Gong: artificial intelligence for a non-artificial revenue lift

Gong, a “conversational intelligence” tool for revenue (sales + marketing + growth) teams, provides unprecedented analytics to business leaders with a human & data focus

What do all sales teams have in common?

They want to grow revenue! In enterprise software, this is a particularly salient need, for four key reasons: deal sizes are large, sales cycles are long and complex, switching costs are high, and multi-homing is not an option for most tools (e.g., you only need/want one CRM).

According to an HBR survey*, only 29% of sales leaders say they’re doing a “good job” executing on their go-to-market strategy. Given the complexity of the sales process and the competitive dynamics of SaaS, revenue teams have turned to data analytics to optimize their customer interactions and improve their odds of winning deals.

However, sales teams don’t have the internal capabilities or bandwidth to intake, digest, interpret, and efficiently act on the data available to them. What could be their best asset quickly becomes a drag, as it is extremely difficult to separate the signal from the noise and any time spent analyzing is missed time with a potential customer.

Gong to the rescue!

Gong has developed an AI-powered solution (dubbed “revenue intelligence“) to:

  1. Aggregate sales data across an organization: They capture all instances of marketing and sales outreaches (email, SMS, phone, etc.) and connect this to existing data in the CRM
  2. Deeply analyze each interaction: They review voice/text for key topics (e.g., pricing/budget), keywords (e.g., specific competitors named) and tone (e.g., objection, questions), and track many other indicators (e.g., frequency of communication, or seniority of role)
  3. Provide personalized predictions + recommendations to sales leaders: Gong provides a complete picture of the overall “health” of a sales process pre-close. They share metrics like “likelihood to close” and give recommendations on specific actions to take based on what they predict will lead to better results

This is all powered by a massive data asset: their intake and analysis of thousands of enterprise sales interactions and outcomes across industries, roles and channels. As more customers sign on to their platform, the more datapoints Gong has to make better predictions about “what works” (i.e., what cadence, content and customer reception is correlated with deals closing, versus dying) and “what to do next” (i.e., specific actions users can take at each step, to increase odds of closing).

All of this is packaged in a simple, easy-to-use platform to take the guesswork out of sales forecasting to easily navigate from data gathering to decision-making.

Sales experts… selling, too

Gong captures value by locking down large, multi-year enterprise contracts of their own. They price on a per-seat basis, with an additional fixed, annual platform fee. Since Gong provides a uniquely valuable service (i.e., not a lot of comparable competitors) including a dataset rich with insights that customer can see immediate value from, churn rates are low. Lastly, Gong is able to attract top-tier sales talent, as their product is on the cutting edge of data-driven sales innovation, a topic sales leaders are likely passionate about!

At the forefront of enterprise AI?

Though Gong has devised an innovative solution to ease important pain points in the complex enterprise sales process, being a first-mover in this space, they face a number of challenges.

Firstly, there are a number of lower-cost alternatives (like Chorus.ai and Outreach) popping up that cater to organizations that don’t need the full sophistication that Gong offers. However, I’d posit that this is simply a validation of there being a true need and a market here, versus being a serious threat, as many revenue teams will recognize the superior experience and data capabilities of Gong.

Secondly, there may be an adoption problem among very large enterprise organizations. These larger buyers may require proof points of other large sales teams benefitting from Gong’s platform and data, because Gong’s predictive algorithms have mainly been trained on SMB inputs. Only until they reach scale in enterprise can we assume that Gong’s predictive abilities are truly tailored to the enterprise market, but these initial enterprise customers would be less likely to want to be Gong’s training guinea pig.

Lastly — and perhaps the most existential challenge — is the fact that they act as a point-solution in the broader sales process, but one could argue Gong doesn’t systemically change the mindset and practices around enterprise sales today. Another, more cutting-edge AI player could enter the market and provide significantly more value to customers if they reimagine what the entire B2B sales system looks like, leveraging AI, rather than just augmenting a piece of the prediction and decisions involved.

To realize their potential, AI technologies need new systems that leverage them

From Prediction to Transformation” by Ajay Agrawal, Joshua Gans, and Avi Goldfarb

Opportunities ahead

While the first two challenges are run-of-the-mill hurdles that many software businesses face, I’d like to focus on how Gong might address the last one and turn it into their greatest opportunity. I would encourage Gong’s management team to envision a future where AI capabilities wholly transform what the B2B sales system looks like, to provide groundbreaking (vs. incremental) value to organizations.

To illustrate this, note that much of the enterprise sales process today relies on information asymmetry on both sides of the table. For example, customers don’t know if what the sales reps are promising is going to hold up in practice, and sales reps don’t know the maximum a customer is willing to pay and if they’re evaluating other competitors. While Gong’s predictions today help sales leaders tailor interactions and communications, they don’t alleviate the fundamental pain of information asymmetry. Could system-wide AI help address this? What could Gong do at a broader level to make the sales process more transparent and less costly for both sides?

In order to explore these types of questions, Gong’s organization would need to shift substantially to allow (a) new roles / teams, (b) executive vision / organizational prioritization, and (c) systems change talent for this type of “blue ocean” ideation and experimentation. However, if they are able to harness the power of AI to envision and architect this new system, there is a fantastic market opportunity ahead of them.

*HBR survey sponsored by Gong


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Student comments on Gong: artificial intelligence for a non-artificial revenue lift

  1. Very interesting post Steph, thank you!

    Gong’s AI-powered software can clearly create a ton of value for companies trying to manage their sales process and sales teams. This kind of tool can also be very powerful to A/B test different sales scripts and approaches, systematically recording data about the sales process and outcomes. I can see this being helpful not only for SaaS companies or start-ups, but also for any established company looking to optimize their go-to-market strategy.

    I wonder if this kind of tool also changes the competencies that you would look for in a sales team. My impression of a good traditional salesperson is someone who comingles prediction and judgement and is able to tailor their sales approach in real time based on how the conversation with a customer evolves (i.e. a highly empathetic person). However, the sales people that can make the most out of Gong are probably those who are believers in technology, are comfortable with incorporating input from AI bots during their interactions with customers, and are flexible in their sales approach.

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