Rupak Patel, based in New Zealand, is an operations intelligence analyst at BetterSaver. He runs a team tasked with determining the most effective methods for storing and analyzing customer data at BetterSaver. The variety of his experience includes sales, customer service, finance, and software development. Before joining BetterSaver in 2021, he worked in data science with Pushpay and Samsung.
Rupak reveals how digital investment advisory organizations like BetterSaver manage and analyze customer data. He describes his team's challenges in collecting data from multiple disparate platforms. Having tried countless difficult-to-use tools, he and his team discovered Airbyte and its rich ecosystem of connectors.
BetterSaver gives expert digital KiwiSaver advice that’s 100% personalized to an investor's profile. Since launching their innovative KiwiSaver comparison site in 2018, they have had the aim to generate transparency within the market. A first of its kind for Aotearoa, their ethical analysis holding providers to account caused quite a stir.
BetterSaver wants to simplify the process of finding the right KiwiSaver fund by empowering New Zealanders to make better decisions for themselves. BetterSaver built a platform to give equal access to KiwiSaver advice. BetterSaver wants New Zealanders to achieve their personal financial goals while being confident that their KiwiSaver fund is invested profitably and aligned with their world view.
Unifying data sources across multiple platforms
Our marketing and growth teams had to map out our customers' journeys to determine what keeps our customers loyal to us. The major problem is that customer journey information is spread across multiple CRM platforms, from HubSpot and Intercom to Facebook and Instagram. So, our team needed an efficient way to integrate this scattered data and measure marketing effectiveness.
"The problem that we're trying to solve is that we have a CRM, and we have a messaging service. We use messaging services like HubSpot and Intercom to obtain customer data and collect marketing analytics from Facebook and Instagram. Our marketing team wants to analyze this data holistically to determine whether the marketing budget is well-spent and how we can improve."
Our old data architecture could not support our data pipeline efficiently because:
Tedious workflows with external infrastructure
Our data teams previously relied heavily on custom bespoke Python scripts and Intercom to connect our data infrastructure and analyze customer data. However, external third-party solutions require data extraction, transfer to off-site servers, and subsequent pushback to our database. As a result, my team researched transparent, secure, internally-manageable alternatives that could handle the sensitive information in our infrastructure.
"Our business deals with a lot of PII data, and we didn't want to waste hours trying to find out where and how third-party solutions are storing our data. If data is stored and processed on our GCP infrastructure, it is much easier to manage."
How did we discover Airbyte?
Given the amount of personally identifiable information (PII) involved in BetterSaver's data infrastructure, my team was hesitant to use external third-party solutions to store sensitive data. In search of a solution that allows us to handle and store all data in-house using Google Cloud Platform, our team discovered Airbyte.
Simpler customer experience data aggregation
Our data team uses APIs from Airbyte for blending customer data from different sources and bringing them into Google BigQuery for analysis. Using this solution, our marketing team has complete visibility into the customer journey across all channels and can pinpoint where customers abandoned the journey.
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights. Now, we can easily tell how any customer entered our platform, how much they cost, and their ROI. The marketing team can easily figure out where money is spent on projects that don't yield results or at what point in the product journey customers are dropping off."
Seamless and repeatable data analysis
My team leverages Airbyte's docker image and documentation to get up and running quickly on a local machine. We can then easily deploy the solution to the Google Cloud Platform.
"With the Airbyte GUI, all we had to do was copy and paste the API key and fill in details to set things up. Then, if there is a connector, simply create a connection, and it does the job. The docs and UI were approachable and easy to use, especially for someone from a non-technical background. The key is that everything is now repeatable, and if anyone wants to re-run a report, just push a button, and you get newer results and a new set of analysis."
How do we feel about using Airbyte?
Today, we use Airbyte's APIs to integrate customer experience data from numerous sources into BigQuery to power our Jupyter notebook dashboards. This solution saves us a lot of time and provides us with a more secure internal solution. Furthermore, as my team continues to scale our use cases while employing Airbyte, we are confident in its capabilities due to the constant support from community experts.
"We had an excellent experience working with the Airbyte community. Typically, we post a question, and we get a response within 24 to 48 hours. So, we are pretty comfortable using Airbyte for the long term. At least shortly, we don't feel the need to hire a data engineer, and that's going to save the company money, which is especially beneficial for us as a startup."
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