Ed King is the founder and CEO of Openprise.
If the first era of RevOps was defined by the accumulation of data, I believe the next era of RevOps will be defined by the operationalization of data. Any fool with a dollar can acquire data, but organizations that can successfully make data work for them will most likely emerge as winners in the coming decade.
What does operationalizing data mean? It means focusing your revenue teams on shared data and shared goals. Here is a simple framework to make your data good, yours, and useful—the three essential steps to operationalizing data. (Full disclosure: My company offers many of these solutions, as do others.)
Step 1: Make data good.
Before you can do anything with it, data quality must be good; otherwise, it’s a simple matter of garbage in, garbage out. Here are three characteristics of good data.
1. Accurate
Good data is acquired from reputable sources. Look for both public and private data sources to enrich and validate your data, including reference data like the U.S. Postal Service ZIP code table, data services, and government open data sources.
2. Up To Date
Dynamic data changes frequently and can rapidly become stale. For example, the average tenure of a Chief Marketing Officer at a technology startup is about 24 months. Relying on a data enrichment vendor to update your database may be economical, but tracking your champions in real time may require additional technology. I recommend automating data maintenance tasks to help keep your data up to date.
3. Noise-Filtering
One of the toughest challenges in data quality is isolating the good from the bad. For most companies, this means three things.
• Removing and merging duplicate records.
• Unifying, then removing, redundant data fields.
• Removing records that don’t align with your go-to-market (GTM) motions.
With current technology, it is possible to filter out vast quantities of irrelevant accounts from your CRM. Just imagine how much simpler GTM motions can be with just a fraction of your usual targeted accounts.
Step 2: Make data yours.
Great! Your data is clean. However, it’s still just generic data that your competitors also have. To transform it into a competitive advantage, you need to make the data yours by adding business context so the data can support your GTM motions. Here are three important steps in making data yours.
1. Segmentation
Segmentation is how you interpret data for your business needs. For example:
• A 500-employee company may be a commercial account for Salesforce, but it’s an enterprise account for Hubspot.
• An enterprise architect is an influencer for Salesforce’s Sales Cloud but a decision maker for Salesforce’s MuleSoft.
• To an Internet of Things vendor, Coca-Cola is not a beverage company, and New Jersey Transit Authority is not a transportation company. They are both smart vending machine companies.
Good segmentation makes it possible to find the right prospects and engage them precisely.
2. Scoring And Grading
Strong GTM teams don’t treat all their customers and prospects the same; some are more special than others. Once you have your data properly segmented, scoring and grading levels up the business context to help your marketers and sellers target precisely. It can also help them allocate limited resources: If your product is better-suited to large companies, spending six months targeting an enterprise account that pays 10x is most likely a better investment than an SMB account that takes three months to close.
3. Account Hierarchy
Successful sales teams are highly organized, especially when targeting enterprise accounts with multiple business units and locations. Getting company hierarchy data from vendors is just the start. Each global sales organization is uniquely organized, and RevOps should structure raw hierarchy data in a way that best supports how the company sells.
Step 3: Make data useful.
Even high-quality data that fits your organization like a glove can still be limited in effectiveness. To be useful, data needs to be easily accessible to different stakeholders so they can form insights and take action. Useful data shares three characteristics.
1. It’s available in the user’s preferred application.
Most business users have apps they are highly proficient in. These apps can be the best way to deliver data, enabling users to interact in a familiar environment. For example, I’ve found that account executives typically want to access data in the CRM, while sales development reps want to use data in prospecting tools like Salesloft. Customer success managers may want data in Gainsight, and executives may prefer a Tableau dashboard. A capable data platform should be able to deliver the same high-quality data to different apps for different users.
2. It’s accessible at the right time with the right context.
Existing apps don’t always have the right user interface to enable efficient interactions with data. For example, marketing automation platforms are usually the system of record for prospect records, but not all of them allow campaign managers to build lists. A simple custom app that provides access to relevant records and segmentation parameters, with the ability to populate the target campaign easily, can make that data more useful.
3. It’s secure and compliant.
Security concerns and privacy regulations like GDPR can make it difficult to share data with a partner or vendor. More advanced technology and custom apps can make collaboration easier, enabling data vendors to send their lists for automated evaluation and instant feedback, joint-marketing partners to compare lists without exposing personally identifiable information and channel partners to register deals and get real-time updates on current opportunities without needing access to your CRM.
Summing It All Up
I believe the evidence shows that the companies that will outperform in the next ten years are companies that can effectively operationalize their data so GTM teams can move at full speed and weaponize that data for competitive advantage, internally and with business partners. I hope this simple framework of “make it good, make it yours, make it useful” will help you determine how to best operationalize your data and create efficiencies.
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