RevOps + AI/ML - from Analysis to Action

I spent much of my early career in the Customer Relationship Management - or "CRM" space (I was even there when the term was coined, but that's another post for another time). CRM was always supposed to be about getting the 3 primary customer-facing functions - Sales, Marketing & Customer Support - working more closely together, using the same set of data, and aligning on process.

Yet CRM was long associated with another term - "failure" - because for too many organizations, they never really achieved the "360 degree view of the customer" that CRM promised to deliver. Many customers - and software vendors - never got beyond "point solutions" that provided some level of automation and insight within the functions, but too rarely between them.

More recently - I've have the opportunity to work with many leading-edge organizations that reinvented how customer data can be to gain deeper understanding or their customers. Yet despite all of the progress that's been made from an analytics perspective - there remains a massive gap in many organizations in translating analysis into action. Tom Davenport and Randy Bean wrote a great piece on that gap recently.

So there are still too many organizations for whom the promise of "CRM" and/or "Customer Analytics" remains just that. Because for too many, their market-facing teams - and their "analytics" and "action" efforts - continue to operate in silos, with each team doing their work independently of the others.

At the same time, given all of the rightful attention being paid right now to Artificial Intelligence & Machine Learning (AI/ML), and the rapid adoption of ChatGPT, DALL-E and related technologies - many Marketing, Sales, Customer Success and other business execs are asking themselves "how can I use this technology" and "where does it fit"?

RevOps - new idea or just a new buzzword?

We're also recently hearing more about a new topic - Revenue Operations - or "RevOps". The premise of RevOps is to break down the silos, and to drive increased collaboration, greater communication, and the sharing of best practices across departmental lines - as well as to suggest and initiate actions that improve results. It is a framework for aligning all the functional areas that generate revenue around the shared goal of driving growth - using data-driven insights and a holistic approach to improve efficiency, increase revenue, and enhance the customer experience. In other words, it builds on prior efforts by driving analysis to action.

For RevOps to finally help us achieve not just the "360 degree view of the customer" but also 360 degree coordination of activities with them - it needs to integrate - and operationalize - all the essential parts of the business that contribute to revenue growth. Therefore, RevOps is an "and" - not an "or" topic - to be successful it needs to span multiple teams and departments, and coordinate their activities by suggesting actions that drive success - from the company's perspective, and of course also the customers'.

I am optimistic - because as I have had a chance to work with customers, it is clear that AI/ML can help RevOps initiatives bridge the "analysis to action" gap - and succeed where prior efforts may have fallen short.

How AI / ML can enhance RevOps

So how can AI and ML be used to improve processes, automate manual tasks, and generate insights into customer behavior? Here are some specific ideas:

1. Predictive Analytics: A perfect use case for ML - and one we're doing every day at Squark - is to ingest customer data, build & refine models, and identify patterns to inform predictive analytics and suggest recommended actions - i.e. what are customers likely to do - and what can we do to influence more positive behaviors and outcomes. For example, predictive models can help determine which customers are most likely to churn, which products are likely to sell the most, and what offers should be made to specific customers - and suggest actions to prioritize leads, and increase productivity, and head off potential issues before the customer complains - or churns.
2. Customer Experience: AI and ML can of course be used to improve the customer experience by automating and personalizing interactions. For example, chatbots and virtual assistants are more and more frequently being used to answer customer queries in real-time and provide support. By analyzing customer data, AI and ML can generate personalized recommendations and offers that are more likely to resonate with specific customers and increase the likelihood of satisfaction & success.
3. Customer Lifetime Value: Building on the prior point, Customer Experience in the near-term has direct impact on Customer Lifetime Value (CLV) - and corporate growth and profitability - in the long-term. Positive customer experiences can lead to lifelong loyalty and exponential growth, while negative experiences can damage a company's reputation and negatively impact both near-term revenue and long-term profitability.
4. Forecasting: AI and ML can be used to develop more accurate revenue forecasts by analyzing historical trends and data. This helps companies to make informed decisions about sales targets, budgets, and resource allocations.
5. Sales Targeting: AI and ML can be used to optimize sales targeting by identifying the most profitable customer cohorts & segments - and targeting them with personalized offers and messaging with the highest likelihood of response. This approach increases the likelihood of closing deals while reducing the cost of customer acquisition - increasing sales productivity and reducing cycle time.
6. Process Automation: AI and ML can be used to automate manual tasks, freeing up time for RevOps teams to focus on value-added activities. For example, AI and ML can be used to automate data entry, document management, and order processing, reducing the risk of errors, improving customer experience and increasing efficiency.

In short, by leveraging the power of both generational & computational AI & ML, I do believe that businesses can make their RevOps efforts significantly more successful. And by doing so, they can scale more efficiently, streamline operations and optimize their revenue generation potential - while identifying and prioritizing their most profitable customers, channels, and processes.

Analysis can finally translate to action - and the promise can - finally - become a reality.