#79: Building a Data-Driven Culture: Three Mistakes to Avoid
Hello,
This newsletter will take you about 4 minutes to read.
I. Spotlight: Building a Data-Driven Culture: Three Mistakes to Avoid
When one of the largest global telecom companies grappled with high customer attrition, the marketing team used a heuristics-driven approach to retain customers. For example, if a customer hadn’t made any outgoing calls in three weeks, the company would roll out a promotion.
However, this approach didn’t deliver results, and customer retention was at its lowest in years. Based on the weekly business performance reviews, the CEO knew it was time to try something different. He turned to data science tools and a cross-functional effort to solve the retention challenge.
The marketing team deployed machine learning algorithms to study customer usage patterns and predict churn. Simple techniques like decision trees helped spot factors such as billing amounts and outgoing call patterns — both good predictors of a customer’s propensity to leave.
Tests run on historical data indicated that this approach could improve customer retention by 39% — a hopeful sign. Then the data science team brought in more advanced AI firepower, adding techniques such as neural networks for deeper pattern-spotting.
This approach turned out to be far more accurate and effective, with the potential to improve customer retention by 66% — a considerable uplift. A four-week pilot run with high-value customers confirmed the results.
The data science solution looked ready for a full rollout. Then things turned south.
The marketing product managers refused to use the solution. They found it hard to trust an algorithm that spat out a list of at-risk customers with little explanation. What’s more, many of the data-backed recommendations were counterintuitive.
II. Industry Roundup:
1. Article: AI models have favorite numbers because they think they’re people
04 minutes | Tech Crunch | Devin Coldewey
AI models, like humans, struggle with randomness. For example, when asked to choose a random number between 0 and 100, they often avoid extremes and favor numbers like 47 or 72, mirroring biases seen in human choices. This behavior stems not from understanding but from mimicking patterns found in their training data, showcasing how AI often replicates human tendencies without actual comprehension.
2. Article: The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
11 minutes | McKinsey | Alex Singla, Alexander Sukharevsky, et al.
In 2024, 65% of organizations are using generative AI regularly, nearly double from ten months ago, according to a McKinsey Global Survey. Despite emerging risks like inaccuracy, companies increasingly integrate AI into various business functions, leading to significant operational benefits and increased revenue. The survey highlights a rise in AI adoption globally, with professional services showing the largest increase.
III. From my Desk:
1. Article: Turning Trash Into Treasure: How AI Is Revolutionizing Waste Sorting
06 min | Forbes
Did you know the US has lost land roughly the size of the state of Maryland to landfills? The waste crisis is devastating our planet. My latest for Forbes on how AI is helping sort trash, improve recycling & drive sustainability. The article shares the top challenges with waste sortation today & highlights AI-driven robotic solutions. Check out expert inputs from WasteExpo, AMP, Glacier, and EverestLabs.AI.
2. Post: At the Databricks Data+AI Summit in San Fransisco
01 min | LinkedIn
I was at the Databricks Data+AI Summit in SFO this week. GenAI was the dominant theme - across the keynote, sessions, and exhibitor booths! Despite the overall hype, it was good to see some meaningful applications and impactful enterprise implementations. I was surprised by the scale of this ecosystem around a single (prominent) product. Overall, it was a well-organized event, rich in networking opportunities and innovative formats for idea exchanges and learning.
-> Check out the Summit Website
Thank you for subscribing and reading the newsletter. I appreciate your attention,
Ganes.
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My Website | LinkedIn | Twitter | YouTube
I’m Ganes Kesari. I publish ‘Data-Driven Future’ to help understand how data shapes our world, explore key trends, and explain what they mean for you today. I speak and write to demystify data science for decision-makers and organizations.
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Struggling to get your team on board with data-driven insights is common. You might want to explore tools like Loyally AI to better track customer behaviors and improve retention strategies. It helped others make sense of complex data and build trust in their predictions.