This is important in your personal life but is just as critical when it comes to new business relationships. According to GE CEO Jack Welch, “You’re going to be defined by your first 90 days. You’ve got to act.” He was referring to starting a new job, but the concept also applies when beginning an engagement with a new customer.
Much has been written about the customer onboarding process – the critical first steps in integrating a client into a new vendor organization. This beginning stage is the time to set the tone for the relationship and begin to deliver on the promises made during the sales and contracting process.
As more businesses have transitioned to an entirely digital world, a new critical component has been added to onboarding a new client – importing client data into the vendor’s systems. This customer data onboarding process is especially important for SaaS companies.
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Why data onboarding is crucial
When SaaS companies create new features or build an updated product, it's all hands on deck and typically a top priority for the organization. What gets lost, though, is that innovation is wasted when the product experience is not intuitive. While crafting the customer journey, a product leader must pay particular attention to their customer’s first steps.
In SaaS products, the data import process – data onboarding – is one of those crucial early steps. Unfortunately, it is often a major pain point for the customer. According to the recent State of Data Onboarding survey conducted by Flatfile, 96 percent of respondents said they had run into some type of issue while trying to onboard their data. Specific problems cited included data formatting, data validation, and column matching. The result: multiple error messages as customers try to unsuccessfully upload data. The frustration is immediate for a new client hoping to quickly use the software product.
As the irritation mounts, a new customer may start experiencing buyer’s remorse, something to be avoided at all costs. A new customer is sure to be asking themselves these questions post-purchase:
- Will I get a return on my investment?
- Will the product really work the way they say it will?
- Will my team see this as helpful, or just another step in their day-to-day?
- If this doesn’t work out, how will it reflect on me?
The last thing a SaaS company wants is a data onboarding process that simply doesn’t work. But unfortunately, instead of automating and improving the process, organizations tend to use clunky workarounds to accomplish data onboarding. These patchwork efforts only transfer the burden of navigating the difficulties of data importing to an already frustrated customer. The customer is presented with complex templates, long support articles, and cryptic error screens, often within the first few minutes of their journey as they simply try to migrate data into the product.
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The complexity of data onboarding
Data onboarding is so challenging due to the killer combination of high variance input – meaning the data is difficult to standardize – and non-technical customers, who might have difficulty translating how their data fits into another system. As a result, building a custom data importer is easier said than done. A well thought out data importer should have all of the following components:
This is the process of taking an aggregation of information (in a file) and breaking it into discrete parts. Essentially, data parsing means the separation of data. Intelligent parsing auto-detects the encoding directly from the file. The most effective data parsing feature provides the technicalities of going from a file to an array of discrete data while streamlining the process for the customer.
Mapping and matching often get used interchangeably and refers to taking the original previously unknown data and matching it to a known target. It’s an absolute requirement that this is done well during the data import process. For example, when configuring the data importer to accept contacts, if one of the fields is “email,” the customer might choose a file where the field is labelled “email address.” Without proper data mapping that import will fail because “email address” does not equal “email.”
Most product teams have an application database and an application programming interface (API), and they want to make sure that the data can seamlessly flow into the database. The way data gets into the system matters, since it must be labeled appropriately. APIs expect a certain data format, and the import will fail if the format is not structured properly. Successful data importing requires flexibility and the ability to plug into however best the data is accepted into the application or database.
Data validation checks to ensure that the data matches an expected format or value. This prevents issues from occurring down the line. For example, if special characters can’t be used within a certain template or other feature, then customers shouldn’t be able to import them. Otherwise, they’ll be frustrated when an error message pops up during use that should have appeared during the import stage. Without data validation, customers might end up having to remove and re-upload data.
Data transformation makes changes to the data as it flows into the system to meet an expected or desired value. Rather than sending data back to users with an error message, data transformation can make small, systematic tweaks to make data more usable. For example, when transferring a task list, prioritization data could be transformed into a different value, such as numbers instead of labels, or numbers that get rounded up.
Solving the data onboarding dilemma
While many companies try to solve the data onboarding problem by building their own data importer, unfortunately, most of these home-grown solutions fail to address most of the issues involved in importing data correctly and are unable to keep up with the ongoing changes in data formats.
With the exponential growth of data and data sources and the fact that most businesses need to import data regularly (weekly, daily or even multiple times a day), the data onboarding process will continue to be a challenge. Comprehensive data import solutions, like Flatfile’s Portal, now exist to help overcome the problems involved with data onboarding. These solutions enable a seamless, successful data onboarding process that ensures an important, favorable first impression for customers.
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Eric Crane, Co-founder and COO, Flatfile (opens in new tab)