Generative AI and applicability in Revenue Lifecycle Optimization
Gokul Anantha
My team and I have been heads down refining our product with early customers. So, it has been some time since I published a viewpoint. Between the last post and now, Generative AI ( (specifically OpenAI with its super-successful launch of ChatGPT4 ), has raised the bar on all AI initiatives. Accenture recently published an excellent take on Generative AI’s impact on work as we know .
Dev, Shuvi and the team at LightSpeed provide another interesting perspective at their increasingly popular handle- hammeredbyai.substack.com
My own dabbling with AI began in 2018 – as product leader for an intelligent communication service offering at SAP – specifically in optimizing transaction workflows by intelligently using context specific communication / messaging channels.
AI is increasingly front and center of our vision to simplify revenue lifecycle visibility & optimization. Our focus being to help operations ( RevOps, in our case) become strategic and drive better revenue outcomes.
It is early days, but here is a viewpoint based on our conversations with Revenue (& RevOps leaders) specifically in small to mid-market enterprises (<$100M in ARR)
Insight 1 : Operational business leaders are less enamored by the promise of automation and much more interested in achieving improved decision science outcomes. Here are some use cases.
1. Predictive/Likelihood models – These are unanswered frequently asked questions. For e.g, Likelihood of lead ( in a particular state) converting, deal in a particular state ( converting), attributes of a deal that makes it more likely to be bottlenecked ( or have longer sales cycle) or even customer churn.
2. Closed loop learning models – We find several interesting sub-use cases
- Adaptive analytics / measures / benchmarks to rapidly changing custom ( application) definitions. For e.g. New business sales motion may have stage lifecycle definitions that evolve over time preventing the ability to benchmark against historical
- Tagging data using embeddings (i.e. text strings matching). Also applicable to data hygiene – i.e. Matching and aggregating contacts, customers, activity from various sources.
- Workflow optimization – optimize workflow rules for lead or deal lifecycle based based on learnings from human action ( for e.g. accept/ reject notification driven actions) and / or based on workflow rule outcomes.
3. Forecast accuracy – An often discussed, but still relevant topic. Enterprises <10M in ARR that are less likely to invest in platforms such as Clari. We also hear discomfort from users of revenue intelligence systems on opacity of the predictive models.
4. Synthetic Data – With Generative AI led synthetic data , enterprises seek to gain from the ability to harmoniously integrate their real-time & historical datasets with suitable AI models at scale .
5. Sales Enablement – AI generated customer facing or training content with elements of closed loop learning model.
Insight 2 : Our customers highlight the need for an integrative approach making datasource integration, curation and application of AI models simpler. In doing so, there is an opportunity for simplification in addressing specific operations verticals.
A future for RevOps
Revenue Operations , by definition, is seen at the center of revenue lifecycle optimization or customer 360 degree initiatives ( our view) . Many revenue operations leaders are doing just that with data lake initiatives underway. That is their starting point.
What if RevOps could gain a real-time revenue data lake at the click of a button?
Gokul Anantha
Gokul started his career heading a CRM service line at HCL Technologies. Over 20+ years , he has led revenue growth at Cognizant, Trianz and most recently at SAP, communication services business unit pursuing both product and sales led strategies. Loglens and its flagship platform BigPicture© is the outcome of personal experiences
