While exploring data in the marketing industry, I always noticed two misleading ways of talking about being data-driven. Some underrate the value of being data-driven, they view it just like a dashboard with numbers representing various things. Others over-complicate the meaning of being data-driven, they hear words like big data, machine learning, or even artificial intelligence. Data-driven marketing is a process by which marketers gain insights and trends based on in-depth analysis informed by numbers. It is more than a dashboard with numbers, it is the insight represented in those numbers. Methods like machine learning, A/B testing, etc. are simply statistics and mathematics based formulas, rather than a portrayed sci-fi like form.
Here are some examples of what data-driven marketing and branding could look like:
- You run an ad on different social media platforms for a month, you find 70% of all new customers through ads come from Instagram ads. Next month, you can focus ads on Instagram more than others.
- You get a new idea of re-establishing your brand by changing the logo from A to B or changing colors from A to B. A/B testing is a statistical way to determine if the change is better than the older version.
- Your site is being designed and you want the visitors to focus on a specific page. Placing that item in a specific location in relation to the rest of the website is optimized by studying that relationship with previous data points. This is machine learning!
There are so many tools that already exist to facilitate building your brand with data. Web analytics tools like Google Analytics does a lot of the backend tracking, Ad analytics tools like Facebook’s pixel do the same thing too! It gets way more interesting when you integrate BI (Business Intelligence) tools like Sisense, Looker, etc. These tools accelerate the insights that would inform the direction of the brand’s improvements needed to succeed.
I will end this with a final realization. Being data-driven is a cyclical process making it an exponential process of improvement. If you are starting a new brand, there is a limited amount of data on what is being created. You started the brand, and start generating new data. That data is used to improve and accelerate the brand, which in return it increases the user engagement and therefore increasing the data gathered.