The Customer Insight Strategy of the Walt Disney Partners Federal Credit Union
In this interview, the CDO of the Partners Federal Credit Union at The Walt Disney Company, discusses customer insight challenges, journey mapping, innovative initiatives, and key learning throughout his career in customer insight, data & analytics.
Juan F. Gorricho is currently the Vice President, Chief Data & Analytics Officer for The Walt Disney Company’s Partners Federal Credit Union.
In this role, Juan leads the data and analytics strategy development and execution for Partners, one of the top credit unions in the US, exclusively serving the more than 100,000 cast members of The Walt Disney Company.
Juan has more than 15 years of experience in the data and analytics space including multiple speaking engagements.
In his prior roles with Disney, Juan led multiple multimillion dollar projects to implement business intelligence and analytical solutions for key lines of business such as Labor Operations, and Merchandise.
In this interview Juan discusses why member insight is at the heart of the strategy of the Walt Disney Company’s Partners Federal Credit Union, why new tools and technology aren’t always the answer to solving industry challenges, and he shares the early stages of an innovative new system of analytical solutions they’re working on that is interconnected, and creates synergies through connectivity.
SEE ALSO: The Big Book of Customer Insight, Data & Analytics
Firstly, can you tell me about your history in customer insight and your key responsibilities as the Vice President, Chief Data & Analytics Officer, Partners Federal Credit Union at The Walt Disney Company?
The majority of the roles I have led at Disney and before have relied heavily on deep and detailed understanding of customers to drive strategy. Since early in my career, I have had strong passion for the use of data to shape strategies, particularly in customer-centric activities.
In my role at Partners Federal Credit Union, I have two main responsibilities in this regard. One consists on building a solid and rich data foundation that enables us to understand our customers or members, as we call them in the credit union space.
This includes having a complete, 360 degree view of key attributes about them as well as about their activities and interactions with us. The second main responsibility consists on delivering key, member-centric insights to my peers in the lines of business for them to execute strategies that will create memorable experience for our members.
What does the wider customer insight, data and analytics strategy at the Walt Disney Company’s Partners Federal Credit Union entail?
Member insight is at the centre of our strategy. Knowing our members well is at the core of our vision, “Make all financial dreams come true”. We need to know our members, understand what they need, and develop products, services, and experiences that leverage that knowledge about the members to meet their needs. We capture this strategy with two principles in our strategic plan: know our members and show our members that we know them.
As outlined in the prior question, the first statement drives us to continuously build richer views of our members, understanding their characteristics and behaviours. This consists on bringing together transactional data but it also entails enriching the view of the customer with external data from third party vendors as well as internal, behavioural data that may not be transactional in nature.
The second statement drives us to develop products, services, and experiences that are relevant to what our members need. In this regard, it is critical for my team to have strong partnerships with the lines of business that lead member experience activities in order to understand exactly what their needs are and deliver data products that are timely and relevant to their activities.
What are your biggest challenges when it comes to insight, data and analytics right now? And what steps are you taking to overcome these?
The challenges are not very different across companies and industries. There are technical challenges such as a lot of very different source systems with a myriad of data quality issues, to cultural and organisational challenges such as the level of maturity of the organisation to take action with the insights and data products we are delivering.
It is critical to address these challenges head on in order for key initiatives such as personalisation to be successful. With inaccurate and/or incomplete data, personalisation strategies will be irrelevant and may not be timely. If lines of business do not know how to act on the outcome of personalisation centric data products, we will miss opportunities to show our members we know them.
In my experience, and it seems to be a common theme across my peers in other industries, people and culture challenges tend to be the harder ones to address. The best way to overcome these challenges is to forge strong partnerships with the lines of business tasked with managing member experiences to ensure that they come along for the journey.
This translates into making sure that the data and analytics strategies are developed keeping a balance between people, process, and technology.
In this space it can be tempting to invest heavily on technology alone by securing lots of data and/or sophisticated technology. This is easy. This usually ends up on overlooking and underestimating the importance of ensuring that the business processes and the people leading them are ready to leverage the outcome of the data products.
There has been a lot of change in the way organisations get customer data, with survey fatigue coming into play and ensuring the gathering is non-intrusive.
What is the process of gathering customer insight and data at the Walt Disney Company’s Partners Federal Credit Union?
We gather data in several different ways. Qualitatively, we rely heavily on touch point and experience surveys, as well as focus groups. We conduct traditional surveys after our members have had interactions with us through digital and non-digital channels. These give us insights into the experience within a touch point.
We also conduct more comprehensive, experience focused surveys that gives an idea of the satisfaction with the overall experience our members have at Partners. Additionally, we conduct focus groups regularly to gather feedback first hand from our members.
Quantitatively, we have started collecting multiple data points in our business processes that allow us to understand the performance of the business processes. As mentioned earlier, this includes transactional data as well as non-transactional data about what our members experience is through the channels. For example, we will soon start collecting and leveraging log data about our online and mobile banking application to enrich our view of the member experiences through those channels.
We are also looking at piloting transcribing voice calls and performing text analytics on the transcription in order to use the data to determine member sentiment. System based strategies such as these allow us to gather more data about member experience in a non-intrusive way.
Once you have collected the customer data it can be easy to drown in a sea of big data. How do you segment the insights and accurately map the customer journey?
The most important way to avoid drowning in data is by starting with the end in mind: how are you going to use the insights you derive from the data? This comes from forging strong partnerships with business partners, which will allow data teams to understand what the most relevant and impactful uses of data and insights are.
Customer or member experience journeys are certainly a great way to start because they are business centric and will highlight the most critical data points that are needed. Data efforts tend to be less successful when data teams start from the bottom up, bringing all data together without any specific business strategy or direction in mind – kind of a “build it and they will come” approach to data.
Going after the data without business direction will take data teams in directions that most likely will not be aligned with the most relevant business needs.
And when you have this rich customer view and journey map in place, how do you ensure the insight you’ve collated are made operational at the Walt Disney Company’s Partners Federal Credit Union?
We follow a principle of building end-to-end solutions that work, even if they are not that sophisticated at the beginning. Data teams tend to focus, sometimes, too much on building complex algorithms and solutions that can take a long time. We prefer to start small and to ensure that our solutions work end to end. Once we have tested that the solution works and that it is creating value, we then focus on improving it iteratively.
This iterative process also ensures that the organisation’s maturity evolves with the complexity of the solution. This goes back to one of the points I made earlier about always keeping a balance between people, process, and technology.
Data and analytics are only valuable if they are being used to improve the outcomes of business processes. This implies adoption of the solutions by those who lead member experience areas. And adoption will be driven by relevancy of the solutions, which goes back to the point made earlier about data teams having strong partnerships across the organisation.
Are you working on any innovative initiatives that your peers might be able to learn from?
We have an ambitious goal for having a system of analytical solutions that is interconnected and that creates synergies through this connectivity.
Traditionally, organisations implement data and analytical solutions one a time, in isolation. For example, organisations can have a pricing optimisation engine that operates independently from a product recommendation engine or from the risk scoring models. Our vision is to start developing these components and as they evolve, we are starting to interconnect them to make sure that that their output takes into account all the other dimensions.
For example, our vision is that when we make a product recommendation through the recommendation engine, the algorithm also takes into account the risk, pricing, and profitability dimensions. This will ensure that we are optimizing our recommendation for the whole system rather than for a single domain. We are in the early stages of this initiative but it is pretty exciting to think where this can take us.
What tools or technology are you currently using to make the most of your customer’s data? And how have they benefitted your insight strategy?
Primarily, our technology strategy has been based on leveraging simple and available technology. I think that to get going, companies have already more than enough technology available. Thinking that acquiring new technology will solve the challenges or add business value is a common trap in which many companies fall into.
Additionally, I have led the adoption of cloud computing to support my data and analytics strategy. This allows me to minimise the amount of capital and operating expenses required, aligning them with the business value being created, while giving us access to technology that otherwise would be cost prohibitive to acquire in house such as big data, machine learning or MPP databases.
We primarily work with Amazon Web Services in this front leveraging a pretty nice solution from Zementis called ADAPA to run our analytical models as a service. For model development we use SAS and R, and for data visualization and more traditional BI we use Tableau and SQL Server.
What has been your key learning throughout your career within customer insight, data & analytics that you can share with customer experience leaders that are nearer the start of this journey?
I would say that the biggest key learning has been that ‘perfect’ is the enemy of ‘good’ and that momentum forward is better than perfect trajectory. Organisations that want to get going with customer insights, data and analytics need to do that: get going.
There is a lot of noise in the market about tools and technology which is confusing many companies, making them think that it will take significant amounts of capital investments or armies of data scientists to start.
I firmly believe that companies have more than enough data and technology to get going, and that main thing they really need is an initial idea of relevant use cases where data and analytics can make a difference, and just start.
This interview first appeared in The Big Book of Customer Insight, Data & Analytics. Click on the banner below to download your complimentary copy of the full report.