Interview: Alison Burnham, Scorebig on Optimal, Real-time Pricing through Analytics

We discuss Analytics at ScoreBig, company’s business model, unexpected insights, challenges in customer value management, advice, and more.

alison-burnhamDr. Alison Burnham is Vice President of Pricing & Analytics at ScoreBig Inc. Alison specializes in managing analytics to solve business problems including Pricing, Customer Value Management, Risk Management, and Marketing. At ScoreBig, her group is responsible for the pricing engine that drives the Name Your Own Price format for ticket buying and associated analytics. The pricing engine adjusts for real time market pricing, partner requirements, predicted trends and purchaser characteristics.

Prior to ScoreBig, she worked on the optimization of customer/company value exchange through both her own consulting company and several boutique Customer Value Management Agencies. Alison has a PhD in statistics from McMaster University.

Here is my interview with her:

Anmol Rajpurohit: Q1. What does ScoreBig do? What role does Analytics play at ScoreBig?

scorebig-logoDr. Alison Burnham: ScoreBig enables consumers to get great tickets for live sports, concert and theater events – at savings up to 60 percent below box office price. ScoreBig customers pick their own price on seats from the floor to the rafters and never pay any fees. ScoreBig is the first and only opaque sales channel to move unsold ticket inventory in a way that protects the ticket owner’s brand and full-price sales. Headquartered in Los Angeles, Calif., ScoreBig was founded in 2009 and is backed by U.S. Venture Partners and Checketts Partners Investment Fund. ScoreBig was recently recognized by Forbes as one of America’s Most Promising Companies and by Billboard as one of the 10 Best Start-ups of 2012.

Analytics is the foundation of the ScoreBig offering – since our customers name their own prices, the analytics team is responsible for setting an optimal reserve price at which we will accept, decline or counter offer. We have current and historical data on ticket prices on hundreds of thousands of events across both the primary (e.g. TicketMaster) and secondary (re-seller) markets. We mine that data to inform our own ticket pricing.

AR: Q2. From Analytics perspective, what is the key value in ScoreBig business model that allows customers to define their own price?

AB: We obtain tickets at a large discount to the market prices in order to attract the casual ticket buyer who is turned away by high venue and reseller prices. Our goal is to allow that buyer to try out live events at a discount. Our commitment to our partners (the performers, teams and properties who provide live entertainment) is to discover which of those buyers might be converted to a full price buyer in order to be able to choose their seat location and have a fixed price.
AR: Q3. Have you done any experimentation on the performance of your pricing model against other dynamic pricing models for live events (for example, Ticketmaster)?

AB: We are constantly benchmarking our pricing model against all available inventory data. We strive to be below market but at a price point that optimizes yield for our partners (and revenue for ScoreBig).

AR: Q4. So far, what has been the most memorable, unexpected insight you got from Analytics at ScoreBig?

event-seatingAB: We expected to see that value conscious buyers would buy last minute and tend towards the cheaper seats. From very early on it was clear that the majority of our buyers buy well in advance and actually tend towards the greater value seats – seats in prime locations with big discounts but still a high absolute price.

AR: Q5. How do you define and measure Customer Lifetime Value?

AB: Customer lifetime value is measured for us with an actual return for each customer – for every transaction we know revenue, coupon costs, cost of goods sold so we can measure lifetime value very accurately. We look at quarterly trends since ticket buying is in general a purchase made only a few times a year vs more frequent purchase verticals like grocery, telecommunications, and restaurants.

AR: Q6. What are the most underrated challenges of Customer Value Management?

divided-customer-attentionAB: In an e-commerce world today the challenge is completely attention – when we get our repeat customers to our site our conversion rate is high – but keeping ourselves top of mind in today’s very cluttered e-commerce space is our challenge. We use email to connect to our customers which raises the second challenge which is to keep an email communication stream relevant and fresh.

AR: Q7. How has the recent surge of interest in Big Data and Machine Learning impacted eCommerce?

AB: In general it has opened up a huge range of possibilities for personalization which in many cases companies are just starting to be able to take advantage of. However, with that range of possibilities comes the challenge of staffing to take advantage of it. There still remains a shortage of skilled data scientists to manage and mine that data.

AR: Q8. What motivated you towards working on Analytics?

numbersAB: I've always loved math and numbers, and while studying it in university found that the math area I liked the best was statistics. The combination of elegant math and clear applications to the real world was very appealing. Since graduating I've been lucky enough to work in a huge range of verticals from car manufacturing, to nuclear reactor engineering, finance, and retail. It’s always new business problems and challenges which I really enjoy.

AR: Q9. What is the best advice you have got in your career?

AB: The best advice I got was early on in my career when I was consulting in the automotive industry. The advice was to explain my results as if I was explaining them to my mother. To be clear this is not the same as ‘dumbing it down’ - a phrase I hear a lot and really dislike. Instead it’s about telling a story and adding in technical detail where required to prove a point but without unnecessary complications. Often when explaining analytical results as analysts we can’t resist throwing in details to show how smart we are. But in general the story can be told much more effectively without that.

AR: Q10. What key qualities do you look for when interviewing for Data Science related positions on your team?

AB: Good communications, and strong curiosity. I look for someone who is technically strong but can also tell a story. I generally ask them to describe a project they did to me as if I was their client.

AR: Q11. What was the last book that you read and liked? What do you like to do when you are not working?

AB: I love mysteries – the last book I read and liked was Lynda La Plante’s Backlash. I hike, go to concerts, sports and theatre (a great work perk!), cook and read.