10 Common Mistakes while Building an AI Model for your Go To Market

10 Common Mistakes while Building Machine Learning Models & How to GTM Leaders Can Learn More About These Mistakes.

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In today's data-driven GTM landscape, machine learning models have become indispensable tools for businesses seeking to optimize their go-to-market (GTM) strategies. At Forwrd, we've built and tested thousands of GTM AI models, meticulously evaluating their performance and uncovering valuable insights along the way. 

While evaluating the accuracy and quality of models built using Forwrd we have identified certain common mistakes in model builds that can significantly impact performance, leading to suboptimal results and missed opportunities.

The mistakes we'll discuss in this article aren't just theoretical concerns – they're real issues we've encountered and overcome in our work with diverse clients across various industries. 

From SaaS startups to large B2B enterprise clients, we've seen how these pitfalls can hinder the effectiveness of AI model builds. 

At Forwrd we guide our clients on their way to build optimal GTM AI models which have surpassed industry standards and yet by sharing these insights, we aim to help marketers and GTM analysts alike to avoid these common traps and build more robust, effective models that drive tangible business results.

As we look into some common mistakes, we'll not only explain what it means and provide relatable examples but also offer practical way in which marketers can work with their analysts while building an AI model. Whether you're just starting your journey in AI-driven marketing and revenue analytics or looking to refine your existing models, these insights will prove invaluable in enhancing your machine learning initiatives.

‍Let's explore the 10 common mistakes in building machine learning models, their implications, and how to avoid them:

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1. Insufficient Data Preprocessing:

What it means: If the data going into your model isn't clean or organized, it won't produce accurate results. Think of it like cooking: you need fresh, prepped ingredients to make a good dish. Similarly, in predictive modeling, messy data leads to unreliable predictions.

Example: If you're analyzing customer profiles and your location data has variations for the same country (e.g., "US," "USA," "United States"), this inconsistency can affect the model’s accuracy. You need to standardize these terms into one category to get meaningful results.

How to solve it: When discussing data with your team, focus on fields that may be messy, like geographic locations or customer engagement data. Ask questions like, "How are we ensuring consistency in this field across different sources?"

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2. Data Leakage

What it means: Sometimes, information that shouldn't be included in your training data sneaks in and makes the model look better than it really is. In real-world use, the model will likely fail because it’s been “trained” with data it wouldn't normally have access to.

Example: Let's say you're predicting which customers are likely to churn. If you accidentally include information in your training data that you wouldn't have known before a customer churned (like the date they canceled their subscription), your model will perform unrealistically well in training but fail in real-world scenarios.

How to solve it: When speaking with your data scientist, ensure that the data being used to train the model is truly "unknown" at prediction time. You can ask, "Are we using any data here that we wouldn’t have access to before the event happens?"

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3. Overfitting

What it means: Overfitting happens when a model learns too much from specific patterns in the training data, including irrelevant noise. This makes it perform well in testing but poorly when used on new data.

Example: Imagine creating a model to predict which email subject lines will result in high open rates. If your model learns to associate success with very specific words or phrases that happened to work well in your limited training data (like "Flash sale on Tuesday!"), it might fail when applied to new campaigns with different contexts.

How to solve it: Like teaching a student, you don’t want them to just memorize answers; you want them to understand concepts. Ask your data team if they are exposing the model to varied data to avoid this. A good question is, "Are we ensuring the model generalizes across different types of data, not just this specific campaign?"

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4. Ignoring Feature Selection

What it means: This is when too many irrelevant features are included in the model, which creates noise and reduces its effectiveness.

Example: In a B2B setting, when predicting which leads are most likely to convert, you might be tempted to include every piece of data you have about a prospect. However, including irrelevant information like the prospect's industry buzzwords used on their website or the number of employees listed on LinkedIn (which might not be accurate or updated) could distract the model from truly important factors like engagement with your content, website visits, or specific pain points expressed during interactions.

How to solve it: Think of this like choosing only the essential ingredients when cooking. Encourage your team to prioritize the most important data points and ask, "Which features are we focusing on to predict conversions, and how are we evaluating their relevance?"

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5. Neglecting Feature Engineering

What it means: This happens when you fail to create new insights or metrics from raw data that could improve your model’s performance.

Example: In a model predicting email engagement, instead of just using raw data like "time sent" and "subscriber age," you could create a new feature like "time since last interaction." This engineered feature might be more predictive of whether a subscriber will open your email.

How to solve it: Ask your team how they’re combining data to create new insights. You can ask, "Are we creating any new features that combine existing data to get better predictions?"

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6. Improper Validation

What it means: If you don’t properly test the model on various sets of data, you risk getting inaccurate results. It's like tasting a recipe only once before serving it to guests—you won't know if it works for different tastes.

Example: If you're building a model to predict which customers will respond to a particular promotion, testing it on just one subset of your data might give you a false sense of its performance. It's like taste-testing a new recipe with only one person – you need a variety of opinions to be confident in its appeal.

How to solve it: Ask your analyst, "How are we testing the model on different customer segments to ensure it works across the board?"

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7. Ignoring Class Imbalance

What it means: If you have more data on one group than another (e.g., more data on loyal customers than churned ones), your model could become biased and miss key insights about the smaller group.

Example: In fraud detection, if you have far more legitimate transactions than fraudulent ones, your model might struggle to detect fraud because it hasn’t been given enough examples.

How to solve it: Ask your team how they're accounting for this imbalance. You could say, "Are we giving more weight to less frequent but important cases like fraud?"

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8. Overlooking Hyperparameter Tuning

What it means: Every model has settings, like a camera’s shutter speed and aperture. If these settings aren’t optimized, the model won’t perform as well as it could.

Example: Just like using your camera in auto mode might get decent photos, leaving your model’s settings at their defaults will likely work okay but not great.

How to solve it: Ask if your data team is tweaking these settings to get the best results. A good question is, "Are we adjusting the model's parameters for our specific data and goals?"

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9. Choosing an Incorrect Algorithm

What it means: Not all algorithms are suited to every type of problem. It’s important to choose the right tool for the job.

Example: If you're trying to group customers by behavior, a simple linear model won’t work as well as a clustering algorithm designed for segmentation.

How to solve it: Ensure your data scientist explains why they chose a particular algorithm. You could ask, "Why is this algorithm the best fit for our segmentation task?"

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10. Ignoring Business Context

What it means: A model might make great predictions, but if it doesn't align with your business operations, it’s not useful.

Example: A model might suggest sending marketing emails at 2 AM for the best open rate, but if your team isn’t available to respond to inquiries, this isn't practical.

How to solve it: Stay involved in the modeling process and regularly discuss how the model’s outputs will be used in real business scenarios. You can ask, "How does this model’s recommendation fit with our operational constraints?"

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Conclusion:

By avoiding these common mistakes, GTM leaders and analysts can build more effective, reliable, and actionable machine learning models. Remember, the goal isn't just to create a model with high accuracy, but to develop insights that drive real business value. At Forwrd, we've seen firsthand how addressing these issues can dramatically improve model performance and, more importantly, deliver tangible results for our clients' GTM strategies.

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As you embark on your own machine learning journey, keep these lessons in mind. With careful planning, rigorous methodology, and a focus on business relevance, you can harness the full power of AI to transform your marketing efforts and drive unprecedented growth.

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By avoiding learning the behind the scenes of these mistakes, marketers, GTM pros and analysts can learn to build and identify more effective, reliable, and actionable machine learning models for their business goals. Remember, the goal isn't just to create a model with high accuracy, but to develop insights that drive real business value.

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While these mistakes are indeed complex and can be challenging to address, especially for those new to machine learning, there's good news. At Forwrd, we understand the intricacies involved in building effective AI models for go-to-market (GTM) strategies.

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That's why we've developed a specialized platform designed to simplify the process for GTM managers. Our tool guides you through the steps of model development, helping you navigate these common pitfalls with ease while building self learning GTM AI models for you. From data preprocessing and feature selection to model validation and interpretation, our platform provides intuitive interfaces and built-in best practices to ensure you're on the right track.

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By leveraging Forwrd's tool, GTM managers can harness the power of AI without getting bogged down in technical complexities, allowing them to focus on what they do best – driving business growth and customer engagement. With Forwrd, you're not just avoiding mistakes; you're empowered to create sophisticated, effective AI models that deliver real business value.

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