Over the last few years, RevOps has emerged as one of the key functions of any business. An effective RevOps team acts as a strategic function that aligns different silos of business together and optimizes for revenue generation and business growth.
One of the key ways that RevOps does this is through data. And historically, companies have always used past data to make predictions on the future. But here’s the problem: Data, especially when it’s derived from multiple sources, can be overwhelming in volume and exceedingly difficult to rationalize. This makes it challenging for top executives to truly understand the meaning behind the data they are gathering and analyzing.
Emerging technologies, such as artificial intelligence, can help businesses identify previously hidden opportunities and improve forecasting capabilities.
However, using AI in RevOps isn’t a simple case of plug-and-play. There are challenges in both implementation and usage that you’ll need to address. We could get straight into it, but first…
What are the challenges faced by RevOps?
Before implementing any technology, understanding the problems that need to be solved will lead to selection of a solution that is most effective for your specific use case.
While bringing together traditionally siloed departments and trying to align them towards revenue goals, there are a lot of unique challenges that occour. For example, each department works individually, and this creates a lot of fragmented data for the RevOps team. The alignment towards a common goal itself becomes an issue, because despite the existence of RevOps, each team has its own goals and metrics that they’re keeping an eye on.
AI driven solutions can be of great use in streamlining and analyzing data. By automating data integration, cleansing, and analytics, AI empowers RevOps teams to extract valuable insights from vast amounts of information organizations. This, in turn, enables organizations to make data-driven decisions and optimize revenue operations.
But how can you do all this with AI?
AI in RevOps: A Deep Dive
We’ve spoken a little bit on the different things AI can do for business. Like any other technology, AI can be implemented in almost any business process, provided it is done properly and there’s a structure set up to ensure you get the most out of it. The benefits of AI can then be felt across the board. What does this mean? Implementing AI in RevOps, for example, doesn’t benefit just the RevOps team. There are ripple effects of this increase in efficiency that provides actionable feedback to other teams as well. For example, your sales team will see a boost in productivity, not just due to a tightened process, but also due to accurate and powerful training delivered to your reps at the right time.
Let’s now get a thorough understanding of the benefits that AI gives RevOps teams:
1.AI for Planning and Forecasting
Imagine this: Your task is to create a business forecast. You need to forecast demand, and then suggest where and how to allocate resources. For this, you’ll need data from a myriad of sources, analyze them, draw conclusions, and then make predictions that can be significantly relied upon. Sounds difficult? Of course. But what if you had an AI friend to help you out?
AI for planning and forecasting involves using machine learning algorithms to analyze data, identify patterns, and generate accurate forecasts based on the same. By automating this process, businesses will be able to make more informed decisions, optimize allocation of resources, thereby leading to better results.
Organizations can also proactively adapt to changing market conditions, anticipate demand, and align their business strategies accordingly.
AI powered planning and forecasting solutions, such as Clari and HubSpot, use advanced analytical techniques to process large amounts of data. This includes historical data, market trends, and customer behaviors. Once this is analyzed, they generate accurate forecasts and assist businesses in making data-driven decisions.
Clari, for example, combines AI and automation to provide sales teams with real time insights into their sales pipeline. By analyzing data from different sources such as CRM, emails, and calendar events, Clari can generate accurate sales forecasts, identify potential risks and opportunities. This can help the sales team prioritize and focus their efforts on areas that need it the most.
HubSpot’s AI-powered forecasting tools, however, leverage historical data and machine learning algorithms to provide reliable sales predictions that can help businesses allocate their resources more effectively.
2.AI for Process Optimisation and Operational Efficiency
A key source of frustration for people working in operations is that there never seems to be enough time. There’s always a fire to fight, or a process to optimize, and with rapidly changing environments, the optimisation process can be infinite. Besides, what do you do when processes are changed, and then you’ll have to do the whole thing all over again?
AI plays an important role in optimizing business processes and improving operational efficiency. By leveraging natural language processing and machine learning, AI tools can analyze vast amounts of data, provide insights, and suggest improvements. These tools automate tasks, streamline workflows, and enhance overall productivity, enabling businesses to achieve operational excellence. The application of AI in this domain can range from automating repetitive manual tasks to optimizing complex business processes.
ChatGPT is an excellent example of AI for process optimization and operational efficiency. By utilizing natural language understanding and generation capabilities, ChatGPT can analyze and interpret text-based data, understand user queries, and provide relevant and accurate responses. When integrated with your existing platforms, this AI-powered tool can automate customer interactions, handle routine inquiries, and even assist employees in various departments, such as HR or IT support, by providing real-time assistance and guidance.
Everstage’s EverAI is a great example of this. With a smart integration, users of everstage can now get summaries of their compensation contracts, and get any questions around them answered as well. This reduces the workload of Operations folks, who no longer have to spend hours going back and forth with sales reps, explaining their contracts and resolving their queries.
3. AI in CRM and Sales Processes
Look, let’s admit it: Sales is incredibly complex. It’s not a simple matter of telling people what you have and then waiting for them to buy it. There might be many prospects, but you don’t have unlimited time. So, you’ll need to correctly identify where you need to focus your efforts, and also know how to talk to the people you’re talking to. What are their concerns? How promising is this lead? How do I optimize and personalize my conversations with them?
AI has transformed customer relationship management and sales processes, enabling businesses to provide personalized experiences, automate lead scoring, and optimize sales strategies. AI-powered tools in this domain analyze customer interactions, extract valuable insights, and assist sales teams in making informed decisions. By leveraging AI, businesses can enhance customer engagement, improve conversion rates, and drive revenue growth.
Gong is an example of an AI-powered platform that uses natural language processing and machine learning algorithms to analyze sales calls and meetings. By identifying conversational patterns, sentiment analysis, and key talking points, Gong provides valuable insights into sales team performance, customer preferences, and potential areas for improvement. HubSpot NLP and Salesforce AI also offer AI-powered solutions that automate lead scoring, analyze customer data, and generate actionable insights to optimize sales processes.
4.AI in Analytics
As we’d discussed earlier, data is crucial to the functioning of RevOps teams. Small amounts of data might be easily manageable, but what happens when you have a LOT of data, from different sources, that all need to be analyzed? It’s a given that the more complex the data is, the more chances there are for errors, and the more time it will take to do a thorough analysis. This is why AI can be of great help in this area.
AI has greatly impacted the field of analytics by automating data analysis and visualization tasks, uncovering insights, and enabling data-driven decision-making. AI-powered analytics platforms can process vast amounts of data, identify patterns, and generate visualizations, allowing businesses to gain actionable insights and make informed decisions faster.
Tableau is a popular AI-powered analytics platform that leverages machine learning algorithms to analyze and visualize complex data sets. It provides businesses with interactive dashboards, data exploration tools, and predictive analytics capabilities. HubSpot NLP Querying is another example, where natural language processing is used to query and analyze data within HubSpot's CRM platform. Users can ask questions in natural language, and the AI-powered system understands the intent behind the query, retrieves relevant data, and presents the insights in a user-friendly format. Thoughtspot is yet another example, offering AI-driven search and analytics capabilities that allow users to search for specific data points and instantly receive relevant insights and visualizations.
These AI-powered analytics tools empower businesses to uncover hidden patterns, identify trends, and make data-driven decisions efficiently. By automating data analysis and visualization tasks, organizations can save time, improve accuracy, and gain a competitive edge in today's data-driven business landscape.
5.AI in Customer Support and Success
For any business to succeed in today’s market, customer satisfaction is key. And customer satisfaction, understandably, depends a lot on the quality of support they receive.
AI-powered solutions have transformed customer support and success processes, enabling businesses to provide faster, more personalized, and efficient support to their customers. AI-driven chatbots and support platforms leverage natural language processing and machine learning algorithms to understand customer queries, provide real-time assistance, and offer 24/7 support.
Intercom, for instance, utilizes AI-powered chatbots to engage with customers on websites or messaging platforms. These chatbots can understand and respond to customer inquiries, provide product recommendations, and even perform basic tasks, such as order tracking. Freshdesk and Zendesk offer AI-powered support platforms that automate ticket routing, provide suggested solutions, and assist support agents with relevant knowledge and information. By leveraging AI in customer support, businesses can enhance customer satisfaction, improve response times, and optimize support operations.
6.AI for Sales Cycle and Prospection Optimization
One of the key things businesses should be focussing on is their process of prospecting leads. If businesses can correctly identify the key metrics that influence the quality of the lead, and identify where and how their focus should be targeted, they are more likely to see more fruitful conversations and shorter sales cycles.
AI has revolutionized the sales cycle and prospecting optimization by automating lead scoring, prioritizing sales efforts, and providing market intelligence. AI-powered tools in this domain analyze vast amounts of customer data, identify key indicators of lead quality, and provide actionable insights to sales teams, enabling them to focus their efforts on the most promising prospects.
H2O.ai, Akkio, Madkudu, and Clay are examples of AI-powered platforms that specialize in lead scoring and market intelligence. These tools utilize machine learning algorithms to analyze customer behavior, demographic data, and past purchase history to predict the likelihood of conversion and prioritize leads accordingly. By leveraging AI-powered market intelligence, sales teams can gain a deeper understanding of their target audience, identify market trends, and make informed decisions to optimize the sales cycle and improve overall revenue generation.
Conclusion
The integration of AI in Revenue Operations is reshaping the way organizations approach revenue generation and growth. By leveraging AI's capabilities in data management, analysis, forecasting, and customer engagement, RevOps teams can unlock new levels of efficiency, effectiveness, and optimization.
This means that the future of Revenue Operations is undeniably intertwined with AI. Embracing this transformative technology will eventually evolve from being a competitive advantage to an absolute necessity for organizations aiming to thrive. Like with everything else, early adopters will have an advantage, and so the time to harness the power of AI in RevOps is now. The potential for organizations to achieve unprecedented efficiency and growth has never been greater.
RevOps is on the brink of an exciting transformation as AI takes center stage. Soon, there will be a revolution in the way organizations approach revenue generation and growth. With AI's extraordinary prowess in data management, analysis, forecasting, and customer engagement, RevOps teams are poised to unlock unprecedented levels of efficiency, effectiveness, and optimization.
So, what are you waiting for? Take the plunge into the world of AI, and watch as your operations get more efficient, propelling your business to new heights. The future is here!
