Technology magnifies the quality of the people, processes, and data behind them. We need data to empower us and AI to scale us. Marketers can put their energy into creativity, imagination, and strategy with better use of AI / data. Then, let’s look to manage our bias and use the opportunity to develop advantages from them!
These biases create decisioning problems in our insight, segmentation, and orchestration processes. Awareness is your strongest asset.
Awareness: The most common bias. It is having a tendency to interpret new information as confirmation of our own 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.
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, and 3) creating a diverse team or collective perspectives. Nosce te ipsum. (Know yourself)
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 marketer who is so invested in a losing campaign 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 your 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. Marketers will recognize this when their AI or statistical models applied to new datasets 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.
Fix: Solve overfitting by split up data sets, say training vs. testing. Also, cross-validate over such multiple sets. Solve underfitting by incorporating confidence levels and monitoring them.
Advantage: Neither is useful. But they can be great discussion points with your MarTech vendors with intelligence capabilities
Outliers / Ethics
Awareness: Outliers are data points significantly above or below the norm or outside the pattern. Relying on such numbers at face value may not paint an accurate picture. Busy marketers have a tough job of translating conversations analog to digital and back again. Outliers are not to be dismissed, if they are your customers and treated equally.
Fix: Tread carefully. Ignoring outliers is sensible in some situations and completely irresponsible in others. Have a complete understanding of the context of the problem. Also overlay them with any financial, ethical, and social constraints. Ensure your team are also aware.
Advantage: Studying these reinforces your teams’ purpose. Use it to improve processes and feedback for the model/algorithm. Statistical outliers can uncover new opportunities or the need to reinvest in campaigns.
Is there a Magic Wand?
Yes! Didn’t think I would say that, right?
- Awareness, education and training are great first steps.
- Build teams with diverse perspectives.
- Ensure inclusion of thoughts, ideas and constructive comments.
- Strive for transparency in the process and models/algorithms.
- Marketing executives managing these teams should immerse themselves in these conversations. Also, take the opportunity to work with data. These leadership investments build upon the AI and data culture you want to reflect and scale.
We are human. Our collection, interpretation, AI application of data will magnify who we are and how we operate. Awareness of biases and iteratively improving upon them will help leaders feel more confident in their marketing AI-driven decision-making support.