Cut Churn, Boost Revenue: The Unsexy AI Strategy That's Secretly Lucrative

In today’s Future Friday…

• Turn Messy Data Into a Profit Machine (This "Boring" Strategy Works)

• Why Your Marketing Team Needs a Data Science Crash Course

• Customer Churn is Costing You: This Technique Reveals the Fix

• Google Just Made it Easier to Build Your Own AI Assistant

• Your Next Ad Could Be Made By OpenAI's Text-To-Video AI Model

AI can't fix messy data or a lack of strategy. Without a structured approach, even the most cutting-edge AI tech won't deliver the results you expect. Join us as we unpack how you can transform your data into a powerful asset for growth and innovation.

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TOPIC OF THE WEEK

This 'Boring' Strategy Will Transform Your Data into Revenue

Sometimes, the most valuable AI strategy is the one that seems a bit... boring.

Let's face it, digging through your data, cleaning and then sorting it doesn't sound as exciting as experimenting with the latest tech.

But rushing to advanced AI tools without the right foundation could mean you're building on shaky ground. It might sound counterintuitive, but the key to successful AI isn't always the most cutting-edge tech.

There's a smarter way to capitalize on your data, and we’ve uncovered that in our latest podcast episode with Neurons Lab co-founder Alex Honchar.

Alex outlines the four "boring but fundamental" stages of data analysis that make AI truly effective. Mastering these stages forms the backbone of a data-driven decision-making process:

Stage 1: Descriptive Analytics = What Happened

Deep dive into historical data to understand past behaviors and outcomes.

Alex emphasizes the importance of visualizing data to uncover trends and patterns. It also involves data cleaning and the initial analysis to set the stage for deeper insights.

Why it's important: Establishes a baseline understanding of your data before moving to more advanced analysis. 

Stage 2: Diagnostic Analytics = Why It Happened

Now analyze clean data to diagnose the reasons behind specific business outcomes.

Alex suggests asking targeted business questions, such as why certain customers churn, and segmenting data to identify common characteristics or behaviors in specific customer groups.

Why it's important: Drives insights that inform actions and helps you target problems at the root cause.

If there are specific timing, specific size of the order, and specific behavioral patterns, you can visualize it and then try to diagnose what is happening with them again. 

Stage 3: Predictive Analytics = What's Next

Only after you have your visualizations and clean data can you predict the future, like forecasting who might churn in the future. 

Why it's important: Enables proactive decision-making and resource allocation.

Stage 4: Prescriptive Analytics = Making It Happen

Here's where you can start using generative AI. The final stage involves using the insights gained from the previous stages to create a game plan.

Armed with insights from previous stages, you now use AI to create personalized messages for churners.

Why it's important: Helps businesses take the best course of action based on data-driven insights.

ℹ️ Why This Matters Today

Most AI projects fail, with up to 85% of ML projects falling through due to lack of strategy and poor data foundations. Data analysis ensures you spend money on the AI tools and strategies that will actually work for your specific business.

Without these stages, you're flying blind, making decisions based on gut feelings instead of solid insights.

🏆 Golden Nuggets

• Clean data is key. Gartner estimates that poor data quality costs organizations an average of $12.9 million per year.

• It's okay to go back and adjust. As you learn more about your data, you might need to revisit the initial stages to refine your approach. 

• The "boring" stuff matters most. Don't get tempted to skip straight to the fancy AI tools.

Different-sized companies need different tools and approaches

The common denominator is understanding how the 4 stages of data analysis align with your company's specific needs and resources. Let's break down how businesses of different sizes and types can leverage AI:

Small Businesses

If you're a business with under $10 million in revenue, don't underestimate the power of the descriptive and diagnostic stages.

Alex said it doesn’t even require predictive models to uncover why customers behave the way they do. Even simple dashboards like Amazon Quicksight Q can reveal valuable trends in customer behavior, sales, or website traffic. 

Focus on identifying why certain patterns occur – are discounts working, why are customers churning, and what products are most popular? 

Mid-Sized and Larger Businesses

Bigger companies would want to experiment a little and then double down on advanced analytics (predictive and prescriptive).

This is where Alex emphasizes that "your marketing department has some ideas on how to engage customers...and those marketing ideas are typically related to some different segments." The focus shifts to:

1. Predictions: Identify churn risks and high-value customers and tailor your interventions.

2. Agility: Test different discounts, offers, and messaging with A/B testing. Don't just guess, get data-driven results.

A technique called uplift modeling helps you see who responds best to which treatments, improving your ROI.

At this scale, predictive analytics can help you anticipate who's likely to spend more or churn, allowing you to intervene before it's too late.

You might need some machine learning expertise at this stage, according to Alex. But the payoff in targeted campaigns and proactive customer retention can be huge.

💡 Ideas to Marinate

• E-commerce and digital marketplaces generate tons of data and are perfectly positioned to leverage AI.

• These digitally native businesses can mine data for insights to personalize customer experiences and optimize pricing.

• However, businesses handling confidential information (like those in finance or healthcare) need extra caution. Consider private AI models hosted securely to protect your unique data.

Choosing Your AI Tools: When to Go Public vs. Private

You've got data, you've got goals, but which AI models are the best fit?  When it comes to tools like OpenAI's API, Alex reminds us that "the current take is that if your data is not exactly unique...it's safe to use." 

Ask yourself these two questions:

Is it out there already?  If the info you're working with (like dog breeds and customer preferences for common products) is widely available online, a public API can often be a cost-effective, convenient solution.

Have you got secrets?  For highly sensitive data (think those defense protocols Alex mentions), a private model is essential. "I wouldn't let this data out because it's really unique, custom, and sensitive."

You might need to get a bit creative to find the middle ground. Consider:

• Anonymizing Data: Remove personally identifiable information before using public APIs.

• Hybrid Solutions: Combine public APIs for some tasks with private models for the sensitive stuff.

Alex said you don't need to send "the name of a customer, the address, and the age" to craft personalized emails. Think in terms of what's essential vs. what's just extra baggage.

⚒️ Actionable Steps

1. Assess Your Data Landscape

• Size: How much data do you have? Is it structured or unstructured?

• Type: What kind of data is it (Sales, customer behavior, product info, etc.)?

• Sensitivity: Does it contain personally identifiable information or confidential business insights?

2. Define Your Business Goals

• Quick Wins: Are there any immediate questions you can answer with descriptive and diagnostic analytics (e.g., identifying top-selling products, uncovering churn patterns)?

• Strategic Growth: What would you like to predict to drive growth (e.g., customer lifetime value, market trends)?

3. Evaluate Your In-House Talent

• Small Teams: Start with basic visualization tools (Quicksight Q) and focus on simple insights. Consider outsourcing for more complex tasks.

• Growing Teams: Explore hiring data analysts or upskilling current employees in data science basics.

4. Match Tools to Your Needs

• Common Data, Public APIs: Experiment with OpenAI and other readily available models.

• Specialized or Sensitive Data: Investigate private, open-source models that can be hosted securely on your infrastructure.

• Hybrid Approach: Consider using public APIs for some tasks and private models for others based on sensitivity.

CAVEMINDS’ CURATION

The Secret to Stopping Customer Churn

Marketing campaigns are a gamble. You want to reach only those who'll actually be swayed to buy, donate, or vote – whatever your goal is. 

Target the wrong people, and you've annoyed them into not buying. Worse, you might waste marketing dollars.

It's a common problem, and what Alex points to as a lack of data-driven precision. You need a way to pinpoint the persuadable customers. 

That's the power of uplift modeling. This machine learning technique goes beyond "who might buy?"  

Uplift modeling predicts who'll buy because of your campaigns.  We're talking about isolating those who need that extra nudge to take action.

Here’s how it works:

1. Collects data on past sales, customer profiles, and how people responded to previous campaigns.

2. Splits the data into groups, randomly dividing customers into a "treatment group" (who receive the offer) and a "control group" (who don't).

3. An ML algorithm analyzes the data. It looks for patterns in how the two groups reacted differently to your campaign.

4. The model reveals what makes someone a "true responder" –  people who are actually swayed by your marketing to buy, donate, etc.

5. Now you know the type of person your campaigns work best on. Focus future marketing only on new customers who share those winning characteristics.

🏆 Best Use Cases

Uplift modeling is particularly useful in these marketing strategies:

• Personalized Promotions - Give the right discounts to the right people. Avoid giving discounts to customers who were going to buy anyway!

• Churn Prevention - Figure out who's about to leave and offer them something special to stay. Don't waste time on people who are already gone.

• Customer Acquisition - Target people who look most like your current top customers to save money on advertising.

• Cross-Selling & Upselling - Know which extra products or upgrades specific customers might actually want, increasing their spending.

This is where you already might want to have some expertise in machine learning, but Alex said there's almost no one that offers a holistic solution to this.

Read next: AI Playbook #1: The Email AI-Agent That Turns Hours into Minutes

Neurons Lab combines deep AI knowledge with intellectual proprietary expertise.

While they offer consulting, their focus is on "co-innovation." They work with clients to define business goals and actively partner in building AI solutions that address those goals.

NEEDLE MOVERS

Google Releases Open Source AI Models for Common Tasks

Google has made it even easier for developers to get started with AI!

It’s released a series of open-source models, called GEMMA, for tasks like text generation, translation, and image classification. This could make incorporating AI features into apps and websites more accessible for businesses of all sizes.

OpenAI Takes Aim at Video with New "Sora" AI Model

OpenAI is taking generative AI a step further with Sora. This new model can turn simple text prompts into short, eye-popping videos.

While still in its early stages, Sora has huge potential. We could be looking at a future where custom commercials, music videos, or even tailored training materials are just a few text prompts away.

For some, the future might look scary. For others, it is extremely exciting…

No word yet on pricing or public release, but this is definitely one to watch.

That’s all for today!

Continue Reading

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