Jeff ‘John’ Tanner, Jr., Ph.D. and author of Analytics and Dynamic Customer Strategy
Dean, Strome College of Business, Old Dominion University
Dr. Tanner, a noted expert in the field, offers advice based on his extensive research on trends in the use of data analytics in business-to-business and business-to-consumer marketing. His clients include major companies including IBM, Pearson-Prentice Hall and Cabela’s.
When it comes to analytics, does it really matter whether your data is big or small? Or does it matter whether you have the right data to make the right decision when you need it?
Rather than worrying about whether data are big or small, what most marketers should take away from the three Vs of Big Data are:
- Data, if available, can support more decisions (Variety)
- Data, if available, can accelerate decision making (Velocity)
- Data, if available, can add value (Volume).
Don’t worry about whether your data are big or small; rather, think instead about the three Vs as benefits of data-driven decision making.
But take note of the caveat: If Available. If you don’t have the data available at the time of decision making, you don’t have time to get the data.
That’s why a sound data strategy is necessary before you need the data. And to start, let’s consider how you use data.
Decision makers use data for three things:
- to identify problems or opportunities,
- to make decisions, or
- to gain insight
Most organizations stop at #1 – if you think about traditional reporting systems, all they tell you is that things are either working or they’re not. The real value of data, though, is in making decisions and gaining insight.
I worked with Cabela’s on a project involving cart abandonment on their online store. People would fill up a shopping cart at Cabela’s online store and then not purchase it. We used data in two ways. One was to determine whether there were products with a greater likelihood of purchase if they went into the basket first – we looked for the opposite of cart abandonment. This exercise then gave us a list of products to promote, which resulted in a 400 bps improvement in margin while also doubling revenue compared to the usual marketing campaign. That’s using data for decision making.
Then, we dove deeper into the data in order to understand why things happened as they did. This insight then gave us ideas for additional opportunities. But here’s where the strategy came in. We already had significant data on households so we were able to do the data mining that made insight possible.
What about this example can apply to your exhibition?
No matter the environment, retailer, business-to-business exhibition organizer or otherwise, the principles of a data strategy are the same.
First, get your data together. The biggest challenge I see in using data for decision making is that the data is scattered in different systems. People won’t share data and companies are slow to build the data warehouses necessary to bring the data together. Executives, though, who see the power in data can make this happen. To help them see the power in your data, find out the questions they want answers to and illustrate which ones can be answered with a single complete view of your customer.
Second, define your data. A master data definition table tells you what data you have, the source, and what it means. For example, define “customer.” In some organizations, an attendee customer in the data is the individual and sometimes it is the decision location, not the individual registrant. The individual registrant is then linked to the decision location. In other settings, an attendee customer is a member to the association who is an attendee to an exhibition though may be a buyer of other non-exhibition offerings such as online webinars, education only conferences, emedia or print publications among other offerings. Such definitions are important for linking transactional data (How big is a customer? Depends on how you define customer.) and other forms of data.
Note that this language should be agreed upon across units, for example the group which manages exhibitions and the group which manages conferences. There’s nothing worse than an argument in a meeting only to find out the argument was all semantic.
At this point, you’re ready to begin using data for insight and decision making, much as we did at Cabela’s. But you’ll quickly figure out that there’s data missing. Now you can put into place data capture tools and processes to fill in those gaps.
For example, you might find that you need to add fields to your attendee registration system for all exhibitions, which might also then require adjustments to other non-exhibition events or conferences that are run or managed by your organization. You may also find that you need to add fields in the CRM system so that you can test different approaches to attendee marketing campaigns or to tighten campaign management and develop a lead nurturing system or an approach that achieves a higher pre-registration show up rate. With data, for example, you can determine whether a pre-show invitation that was opened not only predicted show attendance but was also predictive of post-show engagement. Documenting the effects of each interaction helps optimize the value of each marketing event.
Many organizations have adopted a “collect everything, sort it out later” mentality. That’s fine early on. As you gain experience, however, you’ll recognize which data is more important. Keep in mind that while data storage may seem relatively cheap, the cost of acquisition (especially if that involves customers giving up data) is not always insignificant. And remember, data needs to be maintained, kept current.
The goal is to have an intelligent conversation with your customer through all channels. Your data helps your marketing listen and respond. Thus, as your data strategy matures, those opportunities for capturing important pieces of information about the customer can be recognized and leveraged.
And that means thinking about all sources of data. When you talk with a friend, you aren’t just listening to what is said. You’re also reading body language, taking in contextual cues, and much more. New sources of data can give you those contextual cues. For example, Marketo salespeople use LinkedIn photographs to determine how to first contact a potential prospect. If the person is smiling, reach out one way. If not, use a different approach. How did they learn? They did so through using data for insight.
Your data may never meet the definition of Big Data. But who cares? What’s really important is that you leverage the data to make better decisions faster.
If you are interested in learning more about how business-to-business exhibition organizers are using data analytics today, two new CEIR reports are now available: