Mike Tong has more than 10 years of experience running GTM operations and strategy for companies in the fields of data and technology within McKinsey TMT, AtSpoke, Splunk and the VC company B Capital.(AI/ML)
As of 2019, I was the leader of my sales and strategy for growth of an investment-backed AI company named atSpoke. The company that Okta eventually bought, utilized AI to complement traditional IT services management as well as internal communication within the company.
At an early stage the rate of conversion was extremely high. If our sales staff could speak to a potential customer — and that the prospect engaged with the product they’d more often than not be a client. The challenge was getting good prospects to communicate to the sales team.
The old SaaS strategy for generating demand did not work. The process of buying ads and creating communities with a focus around “AI” were both expensive and attracted people who were not financially savvy. The purchase of search terms to describe specific value propositionsfor example, e.g., “auto-routing requests” did not work since the concepts were novel and nobody was searching for these phrases. Additionally, terms such as “workflows” and “ticketing,” that were more popular which put us in directly competition with giants such as ServiceNow as well as Zendesk.
As a consultant for enterprises in the early stages of their growth as part of the B Capital Group’s Platform team, I have observed similar patterns throughout the majority of AI, machine learning, as well as advanced analytics company I have spoken to. A healthy pipeline generation process is the biggest issue in this sector, yet there’s not much information on how to tackle the issue.
Keep a reference to the categories recognized in the early days of messaging even if the topic isn’t the primary focus of your business’s value offer or the reason why customers will eventually sign an agreement.
There are four major issues that stand in process of generating demand in AI/ML businesses and strategies to address those issues. Although there isn’t a silver solution, there is no secret AI buyer’s meeting at Santa Barbara or ML enthusiast Reddit thread, these suggestions can help you plan your marketing strategies.
Challenge 1. AI/ML categories aren’t yet established
If you’re reading thisarticle, you’ve probably heard the tale that is Salesforce in general and “SaaS” as a category However, the brilliance of the concept is worth not being forgotten. When the company was founded in 1999 the term “software as a service” was not a thing. At the time there was no one contemplating, “I need to find a SaaS CRM solution.” The media called the company was an “online software service” or an “web service.”
Salesforce’s initial marketing was focused on the shortcomings of sales software that was not as sophisticated. Salesforce famously organized the ” end of software” protest in the year 2000. (Salesforce still employs that same message.) Chief Executive Officer Marc Benioff also made a habit of reiterating the phrase “software as a service” until it became popular. Salesforce invented the market they controlled.
AI and ML companies are also faced with the same dynamic. Although the terms machine learning and AI are not brand new, specific solutions such as “decision intelligence” don’t fall into a single grouping. Indeed, even putting together “AI/ML” companies is awkward because there’s a lot of overlap in businesses intelligence (BI) and data automated predictive analytics, and. Businesses in more modern categories could be mapped to terms such as Continuous Integration or Container Management.