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The more sophisticated the use of AI with humans, the more human we need to be.
Earlier this year, I sold an AI algorithm for consumer behavior and decision intelligence. This is his third time taking Algorithms/AI, but the challenges are the same each time. Prejudice is underrated.
Technology enhances the people, processes and data behind it. The more sophisticated the use of AI with humans, the more human we need to be.
CX professionals and leaders can make better use of AI/data to focus their energy on creativity, imagination, and strategy. So let’s take the opportunity to manage our biases and create advantages from them!
These are the regular biases we’ve found to cause decision-making problems in the process of insight, segmentation, and orchestration. Remember that consciousness is your strongest asset.
cognitive bias
Awareness: The most common bias. They tend to interpret new information as confirmation of existing theories.
repair: Overcoming confirmation bias starts with using more quantitative analysis. Also, adopt an objective perspective to process and interpret data from engagement efforts. Be at ease and appreciate those who disagree with your views and respect you.
advantage: This is an opportunity to create stronger team dynamics when making decisions. You do this by:
- Developing a good data culture.
- Leverage team evaluations to understand decision makers.
- Create diverse teams or collective perspectives. Nocetaipsum. (know yourself)
Related Article: The Impact of ChatGPT on Customer Experience and Marketing
irrational escalation
Awareness: Common among investors, entrepreneurs, and high-growth marketing leaders. Tendency to dismiss or ignore new research evidence that overturns or undermines existing decisions. Leaders who have invested in not losing their CX initiatives completely ignore the data showing the pitfalls.
repair: Be open to abandoning deep investments. Also, when considering new research, think as objectively as possible. Define a deadline or have a research or advisory support group help mitigate this impact.
advantage: Take advantage of parachute metrics that show changes in speed. Examples include accelerated customer satisfaction declines, campaign engagement, and top funnel inquiries.
A buffer is provided by notifying yourself before the stakeholder does. Then you can make the right decisions and better prepare your story. Top startup founders and corporate CEOs use these metrics to manage their ups and downs. Failure is part of business and marketing, but how you handle it can make or break your career.
overfitting and underfitting
Awareness: Overfitting involves overly complex models that fit the data too well. Many market development leaders perceive this when AI or statistical models applied to new datasets are consistent but inaccurate on average. Example: Nearly all prospects meet some “eligible” criteria, but that’s not true.
Underfitting occurs when a model or algorithm fails to capture the underlying trends in the data. The model is too simplistic and does not reflect the data well. Using the previous example, the underfitting model/algorithm will occasionally generate the correct eligible prospects.
repair: Solve overfitting by splitting the dataset, such as training and testing. Also, cross-validate against multiple such sets. Resolve underfits by incorporating confidence levels and monitoring them.
advantage: Neither help. But they can be great discussion forums with intelligence-capable martechs, sales techs, and customer data vendors.
Outliers/Ethics
Awareness: Outliers are data points that are significantly above or below the norm or outside the pattern. Relying on such numbers at face value may not give you an accurate picture. A busy leader, especially one who leads his CX across the organization, has the daunting task of converting analog conversations to digital and vice versa. Outliers should not be dismissed if they are your customers and are treated equally.
repair: Tread carefully. Ignoring outliers is wise in some situations, but completely irresponsible in others. Please fully understand the background of the problem. It also superimposes financial, ethical, and social constraints. Make sure you know your team as well.
advantage: Studying these will strengthen your team’s purpose. Used to improve model/algorithm processes and feedback. Statistical outliers can reveal new opportunities and the need to reinvest in campaigns.
Related article: Can AI Marketing Transform Your Business?
The best AI is the best you
With proper preparation and regular context checks, organizations should:
- An important first step is to develop a plan for awareness, education and training.
- Build a team with diverse perspectives.
- Include thoughts, ideas, and constructive comments.
- We strive for transparency in our processes and models/algorithms.
CX and GTM executives who manage these teams need to immerse themselves in these conversations. Also, take advantage of the opportunity to work with data. These leadership investments are based on the AI ​​and data culture we want to reflect and scale.
Collecting, interpreting, and applying AI to data expands who we are and how we operate. Recognizing biases and iteratively refining them gives leaders confidence in marketing AI-driven decision support.
We use data to empower and AI to augment.
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