We just launched a new brand identity, but the board wants "hard numbers" on the ROI. What Data Science techniques, like A/B testing or market basket analysis, can be applied to measure something as subjective as brand perception and its impact on sales?
3 answers
To quantify brand impact, you should look at Marketing Mix Modeling (MMM). This uses statistical techniques to attribute sales to various factors, including brand awareness campaigns versus direct-response ads. You can also run "Brand Uplift" studies using randomized control trials (RCT). Show the new brand creative to one group and the old (or none) to another, then measure the delta in search intent or purchase probability. Finally, use Natural Language Processing (NLP) to perform sentiment analysis on customer reviews to see if the brand "vibe" has shifted statistically in the public eye.
What tools are you using to aggregate your data? If your brand touchpoints are scattered across different platforms, creating a unified data lake is your first step before any complex modeling can happen.
Simple A/B testing on your landing pages with the old brand vs. the new brand is the fastest way to see the impact on conversion rates. It’s the most direct "hard number" you can provide.
Definitely, Lisa. If the new brand doesn't convert better or at least equal to the old one, the visual changes aren't resonating with the bottom line.
We are currently using Google BigQuery to pull in data from our ads, CRM, and social media. My challenge is the "soft" data—like brand recall. Is there a way to integrate survey-based brand equity scores into a linear regression model effectively?