INBOX INSIGHTS, July 23, 2025: Using AI for KPIs, Which AI Models to Use Part 2 - Trust Insights Marketing Analytics Consulting

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INBOX INSIGHTS: Using AI for KPIs, Which AI Models to Use Part 2 (2025-07-23)

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Using AI for KPIs

A reader recently asked us: “How might you use AI in the process of evaluating KPI performance and next actions?”

This is exactly the kind of pattern recognition that AI was built for. But here’s the thing: just throwing AI at your KPI problems won’t magically solve them. You need a framework.

Enter the 5P Framework. It’s what I use to tackle any analytics challenge, and it works perfectly for incorporating AI into your KPI evaluation process. Let me walk you through how this actually works.

Purpose: What Problem Are We Actually Solving?

Before we get excited about AI doing all the heavy lifting, we need to be crystal clear about what we’re trying to accomplish.

Most people say they want to “improve KPI performance,” but that’s not where you should start. Here are the real questions we should be asking:

Which metrics actually impact our business goals?
What patterns in our data are we missing?
How quickly do we need to identify and respond to performance changes?
What decisions will we make differently based on better KPI analysis?

The AI Connection: AI excels at answering specific questions, but it’s terrible at figuring out what questions to ask. That’s still our job.

For example, instead of asking AI to “analyze my KPIs,” ask: “What factors correlate with our highest-converting traffic, and how can I identify those patterns earlier?”

People: Who Actually Cares About This Output?

This is where most KPI initiatives fall apart. We create beautiful dashboards that nobody looks at because we didn’t think about who needs what information and when.

Internal Stakeholders:

Marketing team: Needs to understand traffic quality and source performance
Sales team: Wants to know which leads are most likely to convert
Executive team: Requires high-level trends and alerts for major changes
Operations team: Needs operational metrics that impact customer experience

External Considerations:

Customers whose behavior creates the data
Partners or vendors whose performance affects your metrics
Regulatory requirements for certain industries

The AI Advantage: AI can personalize insights for different stakeholders. Instead of one generic dashboard, AI can generate role-specific reports and alerts.

Your CMO doesn’t need to know that mobile page load time increased by 0.3 seconds. But they do need to know that mobile conversion rates dropped 5% and it might be related to site performance.

Process: Making This Repeatable and Consistent

Here’s where the magic happens. A good process turns chaotic data wrestling into systematic insight generation.

Traditional KPI Review Process (probably sounds familiar):

Pull reports from multiple tools every Monday
Copy numbers into master spreadsheet
Calculate month-over-month changes manually
Send summary email with green/red status indicators
Panic when something’s red
Schedule emergency meeting to figure out what happened

AI-Enhanced Process:

Weekly Automated Analysis:

AI pulls data from all connected sources
Identifies statistically significant changes
Correlates changes across different metric categories
Generates narrative summaries for each stakeholder group

Exception-Based Reporting:

Continuous monitoring for unusual patterns
Automated alerts only when action is needed
Context-rich notifications that include potential causes

Let me show you this with a real example using e-commerce KPIs:

Sample KPI Map: E-commerce Performance

Traffic Metrics: Sessions, users, traffic sources, mobile vs desktop
Engagement Metrics: Pages per session, time on site, bounce rate
Cart Metrics: Add-to-cart rate, cart abandonment rate, average cart value
Conversion Metrics: Purchase rate, revenue per visitor, customer acquisition cost

Traditional Analysis: Look at each metric separately and manually try to find connections.

AI-Enhanced Process:

AI analyzes all metrics simultaneously every day
Identifies that cart abandonment spiked 15% yesterday
Correlates this with increased mobile traffic from a specific campaign
Discovers mobile checkout process has a technical issue
Alerts appropriate team with specific action items
Continues monitoring to confirm fix effectiveness

The process becomes: Monitor → Detect → Correlate → Alert → Act → Verify.

Platform: What Tools Do We Actually Need?

You don’t need to completely overhaul your tech stack to get started with AI-enhanced KPI analysis. Let’s be realistic about what’s actually required. You probably have all these tools already.

Minimum Viable Platform:

Your current analytics tools (Google Analytics, whatever CRM you’re using, etc.)
A way to export data (CSV files work fine to start)
Access to AI tools like ChatGPT, Claude, or Google Analytics Intelligence
A shared location for insights and actions (could be as simple as a shared document)

Intermediate Platform:

Automated data connections (Zapier, webhooks, or native integrations)
Dashboard tools with AI features (Tableau, Power BI, or specialized analytics platforms)
Automated alerting systems
Centralized reporting location

Advanced Platform:

Custom AI models trained on your specific data
Real-time streaming analytics
Predictive modeling capabilities
Integrated action-taking (automated bid adjustments, inventory reordering, etc.)

Start Simple: Most businesses should start with the minimum viable platform. Export your KPIs to a CSV, upload it to ChatGPT, and ask: “What patterns do you see in this data that I should investigate?”

Performance: Are We Actually Solving the Problem?

This is the P that everyone skips, and it’s why most KPI initiatives fail. We never go back and check if our new AI-enhanced process is actually working.

How to Measure Success:

Speed to Insight: How quickly can you identify and understand changes in performance?

Before AI: Took 3-5 days to notice and investigate a conversion rate drop
After AI: Alert within hours, root cause analysis within a day

Decision Quality: Are you making better business decisions?

Track decisions made based on KPI insights
Measure outcomes of those decisions
Compare to decisions made with old process

Time Investment: Are you spending time on analysis or action?

Before: 6 hours/week wrestling with data, 2 hours taking action
After: 2 hours/week reviewing AI insights, 6 hours implementing improvements

Stakeholder Satisfaction: Are people actually using the insights?

Survey your internal stakeholders quarterly
Track dashboard usage and alert response rates
Measure time from insight to action

Your 5P Implementation Blueprint

Ready to put this into practice? This is your reusable blueprint. Work through each step at your own pace—some organizations can knock this out in a few weeks, others need a couple months. The key is being thorough at each step.

Step 1: Define Your Purpose

Write down the top 3 business questions your KPIs should help you answer
Be specific: instead of “improve conversion,” try “identify which traffic sources produce customers with highest lifetime value”
Test each question by asking: “What would I do differently if I knew the answer?”

Step 2: Map Your People

List everyone who currently receives KPI reports or who should be making decisions based on the data
Interview 2-3 key stakeholders about what insights they actually need and how they would use them
Document what each person cares about most and how often they need updates

Step 3: Design Your Process

Map your current KPI review process from data collection to action taking
Identify the biggest time-wasters and pain points (usually manual data copying and correlation hunting)
Design your ideal process: what would automated pattern recognition and alerting look like?

Step 4: Choose Your Platform

Start simple: CSV exports + AI tools like ChatGPT or Claude for pattern analysis
Identify your next evolution: automated data connections, specialized analytics platforms, or custom solutions
Don’t over-engineer—pick tools that solve your current problems, not theoretical future ones

Step 5: Measure Performance

Set baseline measurements before implementing AI: how long does insight generation take? How quickly do you act on findings?
Define success metrics: speed to insight, decision quality, stakeholder satisfaction, time allocation
Build in regular check-ins to assess whether the new process is actually working

Step 6: Implement and Iterate

Start with one set of connected KPIs (like the e-commerce example above)
Test your new process for at least a full business cycle before making major changes
Refine based on what you learn, then expand to additional KPI sets

The Reality Check

AI isn’t going to turn you into a data analytics expert overnight. But if you approach it systematically using the 5Ps, it can absolutely help you spend less time drowning in dashboards and more time taking action.

The goal isn’t to automate everything—it’s to automate the repetitive things so you can focus on the interesting decisions that actually move your business forward.

To the reader who sparked this post: I hope this gives you a concrete framework for incorporating AI into your KPI evaluation process. Start with Purpose—get clear on what questions you’re really trying to answer. Everything else will follow from there.

Grab your copy of the 5P Framework!

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In this week’s Data Diaries, let’s continue on with our recommended basic AI tech stack. There are a lot of tools to choose from, and a lot of vendors with their hand out, ready and waiting to take your money for things you might or might not need.

As a reminder from last week’s issue, probably 80% of the things you want to do in AI, you can do right inside a tool like Claude, Gemini, or ChatGPT.

From the remainder, there are a few different ways to think about what the best tools are, but we’re going to approach it very simply, from a modality perspective.

For example, in the category of audio, there are a few options:

For voice cloning of a real person’s voice, Eleven Labs is hands down the winner
For synthetic voices that sound real, Google TTS is the best and cheapest choice
For music, Suno currently holds the top spot for making songs
For transcription, NVIDIA Parakeet and Voxstral are the current best models; Fireflies.ai is the general best transcription service

In video, there are also some specific options:

For cloning a person, HeyGen is still the gold standard
For general video production, Google Veo 3 is the winner by a long shot, OpenAI Sora as a distant second

For image generation, different options for different purposes:

For images with text, ChatGPT is by far the best
For realism, Google Imagen 4 and Midjourney are excellent
For low cost, Flux 1 Dev that you run on your own hardware is still near state of the art

For coding, each LLM ecosystem has a great coding tool, plus some other tools:

OpenAI customers should use Codex
Claude customers should use Claude Code
Gemini customers should use Gemini CLI
Inside your IDE, Cline is the best coding agent

And finally, for local AI on high end laptops, these are the current best:

For writing fluency, Mistral Small
For execution speed and tool calling, Qwen 3 30B AA3B
For more complex instructions, Gemma 3

As we write this, it’s late July 2025, so there’s a good chance that in no time at all, this list will be out of date, but for the many, many, MANY people who ask, “What should I be using?” this is a good starting point. And we want to emphasize that this is a STARTING point. With 1.9 million models and tools to choose from, chances are there’s something out there that satisfies your specific needs – but this is a great starting point.

Here’s a roundup of who’s hiring, based on positions shared in the Analytics for Marketers Slack group and other communities.

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