Let's introduce a new concept in the big data discussion. Too often, we lump all CRM data types together without...
By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers.
differentiating between their key categories. We also focus on extraneous details such as how, where and how long to store it. But what's that got to do with using data to derive actionable information and to drive better business?
In fact, all data is not the same, so the discussion needs to be about the CRM data types we have and whether they are sufficient to derive business insight. Data scientists divide the data realm into two parts, quantitative and qualitative, and each part again into halves.
In business we work with all kinds of data, so understanding its diversity is critical to gleaning its meaning. Marketing uses a lot of qualitative data and finance uses quantitative data, and perhaps that's a reason the front and back offices have communication troubles. Interestingly, marketing has recently found ways to turn qualitative data into the quantitative stuff the back office craves by scoring it and analyzing the results. This little trick has been responsible for much of the marketing automation surge lately.
This data is measurable -- your height as you grow, the quantity of goods bought or sold, revenue, and profit are all quantitative data, and there are two types of quantitative data.
Data that cannot be logically divided mathematically and where there is no true zero point, is interval data. Temperature is a good example. While there is a zero-degree reading in most temperature scales, it is arbitrary and capable of being exceeded. Only the Kelvin scale has an "absolute zero." Also, it makes no sense to divide one temperature by another.
Too often, we lump all data together without differentiating between its key categories.
Ratio data has a true zero possibility, such as profit and loss. Elapsed time can also be considered to be this kind of quantitative data.
Quantitative data is also known as categorical data because it represents distinct categories, not numbers. You can't perform math on categorical data, and there are two types:
Hair color and postal codes are nominal data because they have no order: Blond does not necessarily come before or after brown. Your postal code does not depend on your friend's, nor does it make sense to perform math on it.
Letter grades are ordinal data because they have an order, but B times two means nothing. A digital photo file is qualitative data that is also ordinal, in that the order of the data's rendering (i.e., transformation into a picture) matters quite a bit.
Over the past decade, we've become partial to social media and the mountains of data it generates. Often that's qualitative data revealing people's attitudes, needs and intentions. Social data alone might be good at giving you the zeitgeist of the times in the market, but by itself it doesn't provide a vivid picture of specific demand that you can satisfy.
Marketing and sales also use a great deal of nominal data, which can be bought from list providers. It can include names and contact information about companies and their executive decision makers, but not necessarily anything about their aspirations, goals and the business problems they need to solve. This can be the most valuable and hardest-to-get information.
For more on CRM data types and data analytics:
CRM analytics and traditional data mining
CRM analytics reveals more about customers
That's where data diversity begins to matter -- right at the intersection of completeness and relevance. When you start talking about those two big ideas, you are no longer discussing data or even information; you've crossed the line into knowledge, or insight as some would call it.
The nominal data about names and addresses is relatively static. It changes over time, but keeping it accurate is a manageable task that many vendors provide technologies for. Social data has value but its rapidly changing information needs to be tempered through triangulation with data from providers and market intelligence as well. The CRM data types you can capture through intelligence-gathering on the Web -- reading through press releases, news stories and official reports -- can complete a picture and upgrade your data to knowledge.
Knowledge is power
Knowledge is what you take action on, including deciding which opportunities to invest your limited resources in. Prior to the digital information age, gut instinct often substituted for knowledge because the intelligence-gathering was costly and time-consuming. In that slower age, you could get the information, but by the time you had it in hand, it was usually too late to do much about it.
Today we've got more tools to capture this essential data and time to act. But curiously, what might hold us back is the lack of business processes that adequately exploit intelligence-gathering. Changing the situation starts with understanding that all data is not the same. We know this intuitively, but once we bring the idea into our conscious minds, we can begin evaluating the CRM data types we collect and then figure out how we can upgrade them to business knowledge. Intelligence-gathering might be the next frontier in the marketing revolution, and as usual, the first ones into the breach will get the biggest returns.
Dig Deeper on CRM analytics and business intelligence
Denis Pombriant asks:
How are you addressing CRM data types and data diversity at your company?
0 ResponsesJoin the Discussion