By Mary Tucker, CEIR Sr. Communications & Content Manager
Predict, CEIR’s Annual Exhibition Industry Outlook Conference featured experts from within and outside of the exhibitions industry taking a deep dive into the macro trends and global policies that have real world effects on the business of events. One of this year’s business experts was Dr. Peter Fader, who delivered the keynote, Establishing Competitive Advantage: Implementing a Customer Centricity Model.
Peter is the Frances and Pei-Yuan Chia Professor of Marketing at The Wharton School of the University of Pennsylvania. His expertise centers around the analysis of behavioral data to understand and forecast customer shopping/purchasing activities. Peter works with firms from a wide range of industries such as telecommunications, financial services, gaming and entertainment, retailing, and pharmaceuticals to focus on customer relationship management, lifetime value of the customer and sales forecasting for new products.
Much of his research highlights the consistent (but often surprising) behavioral patterns that exist across these industries and other seemingly different domains. In addition, Peter co-founded predictive analytics firm Zodiac in 2015, which was sold to Nike in 2018. He then co-founded (and continues to run) Theta to commercialize his more recent work on “customer-based corporate valuation.”
Peter is the author of three books, Customer Centricity: Focus on the Right Customers for Strategic Advantage (2020), The Customer Centricity Playbook with Sarah Toms (2018), and The Customer-Base Audit with Bruce Hardie and Michael Ross (2022). He has also won various awards for his research and teaching accomplishments, including being named by Advertising Age as one of its inaugural “25 Marketing Technology Trailblazers” in 2017 making him the only academic on the list.
In part one of our interview with Peter, he shares his vision for how customer centricity will affect future business and how it plays into business-to-business (B2B) exhibitions.
In your presentation, you noted that the two approaches companies have historically taken to getting customers’ attention consisted of either standing out as the “best in their class” or establishing a reputation as a “good product for a good price.” However, these approaches are no longer as effective due to factors like commoditization, globalization, intensive competitive intelligence, media saturation, well-informed customers, etc.
At what point did the need for a customer centric model become pervasive in consumer business and where do you see the B2B exhibition industry fitting into the timeline/bigger picture?
Peter: I will take some credit for calling attention to this third dimension, this customer centric model, and shouting about it endlessly but there are a couple of very important precedents for it. And it’s not only to give credit where it’s due, but a lot of learning can take place from these examples.
First and foremost is old school direct marketing. I first heard about these ideas from the Franklin Mint, which created a lot of collectible cars, gold-plated monopoly sets and other tchotchkes like that. They didn’t really care what they produced because they were more interested in what their most valuable customers wanted. They are the ones, more than anyone else, who came up with the notion of “lifetime value” and started to really try to measure and leverage it.
You still see a lot of that today with companies like QVC, which is cut from that cloth. They tend to be disparaged because when you hear the term ‘direct marketing’ you think about late night infomercials and think, “Oh, that’s not going to apply to us,” and that may be true. But the practices behind the scenes are actually quite good, so let’s give big credit to the old school direct marketers.
Secondly, there are examples of companies doing this on a one-off basis. Two of the companies I often talk about have similar stories in that they were getting pummeled within their highly competitive industries, and they just couldn’t find their way out through being the best or scaling faster than others. So, they turned to this third dimension out of desperation.
One of them is the Harrah’s Casino chain. They reached a point where they simply could not compete with the newer casinos entering the market. They couldn’t come up with better games and they didn’t have better real estate. They were boxed into a corner and out of pure desperation they hired a Harvard professor, Gary Loveman, who became the CEO.
They rose to the top doing basically all the things that I talk about such as tagging and tracking individual customers, building a really robust loyalty program, and using it to drive not just insights, but real actions. What kinds of games should they have? What kinds of restaurants should they open? What kinds of entertainment should they bring in? That’s customer centricity!
Eventually they bought out Caesar’s Palace/Caesar’s Entertainment, which they still are today. Needless to say, the other casino chains were not going to simply stand by and applaud for them. They said, “We could do it better. We have deeper pockets.” And so it’s been harder for them to hold onto it.
Same exact story with company number two, which was Tesco, the UK-based grocery retailer. In response to struggling to keep up with competitors, they created a loyalty program and rose to the top in similar fashion. Like the previous example, all the others have been playing catch up and doing a pretty good job of it.
Whether companies do this systematically – like the direct marketers – or on their own, what I’ve tried to do is collect a lot of these examples, extract the common and best elements of each one and pull together a more robust, more generalizable approach to this.
There is no reason why these same stories cannot apply just as well in the B2B exhibitions space. I guess B2B exhibitions would be coming into this when the concept is fairly established, but the practices for most companies continue to be very eccentric. They might say the right things, but when it comes down to it the questions become, How much product did you sell? How did you keep your costs down? Are they prioritizing customer lifetime value and customer-based corporate valuation? These factors are rarely put ahead of the product-oriented metrics.
Because of that, I think it’s actually still pretty early stages for this industry. I also think the exhibition space is in a better position to benefit from this model, which is one of the things I kept emphasizing at Predict. B2B exhibitions can apply this concept better than a lot of other industries that are making more noise about it such as pharmaceuticals, retail banking and others because you don’t have a lot of the regulatory barriers. I think you have a lot of room to embrace and maneuver with this strategy.
You have a much more focused target rather than casting a huge net out to the entire universe because you are dealing with a smaller group of people that you can get to know. But you still need these best practices in place to do this effectively. These ideas come naturally to B2B because you understand your customers much better. You have relationships with them. The way it has worked in the past, we could try to be everybody’s best friend but that’s not going to work today.
For example, we take only certain clients to play golf or to the Super Bowl, or whatever else. We’ve often shown that kind of favoritism in B2B. What we haven’t seen on the B2B side that we see in B2C would be the metrics and quantitative accountability that customer lifetime value offers. All too often, who are we taking to the Super Bowl or to play golf? It’s the customers we like better or the ones we’d rather spend time with. I suggest removing that human element and replacing it with a more quantitative approach.
Let’s have the numbers and drive these decisions. And I can offer lots of examples of companies that have done just that. They’ve said, “Now we’re going to hold you accountable about who you take to golf by using customer lifetime value and related metrics.” The customer lifetime value arises on the consumer side, but the differential relationship management arises on the B2B side. Let’s mash them together and enter a new dimension of marketing B2B events.
You define Customer Lifetime Value (CLV) as a prediction of each customer’s profitability over their relationship with a company (past and future). In relation to exhibitions, this translates to 1) how long it will be until an attendee/exhibitor/sponsor has no need to attend my show 2) how many interactions of value they will have at my show and 3) when those interactions happen, how much value is being created for each.
What data points do you recommend show organizers use to determine CLV and how much data does it take to make an effective CLV determination?
Peter: Let me flip that around in terms of how little information do you need? For example, if you give me all of the data you know for each attendee/exhibitor/sponsor such as how often they participated in activations at one of your shows, that’s great. That’s fun. That’s terrific. But it’s also sometimes hard to get that data and hard to manage. It can get difficult to share data due to privacy issues, especially when there is so much data. What we want to do is incentivize people to not only be willing to share information, but to want to do it.
So make it worth their while to use the smart badge, or raise their hand, or engage with the mobile app or loyalty program, or whatever tracking technology we’re talking about. Communicate that they will miss out by not doing so. Zodiac commercialized this with Nike, which was successful in getting customers to actively engage. For instance, at their flagship store in New York they have fun experiential offerings, but you can’t really get the full value out of them unless you have the mobile app loaded and open. They make people want to take advantage of all the fun stuff and get past that understandable hesitation of having their activity tracked.
How little data do we need? I’ve asked these questions almost out of pure academic interest. Do we really need to track a ton of information for each person? What if some of it is missing? What if we have it rolled up so that I don’t know exactly when you went to shows but I know how many shows you attended this year?
I’ve spent the last 10 to 15 years asking more and more of these questions to basically get less and less data. Then I met my Ph.D. student, Dan McCarthy, who became my co-founder of these various companies and was asking the same questions from almost a Wall Street analyst side. If you’re an investor in a company and knocking on their door, there’s no way that company will give you all of its transactional log data. That’s ridiculous! But what would be some aggregate metrics that they would be willing to share that don’t identify any one individual and would really tell you about the health of the company? It turns out that we’ve answered that question in our academic research.
The answer for B2B exhibitions would be, how many unique people have shown up at one of your events in a given year? And among those people who showed up, how many events on average did they attend? That’s it. Give me those two numbers rolled up in an aggregate level either on a year-by-year basis, or maybe a quarter-by-quarter basis, and I could reverse engineer it to run the same exact models that I could run if I had all of the granular data for individual by individual, event by event.
Which leads me to the Holy Trinity of data, RFM, which stands for recency, frequency and monetary value. Our forefathers in direct marketing gave us this rubric and I’ve been parroting it for 40 years since. I’ve also been applying it to all kinds of bizarre domains where you wouldn’t think it has any relevance at all. Things like animal tracking, library book borrowing and doctors without borders – all kinds of domains that you’d think would be very different than people buying collectible cars and gold-plated monopoly sets, etc. RFM is the natural answer.
In your industry, questions such as, when did you last attend an event? (Over some reasonable horizon, let’s say, the last two or three years.) How many total events did you show up at? How much money did you spend, or how much time? I haven’t analyzed the data for this industry, but I can guarantee you that RFM would be incredibly predictive of how many more events attendees will go to and over what horizon.
It still requires individual level data for each person; we could roll it up even further and achieve very little loss in model performance. Without getting too deep into that, I propose that you don’t have to collect a thousand data points. I have found that we can get just as far with very little data. Which is great because it makes it much easier to implement strategies and compare results across different events.
