Like most companies, you probably have a thirst for information when it comes to your customers. You simply want to know everything about them; psychographics, demographics, satisfaction, purchasing habits and the list goes on. Often times this thirst results in a significant (translation = expensive) infrastructure investment to manage your data and to drive an exceptional customer experience through highly integrated systems. Once you’ve integrated your sales, accounting, manufacturing, customer service, marketing and quality data how do you translate this into value for your customer and drive a Return on Insight?
Many companies have made managing their data a number one priority, but with the vast amount of data available they are struggling to understand it. Translating data into insight is arguably an arduous task. While many software programs including Microsoft Excel 2007 are capable of running an algorithm or confidence interval there is a fine line between what is statistically significant and patterns that have meaning. Extrapolating insight from data is a science that was designed for the Data Guru.
Data Guru’s have an aptitude to see information through a polarized filter, meaning that they can block out elements that create noise to identify patterns and trends that matter. They’ve moved well beyond one-message-fits-all-customers to a calculated equation, which customizes each interaction with the customer to optimize relevancy and value. The data guru goes beyond new age database marketing to interpreting outliers in sales patterns, methodically reducing operational expenses and forecasting sales well into the future. Fundamental to their data analysis is a strategic view-point on why things are happening and picking out the golden nuggets that matter. Here are a few guiding principles:
1. Data doesn’t lie, but people often do. What you trust is up to you.
2. Insightful findings are in your data, but biased opinion may mask it. Subjectively searching for a specific validation may cause you to miss objective insights and more importantly the whole picture. The insight effect occurs when an unbiased analysis provides significance and meaning to a company regardless of whether it was the expected outcome.
3. Many patterns are statistically significant, but not all patterns are significant from a business perspective. In some cases, an outlier in your sales data says more than the upward trend – you need to see beyond the data to present the figures that truly matter.
4. There are many ways to visualize information, but not all visualizations are proportional. Be weary of graphs that are not made to scale. Simple example: if you show a cumulative graph of your sales year-over-year the result will be an upward trend; unfortunately, this does not mean your sales have increased year-over-year. Flowing data picked up on the following misrepresentation by BP.
BP Cumulative Graph on Oil Spill
In contrast here is a Proportional Visualization of the top Social Media Subscriber Base
built in Many Eyes
5. Statitistics have value, but not all statistics can be taken at face value. What works for one company or brand may not work for yours. When all else fails refer to principal #1 and trust the numbers.
With shrinking marketing budgets and slowing sales due to economic conditions, companies are struggling to stretch their marketing investments. A great way to focus your marketing efforts is to invest in areas your customers value. If you haven’t spoken to a Data Guru, I encourage you to start the conversation.