Recalibration and Bias Checking
It might be due to fatigue from operating in the COVID-19 era, but we are beginning to see a number of companies struggling to find their flow and get their momentum back. Marketing is not just a hose to turn on & off. It’s a discipline and set of systems. Given the tectonic shifts in markets and behaviour, recalibrating your plans is probably necessary. While you are recalibrating, have someone double check your key assumptions and business model drivers.
In research, the concept of “researcher reflexivity” refers to reflecting on how one’s personal assumptions, biases, and experiences impact their work. Reflexivity is also incredibly important in businesses and organizations, and needs to become common practice at the C-suite and boardroom level.
This past year has been unchartered territory for all of us, and our biases can influence the direction we take. Are we short-term action takers, or long-term strategists? Are we really data driven decision makers? All of these instincts can significantly shape our decisions. But, beware of systemic and personal biases. (note: read the book “Thinking, Fast and Slow” for a great discussion of biases and behavioural science).
Certain biases are not inherently “good” or “bad”, but a lack of awareness of biases can lead to blind spots. Some examples of bias in marketing: not really knowing your customers as intimately as you think you do, mis-pricing, downplaying new entrants/competitors, incorrectly sizing your obtainable market, underspending in a digital tactic like email, or missing a new trend or technology.
Some business execs talk a good game about their company’s value proposition and marketing. But, markets have changed and smart leaders are using research to double check the market segments and key value attributes. Market positioning is not just grids and some words…it’s foundational to your whole marketing plan. Leaders are recalibrating and asking a lot of questions to make sure they are focused in the right areas and opportunities with the right value prop and content. (note : now there are techniques and algorithms that optimize based on probability models).
Of course, the “unknown unknowns“ (things we aren’t aware of, and don’t understand) can be the most detrimental to organizations. What we don’t know, we can’t address. And when we don’t understand, we can’t create an action plan to tackle a challenge. That’s why it’s so important to both identify and understand our biases. Take time to ask the tough questions, and explore “what ifs”.