Many conference organizers are familiar with the saying location, location, location!
They know that their conference location has to be attractive to their prospective customers or they won’t attend.
Some organizers know the 4Ps of marketing: product, promotion, place and price.
So how many meeting professionals are familiar with the 3Vs of big data?
Defining Big Data
Big data is traditionally defined as:
Data sets whose size is beyond the ability of commonly used tools to process it within tolerable time.
Part of using data appropriately is asking the right data questions in order to create business competitiveness.
There’s no question that an organization’s volume of data is growing dramatically.
Most conferences’ data is also growing. Social media, RFID, the Internet of Things are just a few data sets that conference organizers rarely consider. Unfortunately even most traditional conference data is often overlooked, forgotten and never even touched in lieu of planning the next big meeting.
Traditional 3Vs Of Big Data
The 3Vs of big data are variety, velocity and volume.
Let’s take a look at each of these data sets as applied to conferences and meetings.
Data variety is exactly as it sounds. It comes from registration reports, evaluations, purchasing habits, exhibit and sponsor sales, VIP upgrades, lodging reports, food and beverage spend, session attendance numbers, pre and post conference events, special event parties and more. It also comes from social technology including community posts, check-ins, Twitter feeds, Facebook posts, conference mobile apps, photos, audio, videos, web, GPS data, sensor data, relational data bases, documents, SMS, pdf, flash, etc.
Traditionally, conferences have relied on data through batch processing. The organizers take a chunk of data and send it to the server to wait for delivery of results. This works when the incoming data rate is slower than the data processing rate and when the result is useful despite the delay. With new sources of data from social and mobile apps, the traditional batch process disintegrates. Real time, near real time, periodic and batch are all velocity rates conference organizers need to consider.
Data volume is fairly obvious. It is the size of the data from kilobytes to petabytes (one million gigabytes). A text file is in kilobytes. A sound file is usually in megabytes and a video in gigabytes. Data is generated by employees, customers, potential customers, suppliers, vendors and partners. The data is also generated by a group of machines. For example, mobile devices send a variety of information to the company network infrastructure through websites, SMS and phone calls.
What’s clear today is that the traditional association management system data base is not enough. Many are not built with off the shelf features that can collect and easily report the data conference organizers need to make better decisions.
However there are other data collection and mining systems that organizations can use to help them. Marketing automation and demand generation systems can help conference organizers know when specific customers are ready to purchase registration. Social technology aggregation and dashboards can help organizers collect sentiment and potential customers pain points for programming.
So what? Here’s what. The successful future conferences will collect and analyze better data. Successful organizers will ask better questions and use better predictive analysis tools to plan programs that will meet the needs of their customers. Volunteer committees will serve as advisors instead of actually picking conference locations, food and beverage, speakers and topics.
Until conference organizers get serious about using big data, today’s conference will continue to be hit or miss.
What are some of the strategic questions organizers should ask of their data? What are some tools that you use or seen other meeting professionals use to collect and mine data? (Note, I’m looking for personal recommendations, not solicitations by companies!)
Adrian Segar says
The cover story of today’s Wall Street Journal has another view of “big data” by Doc Searls, one that I happen to agree with. Here’s an excerpt:
“…As a result, big business continues to believe that a free market is one in which customers get to choose their captors. Choosing among AT&T, Sprint, T-Mobile and Verizon for your new smartphone is like choosing where you’d like to live under house arrest. It’s why marketers still talk about customers as “targets” they can “acquire,” “control,” “manage” and “lock in,” as if they were cattle. And it’s why big business thinks that the best way to get personal with customers on the Internet is with “big data,” gathered by placing tracking files in people’s browsers and smartphone apps without their knowledge—so they can be stalked wherever they go, with their “experiences” on commercial websites “personalized” for them.
It is not yet clear to the perpetrators of this practice that it is actually insane. Think about it. Nobody from a store on Main Street would follow you around with a hand in your pocket and tell you “I’m only doing this so I can give you a better shopping experience.”
Don’t get me wrong; data is important. But I don’t see the size of data sets available from conferences with even tens of thousands of people as beyond the reach of analytic tools and techniques that have been around for years. Sure it’s possible to miss important trends or insights by poor analysis, but that’s nothing new. Forty years ago I would routinely analyze datasets with trillions of data items, using the creaky mainframes then available. We did just fine then. There are places where big data approaches make sense today, but, in my opinion, conference organizing isn’t one of them.
Samuel Smith says
I think you are onto something with your article. I think we should be looking more closely at the data that we are collecting (or should be collecting) during events. When you start talking about conferences with 100+ education sessions, social media data and a hybrid component the data can start to get unwieldy pretty quickly.
When I was a youngster, I built data warehouses that helped Ford Motor Company make proactive decisions instead of reactive decisions. We would pull together data from different systems and different areas of the company to develop insights that we couldn’t gather from just looking at one set of data. One thing that I took away from that work is that the data always has a story to tell the business.
Now that we have RFID, Social, Hybrid, Wifi and other datasets emerging in our events, we need to be able to bring those together with our attendance, feedback forms, sessions, F&B, Rooming and other data to see what kind of story it tells about our conferences. Here are some things that we could learn:
– For each attendee segment, what is their typical conference behavior/experience?
– What are people doing when the are not at the general session? Maybe they are watching online from the hotel room because they have conference calls with people from the home office.
– Are attendees moving in groups through content tracks like we plan? or do they skip around?
– What could we learn if we segmented the feedback form data by attendee profile type? Are the people that give the sessions high ratings in our target attendee segment? or people that found the session title interesting?
– What ideas and topics make social media conversation explode around an event?
– Are people really skipping the tradeshow floor or does it seem that way to some vendors?
Thanks for the great topic. Wish I had time to read your blog more often.
Amith Nagarajan says
I understand your viewpoint but have to respectfully disagree on this. I believe that big data is not about the ability of a system to handle the volume of the data from a storage and analysis perspective, but to be able to offer insights that a human would be unlikely or unable to discover unaided. What I mean by this specifically is that in the area of meetings and events, we have an enormous amount of data flowing in on attendees, speakers, exhibitors, and so on. For example, tracking session evaluations in real time from mobile apps that tie back into an AMS, we know so much more about attendee views than ever before. But, what does that actually mean? How can that information be harnessed to improve the overall experience. And, most specifically, how can that information be leveraged to drive one to one communication back to that attendee in the future.
When you take into account the variety of data feeds that exist beyond traditional record keeping we have had for years in the events business, the explosion of raw data is astounding. Looking at venue related data, location-aware devices, social stream data, not to mention using video and motion analysis to evaluate the patterns of traffic at events (in a de-identified way), you can quickly see terabytes of information coming together. Add that to being able to analyze trends across multiple conference data sets and the challenge grows further.
All capable enterprise systems these days can handle fairly large traditional data sets, and allow a user to ASK for information. The most promising area of Big Data and Data Mining is the ability to automatically probe for undiscovered patterns and correlations, particularly amongst the new data types that we have not historically had the ability to capture. We may discover information that drives engagement at our conferences based on factors that we would never have thought to manually evaluate.
While a central database can do many of these things, I believe that the coupling of Big Data tools that include these types of forward looking, predictive and pattern oriented discovery abilities are where Big Data really will shine for events. When you take into account the massive amount of data coming from the social stream, real time venue/video related data capture, direct and implied feedback technologies – we have a real opportunity to gain new insight.
Like anything else, I don’t expect a large # of event managers to put all of this to work right away but I think that foreseeing the future tidal wave of data that can come from these sources and how they can be of value calls for further exploration of how Big Data concepts and tools can help event managers.
Dave Lutz says
@Adrian, thanks for your reply and differing points of view! We serve the B2B space where conference organizers provide programs and services to their industry and help their members. None of them would be successful if they had the same mindset that consumers have about their cell phone provider. For these organizers future decisions that are validated by data get approved. Hunches often are sent back for proof.
@Sam, great to hear from you…also neat to learn of your early geek story with Ford! The RFID and NFC technologies provide an interesting twist. Over time, attendees at professional events may not be super crazy about being tracked. What are your thoughts on that? For consumer or corporate events where checking in has personal value, the sky’s the limit!
So much of what you refer to can be leveraged in the next generation conference apps. They’re not too far away from providing a journal to attendees of what they learned, who they met and what products they’re interested in. Conference organizers that are able to mine that data and make amazon-like recommendations will be appreciated by their best customers. Show me, you know me, right?
@Amith, appreciate your insight on this important topic! You’ve brought up quite a few more things that need to be considered before jumping on the big data band wagon! As an AMS provider, it must be challenging to determine which data to make part of each member record and what should be analyzed though other tools and then applied to the Association’s business.
One of the cool things that Jeff and I tripped across last year was SaaS based Idea Management solutions. When you think about big data, one of the challenges has been to synthesize it and make it actionable. We learned about two products – Spigit and Bright Idea. What occurred to us is that solutions like these may be just what an association on data overload needs. What’s your take on this class of products?