Data visualizations must fit your brand, making it a brand identity too. Marketers may not even realize the mistakes they make when using Excel to create a chart and post it. Data must be visually appealing and meaningful for clients.
Chris Sherman, VP of Event Editorial, Third Door Media, Inc. (@CJSherman)
Annie Cushing, SEO and Analytics Consultant, Annielytics.com (@AnnieCushing)
Carry over branding into specializations
Branding is the new black and oversights in market visualization may cost marketers a lot. Annie suggested to carry over branding into your visualizations and shared strategies.
Brushing up logos and websites
Pepsi spent $1 million re-doing its logo, while Symantec spent $1.3 billion. GAP spent $100 million as well, but the Internet went berserk and they had to retract the logo after just 6 days. Companies are not just spending on logos, but also websites.
Avoid using default Excel colors in charts
Annie was critical of the pie charts and bar graphs published by companies. Many of them use the default colors in Excel leaving little visual impact on the people.
Use custom color pallet, fonts and themes
Steps to branding visualizations begin with finding the branded colors and complementary colors. Use color picker to convert hexadecimal values to RGB. Annie suggested Colors on the Web to find complementary colors. It is a tool that uses color theory to show pairings. Create a custom color pallet and custom fonts. She also talked about customization in Mac.
Custom themes are advanced feature. These are burritos for Excel.
Creating own chart and workbook templates
Using your own chart templates will be of big help. Creating workbook templates eases up things for the marketer. Annie herself builds a lot of dashboards having the same tabs – custom tables, pivot tables, text boxes – for clients. This is what is called DNA.
Avoid ‘forever’ data
You would do well staying away from ‘forever’ data as you don’t know where to begin. It is a sea of data which your users will find hard to decipher. Condensing the presentation will help them eking out the meaning.
Data cleanup is important. She used one of her own examples from earlier in the presentation to drive home how it could be cleaned and changed.
Spring load visualizations
In case you plenty of data, you must find ways to make it easily comprehensible. Digestible data is always better.Branding Your Data Visualizations: #SMX New York 2014, Day 1!,