– Many brands and agencies claim to be “data-driven” but lack a true understanding of their data.
– Context is crucial when analyzing data, as data without context is meaningless.
– Collecting data is important, but it must be used to improve the customer journey and inform strategies for better results.The key action item is to ask deeper questions and seek context when analyzing data in order to truly be data-driven.I love it when an agency or brand claims to be “data-driven,” but then when you ask them questions about data, things start to unravel. Simply saying you are data-driven doesn’t actually make you data-driven. Most brands and agencies don’t truly understand their data. When you ask them about why they believe certain things based on the data, everything tends to fall apart. It’s similar to screenshots shared on LinkedIn – there’s never enough context, just a version of the story. In the ecommerce space, there are many people who promote strategies without clean and clear data. There is a distinction between being “data-driven” and being “clean data-driven with context,” but no one talks about it. Be the annoying kid and ask deeper questions. Why? Why? Why? I wrote a post about contextual marketing and how important it is to provide context in your copy, images, and everything else. People want you to sell to them, but they also want you to provide context as to why something is better or the right choice for them. You can collect zero party data in various ways, such as progressive profiling via email, quizzes, post-purchase surveys, general surveys, reviews, support tickets, signup forms, and popups. However, if you’re not using that data to understand trends by source, campaign, ad set, ad, orders, revenue, conversion rate by individual and combination, or modeling, then what’s the point? Data without context is meaningless. This is why your quiz partner, email partner, and SMS partner all claim attribution from the same person – they have no other contextual element to work with, which is why they can’t accurately predict much of anything. Dirty data makes context impossible. Yet, they all consistently claim to be responsible. If every one of your tools was truly responsible, your revenue would be much higher.https://www.linkedin.com/in/jivanco