“Data quality is still not solvable with a multi-select pick list or a set of 20 checkboxes.” - Jessica Zangre
Poor data quality costs companies millions of dollars a year. On top of that, some marketers still aren’t tracking how campaigns are impacting revenue. Worst of all, there are companies that fail to respond to web-generated leads within 24 hours, if ever. Our panel of data experts share their top data challenges that you can automate in 2024.
“You may have a data set you like. But then something changes, like needing new personas, and now you need a different data set. It's hard to keep everything clean when your priorities shift around.” - JD Nelson
Ensuring and automating data quality is a huge challenge because it’s overwhelming. People don’t know where to start. One of the biggest hurdles for data is tech debt. When you join a company as a new RevOps leader, you have to guess why your predecessor made their choices, then attempt to fix any issues.
Give yourself some grace, avoid unrealistic expectations about your data, and don’t worry about making things perfect. It’s more important to communicate with your stakeholders about data challenges so you can work together to solve your issues. Keep your house clean, but know that you can’t fix everything. If most of your data is clean, your processes will still work better.
“Syncing your data platforms can affect the quality of your data. Sometimes, Marketo and Salesforce just don’t play nicely together.” - Laura Marzola
We can focus too much on creating the best 1:1 sync with our main CRM and marketing automation platforms. But what about all of the other tools like Salesloft, Gong, Revenue.io, and others? Don’t forget to automate how that data is pulled into your campaign strategies.
In recent years, RevOps leaned on tools like Process Builder from Salesforce to handle campaign attribution. Unfortunately, these automations can overwhelm your systems, aren’t scalable, and eventually break. That’s why there’s a big trend of bringing campaign operations back in-house where RevOps can ensure their data is stored and attributed correctly.
“Today, we can't live without lead-to-account matching but we still deal with challenges around duplicates and enrichment.” -Jessica Zangre
Everyone has a different way of handling lead-to-account matching. Maybe you do a round robin. Perhaps you create multiple accounts for different geographies. There’s no cookie cutter approach.
Data quality problems become especially clear when leads fill out web forms and aren’t attributed correctly. It’s embarrassing to reach out to a new lead only to find out their company has been a customer for 10 years. Make sure to review your guardrails before you set up any automations. Only Ops should be able to create new accounts.
Good data quality is the foundation you need for your GTM processes to work efficiently. Before building out new automations, make sure to review your data holistically so you can understand how changes will affect your long term revenue goals.
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