faster budget-allocation decisions.
6. Predictive Lead Scoring
Apply machine-learning classification to behavioral, acquisition, and customer data to identify leads with characteristics associated with higher conversion rates, order values, or lifetime value.
Built for Mid-Market Data
Predictive analytics doesn’t have to require an enterprise data-science department.
DigiBlender focuses on lightweight, practical predictive models designed around the data a mid-market business already has.
As additional data becomes available each year, models can be retrained, validated, and improved against actual business outcomes.
The more quality historical data available, the more opportunities we have to test, validate, and improve the models.
Why Machine Learning Fails on Disconnected E-Commerce Data
90% of predictive AI projects stall because Shopify orders, email metrics, and Google/Meta ad spend live in disconnected silos. By centralizing schemas during our Rapid Implementation Sprint, your business becomes ML-ready without expensive data engineering re-work.
From Marketing Reporting to Marketing Intelligence
Traditional marketing reporting looks backward. A dashboard might tell you:
Those numbers are useful. But they don’t necessarily tell you why something happened, what might happen next, or what you should investigate.
The AI Marketing Analysis Agent goes deeper into what happened and why.
Predictive Analytics takes the next step by using historical patterns to estimate what may happen next. Together, they create a progression:
DESCRIPTIVE
What happened?
DIAGNOSTIC
Why did it happen?
PREDICTIVE
What is likely to happen next?
PRESCRIPTIVE
What should we consider doing about it?
What You’re Actually Building
You’re building an AI-powered marketing intelligence capability around your company’s own data.
The AI Marketing Analysis Agent continually develops a deeper historical understanding of your marketing performance.
The Predictive Analytics models use that historical information to identify potential future outcomes and opportunities.
The Annual Marketing Intelligence Analysis brings those findings together into an actionable strategic view.
As your data grows, the system’s historical context grows with it.
The Bigger Idea
Most companies already have enormous amounts of information about their customers, marketing, campaigns, websites, advertising, and sales.
The opportunity is to turn that existing data into intelligence.
We use AI to analyze what happened.
We use predictive analytics to estimate what may happen next.
And we combine both with human business expertise to determine what deserves action.
Your Data + AI Analysis + Predictive Analytics + Human Expertise
From hindsight to insight to foresight.
Turning the marketing data you already own into better business decisions — year after year.
Where to start
Fixed-fee, no open-ended hourly billing or surprise invoices. Because I already know your stack, you can go straight to the Sprint — no audit required.
AI Opportunity & Readiness Audit
Two deep-dive interviews, a readiness scorecard, and a 90-day roadmap ranked by ROI — scoped to the stack I already know, not a generic checklist. Only needed if your stack has changed since we worked together.
Rapid Implementation Sprint
One production workflow, shipped — starting with Your AI Marketing Analysis Agent: an automated data pipeline, standardized schemas for predictive forecasting, fully integrated, with a 14-day hypercare period after launch.
Fractional Chief AI Advisory
Ongoing optimization once the first workflow is live — bi-weekly executive syncs, vendor vetting, and architecture guidance without lock-in contracts.
Worth a short call?
No proposal, no deck, no pressure — just a direct conversation about whether this is useful for your business right now. If it’s not the right time, I’d still love to catch up.
Data to Decisions: Augmenting Your Leadership Team