The Future of RevOps Platforms should make you at least a 4x operator
This week’s newsletter is entirely unlocked courtesy of Revlitix
At this point I think we must all be a bit tired of hearing about AI. I’m there with you. But I think I would be missing out on an opportunity of the decade if I did. Let me tell you about my own journey of my first 10x moment. What I mean by that is that I was able to do a piece of my work that would normally have taken me an hour into 6 minutes. It’s a figure of speech but I really did create those gains for myself.
In fact, the the term "10x developer" refers to a somewhat mythical notion in the software development world about a programmer who is purportedly ten times more productive than an average developer. This concept is rooted in the belief that certain developers have an extraordinary capacity to produce code, solve problems, and deliver projects much faster and more efficiently than their peers. The factors contributing to this high level of productivity are often debated and can include a deep understanding of the codebase, strong technical skills, the ability to abstract and automate, or simply a knack for finding efficient solutions quickly.
The same could be and will be true in Revenue Operations.
BI tools or spreadsheets are not purpose-built for sales and marketing use cases. Most companies struggle with reporting Sales, Marketing, SDR performance, and budget data into one unified GTM view. People using Revlitix don’t struggle to report Sales, Marketing, SDR, and budget data in one view. Take a Product Tour and Get a $100 Gift Card.
So when was this moment for me. Back in the days at Google I was a financial analyst in their FP&A group. I felt the weight of a heavy workload. Budgeting, forecasting, accruals and expense management, vendor cost management, headcount planning, you name it. I did it. And my nights and weekends took a backseat.
I picked up a little book called Automate The Boring Stuff. It was a Python book. I learned the concepts and quickly implemented it into my daily grind. The rote, mechanical, and routine work was crushed down to minutes. Albeit it did take me some time to get my scripts in order. But I learned the following:
Webscraping
Pandas analytics
Text parsing
Visualizations using Bokeh
Working across multiple applications
Using APIs to input/output data from Salesforce to Google BigQuery to Google Sheets back to Salesforce
Name a tool and I could get the data from one place to the next
I was proud of that work. I CRUSHED a 60 hour work week down into 20. It gave me the freedom (time) to reallocate to studying my vertical and amping up my ability to provide strategic support.
The tools coming down the pipe make those gains of mine seem like child’s play
Recently I sat down with Madhu Puranik, founder of Revlitix, on where AI and big data is taking GTM tooling. He was kind enough to join my Monthly Pavilion COO call. (FYI I co-chair Pavilion’s COO group and we have monthly speakers join us in a tell all format). My favorite part of the presentation was that he took screenshots of multiple tools aside from his own.
1. Enhanced Decision-Making Through Executive Summaries:
The importance of providing executive summaries that encapsulate actionable insights from complex data analysis cannot be overstated. By equipping the ops team with executive summaries, a CRO can ensure that leadership is presented with contextual, data-driven recommendations, enhancing strategic decision-making and operational efficiency.
One use case Madhu and I both agree on is that AI will help, assuming all of the math in the background pencils out, is creating an executive level summary. I preach that one way operators could become more “strategic” is to take the time to put the data in context of what’s happening with the business.
What is this data telling me?
What is this data NOT telling me?
Based on where we are and where we want to go, is this data suggesting we’re pacing ahead? Behind?
What can do we about our current situation? Double down? Adjust things?
A tangible outcome of this is to create an Executive Summary slide or memo. It should feature the top headlines and AT LEAST three bullets. Don’t just read out the data.
“On a month-over-month (m/m) basis our pipeline increased from $14M to $16M, representing a 14% increase”
BORING!
How about this instead.
“[GREEN] Pipeline Generation KPI: pipe increased from $14M to $16M (+14% m/m) representing 105% of pipeline pacing YTD (year-to-date) as we head into conference/event season.”
A little bit better with some context. Here’s what a summary could look like with this new generation of tooling. It’s all created automatically. It speeds up the Revenue Operator’s ability to mine and surface insights.
2. Proactive Problem Identification with Signals:
Chris Walker has recently (as he always does) been pounding the pavement on the podcast circuit espousing the notion of signals. I couldn’t agree more. Let’s give “signals” a definition. Signals is the ability to sift through vast amounts of data to identify and alert on key business issues.
This is essentially finding the "needle in the haystack" AT SCALE.
By leveraging signals for audits and employing NLP for contextual matching, a CRO can proactively address potential revenue leaks or opportunities, ensuring nothing critical is overlooked.
The most tangible method of showing a signal for a Revenue Operator is the traffic light system. If you’re building it out manually it’s a simple table that could look like this:
KPI
Definition
Benchmark (3rd party, internal target, or pacing)
Actual
Traffic light color (could color code the text or background of the Actual column)
The new tooling available allows for customizable signals in the admin panel. What’s that worth. For many Revenue Operators if you don't know what's wrong and you don't fix it, it leaves unmitigated business risk.
Not saying this is true but imagine your RevOps team has 10 different things to do. They say they don't have enough bandwidth. Paying attention to marketing, sales, and CS metrics is one of those spinning plates in the air. Overloading the Ops team increases the risk of one of those plates falling over.
Manual monitoring may be the only option available for the business.
What's happening, where are things breaking?
Where's the funnel?
What are the gaps?
Which lead sources are contributing the most pipeline?
etc.
Bringing in AI and NLP could expedite answering these double click questions. Are we getting closer to a 10x operator?
3. Customizable Tech Stack for Automated Audits:
Automating audits and customizing the tech stack to match business requirements can significantly reduce manual effort and errors. For a CRO, enabling teams to automatically verify lead-opportunity matches and source attribution ensures that the sales funnel is optimized and attribution models are accurate, ultimately safeguarding revenue integrity.
Let’s say for example we want to set up two different types of compliance checks:
If my win rate goes below 30% for a particular team, notify the channel
If this person is not following the process, notify the Ops team and managers. Example: lead conversion but certain fields are not filled out
Google Ads are attracting the wrong personas or search behavior. The Marketing Ops team wants to know immediately to address. Example: henever the search term contains the word free, notify the channel
Configuring these should be easy to do across my tools.
4. Agile Reporting and Analytics:
Salesforce has its reporting and dashboard architectural constraints. I’ve often reached to build it out myself using a datawarehouse and BI layer (Tableau for example). A platform that offers customizable, drag-and-drop reports and integrates AI for predictive insights allows a CRO to quickly adapt to changing market dynamics, ensuring that sales and marketing efforts are aligned with corporate goals.
In the absence of BI tooling you’ll often find operations teams building these metrics out into a giant spreadsheet. It’s not uncommon.
Digging into any specific channel could yield even more in-depth summaries.
Aside from the headline metrics I prefer to show the sub-detail patterns to my leadership team. Why is my pipeline underperforming?
To get to your goal, again, based on your historical data, based on your seasonality, a comprehensive level of detail is needed to discover why. What do you need in terms of pipeline to hit that goal from any particular source.
Can you then break this down by teams if you want?
Can you do so by geogrpaphy?
By industry?
Does your forecast suggest meeting target?
Why is that the case?
Is it because demo rates going down?
Or a win rate issue?
Or an MQL volume issue?
Average deal size issue?
The truth is that companies have an abundance of data. But Revenue Operators have bandwidth issues to analyze all of the data to a 2nd and 3rd degree. My hope is that the moment is here for Big Data (machine learning, analytics) and Generative AI (summary and context) is going to lighten the load for operating teams.
Go forth and operate everyone.
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