How BODi builds a foundation for customer 360

How BODi builds a foundation for customer 360

Aarthi Sridharan, VP of Data Insights and Analytics at BODi, examines her experience leading a complex data migration project to achieve customer 360 in the rapidly evolving fitness industry.

Aarthi Sridharan, VP of Data Insights and Analytics at BODi, examines her experience leading a complex data migration project to achieve customer 360 in the rapidly evolving fitness industry.

Cloud migration

More about the episode

In this episode of Data Drip, Aarthi Sridharan, VP of Data Insights and Analytics at BODi, reflects on the challenges of migrating from multiple on-premises data warehouses to a unified cloud-based system and highlights the most important lessons she learned about planning, adapting and managing a major multi-year project. 

All of this is fundamentally motivated by the growing demand for personalized fitness and wellness programs.

“It's very important to help the customer throughout their fitness journey because, after all, a fitness journey is just not just one or two days. It's a lifetime thing.”

Hear the full episode to learn more about:

  • Why the ability to support customer 360 is so essential in rapidly changing industries like fitness
  • The actions it takes to successfully execute an infrastructure modernization program
  • The importance of leadership buy-in, phased planning and flexibility

Watch the episode


Kelly Kohlleffel [00:00:00]

Hi folks. Welcome to the Fivetran Data Podcast. I'm Kelly Kohlleffel, your host. Every other week, we'll bring you insightful interviews with the brightest minds across the data community. We're going to cover topics such as AI, ML, gen AI, enterprise data and analytics, various data workloads and use cases, data culture and a whole lot more.

I am pleased to be joined by Aarthi Sridharan. She is Vice President of Data Insights and Analytics at BODi. I knew them as the Beachbody company. 

Very cool stuff, we're going to talk to Aarthi about. She is an accomplished data and technology leader, with vast experience in data engineering, management and analytics. 

She also has expertise in dimensional modeling, data migrations, master data management, big data and cloud data platforms. Aarthi spent the last 18 years at BODi building the organization's data architecture, data engineering, QA and analytics teams.

Aarthi, welcome to the show. It's wonderful to have you in today.

Aarthi Sridharan [00:01:03]

Thank you so much, Kelly. I'm very happy to be here.

Kelly Kohlleffel [00:01:06]

I am looking forward to diving into BODi's data journey. Before we talk about that, give everyone a bit of perspective about who BODi is today and your current role.

Aarthi Sridharan [00:01:22]

Absolutely. BODi is a health and fitness company. As you mentioned, formerly we were called the Beachbody Company. We are the makers of famous home fitness videos like P90X, Insanity, 21 Day Fix and many more programs. We have a streaming site that customers can subscribe to and access workout programs from the library.

Apart from digital subscriptions, we also offer nutritional products, for example, Shakeology, which is a meal replacement drink. As well as pre-and post-workout recovery drinks and a lot of other nutritional products. The third product we sell is stationary bikes with a tablet so you can stream bike-related workouts and do the workout itself.

Kelly Kohlleffel [00:02:36]

Very cool. There’s so much to talk about as we go through this. When you look at where we are today, what are some of the key industry trends in this health and fitness space that you're seeing right now?

Aarthi Sridharan [00:02:50] 

First and foremost, people want the flexibility of working out at home or outside the house. So, hybrid fitness is a big thing now. 

The second thing is, it's not just about physical fitness anymore. It's also about mental fitness and mindful eating. It's holistic. The third thing is the use of technology and integrating technology to monitor our fitness goals.

Customers want to monitor their heart rate when they're working out and doing cardio. They want to monitor their sleeping patterns to see if they're getting enough sleep. They want to monitor what they're eating and keep a tab on all the things they want to eat. To make sure they are thinking about complete fitness — not just about building muscles, working out and losing weight.

It’s a very positive change and these are all the things that we are addressing at BODi.

Kelly Kohlleffel [00:04:09] 

How does data play in? At BODi today, how is the organization thinking about the use of data and analytics playing into these three different dimensions that you talked about?

Aarthi Sridharan [00:04:18]

At BODi, we always focus on the customer first. When we acquire the customer, when they come to our site, the first thing we want is to help them select a product that fits their goal.

Not everybody wants to do the same thing. We have different types of workout programs for beginners, intermediate and advanced-level customers. We want to recommend the right product based on a survey they take. That recommendation engine is very key because they want to do the workout at home — so it has to be a great experience.

After we acquire the customer, we want to be able to give them a great onboarding experience. Once they sign up, what do they do next? So we want to tell them how to use our products, what are all the different products and how they can build their fitness goals by using all these programs.

We have something called a “BODi Block”, which gives you a schedule of workout programs, five days a week for three weeks a month. So, your schedule is all set. All you have to do is log-in, in the morning, and do the workout that is assigned.

The third thing is that engagement is important and we want to monitor customers' engagement and support them in their fitness journey. So if they're doing great, we send out a notification or email saying, “Great, you did four out of the five workouts this week, which is awesome, keep up the good work.”

Or if we see that they are not engaged, for whatever reason, and are off track — we want to give that gentle reminder saying, “Hey, you may want to get back on track because we see that you have worked out once this week.”

It's very important to help the customer throughout their fitness journey. After all, that fitness journey is not just one or two days. It's a lifetime thing and you want to lead that disciplined lifestyle to take care of yourself.

Kelly Kohlleffel [00:6:57]

Terrific. I want to explore a little bit. You've been at BODi for a while, you've had so much experience going on-prem to the cloud and not just an on-prem source to the cloud, but literally taking infrastructure, moving to the cloud and doing everything from a data program standpoint.

What I’d like to do before that, talk me through when you got to BODi, what was the biggest data challenge that you had at that time? You may have solved it now. What is the biggest data challenge currently that you have going forward?

Aarthi Sridharan [00:7:29] 

The challenge was that we had three disparate data warehouses. That was because when we originally started the company, we were a 100% direct marketing company. We had a data warehouse to house customer information and order management information.

Then, obviously, we had to build a digital streaming platform. Because believe it or not, earlier we were selling DVD and VHS tapes. 

Kelly Kohlleffel [00:8:07] 

I was a customer. I was a customer.

Aarthi Sridharan [00:8:07] 

Exactly. We had to build this digital streaming platform. When we did that, we had to create a second data warehouse to host the streaming data.

And then we had a third data lake to house the social media data, web analytics data and so on. This is a typical growing pain when a company grows really fast. As a result of these three disparate data sources or data warehouses, when we wanted to look at a 360-degree view of a customer, we had to get data from these three data warehouses.

We had to stitch the data together and merge them to analyze it — which takes time. Managing and maintaining three different data warehouses and different technology stacks is also expensive and a lot of work. So what we wanted to do was, we wanted to centralize the data warehouses and also move to the cloud. Which is what we did.

That was building the foundation. Now we have one central data warehouse, which is on the cloud. Now that we have finished the foundational work, we’re able to focus on the analytics. 

First and foremost, for people who want to take this route or journey, is that you want to get buy-in from your leadership team. When I say your leadership team, not just your own leader, but across the organization.

That’s because data cuts across the organization. You want all of your C-Suite to support you in this journey, which is key. Once you do that, you want to have a thorough plan. In our case, we did planning down to inventory of all the tables we had, all the ETL jobs, all the reports and all the downstream applications that use the data.

That makes the execution of it easier and you gain the confidence that you can come up with a realistic plan when you plan to that detail. The third thing is the change management aspect. 

You could build a fantastic data warehouse, but at the end of the day, if it's not being used, you don't gain the value of doing all the hard work. Definitely, involve the stakeholders from the beginning and make sure they're part of the journey.

The phased approach is another one. In our case, we had three different data warehouses. We moved the first one, which is small. When I say move, it wasn’t just the data warehouse, we moved everything including the downstream applications over so that we could learn from it and apply it in subsequent phases.

Kelly Kohlleffel [00:11:33]

Those three data warehouses were all on-prem at the time?

Aarthi Sridharan [00:11:40]

It was a combination, some were on-prem and some were on the cloud.

Kelly Kohlleffel [00:11:46]

You talked about the fact that you had this end-to-end that you wanted to move to the cloud. How did you avoid being overwhelmed by the number of ETL processes, the scheduling and the orchestration that you had in place? A lot of those things had to shift.

What would you have done differently now knowing what you know, based on those past experiences?

Aarthi Sridharan [00:12:12] 

Account for more time for data validation. In the first phase, we underestimated the level of effort it would take for data validation, which is a big piece since we served multiple departments, including finance and accounting.

When you serve those departments, you want data validation and data reconciliation to be done down to the penny.

The other thing is being mentally prepared for changes to happen because typically, depending on the size of the data and data warehouses in your organization, these are multi-year projects. And things could change. You could plan for something and things could change.

In our case, we started the planning, and then COVID hit.

Kelly Kohlleffel [00:13:10]

So you had the legacy side, but let's say you had a business unit that said, “Hey Aarthi, I've got a brand new data product or analytic report that hasn't been created before. It's not part of that legacy.” Did you just go ahead and instantiate that in that new cloud infrastructure? Giving yourself a chance to show some of that value quickly while this phased implementation was going on or phase modernization was going on?

Aarthi Sridharan [00:13:41]

We moved not just the data warehouse. We tried to take all the downstream reports, rewire them to the new data warehouse and hand them over to the stakeholders so they could touch and feel it.

So we planned it out in such a way that we could create any new dashboards or reports in the new data warehouse and deliver them to the stakeholders quickly.

Kelly Kohlleffel [00:14:09]

Excellent. How did you make data validation adjustments in the early days of this process and how would you do it today for something you are modernizing or migrating over?

Aarthi Sridharan [00:14:23] :

I would have seen if I could have leveraged any of those data quality monitoring tools as part of the migration itself.

For various reasons, we couldn't do that, increased scope, increased budget and so on. We had to come up with custom-built scripts in-house to do the data validation. When you have to build something, custom and from scratch, it takes time, effort and energy.

That's something I would have done slightly differently, 

Kelly Kohlleffel [00:14:58] 

Let's say you've got a budget, you could build or buy. How do you balance that out for your data program today? Where do you look to build? Where do you look to buy? Where's the inflection point for one versus the other?

Aarthi Sridharan [00:15:12] 

It's important to understand the ROI to some degree because there are some initiatives where you're just testing the waters.

You don't want to spend millions of dollars on something to test the waters. You want to build a data product to support that experimentation. If that works, then you can probably go and invest more. In my mind, it boils down to what you're trying to achieve, what problem you're trying to solve or what question you're trying to answer.

What is the ROI on that? And based on that, you can make decisions. We do have products that we have built in-house. But when we buy a product, we have to keep in mind that there are several cost factors. It's not just the initial licensing cost. You have to consider the implementation cost and if there’s any customization that we have to do to support our company's goals.

We have to consider customization costs, training costs and ongoing maintenance. So, you would want to consider all of these costs and see if it aligns with your long-term strategy and if it makes sense to buy that product. That's the deciding factor.

Kelly Kohlleffel [00:16:55]

You talked about the importance of planning when going from on-prem to the cloud. What was key to you to get the C-Suite and key business stakeholders in and on board at the beginning of this planning phase to have everybody moving in the same direction?

Aarthi Sridharan [00:17:14] 

You can't go to your CEO or CFO and say, “I'm going to stop everything that I'm doing and just do this project for the next three years.”

That's not going to work. You need to be able to support what you're doing in addition to doing this project. It's a lot of tiny little things that you’ll have to plan out. Once you understand the scope of it, it's important to communicate.

So we tried to lay down a plan and indicate the milestones. How do the C-level executives gain the confidence that we are moving in the right direction? It's very important to indicate those things and highlight what milestones you’re going to achieve. 

That way it's easy for them to track the status. And also be forthright about the pitfalls. The way you communicate with them should give them the confidence that you have thought through everything.

Kelly Kohlleffel [00:18:35] 

Amazing. Your background is incredible. I think about how every single organization today is trying to get a greater level of diversity in leadership. You’ve done an outstanding job at growing your career at BODi. What would you say to encourage other women to break into tech?

Aarthi Sridharan [00:19:00] 

Whenever I have the opportunity, I try to talk to and mentor people who ask me for suggestions and talk about their careers, mainly in data. And for the women in my organization, I encourage them to try new things, maybe not in their realm and encourage them to get out of their comfort zone. 

I also attend career day at schools. I was surprised by how many kids knew about data and analytics and the questions they were asking. It was fascinating.

When I was young in the field, I remember going and asking for help and people were generous and kind. They went out of their way to help me out.

I feel it's my turn to give back to the community and help everybody in the data community with my learnings.

Kelly Kohlleffel [00:20:05] 

That's great. There’s no more giving community, than the data community. Everybody's willing to share. We may not all have the same opinion or perspective, but it's really interesting to get that and learn and decide what's the right direction for you. Whether it be a tech, a way to work with people in your organization or a particular approach or process that you want to take on.

Well, this has been fantastic, Aarthi. I appreciate you joining the show and I'm looking forward to keeping up with everything you're doing at BODi.

Aarthi Sridharan [00:20:39]

Thank you so much and thanks for the opportunity. This is awesome.

Expedite insights
Mentioned in the episode
Why Fivetran supports data lakes
How to build a data foundation for generative AI
How to build a data foundation for generative AI
more effective at replicating data for analytics
less data team time to deliver insights

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