The BigLittle Origin Story : Redefining the Future of RevOps
TLDR: In RevOps, the big and strategic projects and the day-to-day little tasks are inherently at conflict with each other. One requires analysis at forest level (end-to-end visibility and governance) while the other requires leaf-level triage (agile detection and automation), both of which are challenging with today’s siloed tech stack. Both tasks require dramatically different approaches and tools which if used inappropriately will result in sub-optimal decisions and inefficient execution. A two-pronged approach similar to the big.LITTLE computing model offers a solution to this problem.
This blog tells the origin story behind the name of our company.
Big.LITTLE
In processing architecture, big.LITTLE from ARM is a heterogeneous computing model that uses two types of processors. The LITTLE processors are slower and power efficient, while the big ones are powerful and maximize compute performance. This architecture allows for efficient handling of two different kinds of processing requirements. BigLittle.ai is a software SaaS company and has no association with the big.LITTLE processor. However, the solution approach is very analogous to the world of RevOps where teams are regularly faced with juggling two very different but competing priorities.
1. The day-to-day "Little" fires
The typical RevOps manager’s day comprises many critical tasks that are central to “Running the Business”. It includes:
As a RevOps leader and practitioner, these tasks pile up at a rapid pace. For instance, a key revenue metric for the month may be falling behind, certain committed opportunities may be lost or at risk thereof, a customer may be asking for an extra-ordinary discount approval, reps may not be very diligent about keeping Salesforce up to date, a few new reps are behind on their enablement on a new product or another has quit. All of these little fires impact revenue in some way. The longer these “little” fires burn, the more time they take away from the equally crucial, strategic activities.
However, dealing with these little fires requires constant monitoring of the numerous tools in the GTM stack. The fact that these tools provide narrow, verbose and sometimes contradictory perspectives makes it difficult to analyze the problem. All of these challenges, first, make it difficult to detect a fire that may be burning and second, increase the risk of misdiagnosis in a timely manner.
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2. The 40000-foot-view strategic "BIG" storms
These are the periodic, strategic projects that RevOps teams undertake to set the course of the company’s GTM efforts. They include:
These “big storms” could occur as planned events or appear out of the blue. Either way, to handle any of these strategic initiatives effectively, the RevOps team needs to have visibility of the inner workings of their end-to-end revenue flow. How did leads flow through to opportunities and revenue? What contributed to the current levels of conversion rate, sales cycle and ACV? What is the current state of their RevOps and which aspects should be retained as they roll out new changes? Are these revisions being consistently applied throughout the org and are they effective in driving the desired improvement in revenue or ROI or productivity? To obtain the end-to-end visibility, data has to be pulled together from disparate tools across operational silos. This is a tech-intensive task requiring time and resources beyond the available capacity of most RevOps teams. The result is a resource-constrained best-effort solution. The same applies to the governance of any changes rolled out as ongoing results tracking and impact analysis faces the same resource constraints. This repeats quarter-over-quarter.
BigLittle.AI Exists to Address These Problems
Solving for both the big storms and the little fires requires different approaches. The former necessitates analysis at the forest level – i.e, broad understanding of the macros across all RevOps and how to impact them – while the latter demands attention at the leaf level, i.e, find a easier way to find and resolve issues as soon as they happen or even before they happen. No tools in the market provide this broad visibility and deep detection capabilities at the same time. This is the problem BigLittle.ai was built to solve. Our solution framework is centered on two central tenets.
Big Things come in Little Packages
At BigLittle, we are helping define and build this future of RevOps. We are a young company with big ambitions to empower RevOps with the technology to tackle both the big storms and the little fires.