5 RevOps hacks to make your B2B data ready for anything
The release of ChatGPT and other powerful AI models is the latest reminder of technology’s immense potential. Spend a minute playing with it, and it’s hard not to marvel at its capabilities and imagine the possibilities. But it’s equally hard not to miss its limitations. Technology may ultimately be our savior or ruin, but right now, it’s clearly NOT the answer to everything. Case in point: the persistent problem B2B companies have with poor data quality.
Thanks to mass digitalization and the technological innovations that drove it, your customer and prospect data is now a magic door behind which lie all the amazing things you want to do with it, like personalization and ABM. And your data quality is the key to unlocking it. However, technology alone can’t solve the data quality issue, or 66% of businesses wouldn’t be saying they “don’t trust their data enough to use it effectively.” They keep trying, though. As a rule, B2B businesses typically have more marketing technology than they need, with nearly 65% deploying 10 or more tools. Unfortunately, these solutions, intended to do everything from data cleansing through lead routing, often just suck up resources and gum up the CRM or marketing automation platform (MAP), ultimately exacerbating the whole data quality problem.
In a recent webinar discussion, RevOps pros from Openprise, Hamer Marketing Group, and Navigate Consulting Group took on the human and organizational issues preventing companies from doing the most with their data. The three panelists—Jasmine Chung, Kyle Hamer, and Melissa McCready—shared their collective advice for helping businesses effectively enrich and segment their data and keep their funnels moving smoothly. While the tips and tricks flowed quickly over the 50-minute program, here are the top five you can start using today to whip your data into shape and keep it operational for any advanced marketing or sales motion.
- Create a data dictionary or glossary of terms and definitions everyone can agree on. As the game of telephone so aptly demonstrates, no objective fact or definition, regardless of how simple, can withstand the pull of subjective interpretation. Any definition—whether it’s what makes for a marketing qualified lead (MQL), who constitutes your buyers group, or how you divide your selling territories—is meaningless unless it is clearly documented, agreed upon, and made accessible to everyone in the organization who depends on it. Even if everyone is on the same page initially, teams get turned over and definitions change or get lost over time, eventually leaving everyone to operate under their own assumptions. Bottom line: if you don’t agree with what the data means, you’ll never be able to use it effectively.
- Make sure you completely understand what data you’re enriching, who’s using it, and why. Data enrichment is a powerful tool that, in the right hands, can give you the extra information you need to refine your targets and maximize your campaign spend and resources. If you’re not careful, though, it can just as easily send you and your team down a rabbit hole, chasing the wrong leads and accounts and burning budget and time on unneeded updates and API calls. What you’re enriching will depend on your business and strategy, but if, for example, you have an ideal customer profile (ICP) identified and it’s centered on finance, don’t pick an enrichment vendor that specializes in technology. You also want to be careful to only enrich the fields you use and ensure you have a way to rationalize the fields between vendors and your database. Don’t discount velocity, either; if sales needs to initiate contact immediately, consider enriching the record downstream. The data you end up with needs to be served up to the end user—whether that’s sales, customer success, or other business teams—on a silver platter, so it must be lean, useful, and timely. Focusing on the wrong data can subject your enrichment to unnecessary delays, complexity, and expense and completely throw the whole initiative off track.
- When planning your segments, make sure you have the data you need to answer the questions you have. At its core, segmentation is about answering two simple questions: who am I trying to talk to, and what am I trying to say? Somewhere within your data lie your ICPs or ideal buyers. Defining these personas is one of the most important and difficult jobs in marketing. To do it effectively, you need to know not only who to include but also who or what to exclude. For example, your ICP may be very similar to another persona and only separated by one or two tertiary data points, such as an industry subvertical or job subfunction. Without the data, you can’t distinguish and speak directly to them and their experience and needs.
- With both segmentation and enrichment, always consider the context. Digital transformation has removed many of the traditional barriers to global business, but many challenges remain. Language and culture are two of the biggest, and as you travel the globe, the differences in meanings and values can bring chaos to your segmentation and enrichment initiatives. But you don’t need to cross the border to encounter similar difficulties. Even within the same country, job function or purchase authority can vary greatly by industry or company size. For example, the difference in roles and responsibilities between a vice president at an enterprise and an SMB can be enormous. So whether you’re looking at titles, functions, company size, or any other dimension, you need to consider the context and standardize the fields and values so they make sense to you and your business.
- Order, document, and optimize your operations. Full-funnel orchestration is another one of those near-impossible marketing tasks, especially when starting from a place where the data quality is inconsistent or poor. The complexity at every stage—whether corralling and cleaning the firehouse of raw data rushing in from the source, routing the leads to the correct team in the middle, or building and evaluating the attribution models at the end—is immense. It requires rigorous testing, baseline measurement, and iteration at every step. But even more important than the testing and iteration is the order of your operations. While your business and technology will dictate the actual sequence, establishing, documenting, and repeating the steps will give you the consistency you need to get your data to work the way you want. One of the biggest failure points is account management, or what happens when someone leaves the company and the account needs to be reassigned. Creating and documenting the defined steps and SLAs that need to be completed can go a long way toward preventing the avalanche effect that can take out whole sections of your funnel.
Want to learn more? The complete webinar, which includes more tips and real customer examples, is now available on demand. Visit the landing page to watch the video or download the transcript. If you’re ready to start your own data quality and orchestration initiative, schedule a demo with one of our RevOps consultants to see our automated, no-code solutions in action and learn more about how Openprise can help.