Ask most startups about their go-to-market (GTM) these days and they’ll give you lots of numbers. Funnel metrics. MQLs, SQLs, demos, and associated funnel conversion rates. Seen over time, cut by segment. Win/loss rates and close rates as well, similarly sliced. Maybe an ABM scorecard, if applicable.
Or maybe more financial metrics like customer acquisition cost (CAC) ratio, lifetime value (LTV) or net dollar retention (NDR) rate. Maybe a Rule of 40 score to show how they’re balancing growth and profitability.
And then you’ll have a growth strategy conversation and you’ll hear things like:
- People don’t know who we are
- But the people who know us love us
- We’re just not seeing enough deals
- Actually, we are seeing enough deals, but we’re not making the short list enough
- Or, we’re making the short list enough, but not winning enough.
And there are always reasons offered:
- We’re not showing enough value
- We’re not speaking to the economic buyer
- We’re a vitamin, not a pain killer
- We’re not aligned with their business priorities
- People don’t know you can solve problem X with our solution
- Prospects can’t see any differentiation among the offerings; we all sound the same 
- They don’t see us as a leader
- They don’t know they need one
- They know they need one but need to finish higher priorities first
It’s an odd situation. We are literally drowning in funnel data, but when it comes to actually understanding what’s happening, we know almost nothing. Every one of the above explanatory assertions are assumptions. They’re aggregated anecdotes . The CRM system can tell us a lot about what happens to prospects once they’re in our funnel, but
- We’re navel gazing. We’re only looking at that portion of the market we engaged with. It’s humbling to take those assertions and mentally preface them with: “In that slice of the market who found us and engaged with us, we see XYZ.” We’re assuming our slice is representative. If you’re a early-stage or mid-stage startup, there’s no reason to assume that. It’s probably not.
- Quantitative funnel analysis is far better at telling you what happened than why it happened. If only 8% of our stage 2 opportunities close within 6 quarters, well, that’s a fact . But companies don’t even attempt to address most of the above explanatory assertions in their CRM, and even those times when they do (e.g., reason codes for lost deals), the data is, in my experience, usually junk . And even on the rare occasion when it’s not junk, it’s still the salesrep’s opinion as to what happened and the salesrep is not exactly an unbiased observer .
What’s the fix here? We need to go old school. Let’s complement that wonderful data we have from the CRM with custom market research, that costs maybe $30K to $50K, and that we run maybe 1-2x/year and ideally right before our strategic planning process starts . Better yet, as we go about our business, every time someone says something that sounds like a fact but is really an assumption, let’s put it into a “hypothesis file” that becomes a list of a questions that we want answered headed into our strategic and growth planning.
After all, market research can tell us:
- If people are aware of us, but perhaps don’t pick us for the long list because they have a negative opinion of us
- How many deals are happening per quarter and what percent of those deals we are in
- Who the economic buyer is and ergo if we are speaking to them
- What the economic buyer’s priorities are and if we are aligning to them
- When features are most important to customers shopping in the category
- What problems-to-be-solved (or use-cases) they associate with the category
- Perceived differences among offerings in the category
- Satisfaction with various offerings with the category
- If and when they intend to purchase in the category
- And much more
Net — I think companies should:
- Keep instilling rigor and discipline around their pipeline and funnel
- Complement that information with custom market research, run maybe 1-2x/year
- Drive that research from a list of questions, captured as they appear in real time and prompted by observing that many of these assertions are hypotheses, not facts — and that we can and should test them with market research.
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 As many people use “demo” as a sales process stage. Not one I’m particularly fond of , I might add, but I do see a lot of companies using demo as an intermediate checkpoint between sales-accepted opportunity and closed deal — e.g., “our demo-to-close rate is X%”
 I’m not fond of using demo as a stage for two reasons: it’s vendor-out, not customer-in and it assumes demo (or worse yet, a labor-intensive custom demo) is what’s required as proof for the customer when many alternatives may be what they want — e.g., a deep dive, customer references, etc. The stage, looking outside-in, is typically where the customer is trying to answer either (a) can this solve my problem or (b) of those that can solve my problem is this the one I want to use?
 This is likely true, by the way. In most markets, the products effectively all look the same to the buyer! Marketing tries to accentuate differentiation and sales tries to make that accentuated differentiation relevant to the problem at hand, but my guess is more often than not product differentiation is the explanation for the selection, but not the actual driver — which might rather be things like safety / mistake aversion, desire to work with a particular vendor / relationship, word of mouth recommendations, belief that success is more likely with vendor X than vendor Y even if vendor X may (perhaps, for now) have an inferior product)
 As the saying goes, the plural of anecdote is not data.
 And a potentially meaningless one if you don’t have good discipline around stages and pipeline.
 I don’t want to be defeatist here, but most startups barely have their act together on defining and enforcing / scrubbing basics like stages and close dates. Few have well thought-out reason codes.
 If one is the loneliest number, salespersonship is the loneliest loss reason code.
 The biggest overlooked secret in making market research relevant to your organization — by acting on it — is strategically timing its arrival. For example, win/loss reports that arrive just in time for a QBR are way more relevant than those that arrive off-operational-cycle.