Databricks

New logo lands and partner activity are great measures of long term success. I would also but renewal rates, average deal size, and length of contract terms (e.g. 1 vs 2 vs 5 year deals) as very strong indicators of the long term health of an organization that can easily be measured in the short term.

Simple as it seems, I think what really matter is that you present what's in it for them first and foremost. The marketing team will have a very specific POV for what really matters to them, generally some form of attribution of up funnel activities to sales outcomes that helps them to measure ROI. Automation, especially lead routing, is a huge benefit to the Marketing team. Around the ownership factor, I find it best to create clear lines around pieces of the data or tooling architecture that each team should own. For example, the Marketing Ops team owns all data and routing tools as they related to Leads and lead routing, while the Sales Ops team owns everything related to Accounts and account data integrity. They are related so collaboration is key, but it's also very helpful to have clear lines when it comes to final decision making and support for troubleshooting.

There are four key foundational pillars to a RevOps strategy: 1/Talent, 2/Process, 3/Systems, 4/Data. I'd start with Talent and Process changes in the near-term, while starting to build your plans and roadmap for your Systems tech stack and Data architecture. In my experience, you need all these capabilities to operate a world-class RevOps function which can scale with your organization. However, each of these areas have different time horizons to impact. For example, Systems and Data investments are foundational capabilities that will take a longer time to yield a return (18 to 24 mos) whereas you can start to make Talent and Process changes that can have near-term impact to show near-term impact and drive sustained momentum for your strategy. One area which I've found to be high leverage is getting your organization entirely aligned around a single view of the customer journey with clear North Star success metrics and organizational owners who are accountable for driving success at each stage.

While it generally takes more time, look for ways to find joint go to market opportunities or ways you can partner through a third party to build a relationship. For example, co-sponsorship of an event or partnering as keynote speakers to build mutual brand awareness. If there is something in it for the partner they may be willing to foot the bill for early efforts while your work to demonstrate the value and build up a budget for future collaboration.

Start with listening. The number 1 advocate and driver of a revenue strategy is the sales leadership team. Take the time to listen to this new sales leader and understand their underlying objectives while trying to keep the conversation agnostic of technology. Understand how they want to orient the team and where they want reps to focus. In my experience in this scenario, the most critical thing to do is NOT come with an agenda to pitch your plan with this new sales leader first. The majority of times, you are closer to the same objectives than you are apart even if you may not be speaking the same language yet.

The data on this type of dashboard is truly only as good as the input from the sales reps. The most effective way to get this data moving, with accuracy is to hold accountability via the sales leadership team. Opportunity stage progression measurement (aka "deal cycle time") should be a part of a quarterly forecasting motion. To hold sales reps accountable, leaders should work with their team to identify stuck deals that have the potential to make it into the quarter (or any time period) for closure. When the rep talks about a deal closing but the data doesn't match the narrative, the leader needs to push their rep to get into Salesforce and update the opportunity. To supplement this, you can put some visuals on your dashboard around number of deals in a given stage and average time spent. This will spark the "why" questions around how things can move faster and uncover the any data related issues.

The hardest thing to do in this type of transition is to force the sales and marketing teams to pivot twice. What I would recommend focusing your energy on is how you can make the current process as stable as possible until you are ready to "flip the switch" on the overhauled version. Even if the current way of doing things is less than ideal, it is what the teams are familiar with and (hopefully) functions to some extent. Forcing a temporary change will likely slow down the lead gen and qualification process and have a negative impact on revenue. As far as technical areas of focus, lead routing automation is likely going to give you the biggest bang for the buck. Most organizations will put higher value on leads making it through the system faster to a point of sale than having perfect visibility into lead sources or attribution metrics.

The best data dictionaries live in a system of record connected to the data source itself. Tools like Unity Catalog on Databricks or Alation surface the dictionary and governance on the same platform as the end query. This makes is more efficient for users as well as more transparent to keep updated. That being said, I have found it useful to start with a simple, collaborative tool like Google Sheets to get things started. Use this as your structure for a few months until the necessary information is curated and you align on the data elements that really matter. You can do your research on a more robust system at the same time as you generate version 1.

As a RevOps leader, a key capability that I rely on is conversational intelligence to complement a data-driven quantitive approach. The B2B SaaS market and buyer preferences are changing at such a rapid pace and it's super important to stay plugged into what is on the minds of your prospects, customers and partners. I regularly leverage conversational intelligence to listen into calls and get aggregated insights into market sentiment, emerging use cases and identify any friction points that may exist across our customer journey. Conversational intelligence allows us to get into the minds of our prospects, customers and partners and understand what problems they are looking to solve and how they want to solve them. When combined with a data-driven and analytical approach, these insights give me inspiration to proactively launch new plays and initiatives to drive continued customer value.