Machine Learning (ML) and Artificial Intelligence (AI) have their roots in mathematics. From matrices and vector-space models to trigonometric distance calculations, understanding the mathematics behind these models is fascinating. It reveals the underlying fundamentals of how we model the world and predict a model's behavior when deployed in real-world scenarios. However, the true effectiveness of ML/AI initiatives is rooted in deep customer-obsession.

What is NOT Customer-Obsession?

1. ELT-ing TBs of Customer Data: Extract, Load, and Transform (ELT) processes that handle terabytes of customer data are necessary, but simply managing data doesn’t equate to understanding or solving customer problems.
2. Shipping a Bug-Free Model: While delivering a model free of bugs is essential, it is a basic expectation. A bug-free product that doesn’t address customer needs is still a failure.
3. Deploying the Most Cutting-Edge Models: Utilizing state-of-the-art models is exciting and can be beneficial, but it is not customer-obsession. If these models don’t translate into real value for the customer, they miss the mark.

Time and again, we have all come across expensive ML/AI initiatives that deliver zero to minimal value to customers. One common factor in many of these initiatives is the lack of customer-obsession

What is Customer-Obsession in the Context of ML/AI?

There are three pillars of effective customer-obsession:

1. Enable
2. Champion
3. Elevate

Using an example of an ML-AI native CRM, let's expand on these pillars. For simplification, let's consider three distinct user personas:

1. VP Sales
2. Account Executive (AE)
3. Sales Development Representative (SDR)
   
   Each of these personas will have their unique needs for enabling, championing, and elevating.

Enabling: Reducing Customers’ Jobs-to-be-Done

• VP Sales: Enable VPs by providing predictive models that help in strategic decision-making, like quota prediction and team OTE confidence scores.
• AE: Enable AEs by automating tasks like personalized positioning and lead scoring, allowing them to focus more on strategic activities.
• SDR: Enable SDRs by equipping them with tools that streamline predictive lead generation and personalized audiences, making it easier to identify and engage with potential customers.

Championing: Supporting Customers’ Performance KPIs

• VP Sales: Champion their goals by ensuring the CRM provides accurate and actionable recommendations that align with their key performance indicators (KPIs), such as pipeline growth, forecast optimization, and customer acquisition cost optimization.
• AE: Champion AEs by providing tools that automatically prioritize leads based on their performance against quotas, conversion prediction, and sales cycle optimization, helping them to optimize their on-target earnings (OTEs).
• SDR: Champion SDRs by offering insights into their outreach effectiveness, tracking metrics such as response rates, number of meetings scheduled, and lead conversion prediction, lead classifiers, allowing them to refine their approach and improve outcomes.

Elevating: Empowering Customers for High-Leverage Activities

• VP Sales: Elevate VPs by freeing up their time from operational details, enabling them to focus on high-leverage activities like strategic planning, team leadership, and market expansion.
• AE: Elevate AEs by reducing administrative burdens, allowing them to dedicate more time to building relationships, negotiating with clients, and closing deals.
• SDR: Elevate SDRs by automating repetitive tasks, enabling them to focus on crafting personalized messages, engaging more meaningfully with prospects, and improving their overall productivity.

Conclusion

While understanding the mathematical intricacies of ML/AI is important, the true measure of success lies in our ability to obsess over our customers. By centering our efforts on their needs, KPIs, and overall success, we can ensure that our ML/AI initiatives deliver real value and drive significant outcomes.