Building an Insight Engine for RevOps

Data is the lifeblood of business. But the most valuable asset that a RevOps team can lay their hands on is insight. Because in today’s fast changing commercial environment, those who understand their markets the best, and the fastest, are poised to win.

Now, it may be true that we’re constantly producing more data. Especially as the volume of touchpoints on the average purchase journey increases, and measurement instruments grow more advanced. However, more data doesn’t necessarily equate to a better understanding of markets or customers.

For that, we’re still reliant on analysis, synthesis and the power of human capital. So the question that must be asked is: how can sales, marketing and customer success teams leverage insight efficiently?

That’s the role of an insight engine. So let’s talk about what that means, and how you can build one today.

Data silos and operational capability

First, some context. 

RevOps itself is a relatively recent construction. And it’s an important deviation from the standard management structures that came before. Designed to break down the barriers between sales, marketing and operational teams –– RevOps is goal oriented, not function led. Its purpose is to drive revenues, regardless of the skills, channels or teams that might be involved.

It’s this cross-pollination of functional silos and goal-orientation that successful insight engines draw from. Rather than allowing data to naturally fall into the purview of the closest operational hands, businesses that invest in building an insight engine take active steps to foster cross-team data sharing capabilities and a culture of informed decision-making.

In practice, this is a pretty easy activity to set up. Building your insight engine simply involves reconfiguring existing assets and infrastructure. And it’s one that pays dividends as it begins to positively affect organizational culture. Here’s what you’ll need to do:

1. Define data requirements and owners. Start by mapping out the needs of each team. What information do sales reps need to be successful? What do marketers need to know? How can insight improve operational progress towards KPIs? Once you’ve built a list of data points, assign departmental responsibility against each in order to provide clear lines of ownership.
2. Create engaging distribution pathways. After mapping requirements, the next step is to move data from the point of collection and analysis to the people who will benefit from it. For this, you’ll need to select appropriate distribution channels. This might include technological channels such as company intranets, CRM systems or knowledge bases. It can also involve regular process-oriented touchpoints such as review meetings, working groups or regularly scheduled workshops.
3. Formalize management structures. The last piece of the puzzle is an effective management structure that includes regular reviews of your engine, captures new data needs and dedicates resources to continuous improvement.

So, what kinds of insight should your engine produce? The answer, of course, is highly dependent on your context. But we can broadly categorize insights as fundamental truths, tactical knowledge and responsive feedback, based on factors such as frequency of revision and lead time. Let’s look at each category in detail.

Fundamental truths

These are core components of organizational knowledge, derived from active research with clients or customers. Fundamental truths tend to change slowly, and should be rigorously analyzed before distribution.

Examples of business assets that can be derived from fundamental truths include: client personas, market segmentations, competitive analyses and price sensitivity reports. Inputs may include surveys, focus groups, interviews with key clients, intelligence reports and other forms of zero-party data. 

Decisions based on fundamental truths can include pricing strategy, product or service offerings, marketing campaign direction and key sales channels. These are generally large and impactful choices that should be carefully analyzed due to their far-reaching effects.

Lean, tactical knowledge

Tactical knowledge is about being able to move quickly and capitalize on new findings. It’s no surprise that in a modern, competitive environment –– business leaders are asked to do more with less. And to respond fast to changes in consumer markets. It’s these challenges that having access to lean, tactical knowledge can really help.

Integrated research platforms, like InsightHub from FlexMR or Toluna Start, are an ideal gateway to this knowledge –– enabling marketers, product teams and researchers to run quick turnaround projects that place customer opinion at the center of decisions.

Typical data that might fall into this category includes communications evaluations, brand perception tracking, product feature evaluations, new competitor intelligence, A/B testing, behavior or product usage surveys. In short, any question your RevOps team is actively seeking an answer to.

Responsive feedback

The final category of insight your insight engine should handle is responsive feedback. This is about facilitating a two-way relationship between customers and the business. Data that falls into this category includes CX (customer experience) programmes, unprompted reviews and social listening.

What separates responsive feedback from other inputs into your insight engine is that each input represents a one-to-one interaction with a customer. And, more importantly, an interaction that there is an opportunity to individually respond to.

While there’s RevOps value in aggregate tracking of opportunities and threats in review themes, or touchpoint surveys –– there’s also the direct ability to address where things have gone right or wrong. To thank or apologize to customers. To communicate changes that their feedback has influenced. To build individual relationships.

And the good news is that this layer is easy to create. At a basic level, you’ll simply need to choose the touchpoints at which to measure key metrics (whether that’s satisfaction, effort or recommendation) and use a measurement toolset such as Qualtrics CXM or the Medallia Suite to start acting on data immediately.

Delivering RevOps success with data

So, how does this all fit together? If you have a centralized insight team –– your RevOps function may ask for annual strategy setting inputs based on fundamental truths, pass on weekly requests for data that build need-to-know tactical knowledge and put in place pathways for real-time notification of CX anomalies.

Without a centralized insight function, it’s important to both define data gathering responsibilities based on the most appropriate ownership and map out a similar cadence of data. Whichever route you travel –– remember that an insight engine fulfills the same objective as RevOps itself. It transcends departmental silos, is a goal-oriented function, and improves over time through a constant test-and-learn feedback loop.