In the fast-paced Australian business landscape, time is your most valuable resource. Predictive lead scoring uses Artificial Intelligence (AI) to analyse historical data and identify which of your incoming leads are most likely to convert into paying customers, allowing your sales team to stop chasing dead ends and focus on high-value wins.
Traditional lead scoring relies on guesswork—assigning arbitrary points for opening an email or visiting a webpage. Predictive scoring, however, looks at thousands of data points to find patterns you might miss, such as the specific industry, time of day, or sequence of actions that leads to a sale.
Prerequisites
Before you begin, ensure you have the following:- A CRM with Data: You need at least 500–1,000 closed leads (both won and lost) for the AI to learn effectively.
- Consistent Data Entry: Your team must be recording lead outcomes (Closed Won/Closed Lost) accurately.
- An AI-enabled CRM or Tool: Platforms like HubSpot (Enterprise), Salesforce (Einstein), or third-party tools like MadKudu or 6sense.
- Clear Sales Cycle: A defined process of how a lead moves from an enquiry to a customer.
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Step 1: Clean Your Historical Data
AI is only as good as the data you feed it. Before activating any predictive features, audit your CRM. Ensure that "Closed Won" and "Closed Lost" fields are filled out for your leads over the last 12-24 months. What you should see: A list of contacts where the 'Lead Status' or 'Deal Stage' is clearly defined. If you see hundreds of leads stuck in 'In Progress' from two years ago, the AI will get confused. Bulk update these to 'Lost' or 'Nurture' before proceeding.Step 2: Identify Your 'Ideal Customer Profile' (ICP) Attributes
While the AI will find patterns, you need to tell it which fields to look at. Common Australian data points include ABN status, location (State/Postcode), industry type, and annual turnover.Ensure these fields are standardised. For example, 'NSW' and 'New South Wales' should be merged into one format so the AI recognises them as the same signal.
Step 3: Select Your Predictive Lead Scoring Tool
If you are using HubSpot, navigate to Settings > Objects > Properties and search for 'Predictive Lead Score'. If you are using Salesforce, look for 'Einstein Lead Scoring'.If your current CRM doesn't have native AI, you will need to connect a third-party tool via API. For Australian SMEs, HubSpot is often the most approachable entry point for these features.
Step 4: Define the 'Success' Event
In your tool's settings, you must define what a 'converted' lead looks like. Usually, this is a deal reaching the 'Closed Won' stage.Pro Tip: Don't just score for 'any' sale. If you have a specific high-value service you want to grow, consider weighting the AI to look for leads that specifically converted for that service.
Step 5: Map Your Data Points (Features)
Select the properties the AI should analyse. This typically includes:- Firmographics: Company size, industry, location.
- Demographics: Job title, seniority level.
- Behavioural Data: Number of website visits, whitepaper downloads, or webinar attendances.
- Email Engagement: Open rates and click-through rates.
Step 6: Run the Initial Model Training
Once your parameters are set, trigger the 'Train' or 'Build' command. The AI will now run a regression analysis, comparing your 'Won' leads against your 'Lost' leads to find the common denominators. What you should see: A progress bar or a notification stating that the model is being built. This can take anywhere from an hour to a few days depending on the volume of your data.Step 7: Review the 'Influence Factors'
Once the model is built, the software will show you which factors most heavily influenced the score. Screenshot Description: Look for a dashboard titled 'Model Insights' or 'Score Breakdown'. You’ll see a list of attributes with a positive or negative weight. For example, 'Job Title contains Director' might have a +15 influence, while 'Gmail.com email address' might have a -10 influence.Step 8: Set Your Scoring Thresholds
Decide what score constitutes a 'Hot', 'Warm', or 'Cold' lead. Usually, the AI provides a score from 0 to 100.- 80-100: High priority (Immediate sales call)
- 50-79: Medium priority (Marketing nurture)
- 0-49: Low priority (Automated follow-up only)
Step 9: Integrate the Score into Sales Workflows
This is the most critical step. A score is useless if the sales team doesn't see it. Create a custom view in your CRM that sorts all new leads by 'Predictive Lead Score' in descending order.Set up an automated notification: "New High-Value Lead (Score: 92) assigned to [Sales Rep Name]."
Step 10: Monitor and Iterate
AI is not 'set and forget'. Every quarter, review the leads that were given high scores but didn't convert. Did the AI miss something? Perhaps a new industry trend has emerged in the Australian market that the model hasn't accounted for yet. Adjust your parameters and re-train the model if necessary.---
Common Mistakes to Avoid
- Garbage In, Garbage Out: If your sales team doesn't record why they lost a deal, the AI can't learn what a 'bad' lead looks like.
- Ignoring the 'Human' Element: Don't let the AI have the final say. If a lead has a low score but is a well-known brand in your industry, your team should still reach out.
- Over-complicating the Model: Start with 10-15 key properties. Adding 200 obscure properties can lead to 'overfitting,' where the AI finds patterns that don't actually exist.
Troubleshooting
- "My model won't start training": Check if you have enough data. Most AI tools require at least 100-200 'Won' deals and 100-200 'Lost' deals within a specific timeframe to be statistically significant.
- "The scores all look the same": This usually happens when your data is too uniform. If every lead in your CRM has the same job title and industry, the AI has no way to differentiate them. Try capturing more diverse data at the lead capture stage.
- "The AI is giving high scores to spam": Check if you are filtering out 'test' submissions or bot traffic. You may need to add a filter to exclude certain email domains (like .ru or .xyz) from being scored.
Next Steps
Now that you've automated your lead prioritisation, you can look into Automated Nurture Sequences for those 'Medium' score leads.If you're finding the technical setup a bit daunting, or if your CRM data needs a professional clean-up before you can start, the team at Local Marketing Group is here to help. We specialise in helping Brisbane businesses bridge the gap between marketing and sales through smart automation.
Contact us today to discuss how we can help you implement AI in your sales process.