How to Make Data-Driven Marketing Decisions
Master the art of data-driven marketing decisions: cut through biases, analyze trends, and test hypotheses rigorously to boost your strategy's effectiveness. Let the numbers guide you toward smarter, more successful marketing moves.
How to Make Data-Driven Marketing Decisions
Making marketing decisions based on objective data analysis, rather than emotions or hunches, is critical but difficult. Human nature lends itself to all kinds of biases that can skew our decision making. Here are tips for overcoming those biases and truly making data-driven marketing choices:
Become a "Truth Warrior"
The first step is cultivating an active commitment to seeking the objective truth in your marketing data, even if it contradicts your intuitions or emotions. This means consciously trying to remove your own biases and analyzing the data from a neutral, open-minded perspective. Marketing analytics will often reveal unexpected truths that you need to accept and work with.
Look at Long-Term Trends
Don’t put too much weight on very recent ups and downs in your marketing metrics. The recency bias causes us to over-index on the latest results. For most marketing initiatives, you need to evaluate performance over larger time periods to smooth out normal fluctuations. Month-over-month and quarter-over-quarter trends typically provide a more accurate picture compared to week-by-week.
Apply Statistical Significance Testing
Rather than just eyeballing marketing results to determine if changes seem meaningful, use proper statistical techniques. Tests like t-tests, ANOVA, regression, and more allow you to quantify the probability that metric differences are actually due to significant changes in performance rather than normal statistical variation. This prevents you from overreacting to random fluctuations.
Question Narratives Around Result Changes
When marketing results shift, we naturally want to explain why through constructing narratives. For example, if leads from Facebook ads decrease, you might hypothesize it’s due to recent News Feed algorithm changes. Be very cautious about such narratives unless you have rigorous analytical proof to back them up. Keep questioning your presumed causes and dig deeper into the data.
Avoid Reactionary Changes
Related to the point above, when marketing results dip, avoid the temptation to immediately overhaul strategies or replace partners. The confirmation bias leads us to seize on temporary downs as proof of our existing suspicions. But statistically speaking, it’s unlikely a real change in underlying performance versus normal fluctuations. Wait to see sustained evidence over long time periods before making reactionary decisions.
Segment Data for Isolation
Marketing results often combine many complex factors. Try to break data down into more granular segments to isolate the true drivers behind changes. For example, view PPC results by ad group, ad copy, keyword, etc. rather than campaign level only. The more refinement, the better chance you have of narrowing down causal relationships.
Model Out Scenarios
Rather than relying on intuition, use data modeling to anticipate potential results for marketing changes before deploying them. For example, use historical performance metrics and regression analysis to forecast how a 10%, 25% or 50% increase in spending might impact lead volumes. Approaching decisions analytically vs. emotionally leads to better outcomes.
Making data-driven marketing decisions requires vigilance. With the right frameworks and discipline, you can overcome innate human biases and become a decision maker powered by objective data analysis rather than hunches.
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