2.3 Market Research

Primary and secondary research, hypothesis testing, and the data visualizations that communicate findings.

Transcript (177 lines)

0:00 Saturday morning at the flea market, a buyer stands in front of a ninety-five
0:03 dollar Trued Cycles commuter bike and holds up his phone,
0:07 where a similar bike on an online marketplace asks seventy-five.
0:12 He wants to know why Theo's bike costs twenty dollars more.
0:16 Theo points at the orange inspection tag zip-tied to the handlebars,
0:20 the signed twenty-one point safety checklist every rebuild passes.
0:24 The buyer shrugs and walks.
0:27 That night Theo writes one sentence in the notebook:
0:31 an identical bike with a documented inspection tag sells for twenty dollars
0:35 more, and faster.
0:38 Today that sentence is a guess.
0:40 This topic is how a business turns a guess into evidence before it bets real money
0:45 on being right.
0:47 I'm Logan Shaffer, and this is MAMAKO's AP Business,
0:50 Topic 2.3: Market Research.
0:53 Two skills by the end: explain why businesses research a market and use
0:58 secondary sources to assess an opportunity, and test a business
1:01 hypothesis with primary research and put the findings in the right chart.
1:06 Practice questions for this topic are in the next video.
1:09 Let's go.
1:12 Market research means collecting detailed information about markets,
1:15 products, and customer behavior so that marketing decisions run on evidence.
1:20 Theo has been collecting informally since line one of the notebook;
1:24 this topic makes the collection deliberate.
1:26 Everything he gathers lands in one of two families.
1:30 Quantitative data is numerical, so it can be measured and analyzed,
1:34 and it answers how many, how much, and how often.
1:38 Seven bikes sold out of nine is quantitative.
1:41 Qualitative data is descriptive and non-numerical, collected in words and
1:46 images, and it answers why and how.
1:49 A parent saying the signature on the tag is what convinced her is qualitative.
1:54 A real decision usually needs both, because the count shows what happened and
1:58 the sentences explain it.
2:02 Businesses developing a new product use market research to check three things
2:07 before committing significant resources to a launch.
2:10 A product is desirable when it creates value for customers because the solution
2:15 fits a problem they actually have, which researchers call problem-solution fit.
2:20 The parents' problem is that they cannot tell a safe used bike from a dangerous
2:24 one, and a documented inspection answers exactly that.
2:29 A product is feasible when the business can produce and deliver it within its real
2:33 resources, technology, expertise, and time.
2:38 The twenty-one point inspection costs Theo about an hour per bike plus a printed
2:42 checklist and a zip tie, which his Saturdays can absorb.
2:47 A product is viable when it has a realistic path to profit in its market.
2:51 If the tag truly earns twenty extra dollars for one hour of work,
2:55 it is viable, and that is exactly the claim his research has to test.
3:00 Research keeps mattering after launch.
3:02 Businesses with existing products use it to track customers' changing needs and
3:06 wants, to spot openings for product innovation, and to build strategies that
3:11 hold or grow market share.
3:14 Theo's version: the notebook shows commuter demand climbing before the school
3:18 year, which tells him where his August rebuild hours should go.
3:24 To learn about customers, competitors, and the wider market landscape,
3:28 businesses run two kinds of research.
3:31 Secondary-source research gathers quantitative or qualitative information
3:35 that already exists, from external sources like government,
3:39 commercial, and academic publications and databases.
3:43 Primary-source research generates new data straight from customers,
3:47 and it arrives in the next section.
3:50 Secondary sources also expose the outside forces shaping an opportunity:
3:54 political, economic, social, technological, environmental,
3:58 and legal factors, PESTEL for short, like a city announcing new bike lanes or a
4:03 school district cutting its bus routes.
4:06 These sources can report market size in dollars and in total customers,
4:10 market trends, market segments, and the factors driving customer decisions,
4:15 which is exactly the information that signals whether a product could be viable.
4:20 A government census table can tell Theo roughly how many families with school-age
4:24 kids live within riding distance, and a commercial industry report can estimate
4:29 what a used-bike market turns over in a year.
4:32 His main secondary move costs nothing.
4:35 One evening he sweeps sixty local marketplace listings for used bikes and
4:39 logs three fields per listing: asking price, days listed,
4:44 and whether the seller documents any inspection or service history.
4:48 That is rival data, and interpreting it is how businesses assess the landscape
4:53 around a product idea, because reading data that already exists costs far less
4:58 than generating new data.
5:00 The sweep shows untagged commuter bikes clustering near seventy-five dollars,
5:05 and it shows no listing that documents an inspection.
5:08 Secondary research just sized his landscape, and it still cannot test his
5:12 specific twenty dollar claim.
5:15 Testing claims is primary work.
5:19 A business hypothesis states an assumption about a customer,
5:22 a product, or a market, and a business tests that assumption before committing to
5:27 a course of action.
5:28 Theo's is already written in the notebook: an identical bike with a documented
5:33 inspection tag sells for twenty dollars more, and faster.
5:37 Primary research offers a menu: surveys, focus groups,
5:41 one-on-one interviews, experiments, observations, and A/B tests,
5:46 and the right method follows from the type of data the hypothesis needs.
5:51 Surveys collect a large amount of quantitative data that reflects a whole
5:55 population's views, which fits questions like how many local parents would consider
5:59 a used bike at all.
6:02 Focus groups and one-on-one interviews collect deep qualitative data from a small
6:06 number of engaged customers or potential customers, and they allow follow-up
6:10 questions that pull out detailed explanations.
6:14 Experiments and observations capture what customers actually do instead of what they
6:18 say, in a controlled setting for an experiment and in a natural setting for an
6:23 observation.
6:26 A/B testing is a type of experiment that measures authentic customer responses to
6:30 two viable alternatives in a real-world environment.
6:34 Theo's hypothesis is two alternatives with a price gap between them,
6:38 so the method chooses itself.
6:41 Across two Saturdays he racks seventeen comparable commuter bikes.
6:45 Nine carry the signed orange tag and a ninety-five dollar price.
6:49 Eight carry no tag and a seventy-five dollar price.
6:53 By close on the second Saturday, seven of the nine tagged bikes have sold,
6:57 and three of the eight untagged bikes have sold.
7:02 Seven divided by nine is about seventy-eight percent sold.
7:06 Three divided by eight is about thirty-eight percent.
7:09 The tagged rack sold at roughly double the rate while charging twenty dollars more,
7:14 which is authentic customer behavior measured in a real-world environment.
7:18 The numbers confirm the what; he still wants the why,
7:21 so he runs twelve one-on-one parent interviews at pickup.
7:25 The follow-up questions do the real work: most parents point at the signature line
7:30 specifically, and several say the tag saved them the bike-shop inspection they
7:34 would have paid for on their own.
7:36 Qualitative data just explained the quantitative result.
7:42 Hypothesis testing can produce skewed data, and businesses reduce that risk in
7:46 two ways.
7:48 First, design the study with a sample large enough, and populated appropriately,
7:53 to reflect the population being studied.
7:55 Seventeen bikes and twelve interviews is small, and everyone Theo talked to was
8:00 already standing at a flea-market stall, so his results describe flea-market
8:05 parents and may miss buyers who only shop online.
8:09 Second, ask unbiased questions in neutral phrasing, because wording can steer
8:14 the answer.
8:15 Compare two versions.
8:17 Biased: don't you feel safer buying a bike with a signed inspection tag?
8:22 Neutral: what, if anything, influenced your decision today?
8:28 The first plants the answer inside the question, and the second lets the parent
8:32 reveal it.
8:33 Even careful research does not guarantee prediction.
8:36 Businesses routinely have to innovate and respond to problems and opportunities
8:41 using limited, unclear, or contradictory data.
8:45 Theo's evidence covers two Saturdays at one market, and a rainy weekend or a
8:50 holiday crowd could have bent the numbers.
8:53 He tags every bike going forward anyway, and keeps logging sales,
8:57 because the evidence is strong and the notebook keeps the test running.
9:03 Research ends with communication.
9:06 Data visualizations turn rows of data into patterns,
9:09 trends, and insights that stakeholders can grasp quickly,
9:13 so decisions rest on evidence, and the rule is to pick the chart type that best
9:17 shows the specific pattern you mean to communicate.
9:21 Four standard types cover most jobs.
9:23 A bar chart compares individual data points, like one bar per rack showing
9:28 seventy-eight percent sold against thirty-eight.
9:31 A stacked bar chart compares a total and its subcategories at once,
9:35 like monthly sales split into the sixty, ninety-five,
9:39 and one hundred fifty dollar tiers, showing whether the total grew and which
9:43 tier grew it.
9:45 A line graph shows a trend over time, like customers per month climbing toward the
9:49 school year.
9:50 A pie chart shows a part-to-whole relationship, like the share of a market's
9:54 sales each business holds.
9:56 Theo's most useful chart plots two measures against each other.
10:00 Each of the sixty swept listings becomes one dot, asking price on one axis,
10:05 days listed on the other.
10:07 The dots expose a dead zone: undocumented bikes asking above roughly eighty-five
10:12 dollars sit unsold for weeks, while cheaper bikes clear steadily.
10:17 Set his A/B result beside the chart and the insight sharpens:
10:22 the market refuses a high price without proof, and his tagged bikes cleared at
10:27 ninety-five in two Saturdays.
10:29 The chart shows the pattern the sixty notebook rows hide,
10:32 and that insight prices every bike he rebuilds from here.
10:38 That's Topic 2.3.
10:41 A stall-side question became a hypothesis, seventeen bikes and twelve interviews
10:45 became evidence, and sixty dots showed exactly where the market pays for proof.
10:51 The finding raises a bigger question: if buyers pay twenty dollars for the
10:55 documentation, what exactly is Theo's product?
10:59 That is Topic 2.4, Product.
11:02 If this helped, a like tells YouTube to show it to more first-cohort students,
11:06 and subscribing gets you the whole course, in order,
11:09 as it's built.
11:10 And the free AP Business vocab pack, every key term from all five units,
11:15 is waiting at mamako.app, link below.
11:18 The 2.3 practice video is ready next.
11:21 See you next time.

What Market Research Is

Market research is the deliberate collection of detailed information on markets, on products, and on how customers behave, so marketing decisions rest on evidence rather than instinct. What separates research from noticing is that research is planned before the question is answered, and designed so the answer could come back the wrong way.

Everything a researcher gathers lands in one of two families. Quantitative data comes as numbers that can be measured and analyzed. It settles questions of how many, how much, and how often. Seven of nine bikes sold is quantitative. Qualitative data is descriptive rather than numerical. It arrives as words and images, and it settles why and how. A parent explaining that the signature on the inspection tag is what convinced her is qualitative. Real decisions usually need both, because the count reports what happened and the sentences explain it.

Essential knowledge: 2.3.A.1, 2.3.A.2, 2.3.A.2.i, 2.3.A.2.ii

Desirable, Feasible, Viable

A business developing a new product uses research to check three things before it commits real resources to a launch, and the exam names all three.

A product is desirable when it creates value for customers by achieving problem solution fit, meaning it answers a problem those customers actually have. Parents have no way to separate a safe used bike from a dangerous one, and documented inspection answers precisely that.
A product is feasible when the business can produce and deliver it inside its real limits of resources, technology, expertise, and time. An inspection costing about an hour per bike plus a checklist and a zip tie fits inside a Saturday. Feasibility is about capacity, not demand.
A product is viable when a realistic route to profit exists for it in that market. If an inspection tag reliably earns twenty extra dollars for that hour of work, viability holds.

Research does not stop at launch. Businesses with existing products keep researching to track customers' changing needs, to find openings for innovation, and to build strategies that hold or grow market share. A ledger showing commuter demand climbing every August tells a business where to spend next month's rebuild hours.

Essential knowledge: 2.3.A.3, 2.3.A.3.i, 2.3.A.3.ii, 2.3.A.3.iii, 2.3.A.4

Secondary Source Research

Businesses learn about customers, competitors, and the wider landscape through two kinds of research. Secondary source research gathers quantitative or qualitative information that already exists, published by government agencies, commercial firms, or academic researchers. Primary source research generates new data directly from customers, and it comes next. Secondary work goes first for one blunt reason: reading data somebody else paid to collect is far cheaper than collecting your own.

Secondary sources can report the size of a market in dollars and in total customers, its trends, its segments, and the factors driving customer decisions, which together signal whether a product could be viable. They also surface the outside forces around an opportunity, the political, economic, social, technological, environmental, and legal factors from Unit 1, such as a city announcing new bike lanes or a district cutting bus routes.

Interpreting rival data is part of the job. Sweeping sixty local listings for asking price, days listed, and documented service history is secondary research done by hand, and it maps the competitive landscape in one evening: untagged commuter bikes cluster near seventy five dollars, and not one listing documents an inspection. That sizes the opportunity, and it still cannot prove what a tag is worth.

Essential knowledge: 2.3.B.1, 2.3.B.2, 2.3.B.3

Testing a Hypothesis With Primary Research

A business hypothesis states an assumption about a customer, a product, or a market in a form that evidence can support or knock down. An identical bike carrying a documented inspection tag sells for twenty dollars more, and faster, is a hypothesis. It names the variable, the effect, and the size of the effect.

Hypothesis testing then picks a method from the primary research menu, and the method follows from the kind of data the hypothesis needs.

Method	Use it when you need
[[survey\|Surveys]]	A large volume of quantitative data reflecting a whole population
[[focus-group\|Focus groups]] and [[interview\|interviews]]	Deep qualitative data from a few engaged customers, with follow up questions
[[experiment\|Experiments]] and observations	What customers do rather than what they say, controlled or natural
[[ab-testing\|A/B testing]]	Authentic responses to two viable alternatives in a real setting

Choosing a primary research method

A hypothesis with two alternatives and a price gap between them chooses its own method. Racking seventeen comparable commuter bikes across two Saturdays, nine tagged at ninety five dollars and eight untagged at seventy five, produced seven tagged sales and three untagged ones. That is roughly seventy eight percent against roughly thirty eight percent, so the tagged rack cleared at about double the rate while charging twenty dollars more. Twelve one on one parent interviews then supplied the why: most pointed at the signature line, and several said the tag replaced a shop inspection they would otherwise have paid for.

Essential knowledge: 2.3.C.1, 2.3.C.2, 2.3.C.3, 2.3.C.4, 2.3.C.5, 2.3.C.6, 2.3.C.7

Keeping the Data Honest

Testing can return skewed data, and there are two standard ways to hold that risk down. Name both. The first is sample design: a sample has to be big enough, and filled appropriately, to stand in for the population under study. Seventeen bikes plus twelve interviews makes a small sample, and everyone interviewed was already standing at a flea market stall, so the findings describe flea market parents and may say nothing about buyers who shop only online.

The second is neutral question wording, because phrasing steers answers. Asking whether a signed inspection tag makes you feel safer plants the conclusion inside the question. Asking what, if anything, influenced your decision today lets the customer supply it.

Essential knowledge: 2.3.C.8, 2.3.C.9

Showing the Findings

Research ends in communication. A data visualization turns rows of data into a pattern, trend, or insight a decision maker can absorb quickly, and the rule for choosing one is simple: pick the chart that shows the specific relationship you mean to communicate, not the chart that looks impressive.

Chart	Shows	Example
Bar chart	Comparisons between individual data points	Percent sold, tagged rack against untagged
Stacked bar chart	A total broken into its subcategories	Monthly sales split across three price tiers
Line graph	A trend over time	Customers per month climbing toward August
Pie chart	Part to whole relationships	Share of a market held by each seller

Four standard chart types

The most useful chart is often the one plotting two measures against each other. Turning sixty swept listings into sixty dots, asking price on one axis and days listed on the other, exposes a dead zone. Undocumented bikes priced above about eighty five dollars sit for weeks, while cheaper ones clear steadily. Set the A/B result beside it and the insight becomes a pricing rule. This market refuses a high price without proof.

Essential knowledge: 2.3.D.1, 2.3.D.2, 2.3.D.3, 2.3.D.4, 2.3.D.5, 2.3.D.6

Worked examples

Reading an A/B test result

Convert raw A/B test counts into comparable rates and state what the test does and does not prove.

Across two Saturdays Theo racks seventeen comparable commuter bikes. Nine carry a signed inspection tag with a price of ninety five dollars. The other eight are untagged and priced at seventy five. By the close of the second Saturday, seven of the tagged bikes and three of the untagged bikes have sold. Convert the counts into rates and interpret the result against his hypothesis that a documented inspection sells a bike for twenty dollars more, and faster.

Tagged bikes racked: 9
Tagged bikes sold: 7
Untagged bikes racked: 8
Untagged bikes sold: 3
Tagged price: $95
Untagged price: $75

1. Explain why raw counts cannot be compared
Seven and three look comparable, but the two racks did not hold the same number of bikes. Nine and eight are different denominators, so the counts have to become rates before either group can be judged against the other.
2. Compute the sell through rate for the tagged rack
Seven sold out of nine racked is about seventy eight percent.
\frac{7}{9}\approx 0.78
3. Compute the sell through rate for the untagged rack
Three sold out of eight racked is about thirty eight percent.
\frac{3}{8}=0.375
4. Compare the two rates
Seventy eight percent against thirty eight percent is roughly double the sell through rate, and the tagged rack achieved it while charging twenty dollars more per bike.
\frac{0.78}{0.375}\approx 2.1
5. State the limits of what was measured
Seventeen bikes over two Saturdays at one market is a small sample drawn from one place. The evidence supports the hypothesis and does not settle it, and weather or a holiday crowd could bend a result this size.

Answer
78% tagged against 38% untagged. The tagged rack sold at about seventy eight percent against about thirty eight percent untagged, roughly double the rate at a twenty dollar higher price. The hypothesis is supported by a small sample.

Why it matters
An A/B test earns its authority from measuring behavior rather than opinion, and it loses that authority if the two groups are not otherwise comparable. Always convert to rates before comparing, and always name the sample limitation in the same breath as the result.

Revenue from each side of the test

Turn A/B test results into revenue so the marketing decision follows from money rather than from percentages.

Using the same two Saturday test, compute the revenue each rack produced and state which arrangement the business should adopt. Tagged bikes sold at ninety five dollars and untagged bikes at seventy five dollars.

Tagged bikes sold: 7
Tagged price: $95
Untagged bikes sold: 3
Untagged price: $75

1. Compute revenue from the tagged rack
Seven bikes at ninety five dollars each produce six hundred sixty five dollars.
7\times\$95=\$665
2. Compute revenue from the untagged rack
Three bikes at seventy five dollars each produce two hundred twenty five dollars.
3\times\$75=\$225
3. Find the gap
Six hundred sixty five minus two hundred twenty five is four hundred forty dollars, over the same two Saturdays, from one extra hour of inspection work per bike.
\$665-\$225=\$440
4. Convert the result into a decision
The higher price did not suppress sales, so the tagged arrangement wins on rate and on revenue at the same time. Tag every bike going forward and keep logging sales so the test never really stops.

Answer
$665 tagged against $225 untagged. The tagged rack produced six hundred sixty five dollars against two hundred twenty five dollars untagged, a gap of four hundred forty dollars.

Why it matters
Rates tell you what customers did; revenue tells you what it was worth. A research finding is only finished when it has been converted into the decision it implies, which here is a rule about every future rebuild rather than a fact about two Saturdays.

Key terms

8 common mistakes on 2.3

The wrong moves students actually make on these questions, why each one is wrong, and what to do instead. Part of the practice tier.

See what is included

Essential knowledge covered

2.3.A.1 · 2.3.A.2 · 2.3.A.2.i · 2.3.A.2.ii · 2.3.A.3 · 2.3.A.3.i · 2.3.A.3.ii · 2.3.A.3.iii · 2.3.A.4 · 2.3.B.1 · 2.3.B.2 · 2.3.B.3 · 2.3.C.1 · 2.3.C.2 · 2.3.C.3 · 2.3.C.4 · 2.3.C.5 · 2.3.C.6 · 2.3.C.7 · 2.3.C.8 · 2.3.C.9 · 2.3.D.1 · 2.3.D.2 · 2.3.D.3 · 2.3.D.4 · 2.3.D.5 · 2.3.D.6