Q&A with Daniel Brown | Lead Consultant Data Monetisation

Q&A with Daniel Brown | Lead Consultant Data Monetisation 

Name: Daniel Brown 
Role: Lead Consultant Data Strategy and Monetisation 
Time at PTI: June 2019 to present   

Daniel Brown

Daniel is a proven analytical and commercial leader that delivers EBITDA growth utilizing data. Throughout his 20-year career, Daniel has transformed companies by placing data and insights at the heart of their vision to deliver commercial, customer engagement and operational strategies. Daniel has a wealth of experience transforming major brands in the automotive, travel and entertainment sectors including AEG, Group Lotus, Mazda, Aston Martin, British Airways and AXS

Can you provide a view on how the development of data monetisation has changed the way you work with the sports and entertainment industry over the years?

The past few years I have spent a lot of time within the entertainment side of the industry, but also with a sprinkling of sports clubs. What is interesting from my point of view is how over the years, departmental silos across organisations are breaking down. Historically, data used to sit separately within various departments; IT, finance, operations and marketing.  It is becoming clear that these teams are working much more closely together to achieve the common goal of utilising data for monetisation and operational efficiencies

Not so long-ago data was simply the customer contact data that a company put into a CRM database. You may know how many thousands of customer records you hold, then came the marketing thinking of, how many can we contact? However, if you start bringing together every element of the business which has data, then it starts to tell the story of what is happening right across the organisation.  E.g. how does marketing performance impact sales, what activities create higher ancillary spend.

One of the now common discussions I tend to have is that the ‘data initiative’ should sit across the organisation and act as a service to create value for all elements of the business.

How much does a typical club/venue understand about the different streams of data across the business?

If you look at the CEO level, they tend to get their set of reports by each team and as such, they might not fully understand how best to connect departments using data across the entirety to the business helps understand new insight for operational improvement. I’m lucky enough to work with a CEO of a large entertainment company and through our data monetisation process, she now fully understands how to knit data together to help drive the business forward and make better data-based decisions for organisational change. Overall though, I tend to see so much untapped potential in client data that with the right strategy can drive improvements and revenue across the organisation.

With new clients, I tend to advise to start quick and nimble with a small data project. Delivering small incremental wins, built over time provides you with confidence rather than heading straight into a large project and wait three years to see a return.

What’s fascinating about PTI is our ability to bring that all together and get that message across. If we start talking about data and how it serves the whole business, you can start to derive insight from every data point once put into a data warehouse, cleansed and then have the ability to query. This can be your marketing performance, your sales team performance right through to operational performance using the insight to make smarter decisions.

How would you balance transactional data with behavioural data insights?  

People sometimes underestimate how good their data might actually be. If you look at customer-level data, for example, buying match tickets provides rich information on a transactional behavioural level. You can look at who purchased, what did they purchase and when did they purchase in order to obtain insight. Adding historical transactions will further develop the insights you can obtain. Marketing teams tend to use this as a kind of base-level ‘they’ve bought this so they’ll probably buy that’ etc. However, going a bit deeper with your analysis can unlock further insights.

Adding in a behavioural element to your data capture is essential today. You can improve your messages by understanding behaviour, resulting in your entire customer segment being better segmented into more precise groups based on both transactional data and behaviour insight.

I also look to build recency, frequency and monetary models with digital behaviour to identify who you think is going to purchase what and then creating ten variants of communication to improve engagement. Doing this also reduces your operational costs because you’re not blanketing and creating customer churn. Focusing on 200 people based on their needs is more useful and you don’t have to go to 300,000 people hoping for the short-term metric result. This can create apathy across the wider list of customers, which will hurt you in the long term. Marketing teams should realise that a lot of campaigns don’t always work to your goal, but when you learn from each iteration you get better and this will improve response in the longer term.

The term Data-Based Decisions is often used to help drive the consumption of data to run your marketing campaigns operations etc., what’s your view on this?

When we talk about data-based decisions, we need to recognise that this shouldn’t be 100% of every decision that you make. In the past it used to be decisioning based on 20% data and 80% gut feeling or intuition, but today you can reverse it to 80% data 20% gut…this is a recipe that I’ve seen work well recently.

Most organisations have got many years of experience across their team and you shouldn’t underestimate the value of this, then if you have a data and communication strategy as your base and keep analysing and iterating that’s when you can become successful. It really isn’t just about data taking over everything it’s about data informing to learn to help make those decisions with more confidence.

So, what does Data Monetisation aim to achieve for clients?

Data Monetisation aims to achieve an alignment to your business strategy and goals.  It is vitally important that the strategy is fully understood to understand what is needed from the data.  So, it starts at business strategy first.  Based on this we assess all data an organisation has and aligns what can be used via insights to help with the strategy e.g. maximising marketing, ancillary spend, ticket sales, hospitality sales, premium sales, sponsorship growth etc. For example, utilising data insights to explain to potential sponsors the audience/fans you have as well as their engagement with the team and the brand.

Additionally, it is about getting to understand who is in your stadium / venue.  On average you know 30% of the people in attendance.  We create data capture strategies to get to know the remaining 70% and then how to communicate to this fan base.

If you would like to chat about Data Monetisation then you can email him at 

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