I once heard the phrase “heat seekers” used to describe the people who are always first to adopt new technology. I like it. This is just my opinion, but I’ve seen this at 5 out of the 5 startups. During the early days, the first customers are organizations that have ample in-house technical resources; they have “heat seekers.” These in-house engineers can evaluate different solutions using their own fairly objective proof-of-concept criteria. They quickly learn the technology and conduct the trial on their own. For these organizations, the superior technology wins the deal every time.
However, I suspect most companies do not have this in-house expertise. They rely heavily on analyst reports (such as Gartner Magic Quadrant) and references. They may struggle with developing criteria for a proof-of-concept trial. They may also give more weight to ease of use and low admin overhead than technical superiority. These potential customers vastly outnumber the early adopters. In order to grow revenue, you have to sell to ordinary companies — mere mortals.
Advice: Use the experience of early adopters to repurpose/reuse POC plans and create ease-of-use features. Makes sure your product benefits companies that do not have large in-house technical resources (use “wizards” and automation to streamline processes).
Better Advice: One of the great sales execs I’ve met taught the team: “When a customer asks for a POC trial, ask him if they’ll share the POC plan with you. They may well say, ‘I don’t have one, yet.’ That’s when you say: ‘Well, here’s one you can start with.’ That’s when you give them our POC plan. Of course our plan will highlight our strengths – the ones in which we’ve invested the most engineering resources and meet the most customer feature requests. Just be upfront about it.”
AI for Your Business — Not Just Your Product
I find it baffling how many starts up bloviate about machine learning and artificial intelligence in their product offering, but completely fail to use any of it in their own business processes. A classic example is RFP/RFIs. These start out as handcrafted, bespoke documents. But when a company becomes successful, they often receive more RFP/RFI requests than they can handle. Why aren’t automated tools for this standard best practices? We’ve had latent semantic analysis (LSA)* of unstructured text for more than 20 years. However, it seems mostly limited to advertising that is supposed to be “more relevant.” This automation could be a powerful force for streamlining proposals and quotes. This is especially true when it comes to answer GDPR and other questionnaires.
Advice: Start using ML/AI tools for proposals and quotes immediately. Don’t wait until you’re overtaxed and deals are falling through the cracks.
Suggestion: Two systems I much respect are Loopio for RFP/RFI automation https://loopio.com/ and Deal Hub https://go.dealhub.io/demo
* Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations. It could be used to take a body of RFP/RFIs and then generate reusable content for completing new RFP/RFIs. While Wikipedia has an excellent overview article on LSA, the main point of this blog post is we can use natural language processing to eliminate the tedious handcrafted approach to a document that is almost universal in technology sales.
I explore this topic from a different angle in my previous post: We Only Sell to Smart People