Without a clear and quick process your dev, sales, and customer success teams can become overwhelmed by the amount of work required to delight new customers and ingest clean validated data.
Data onboarding is different for every customer — some have hundreds of rows in various formats once a month, while others only need to import large CSV files daily. This variance makes it difficult for teams to develop a streamlined data onboarding process. Instead, companies rely on manual processes and a backlog of requests to import customer data.
The thing is, data onboarding doesn't happen in a silo. The faster a dev team can import data, the faster customers begin to see value in a product. Time to value is important for retention, and anonboarding study by Wyzow found that 55% of customers say they’ve returned a product because they didn’t fully understand how to use it.
So if one team is overwhelmed, that lag negatively impacts all other teams. To help prevent data onboarding from becoming a burden for your teams, we've identified three warning signs that reveal when it's time for a better process and system.
New customer onboarding usually begins with the front-line sales and customer success (CS) teams. They're responsible for gathering data to import into a product so can customers use it.
You get nervous as you ask customers a ton of questions about their data - 'Where is it? What format is it? How much are we talking?' - because you know that you need the 'right' answers to give them a good onboarding experience.
Their data is often emailed as CSV files, which may have vastly different naming conventions and formatting from one company to the next. Not only do these files take time to collect, but cleaning data isn't a core skill of CS and sales teams.
Yet they wind up emailing back-and-forth with customers to correct errors and fill in gaps. Why? Because your dev and data teams are focused on the product and business objectives, the file cleaning and importing tickets and requests get deprioritized making the onboarding process take weeks or even months.
If your team struggles with a backlog of data onboarding related tasks that slow down the customer onboarding process, then it’s time to reevaluate your process.
All of those one-off data cleaning requests have to go somewhere, and they usually fall to technical teams. Dev and product management teams get bogged down with tickets to import customer data, create custom solutions for high-level clients, or help clean up messy data before it's ingested into the company's operational system.
So your frontline teams say to customers, ‘'Of course we can handle your data," but they know that it's going to take you a lot of all-nighters, a lot of stress, significant cost, and some potential missed deadlines to (hopefully) be able to work with the data they have.
Rahi, a company that helps organizations scale faster by improving supply chain efficiencies, ran into this exact issue. They needed to respond to customer purchase orders (POs) faster, but it ran into a number of challenges when receiving a variety of complex customer POs. The data in the POs wasn’t organized, came in different formats, and had to be manually verified against multiple ERP systems before finally loading the order into the management systems.
This manual data wrangling and importing process took Rahi's sales team over 60 hours a week to complete, resulting in longer fulfillment times. To accelerate the process, Rahi leveraged Osmos Pipelines. Instead of the sales team manually cleaning the incoming PO data, the company uses Osmos’ AI-powered data transformations to validate, clean up, and restructure the messy PO data to fit the ERP schema and format.
"We saved over 60% on delivery costs with our largest clients by simply removing tedious copy-paste and manual data wrangling activities," said Rahi CTO Matt Robinson.
By automating data wrangling, your teams can focus on delivering great customer experiences with the time saved.
Custom data onboarding solutions work well — until they don’t. Writing custom Python scripts, building data uploaders, and maintaining data pipelines are all possible solutions, but a lack of repeatable, human-in-the-loop processes limits company growth.
ForMosaic, a resource management software company that enables real-time collaboration, importing clean customer data is critical. They needed one flexible data onboarding solution that could handle different types of scenarios, so they began building a data importer tool. But the team quickly realized that it would take 6-12 months of dedicated engineering time to develop the necessary validations and customizations needed for their customers.
They ultimately decided that building a data importer tool was not part of its core business. Instead, the team decided to configure and embed a smart data uploader right into their application using Osmos Uploader.
"Osmos Uploader gives us all the features we need to provide our end-users a delightful data importing experience, and I get the time back to focus on our core product," said Nima Tayebi, CTO of Mosaic.
Now, Mosaic has the control to handle multiple data importing scenarios, and their customers have the freedom to upload their data to fit the required schema. With Osmos’ custom validations and AI-powered data transformations, Mosaic’s customers are empowered to send clean data every time.
Our team at Osmos ran into this same issue, which is why we've built AI-powered data transformation tools that speed up the data onboarding process.
Osmos Uploader and Osmos Pipelines help makecustomer data onboarding a stress-free and streamlined process. Our no-code technology makes it easy for your teams to quickly validate, clean, and import customer data. That means fewer support tickets, less time spent cleaning and validating data, and more time on improving the product and customer experience.
So if you're ready to scale and speed up data onboarding then try the world’s most comprehensive customer data onboarding solution today.
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