In my last blog, I encouraged you to focus on your attendee’s journey to help grow your event more efficiently, increase your ROI, and create compelling attendee experiences. Philips Healthcare is a compelling example of a company that utilizes big data to obtain true insights.
Many data sources can help exhibitors learn about the attendee, such as CRM/sales data, attendee demographics from registration, RFID in-booth visitor tracking, attendee surveys, and lead collection. Philips took a smart step-by-step approach to selecting which data sources to use when exhibiting at the RSNA (Radiological Society of North America) Annual Meeting. Philips took a multi-year approach to using big data.
The first year, they had a simple strategy of looking at visitor tracking, registration demographics and lead retrieval data. From that, they learned that attendees were staying in their booth an average of 32 minutes, and visiting the booth an average 1.3 times.
The following year, they added an exit survey to the mix, and learned that more than 50% of the attendees were highly likely, and 33% were “absolutely certain” to purchase one or more of Philips’ products within the next year.
By the next year, Philips was benefitting from all five data sources, and practicing predictive modeling. Gartner defines predictive modeling as a commonly used statistical technique to predict future behavior. In predictive modeling, data is collected, a statistical model is formulated, predictions are made, and the model is validated (or revised) as additional data becomes available. Philips used predictive modeling measured by RFID to accomplish two primary objectives:
- Optimize the exhibit design and staffing to be most conducive to driving behaviors that are related to eventual purchase intent and activity.
- Identify the specific behaviors in the exhibit most predictive of eventual purchasers.
During the 4.5-day event, approximately all of the radiology professional attendees received RFID badges. Philips partnered with Alliance Tech and Exhibit Surveys Inc. to utilize iPad and iPad Mini’s for lead response collection, tablet and RFID-enabled survey collection, RFID read points, and time-lapse photography in its 2013 booth.
The following graphic gives a good outline to what technology, survey data and analysis contributed to the predictive modeling that Philips utilized over the last several years.
Of course, we cannot share results data that is proprietary to Philips, but we can tell you that the ability to “tame” big data has clearly impacted logistics, content, and pipeline strategies, as shown below:
The technology and analysis that Philips extracted from big data provided great insights to its stakeholders, including but not limited to the following:
- Overall event effectiveness
- Campaign touch-point effectiveness
- Deep pipeline perspectives
- Cost/performance ratios
- Deep audience demographics
- Check and balance with field data
- Staffing cost controls
- Stickiness of value proposition
- Content reception by visitors
- Net Promoter Score for both stand and brand
The Philips story is an important reminder that having big data doesn’t automatically lead to better face-to-face marketing, but the potential is there. It comes down to how you act on it. Because Philips acted pragmatically, measurement and analysis was incrementally manageable, overcoming its big data challenge once and for all.