Avoid Data and AI Biases for Stronger CX and Marketing Outcomes

The more sophisticated we become in using AI about people, the more human we need to become.

Earlier in the year, I sold an AI algorithm for consumer behavior and decision intelligence. It’s my third algorithm/AI acquisition, and each time the challenge is the same. There is a great underestimation of bias.

Technology magnifies the quality of the people, processes and data behind them. The more sophisticated we become in using AI about people, the more human we need to become.

CX professionals and leaders can put their energy into creativity, imagination and strategy with better use of AI/data. So, let’s look to manage our biases and use the opportunity to develop advantages from them! 

These are regular biases I’ve found to create decisioning problems in insight, segmentation and orchestration processes. Remember, awareness is your strongest asset. 

Cognitive Bias 

Awareness: The most common bias. It tends to interpret new information as confirmation of our existing theories.

Fix: Overcoming confirmation bias begins by using more quantitative analysis. Also, engage an objective perspective to handle and interpret your data from your engagement efforts. Be comfortable and appreciate the people who can oppose your perspectives and respect you.

Advantage: This is an opportunity to create a stronger team dynamic when making decisions. Do this through:

  1. Developing a good data culture.
  2. Leveraging team assessments to understand decision-makers.
  3. Creating a diverse team or collective perspectives. Nosce te Ipsum. (Know yourself)

Related Article: ChatGPT’s Impact on Customer Experience and Marketing

Irrational Escalation

Awareness: Common with investors, entrepreneurs and high-growth marketing leaders. The tendency to dismiss or ignore new research evidence which overrides or undermines an existing decision. A leader so invested in not losing a CX initiative completely ignores the data illustrating the pitfall.

Fix: Be open to abandoning the deep investment. Also, become as objective as possible when considering new research. Define a deadline or have a research or advisory support group help mitigate this effect.

Advantage: Leverage parachute metrics which show you velocity changes. Examples include an accelerated drop in customer satisfaction, campaign engagement or top-of-funnel inquiries. 

Placing yourself on notice before your stakeholders do provides a buffer. You can then make sound decisions and better prepare narratives. Top startup founders and enterprise CEOs use these metrics to manage up and down. Failure is part of business and marketing, but how it is handled makes or breaks careers.  

Overfitting and Underfitting

Awareness: Overfitting involves an overly complex model which fits the data too well. Many Go-to-Market leaders will recognize this when their AI or statistical models applied to new data sets are consistent but inaccurate on average. Example: Nearly every prospect meets some “qualified” criteria, yet it is incorrect.

Underfitting occurs when a model or algorithm cannot capture the underlying trend of the data. The model is too simple and does not reflect the data well enough. Using the previous example, an underfit model/algorithm produces an occasionally correct qualified prospect.

Source link

We will be happy to hear your thoughts

Leave a reply

Enable registration in settings - general
Compare items
  • Total (0)
Shopping cart