Because this month’s blogs have focused so strongly on the use of data in branding, it might appear that metrics and data hold the ultimate answer to crafting an effective brand. The fact is, however, that data without context is meaningless.
Do you remember how, a few years ago, Google famously erred in their estimate of upcoming flu cases? Their algorithm took into account only the number of searches relating to the flu, which was exaggerated by flu-related media coverage and social media mentions. Because Google’s algorithm only included the number of searches and disregarded the context of the results, they overestimated flu cases by 200%.
So while we rely on data at Movéo, we also work hard to put data insights in context, to make sure that we have a full picture of the challenges and strengths of each brand we work with. Here are two key ways to do the same for your brand:
As many of us learned in school, the best way to understand a problem and draw conclusions is by following the scientific method: asking a question, formulating a hypothesis and rigorously testing that hypothesis. Branding is no different.
As brand researchers, we begin with a question, usually focused on understanding what aspects of a client’s brand resonate best with their target audience. After a deep dive to understand a client’s existing products and positioning, we hypothesize what strategy will improve their branding efforts, and test our theories using the most applicable materials and experiments. For example, we might use focus groups and surveys to test where there’s room for a line of high-end medical supplies to improve their place in the market through new messaging. After analyzing the results gathered in the experiment phase to form a conclusion, we can make recommendations for the client going forward.
This real-world experimentation pairs quantitative and qualitative data to offer a more robust set of insights on your brand than either could alone.
Effective branding decisions depend on reliable and accurate data, but obtaining such pristine data isn’t always easy. “Dirty data” is any information that’s erroneous or misleading. That includes data that’s missing, inaccurate, inappropriate, or duplicate. If you’re working from bad data, you’re more likely to waste time on marketing and sales calls to leads that are outside your target market or not properly qualified for the stage of marketing you’ve sorted them into.
You need a standardized system for collecting data, so that collection methods are kept consistent and you can draw valid conclusions from your data. It’s also all too easy to pollute your data by storing what you collect haphazardly or in a variety of different systems. Your brand may benefit from a data management platform (DMP). A DMP can sort, house, and export your data in a way that keeps everything clean and organized.
Data is key to strong branding, but an understanding of how to collect and interpret data is no less important. For more information on crafting a strong brand, check out 10 simple truths about strong brands.
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