# How to Use AI to Predict Journey Path Outcomes
In the digital age, understanding how a customer moves from seeing a Facebook ad to clicking 'buy' on your website is no longer about guesswork. By using Artificial Intelligence (AI) to predict journey path outcomes, Australian small businesses can identify which leads are likely to convert, which are about to drop off, and where your marketing budget is being wasted.
Predictive analytics allows you to move from being reactive to being proactive, ensuring you provide the right nudge at the exact moment a customer needs it.
Prerequisites: What You’ll Need
Before you begin, ensure you have the following ready:
- Historical Data: At least 3-6 months of website traffic and sales data (Google Analytics 4 is ideal).
- A CRM: A system like HubSpot, Salesforce, or even a well-maintained Mailchimp list.
- AI Tooling: Access to a predictive platform (e.g., Polymer, Akkio, or the predictive features within Google Analytics 4).
- Clear Goals: A defined outcome you want to predict (e.g., "Will this lead book a consultation?" or "Will this customer churn?").
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Step 1: Define Your Target Outcome
AI needs a specific question to answer. Instead of asking "What will customers do?", ask "Which website visitors are most likely to fill out our contact form within 7 days?"
In an Australian context, this might involve looking at specific seasonal trends—like predicting pool maintenance enquiries in Brisbane as summer approaches. Be specific about the 'success' metric you want the AI to track.
Step 2: Audit Your Data Quality
AI is only as good as the data you feed it (the "Garbage In, Garbage Out" rule). Check your Google Analytics 4 (GA4) or CRM for gaps. Ensure your tracking pixels are firing correctly on your thank-you pages.
What you should see: A clean spreadsheet or dashboard where every row represents a customer interaction and every column represents a variable (time on site, pages visited, location, device type).Step 3: Export and Clean Your Data
Most small businesses will start by exporting data to a CSV file. If you are using GA4, go to Explorations, create a new path exploration, and export the data.
Remove any personally identifiable information (PII) to stay compliant with the Australian Privacy Act. You don't need names or emails for the AI to find patterns; you just need behaviour.
Step 4: Choose Your AI Predictive Tool
For most Brisbane business owners, we recommend starting with Google Analytics 4 Predictive Metrics or a 'No-Code' AI tool like Akkio.
- GA4: Best for predicting purchase probability and churn.
- Akkio/Polymer: Best for custom journey paths (e.g., predicting if a lead from a specific suburb will become a high-value client).
Step 5: Upload Your Data to the AI Model
If you are using a no-code tool, you will see an "Upload Data" button. Drag and drop your CSV file here. The AI will scan the headers (e.g., Source, Session_Duration, Pages_Per_Session, Converted_YN).
Step 6: Select the "Target Variable"
This is the outcome you defined in Step 1. If your column is named Lead_Status, and the values are Converted or Lost, select this as your target. The AI will now look for patterns in all other columns that lead to the Converted result.
Step 7: Train the Predictive Model
Click 'Train' or 'Run Model'. This usually takes between 30 seconds and 10 minutes depending on the size of your data. The AI is currently running thousands of simulations to see which path most commonly lead to a conversion.
Step 8: Analyse the "Feature Importance"
Once the model is trained, look for a chart called "Feature Importance" or "Top Drivers." This tells you which actions actually predict a sale.
Example: You might discover that users who visit your 'About Us' page and stay for more than 2 minutes are 80% more likely to convert than those who go straight to your 'Pricing' page. This is a vital insight for your marketing strategy.Step 9: Run a "What-If" Simulation
Many AI tools allow you to change variables to see how it affects the outcome. For example, "If I increase traffic from Instagram by 20%, how does that change my predicted conversion rate?"
This helps you allocate your Brisbane-based advertising budget to the channels that the AI predicts will yield the highest quality journey paths.
Step 10: Map the Predicted High-Value Path
Take the findings from Step 8 and 9 and map them out visually. If the AI says the most successful path is Blog Post > Case Study > Contact Form, you need to make it as easy as possible for users to follow that specific path.
Step 11: Implement "Next Best Action" Automations
Use the AI's predictions to trigger real-time actions. If the AI predicts a user is 90% likely to buy but is about to exit the site, trigger a limited-time discount pop-up. If the AI predicts a lead is 'high value', notify your sales team to call them immediately.
Step 12: Monitor and Refine
AI predictions aren't static. Market conditions in Australia change—interest rates rise, or a new competitor enters the Brisbane market. Re-run your models every month with fresh data to ensure your predictions remain accurate.
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Pro Tips for Success
- Don't overcomplicate: Start by predicting one thing (like 'Purchase') before trying to predict the entire customer lifecycle.
- Focus on 'Lead Scoring': Use AI to give every lead a score from 1-100 based on their journey. Your team should only call the 80+ scores.
Common Mistakes to Avoid
Ignoring the 'Why': AI tells you what is happening, but you still need human intuition to understand why*. If a path is failing, look at the page—is the button broken?- Small Sample Sizes: If you only have 10 customers a month, AI won't have enough data to find meaningful patterns. Wait until you have at least 500-1,000 recorded journeys.
- Data Silos: If your Facebook ad data isn't talking to your CRM, the AI only sees half the story. Use tools like Zapier to connect your platforms.
Troubleshooting
| Issue | Possible Cause | Solution |
|---|---|---|
| Model accuracy is low (<50%) | Not enough data or 'noisy' data. | Add more historical data or remove irrelevant columns like 'Internal IP addresses'. |
| AI says everything is a 'Top Driver' | You included the 'Success' variable in the training data. | Ensure the AI isn't looking at the 'Thank You' page visit as a predictor of the 'Thank You' page visit. |
| Predictions don't match reality | Seasonal bias (e.g. Christmas peaks). | Filter your data to look at 'normal' trading periods or include 'Month' as a variable. |
Next Steps
Predicting journey outcomes is a game-changer for ROI. Once you have your predictions:
- Adjust your ad spend to favour the high-probability paths.
- Redesign your website navigation to 'funnel' users toward the behaviours the AI identified as successful.
- Automate your email marketing based on these predictive scores.
Need help setting up your data tracking or AI models? The team at Local Marketing Group can help you turn your data into a crystal ball. Contact us today to discuss an AI audit for your business.