Market Research and Customer Insights Questions

Direct answer

Primary research is information you collect first-hand for this decision (customer interviews, a survey you run). Your own product usage data, support tickets and lost-deal notes sit in between: they are first-party records you already hold rather than data gathered for this question, so some textbooks call them internal secondary data. I use them as primary-grade evidence because they come straight from your own customers, but I remember they were not collected to answer this question. Secondary research is information someone else already collected (analyst reports, government statistics, public company filings, competitor websites, review sites). With two weeks, start with secondary sources for a few days because they are cheap and tell you what to ask, then spend most of the remaining time on primary work for the questions only your own buyers can answer.

How to decide where to start

Write down the decision first (for example "build, defer or drop this analytics feature") and the two or three things you are most unsure about. Then ask of each unknown: has someone already measured this, and would I trust their number for my segment?

• Lean on secondary when the question is about the outside world: how big the category is, who the vendors are, what buyers in general say they want, what competitors already ship.
• Lean on primary when the question is about your buyers' own behaviour and urgency: how they produce reports today, who owns the budget, what would make them switch, what they tried and abandoned.
• Start where one hour of effort removes the most uncertainty. That is usually secondary first, because it also tells you whom to recruit and what to ask.

Worked example

Illustrative plan for an embedded-dashboard feature aimed at mid-size logistics firms:

Twelve interviews give direction, not statistics. Say so in the recommendation instead of quoting percentages from them.

Trade-offs and pitfalls

• Secondary sources can be old or define the market differently from you, so check the date and the definition before using a number.
• Primary samples drawn only from existing customers miss people who rejected you (sampling bias: the people you hear from differ from the people you need to understand). That is why the plan includes non-customers.
• Do not skip primary work because secondary work looks conclusive: a large market says nothing about whether your feature is wanted.
• Do not start with interviews before reading what already exists: you will spend buyers' time asking things a public source could answer.

Direct answer

A 30% workaround rate is good news and a warning. It proves the problem is real enough that people act on it, and it also means we are competing with something that already half works. I would find out what the workarounds are and what they cost users, size the market by what people pay to keep the problem tolerable, build first what replaces the workaround's critical steps, and pivot only if no segment exists where our product clearly beats the workaround's total cost.

Terms. A workaround is anything a person uses instead of a dedicated solution (a spreadsheet, a script, a competitor tool, a manual process). Segmentation means splitting respondents into groups whose behaviour differs.

Switching cost means everything a user gives up by changing tools (time to migrate, retraining, lost habits). A price anchor is the figure customers already compare any price against. A wedge is a narrow entry point where you clearly win, used to get a first foothold. Parity means matching a competitor on a feature, so that it stops being a reason to stay.

1. Segment the 30%

Split by company size, role, frequency of the problem, and workaround type. Compare against the 70% without one: do they lack the problem, or have it and tolerate it? The survey cannot answer that, so ask directly.

2. Follow-up interviews

Interview a handful from each workaround type until answers repeat (new interviews stop teaching you anything new). Ask about the last time they used it, what it costs in hours and money, what breaks, who maintains it, and what they tried before.

3. Verify the workaround

Ask for a screen-share or the artefact itself. Self-reports are often optimistic: a "solution" people mention may be abandoned, rarely used, or a poor fit.

4. Market sizing and prioritization

Count three tiers: no workaround, a free workaround, a paid workaround. A paid workaround shows willingness to pay and sets a price anchor. A free one means we must beat it on effort saved plus switching cost. Size each tier separately, then sum only the tiers you can credibly win.

Worked example (illustrative numbers)

400 respondents, 30% = 120 with workarounds: 70 spreadsheets, 30 paid vendors, 20 manual (70 + 30 + 20 = 120).

Turning that into tier sizes. Suppose the survey is a fair sample of 50,000 target companies (an assumption to check). Then 30% = 15,000 have a workaround and 35,000 do not. Within the 15,000, the survey shares are 70/120, 30/120 and 20/120, so spreadsheets = 8,750, paid vendors = 3,750, manual = 2,500 (sum 15,000). The free tier (spreadsheets plus manual) is 11,250, the paid tier is 3,750. The paid 3,750 prove people pay and give the price anchor. The 11,250 free-workaround companies are the main market, but only the share for whom the time cost exceeds our price plus switching cost is winnable.

Testing the price against the workaround. A spreadsheet user losing 5 hours a week at $40 an hour pays 5 x 40 x 52 = $10,400 a year in time. A $2,400 subscription is about 23% of that, so on time alone it breaks even if it removes about 23% of that work (roughly 1.2 of the 5 hours a week). Buyers also weigh switching cost and the risk that the tool underdelivers, so I would want it to remove well over half of the hours before calling it an easy sell. That comparison is the test because a buyer weighs the price against the cost they already bear. Hours and hourly cost are self-reported, so verify them in the follow-up interviews before using them.

Prioritize in this order, with the reason for each: (1) import from the workaround, because switching cost is the main barrier and 8,750 spreadsheet users start with data they will not retype; (2) the steps that cost users the most time, because that is the value the price is compared against; (3) parity with the paid vendor on the core job, because the 3,750 paid users will not move while a key capability is missing; (4) differentiators last, because they only matter once the first three stop users from leaving.

5. When this is a reason to pivot

• Workaround users say it is good enough, cheap and rarely breaks.
• A paid incumbent is entrenched (it holds the customers and their habits, with long contracts or heavy integration) and we cannot beat it on cost or quality.
• No segment exists where workaround cost exceeds our price plus switching cost, and that is large enough for the plan.

The pivot can be a different segment (for instance the 70% if the pain is real but untreated), a narrower wedge (the workaround's weakest step), or stopping.

Pitfalls

Counting every workaround user as demand double-counts them with the sizing of the whole market, and treating "has a workaround" as "has no problem" throws away the best evidence you have.

Direct answer

I would treat the raw survey as an upper bound for early adopters, not as a read on the whole market. I would measure how skewed the sample is, reweight toward the real population where I have enough mainstream respondents, and replace stated willingness to pay (WTP, what people say they would pay) with behaviour where I can. Then I would give stakeholders a range with a plain statement of what it still cannot tell us.

Correcting the skew

1. Quantify it. Use screener questions (how quickly do you try new tools, where were you recruited) to split respondents into early adopters (people who try new products soon after launch) and mainstream.
2. Reweight (post-stratification). Give each group a weight equal to its share of the real population, taken from an external source or a stated assumption, instead of its share of the sample.
3. Respect small groups. If few mainstream respondents exist, weighting amplifies their noise, so report the uncertainty and collect more of them.
4. Discount stated WTP. People overstate what they will pay. Anchor with behaviour: what they pay today for substitutes, price-page clicks, pre-orders, deposits.

Worked example (illustrative numbers)

200 respondents: 140 early adopters (70% of sample) and 60 mainstream (30%). Assume outside adoption data says early adopters are 15% of the real market and mainstream 85%. Share saying they would pay $20 or more: early adopters 45% (63 of 140), mainstream 15% (9 of 60).

• Raw sample: (63 + 9) / 200 = 36%.
• Reweighted: 0.15 x 45% + 0.85 x 15% = 6.75% + 12.75% = 19.5%.

The raw figure nearly doubles the reweighted one.

Where the plus or minus comes from. The mainstream estimate rests on only 60 people, so chance alone moves it. A 95% margin of error means: if you repeated the survey many times with fresh random samples, about 19 times in 20 the true share would land within that margin of the sample result. The usual approximation for a share is:

margin = 1.96 x square root( p x (1 - p) / n )
       = 1.96 x square root( 0.15 x 0.85 / 60 )
       = 1.96 x 0.0461 = 0.090, about 9 points

So the mainstream share is 15% plus or minus 9 points, an interval (plausible range) of 6% to 24%. It is optimistic, because the sample was not random.

Carrying the interval to the whole market. Hold the early-adopter term fixed at 0.15 x 45% = 6.75%, and swap in each end of the mainstream range:

• Low end: 6.75% + 0.85 x 6% = 6.75% + 5.10% = 11.85%, about 12%.
• High end: 6.75% + 0.85 x 24% = 6.75% + 20.40% = 27.15%, about 27%.

Communicating what remains uncertain

Present "about 20%, plausible range 12% to 27%, and since this is stated intent (what people say they would do, not what they do) it is likely still high" and say which assumption drives it (the 15/85 split). Say what the survey cannot show: whether anyone actually pays.

What to do after the recommendation

• Extra data: a fresh mainstream sample, for instance 300 respondents from a panel recruited without a novelty screen. If it again gives 15%, the margin shrinks to 1.96 x square root(0.15 x 0.85 / 300) = about 4 points, so the interval narrows to about 11% to 19%.
• Cheap experiments: a priced fake-door page (a sign-up page for a product that does not exist yet), pre-orders, a paid pilot with a few accounts.
• Decision rules, set before the data arrives. Break-even is the point where revenue covers running costs. Illustrative derivation: the product costs $240,000 a year to run and sells at $20 a month ($240 a year), so it needs 240,000 / 240 = 1,000 paying customers. If the reachable mainstream market is 10,000 people, that is 10% of them. Proceed if the interval's lower bound (its bottom end) is at least 10%. Modify (re-price, target early adopters first, change packaging) if the interval straddles 10%, meaning 10% sits inside it. Pause if the upper bound (its top end) is below 10%.

Which interval the rule is applied to. The 10% break-even is a share of the 10,000 reachable mainstream people, so the rule must be applied to the mainstream interval (6% to 24%, from the 60 mainstream respondents), not to the blended 12% to 27%, which includes the early-adopter term. On the mainstream interval, 10% sits inside the range, so the call is "modify", not "proceed". The blended range also understates the uncertainty, because it holds the early-adopter share fixed at 45%: that figure rests on 140 people and carries its own margin of about 8 points (1.96 x square root(0.45 x 0.55 / 140) = 0.082). Letting both groups vary gives about 11% to 28% for the blend. Finally, every one of these shares is stated intent that the answer itself calls likely too high, so before comparing with a break-even of paying customers, apply a stated discount (for example, assume only a fraction of "would pay" respondents actually buy, and label that fraction as an assumption). The survey also did not say whether "$20 or more" meant per month, so confirm the price period before using the $240 a year figure.

Pitfalls

Weights fix who answered, not whether stated intent turns into purchases. Fixing one never replaces the other.