WEBVTT NOTE IDER in-house training materials. Captions follow measured phrase audio timings. Hypothetical worked cases; figures are instructional examples. 00:00:00.000 --> 00:00:07.512 A pricing study often begins with the question, what will customers pay? For a commercial decision, that is 00:00:07.512 --> 00:00:07.930 incomplete. 00:00:08.050 --> 00:00:14.595 We need to know how a feasible price changes purchases, costs and the contribution generated from 00:00:14.595 --> 00:00:16.641 the customers we can reach. 00:00:16.761 --> 00:00:23.123 This clinic uses a hypothetical experiment with two prices and the same product. Every number is 00:00:23.123 --> 00:00:26.304 illustrative, not a claim about any current market. 00:00:26.424 --> 00:00:33.878 You will learn why a clear reduction in demand can coexist with uncertain evidence about profit. Keep a 00:00:33.878 --> 00:00:37.605 calculator or spreadsheet available and work through both pauses. 00:00:37.725 --> 00:00:44.301 Our objective is a pricing recommendation that explains its estimand, its experimental design and its 00:00:44.301 --> 00:00:48.685 limits, so another practitioner can reproduce and challenge the reasoning. 00:00:49.355 --> 00:00:55.301 Choose the quantity you want to estimate before choosing a questionnaire. Here it is average 00:00:55.301 --> 00:01:00.059 contribution per eligible offer, including zero for customers who do not purchase. 00:01:00.179 --> 00:01:07.126 That denominator matters: looking only at buyers ignores the customers a higher price drives away. 00:01:07.126 --> 00:01:12.683 Define eligibility, the product, delivery terms and the time allowed to purchase. 00:01:12.803 --> 00:01:19.494 Contribution is realised revenue less costs that change because the purchase occurs, with refunds and 00:01:19.494 --> 00:01:21.278 payment fees treated consistently. 00:01:21.398 --> 00:01:28.297 If each customer can buy multiple units, measure the full customer outcome rather than forcing the data 00:01:28.297 --> 00:01:29.920 into a one-purchase model. 00:01:30.040 --> 00:01:36.887 State whether acquisition costs differ by price and whether they belong in the decision. An explicit 00:01:36.887 --> 00:01:42.021 outcome definition prevents attractive but irrelevant statistics from replacing the commercial objective. 00:01:42.691 --> 00:01:49.027 Before randomising prices, investigate how customers make the decision. Ask them to describe the most 00:01:49.027 --> 00:01:54.940 recent comparable purchase, who approved it, which alternative they considered and what delayed payment. 00:01:55.060 --> 00:02:01.631 Concrete episodes reveal constraints more reliably than asking whether a product sounds useful. 00:02:01.751 --> 00:02:08.693 A firm may value the service but lack procurement approval; a household may prefer it but have cash only 00:02:08.693 --> 00:02:13.442 after payday. These mechanisms guide the offer design and the timing of observation. 00:02:13.562 --> 00:02:21.072 Interview findings also suggest prespecified segments, such as existing users versus new customers. They do 00:02:21.072 --> 00:02:26.078 not establish each segment's population size or causal price response. 00:02:26.198 --> 00:02:32.370 Use qualitative evidence to generate and interpret hypotheses, while preserving a clear distinction 00:02:32.370 --> 00:02:35.219 between explanation and measurement of effect. 00:02:35.889 --> 00:02:42.545 A willingness-to-pay question can help explore a plausible price range, but the answer carries no 00:02:42.545 --> 00:02:43.877 necessary financial consequence. 00:02:43.997 --> 00:02:50.234 Customers may please the interviewer, anchor on the first suggested number or ignore budget constraints. 00:02:50.234 --> 00:02:53.145 An actual offer creates a different decision. 00:02:53.265 --> 00:03:00.102 Make the product, delivery conditions, payment obligations and any refund policy clear, and follow 00:03:00.102 --> 00:03:07.427 applicable ethical and commercial requirements. Record the offer even when the customer declines, so the 00:03:07.427 --> 00:03:08.892 denominator remains complete. 00:03:09.012 --> 00:03:17.266 A purchase still does not establish long-term value: discounts, novelty or temporary urgency may drive one 00:03:17.266 --> 00:03:24.488 transaction. Where repeat business matters, define a follow-up window and track retention and returns. 00:03:24.608 --> 00:03:31.357 The research design should match the decision horizon, rather than treating initial conversion as lifetime 00:03:31.357 --> 00:03:31.807 demand. 00:03:32.477 --> 00:03:40.136 Suppose one thousand eligible customers are randomly assigned to two prices, with five hundred in each arm. 00:03:40.136 --> 00:03:44.191 Keep product quality, marketing copy and fulfilment conditions comparable. 00:03:44.311 --> 00:03:51.604 Save the allocation procedure and assigned price before outcomes arrive. If one customer visits 00:03:51.604 --> 00:03:58.375 repeatedly, preserve that customer's assignment; treating each visit as independent can mix exposure 00:03:58.375 --> 00:03:59.938 and understate uncertainty. 00:04:00.058 --> 00:04:06.737 If customers share prices or purchase for each other, consider a suitable group assignment and analyse at 00:04:06.737 --> 00:04:07.523 that level. 00:04:07.643 --> 00:04:13.639 Random assignment supports causal interpretation because the offer groups are comparable in 00:04:13.639 --> 00:04:19.635 expectation, not because every characteristic will match exactly. Monitor missing records, price 00:04:19.635 --> 00:04:21.134 overrides and stockouts. 00:04:21.254 --> 00:04:27.235 Report assignment-based results so excluding inconvenient customers after randomisation does not 00:04:27.235 --> 00:04:31.041 undo the protection provided by the design. 00:04:31.711 --> 00:04:38.599 At fifty cedis, two hundred of five hundred customers purchase, giving forty percent conversion. At sixty 00:04:38.599 --> 00:04:42.474 cedis, one hundred and fifty purchase, giving thirty percent. 00:04:42.594 --> 00:04:49.678 With a variable cost of thirty cedis, the lower price contributes twenty per purchase, while the higher 00:04:49.678 --> 00:04:50.512 contributes thirty. 00:04:50.632 --> 00:04:57.155 Multiply each margin by its conversion rate: average contribution per offer is eight cedis at fifty and 00:04:57.155 --> 00:04:58.306 nine at sixty. 00:04:58.426 --> 00:05:05.110 The higher price has fewer buyers but a larger estimated contribution per reachable customer. Across 00:05:05.110 --> 00:05:11.348 five hundred offers, those contributions total four thousand and four thousand five hundred cedis. 00:05:11.468 --> 00:05:18.595 These are sample estimates before any differing fixed or acquisition costs. We have not yet established that 00:05:18.595 --> 00:05:22.788 the higher price produces a reliable improvement beyond this experiment. 00:05:23.458 --> 00:05:31.075 Pause the video and calculate two outcomes for each price. First divide total sales revenue by the five 00:05:31.075 --> 00:05:36.577 hundred eligible offers. Then calculate contribution per offer using the thirty-cedi variable cost. 00:05:36.697 --> 00:05:41.666 Identify which price wins under each measure and write a sentence explaining why. 00:05:41.786 --> 00:05:49.376 Do not compare the thirty-cedi margin at the higher price with the twenty-cedi margin alone; customers who 00:05:49.376 --> 00:05:52.502 decline still consume an opportunity to sell. 00:05:52.622 --> 00:05:59.668 Equally, do not choose the lower price solely because it converts more people. The intended decision outcome 00:05:59.668 --> 00:06:00.911 determines the ranking. 00:06:01.031 --> 00:06:08.995 Assume one unit per buyer, no returns and equal offer costs for this exercise. Keep those assumptions beside 00:06:08.995 --> 00:06:13.419 your calculation, because changing them could alter the commercial recommendation. 00:06:14.089 --> 00:06:20.840 The fifty-cedi price generates ten thousand cedis revenue from five hundred offers, or twenty per offer. 00:06:20.840 --> 00:06:26.748 The sixty-cedi price generates nine thousand, or eighteen per offer. Revenue therefore favours the 00:06:26.748 --> 00:06:27.592 lower price. 00:06:27.712 --> 00:06:34.353 Contribution favours the higher price: eight versus nine cedis per offer, a difference of one cedi. 00:06:34.473 --> 00:06:41.284 Lower revenue can coexist with higher contribution because the business avoids variable costs on the 00:06:41.284 --> 00:06:42.646 purchases it loses. 00:06:42.766 --> 00:06:48.760 This distinction is essential when managers reward sales volume while the organisation needs cash 00:06:48.760 --> 00:06:53.470 generation. However, a contribution estimate is not a complete policy decision. 00:06:53.590 --> 00:06:58.981 We must examine sampling uncertainty, repeat behaviour, operational effects and any strategic 00:06:58.981 --> 00:07:05.270 constraints. The result tells us what happened in this sample under these stated conditions. 00:07:05.940 --> 00:07:11.712 Elasticity describes proportional demand response relative to proportional price change. Using the 00:07:11.712 --> 00:07:19.890 midpoint convention, divide the quantity change of minus fifty by the average quantity of one hundred and 00:07:19.890 --> 00:07:20.371 seventy-five. 00:07:20.491 --> 00:07:28.264 That is minus twenty-eight point five seven percent. Divide the price increase of ten by the average price 00:07:28.264 --> 00:07:31.718 of fifty-five, giving eighteen point one eight percent. 00:07:31.838 --> 00:07:38.567 Their ratio is approximately minus one point five seven. The negative sign indicates declining demand as 00:07:38.567 --> 00:07:39.408 price rises. 00:07:39.528 --> 00:07:48.283 This is an arc across the tested prices, not proof that the same elasticity applies at every price. It 00:07:48.283 --> 00:07:52.891 also assumes comparable numbers of offers; otherwise use purchase rates. 00:07:53.011 --> 00:08:00.188 Do not extrapolate a straight demand curve far beyond the experiment without evidence about customer 00:08:00.188 --> 00:08:02.101 substitution and market conditions. 00:08:02.771 --> 00:08:09.624 We first quantify uncertainty about conversion. The higher-price purchase rate minus the lower-price rate 00:08:09.624 --> 00:08:12.071 is minus ten percentage points. 00:08:12.191 --> 00:08:19.091 For two independent samples of five hundred, add the estimated binomial variances: thirty percent times 00:08:19.091 --> 00:08:25.531 seventy percent over five hundred, plus forty percent times sixty percent over five hundred. 00:08:25.651 --> 00:08:32.628 The square root is three percentage points. Multiplying by one point nine six gives an approximate 00:08:32.628 --> 00:08:36.553 ninety-five percent margin of five point eight eight points. 00:08:36.673 --> 00:08:43.076 The interval excludes zero, so the data provide evidence of lower conversion at the higher price under 00:08:43.076 --> 00:08:46.843 this design. This does not yet establish a contribution gain. 00:08:46.963 --> 00:08:53.344 That outcome weights purchases by different margins and has a different variance, which we must calculate 00:08:53.344 --> 00:08:53.743 separately. 00:08:54.413 --> 00:09:01.419 Planning sample size around conversion can leave a profitability question unresolved. Decide the smallest 00:09:01.419 --> 00:09:07.925 contribution improvement that would justify changing prices, then estimate the variability of contribution 00:09:07.925 --> 00:09:08.926 per offer. 00:09:09.046 --> 00:09:15.618 In our simplified binary-purchase case, the lower-price outcome is either twenty cedis or zero, 00:09:15.618 --> 00:09:22.659 while the higher-price outcome is thirty or zero. Their variability differs even with similar conversion 00:09:22.659 --> 00:09:23.129 uncertainty. 00:09:23.249 --> 00:09:29.603 Use pilot data or conservative assumptions to calculate power for the chosen outcome, including 00:09:29.603 --> 00:09:36.411 clustering when assignment occurs by group. Set an observation horizon and analysis plan before looking 00:09:36.411 --> 00:09:37.773 repeatedly at significance. 00:09:37.893 --> 00:09:44.360 Repeated informal checking can create misleading confidence. The practical question is whether the 00:09:44.360 --> 00:09:50.828 proposed test can distinguish a decision-relevant improvement within the available customer flow and 00:09:50.828 --> 00:09:51.325 budget. 00:09:51.995 --> 00:09:59.397 A primary outcome does not remove the need for guardrails. A higher price could improve immediate 00:09:59.397 --> 00:10:05.410 contribution while increasing complaints, refunds or churn. Define those measures and acceptable limits 00:10:05.410 --> 00:10:06.798 before the test. 00:10:06.918 --> 00:10:14.023 Segment analysis should follow a mechanism identified in advance, not a search through dozens of groups 00:10:14.023 --> 00:10:15.800 until one looks favourable. 00:10:15.920 --> 00:10:23.067 Report uncertainty for each subgroup and consider the extra risk of false discoveries. Check whether 00:10:23.067 --> 00:10:27.832 stockouts, delivery delays or competing promotions affected one arm differently. 00:10:27.952 --> 00:10:33.611 If the market changes during the experiment, preserve concurrent comparisons rather than comparing one 00:10:33.611 --> 00:10:36.845 month's low price with another month's high price. 00:10:36.965 --> 00:10:43.681 Finally, distinguish a causal effect in the tested customer population from a claim about every channel, 00:10:43.681 --> 00:10:44.941 season or geography. 00:10:45.611 --> 00:10:52.209 Pause and evaluate the contribution result directly. In this simplified experiment, the estimated variance 00:10:52.209 --> 00:10:58.335 of the difference in mean contribution is zero point five seven cedis squared. 00:10:58.455 --> 00:11:04.758 Take its square root to obtain the standard error. Multiply by one point nine six and place that margin 00:11:04.758 --> 00:11:07.743 around the estimated gain of one cedi per offer. 00:11:07.863 --> 00:11:14.466 Does the interval exclude zero? Then write a recommendation that acknowledges both the clear 00:11:14.466 --> 00:11:17.767 conversion decline and the uncertainty about contribution. 00:11:17.887 --> 00:11:24.972 Assume independent customers, a single prespecified comparison, equal acquisition costs and complete 00:11:24.972 --> 00:11:26.153 purchase records. 00:11:26.273 --> 00:11:33.232 Do not treat a statistically clear demand reduction as proof of commercial success or failure; the 00:11:33.232 --> 00:11:37.581 contribution question has its own outcome definition and uncertainty calculation. 00:11:38.251 --> 00:11:45.217 The square root of zero point five seven is approximately zero point seven five five cedis. 00:11:45.337 --> 00:11:52.189 The ninety-five percent margin is about one point four eight, giving an interval from minus zero point four 00:11:52.189 --> 00:11:55.995 eight to plus two point four eight cedis per offer. 00:11:56.115 --> 00:12:02.004 It includes zero, so this experiment does not establish a positive contribution effect at that 00:12:02.004 --> 00:12:06.715 confidence level. It also does not establish that the prices are equivalent. 00:12:06.835 --> 00:12:14.918 The data remain compatible with a modest loss or a meaningful gain. A defensible response is to assess 00:12:14.918 --> 00:12:20.755 whether further testing has value, considering customer flow, switching costs and risk tolerance. 00:12:20.875 --> 00:12:27.991 If management acts now, describe the decision as one made under uncertainty rather than declaring a proven 00:12:27.991 --> 00:12:28.828 profit improvement. 00:12:29.498 --> 00:12:36.018 Prepare a pricing memorandum that begins with the decision, the population and the primary outcome. Show 00:12:36.018 --> 00:12:41.723 conversion, revenue and contribution together so the reader can see why their rankings differ. 00:12:41.843 --> 00:12:48.432 Include uncertainty around the contribution effect and disclose the assumptions behind it. Then address 00:12:48.432 --> 00:12:55.021 repeat use, customer fairness, channel agreements, capacity and any cost that changes at scale. 00:12:55.141 --> 00:13:02.323 Name the person responsible for implementation and define a review date with a reversal trigger. If 00:13:02.323 --> 00:13:08.159 prices are personalised or regulated, obtain the relevant commercial and legal review before 00:13:08.159 --> 00:13:08.608 deployment. 00:13:08.728 --> 00:13:15.809 A well-designed experiment answers a defined causal question; it does not automatically resolve every 00:13:15.809 --> 00:13:16.820 policy constraint. 00:13:16.940 --> 00:13:22.640 Preserve the allocation data and reproducible calculations so later analysts can distinguish market 00:13:22.640 --> 00:13:24.394 change from measurement error. 00:13:25.064 --> 00:13:31.887 Choose an existing pricing report and check its denominator. Does it describe buyers only, visits, 00:13:31.887 --> 00:13:35.072 unique eligible customers, or all assigned offers? 00:13:35.192 --> 00:13:42.350 Recompute contribution using a consistent unit and include customers who bought nothing. Check whether 00:13:42.350 --> 00:13:48.485 the uncertainty calculation respects repeat customers, grouped assignment and the actual outcome 00:13:48.485 --> 00:13:48.996 distribution. 00:13:49.116 --> 00:13:56.436 Then propose one feasible experiment with a primary metric, minimum worthwhile effect, sample plan and 00:13:56.436 --> 00:14:03.269 follow-up window. Junior practitioners should be able to rebuild each calculation from raw records. 00:14:03.389 --> 00:14:10.033 Senior practitioners should be able to explain why the design answers the commercial question and what 00:14:10.033 --> 00:14:14.186 uncertainty remains. Complete the scored questions and applied tasks below. 00:14:14.306 --> 00:14:20.601 Your aim is to make pricing evidence actionable without confusing a favourable point estimate with a 00:14:20.601 --> 00:14:21.388 reliable improvement.