Data Onboarding

How does data onboarding work?

If data onboarding were as simple as uploading the same type of customer file over and over again, it might be a more manageable process. But raw customer data comes in all types of formats and standardizations. Every scenario is different.

What makes onboarding data even more complicated is that it can involve data from a variety of external sources. It’s up to marketers to comb through this data and correct errors, perhaps enrich the data with additional information, and standardize it as necessary until the data is in an uploadable format for CRMs or marketing analytic platforms.

Once uploaded, marketers must leverage online identifiers and device IDs to match the new customer data with existing, pre-stored data or to group together the new customer data into unknown customer IDs. There are two main types of IDs that marketers to use to track the activity of any given customer:

• Cookie IDs, which track how often a given customer (known or unknown has visited any particular website).
• Ad IDs, which are generated when a user visits any particular application and used specifically for improving advertisements based upon app usage.
  Though the data onboarding process can be tedious given the amount of corrections that need to be made to any given offline data and combined with the huge quantities of online data that any one customer/prospective customer can generate, it is essential to the foundation of marketing. Good data onboarding must be:
  • Accurate. Without accurately-stored customer data, marketers cannot perform any of their lead nurturing activities nor provide an accurate report on their marketing performance. What good is a flurry of marketing activity if you don’t know which customers are generating it? Each customer profile should include all marketing activity and as much contact information as possible.
  • Fast. Accuracy is certainly a critical tenet of data onboarding, but so is speed. Just think about how fast marketing data changes—social media data, online ads or website interaction data needs to be processed in hours, not days in order to provide useful insights. Even in our earlier example of uploading new contacts to a CRM, time is of the essence. Salespeople won’t be content waiting around for days just to get the list of contacts that they can call for potential new business. Marketers must act fast—and with precision—in performing data onboarding.

Online data vs. offline data

When you get down to it, data onboarding can be summed up by the challenge of bringing together online and offline data.

Online data
Online data is any data that has been generated from online activity or that has been stored in an online environment, such as a CRM. This can include everything from web chat data to social media activity to web interaction data.

Offline data
As you can imagine, offline data is everything else that’s not online. This most commonly includes contact lists that are bought, generated from marketing events, or scrubbed from LinkedIn profiles and other sources, but can also be any customer information collected manually from an in-person interaction, such as phone numbers, purchase history, customer surveys, and more.

Marketers know that their customers are primarily searching and shopping online, which makes online data the most valuable information that they can use. However, offline data allows marketers to better understand how online and offline actions are inextricably linked, enabling marketers to ask questions such as:

• What online search of the brand led a customer to purchase an item at the store?
• What in-store experience, either good or bad, led them to open a customer support chat, write about the brand on their social media channels, or send an email?
• How much time is a customer spending online researching a product before entering a physical store?

The benefits of data onboarding

Through data onboarding, marketers can fill the gap between online and offline customer engagement. And better understanding the relationship between the two offers a wealth of opportunities for modern marketers, such as:

• Retargeting customers that engage in offline activities in an online environment
• Building a 360º customer profile that offers a complete view of all historic customer activities
• Offering a more personalized experience targeted to demographics, needs, etc. of each customer
• Identifying high-profile customers that require special attention
• Understanding how to improve the disconnect between an offline and online experience

Data onboarding and data preparation

The data onboarding process must include effective data preparation. As discussed earlier, before offline data can be imported into online systems, it must be cleansed and standardized to fit the requirements of its end destination—in other words, it must be prepared.

Excel is a common tool of choice for this necessary data preparation work, often because it’s the format that customer data is most likely to arrive in. But while manually preparing data with Excel is certainly feasible, it takes time and is error-prone, too. The other downside with Excel is that it’s extremely difficult to record and reuse the specific data preparation steps that were used. When new data arrives in the same format, marketers must manually recreate those same steps, instead of recycling previous work.

Increasingly, marketers are now choosing data preparation platforms for their data onboarding efforts. Data preparation platforms transform the entire data onboarding process to exemplify the iterative nature of data onboarding, instead of the largely trial-and-error based process under legacy tools.

Designer Cloud for data onboarding

Here at Alteryx, the rate at which we’re seeing marketers use the Designer Cloud data preparation platform for data onboarding is huge.

The primary advantage we hear from marketers for using Designer Cloud is the ability to cleanse and prepare data with an intuitive interface—no more piecing together Excel spreadsheets or recruiting data science teams for help. But the automation that Designer Cloud provides is key, too. With the ability to schedule repeated transformations for each new dataset, marketers are often left to review transformed data, instead of preparing it from scratch.

Take Malwarebytes, for example, a malware company whose marketing team is using Designer Cloud to onboard data into their marketing automation platform, Marketo. Before Designer Cloud, the team relied on Excel, but they also needed a lot of additional help from their data science team that cost untold hours in delays. It wasn’t until they adopted Designer Cloud that the team was able to accelerate their process, onboarding 40,000 leads into Marketo—the equivalent of six months work—in just three months.

To learn more about how Malwarebytes is transforming their data onboarding operations with Designer Cloud, watch the video on the left.