How to bring big data into business decisions

A recent article by Forbes.com says, “…an incremental 241% ROI can be generated by applying data to business decision. 91% of CMOs believe that successful brands make data-driven decision. However, only 11% of marketers use data to make business decisions today.”

When I spoke for Digital Cream, a marketing event held by Econsultancy, in Shanghai last November, I asked the audience a straightforward question, “How many of you have employed one full-time staff to handle marketing analytics work?”

I barely saw two to three attendees raised their hands out of around 100. That casual poll was in line with Forbes’ findings.

Clearly there is a big hole in the “Big Data” picture. The marketers indeed are neglecting an important marketing asset – data.

Marketers used to think that data could not provide imminent benefits because it could not been “seen” as conspicuous billboard or eye-catching window display could. The truth is, nonetheless, even organizing an offline event requires decisions being made based on data insight.

Questions like how many buyers at the B2B trade show, how many qualified leads that we can retrieve from the event, what is the purchase intent, how can we visualize the whole lead journey, etc. can be answered only by data.

Big Data Situation In Hong Kong Exhibition Industry

Unfortunately, unorganized data is just a bunch of meaningless numbers that cannot provide answers, not to mention helps us to make business decisions. We need to know the right way to collect and manage it, something that I would like to share with you in this article.

So, what actually is “Big Data?” Let me use Hong Kong exhibition industry as a model to illustrate it.

In 2013, there were 50,205 visitors visited The Hong Kong Gifts & Premium Fair where 1,908 exhibitors participated. Can you visualize how many information would be exchanged at the convention floor between these people? 

Let’s say if each visitor exchanges one business name card with one exhibitor, the information exchange will then produce almost hundred millions of names, titles, email addresses, phone numbers, and so on. And this does not count the data representing the product interest!

If the organizer registers each of the data exchange for post-show analytics purpose, the data that will be processed for the event is likely in a big data situation.

In order to take advantage of this kind of data-driven business intelligence, understanding the definitions as well as the best practice of big data is equally important.

Four Definitions Of Big Data

The 1st definition of big data is about Volume. Yes, the volume must be big enough to be called big data.

Some event marketing service providers have been using a lead retriever solution to help exhibitors to manage and qualify buyers’ data. Here’s how it works: The data that collected at the convention floor include a series of navigation clickstream associated with a business name card image of each buyer.

And then the data will be analyzed for the buyer-and-seller interaction including text of each product, image, audio, video, etc. In a typical trade exhibition, an average size of data exchange happens at any given point of interaction could go up to megabytes of data.

And for a larger event, the data exchange will reach to the aggregated size of gigabytes or even terabytes! The concern of data volume is not only storage but also the bandwidth required for data transmitting, which will lead to the 2nd definition of big data.

The 2nd definition of big data is about Velocity. How can we handle a fast data exchange?

How can we ensure a stable data transmission? How can we handle complex data synchronization?

Imagine you are inside an exhibition venue and want to visualize the visitor traffic for an event using heat map, the amount of data that you need to collect, process, transmit, and then output is large. There are a lot of infrastructural concerns.

The 3rd definition of big data is about Variety. We can further interpret data variety as data source.

Today all our business activities will be inevitably labeled and categorized into different data characteristics, which are generated by different data channels. Using exhibition as an example, an exhibition organizer has to manage many data channels for organizing an event.

For instance, the web traffic generated via the event website, the visitor registration data generated at the registration counters, the comments and reviews of the event amplified over the social media, the keyword semantic obtained via a marketing research on search engines, and so on. It may sound hectic, yet, all we need is a systematic data bucket approach and we can properly manage all the data sources.

The 4th and the last definition of big data is about Value. The value of data in fact is determined by how we treat it.

Are we able to process the data and generate insight, and then provide valuable foresight for our business? If we can’t perceive the value of the data even though we can manage the volume, velocity, and variety issues well, we still fail to achieve the ultimate goal—make informed decisions instead of intuited ones in our business.

And this is essentially the real meaning of big data.