I had the pleasure of speaking about ‘Creating Values from Big Data and Social Media’ with Dr. Cheemin Bo-Linn at the Online Marketing Summit in San Diego on February 12th. Check out my presentation. I would love to hear your comments!
To me, there is “good” big data, which leads to insight and “bad” big data, which contains noise and leads to confusion and bad conclusions. The trick for marketers like us is to separate the signal from the noise. So, how do we do that?
Here is the process I’d recommend:
1. Clearly define the problem to be solved.
2. Identify your knowns and unknowns.
3. Find appropriate tools.
4. Test your hypotheses.
5. Draw assumptive insights.
1. Clearly define the problems to be solved:
For example: “I would like to get more leads from Twitter, what should I do?” Or maybe that’s not the best question asked. Perhaps what you want to ask instead is “Which channels generate the most leads per dollar invested?” Or “Which channel’s leads convert to sales?” You really need to think through your questions.
2. Identify your knowns and unknowns:
What should I do to gain more followers?
The number of current followers per channel, number of followers per day and timing of my posts etc.
What is my optimal post time?
What is the audience discussing?
How does my offering compare with that of my competitors’?
List them all. Some unknowns can be revealed through analysis and becomes knowns. Some unkowns will always remain unknown.
3. Tool selection:
The vital criterion for tool selection is data source. Good data is not free. To access API and other valuable data sources costs money. If you go cheap with data going in, you will get garbage data coming out. Understand the data source and choose your tools wisely.
4. Test your hypotheses:
Repeatedly run controlled tests using the tools you select. Adjust key words, change time frames and filter irrelevant information. Take time to analyze the data.
5. Draw assumptive insight:
Through analysis, insights will be revealed. However, you still need to apply your judgment by taking into account other facts and anecdotes. Gathering appropriate subsets of Big Data is science, but interpreting that data and arriving at insight is art.
We are constantly adding information to Big Data through our social media engagement, CRM and multiple other sources. Therefore, insights need to be refreshed with new information constantly.
Like fashion, data and trends are moving quickly. You need to move with it.
New insights are always there to be discovered.
Big Data represents a brand new world for marketers like us.
Unfortunately, there is no short cut. It takes time and effort to find hidden treasure.
(Thanks for social listening and research data contributed by Bryce Olson and Anne Himmelsbach.)