Sales tech, or sales technology, refers to a range of digital tools designed to enhance sales teams’ productivity, allowing them to focus more on closing deals and driving revenue. Per Hubspot, only 34% of an average sales agent’s time is spent actually selling. As companies aim to optimize their sales processes and drive revenue growth, AI is revolutionizing the sales tech landscape. Preet Grewal, a seasoned expert in sales technology, shares her insights into the emerging trends and disruptions brought about by AI.
Per Forbes, Revenue Operations is the fastest growing job in the US right now. How did you get involved in it?
After my stint at PwC as a Management Consultant working for Oil & Gas, Airlines, Retail etc. I was ready to commit to a single industry vertical. I chose the Technology industry and joined McAfee Enterprise as the Global Sales Operations Lead in the Sales Strategy and Operations team. It was an exciting period, especially with McAfee’s acquisition of SkyHigh, which provided numerous opportunities to impact top line growth by developing advanced analytical solutions for the Sales organization.
Since then, McAfee Enterprise has undergone significant transformations, including an IPO, two divestitures, and a merger, eventually becoming Trellix. Throughout this journey, I have had the privilege of collaborating with incredible colleagues on challenging and high-priority projects. Over time, my role and responsibilities expanded significantly, leading me to support revenue teams throughout the entire revenue cycle.
Recently, I transitioned to the Data Centre of Excellence Team at Trellix, where I serve as a Product Manager. This role has given me a broader exposure, allowing me to work closely not only with Sales and Sales Operations but also with Product, Finance, Marketing, and IT functions.
I imagine you are closely familiar with the CRM space. Salesforce has been a market leader with a quarter of the market share. What are your predictions on it for 2024 and beyond, especially with the growth in AI?
Salesforce has enjoyed almost ~30% compound average growth since 2011. However, I believe it should be concerned about competition from Microsoft Dynamics, especially given Microsoft’s investment in OpenAI. Salesforce’s platform has a poorly designed user interface and functionality that often requires advanced programming skills for customization. While its growth has been fueled by cross-selling its marketing, analytics, and visualization platforms, the core CRM platform is not intuitive or user-friendly for sales reps.
Looking ahead to 2024, Salesforce is likely to face significant challenges as Microsoft Dynamics enhances its AI capabilities for sales-related tasks. Sales reps spend ~70% of their time in admin tasks, including CRM, and can benefit tremendously from tools using a natural language interface and offering automation. Microsoft’s well-known strategy of bundling solutions—spanning Microsoft 365, Azure, and Teams—offers a seamless integration that simplifies the workflow for sales reps. This bundling approach also allows Microsoft to offer substantial discounts, which is particularly appealing to customers as high interest rates are expected to stay in 2024. So for Salesforce, while I don’t think their Annual Recurring Revenue (ARR) will decline suddenly, I do think their growth will slow down. CRMs are inherently sticky due to their extensive customization, and switching platforms can significantly impact a company’s revenue. So I think Salesforce’s customers might be shrinking durations in 2024, slowly switching to competitors. And as competition continues to intensify it might have a tough time acquiring new customers.
In Conversational Intelligence space, companies like Gong or Chorus AI have built incredible products. How do you view their product offerings?
Gong was started with $6 million in seed money in 2016. However, with the democratization of AI and the availability of powerful pre-trained models, small startups can now build comparable, if not superior, products at a fraction of the cost. Transformers have revolutionized the field of large language models (LLMs) and natural language processing (NLP) by fundamentally changing how models handle sequential data. This shift has led to the commoditization of technologies like Gong. Fine-tuning these pre-trained models for specific applications, such as sales call analysis, now requires significantly fewer resources compared to developing a proprietary model from scratch. So, from a purely product standpoint, Gong no longer holds the same competitive advantage it once did.
But based on my research Gong is very popular among customers. You don’t see it maintaining its long-term dominance in Revenue Intelligence space?
Gong is a market leader in conversation intelligence. When it was launched, features like sentiment analysis and tracking the percentage of time spent by sales reps asking questions were novel and highly attractive, giving Gong a significant first-mover advantage and a dedicated customer base. However, this product differentiation has diminished over time. Competitors like Chorus AI now offer very similar features, and the market is becoming increasingly crowded with comparable solutions.
But I do think that one area where Gong has excelled is in its marketing strategy. The company has made substantial investments in brand building and has effectively utilized organic LinkedIn marketing to maintain visibility and engagement. This marketing prowess has helped Gong sustain its market presence.
However, Gong and Chorus AI are not inexpensive solutions. The average license cost per user ranges from $100 to $130, meaning that for companies with thousands of sales reps, the cost of these tools can run into millions of dollars annually.
When Gong raised its last funding round in 2021, its revenues were $120 million, and it got a valuation of $7.25 billion—a multiple of 60x revenue. However, the economic landscape in 2023 is quite different. CFOs are tightening budgets, layoffs are happening, and the advanced AI has enabled startups to develop superior conversational intelligence models at a fraction of the cost. Given these factors, it is highly unlikely that Gong can maintain its dominance in the market.
Other than CRM what other strong use cases does AI have in Enterprise Sales organizations?
I think there is a lot of potential for AI in Sales related functions. For example, Sales enablement can be better. Currently, new reps in Enterprise sales take an average of 6 months to ramp up. We are still caught in the classroom style training (now virtual) that is forgotten completely after a few weeks. This can be improved through AI, through AI mock interviews, or AI suggestions based on the pre-programmed best-in-class rep responses.
Another significant pain point for Sales Leaders and Sales Operations teams is Go-to-Market (GTM) and Territory Planning. This function is crucial, especially as it often coincides with closing Q4, the biggest quarter of the year. GTM and Territory Planning require complex data modeling, and even with expensive software like Anaplan, which demands substantial resources for implementation and maintenance, the results are often static lists and extensive subsets of Excel data. I really think that AI has the potential to revolutionize this process. Imagine an AI model that can simulate different scenarios and automatically optimize each territory based on a variety of factors such as renewal value, customer vertical, trailing twelve months (TTM) revenue, and qualitative factors like sales rep performance, tenure, close rate, and quota potential. This would enable dynamic, data-driven territory planning, providing sales leaders with real-time insights and more effective strategies. AI-driven optimization could significantly reduce the manual effort and resources required, leading to more agile and responsive sales operations.
Another use case worth considering involves the manual entry of customer insights into CRM systems by sales reps. Currently, reps are required to input a significant amount of data, such as competitor information and win/loss comments. This process can be streamlined by automatically extracting these insights from recorded customer conversations. Automating the addition of crucial information would not only reduce the administrative burden on sales reps but also ensure that the data is accurate and unbiased, as it would be sourced directly from customer interactions. This data could then be aggregated and modeled, allowing Product teams to analyze it at scale to determine which features to develop or improve.
Furthermore, this information could be accessed by the Chief Revenue Officer (CRO) through NLP solutions like chatbots, enabling simple and direct queries such as, “Why are we consistently losing Data Loss Prevention renewals in Financial Services?” This would provide actionable insights quickly and efficiently, enhancing strategic decision-making across the organization.
I think startups tackling these problems could disrupt the Sales-Tech market, helping their customers boost market share and revenues.
