Statistics for Sales and Marketing

Using a chi-square test of independence on a 523-person survey, this case links packaging uniqueness to purchase intent and guides decisions on whether to proceed with the new packaging.

Frame clear research questions using descriptive and inferential statistics to analyze sales and marketing data, then infer population insights from sample data to predict outcomes like customer satisfaction and preferences.

Operationalization turns abstract theories into measurable variables, enabling consistent data collection by defining how to quantify concepts like packaging uniqueness and sales performance via a survey.

Define the population as the set of objects relevant to analysis, and explain sampling from a subset of respondents or other units, emphasizing sample size and reliability.

Clarify the difference between a parameter and a statistic, showing how we estimate population parameters from sample data, using margin of error and statistical testing when a census is impractical.

Explore the four levels of measurement, nominal, ordinal, interval, and ratio, and learn how their differences guide selecting the right statistical tests, with real world marketing data examples.

Explore how to use contingency tables to display discrete or categorical data, convert counts to relative frequencies, and interpret cross-tabulations with pandas crosstab in a Jupyter notebook.

Learn how hypothesis testing uses samples to infer population parameters, test null hypotheses in A/B variants, and decide if results reflect chance or true effects using p values.

Define the null hypothesis as no effect and the alternative as the expected difference. Use data to test and decide between two-tailed or one-sided alternatives in marketing analytics.

Learn how test statistics quantify differences between sample data and the null hypothesis to assess significance, with chi-square, t, Mann-Whitney U, and Wilcoxon W. Discover how p values guide interpretation.

Examine the p value, the probability of observing data under the null hypothesis, and how small values support rejecting the null in hypothesis testing; explore significance levels.

Explore how a p value guides the rejection of the null hypothesis and how the significance level, alpha, sets the threshold for significance in sales and marketing.

Learn how degrees of freedom measure independent information used to estimate parameters, with examples showing how constraints reduce freedom and how higher DF improves precision alongside p-values.

Explore the chi-square test, comparing observed and expected frequencies to assess relationships between two categorical variables using a contingency table. Learn about null hypothesis, independence, and goodness-of-fit applications.

Use the chi-square test of independence on a contingency table to assess if packaging uniqueness influences purchase likelihood, concluding no significant association at alpha 0.05.

Assess whether packaging uniqueness influences propensity to purchase by applying the chi squared test of independence, confirming conditions, and interpreting results at alpha 0.05.

Learn to use price per volume to align premium brand positioning, applying a one-sample t test on 100 european SKUs to compare €6.43 with €6.21 at 0.05.

Explore mean and median as measures of central tendency that reveal a data set’s typical value, with practical calculations in Python and a downloadable notebook.

Learn how standard deviation measures the spread around the mean in Sophie's PPV data, using price ranges and Python calculations to interpret distribution.

Understand the normal (Gaussian) distribution, defined by mean and standard deviation, and how symmetry, tails, histograms, and normality testing guide hypothesis testing and t tests.

Explore histograms as graphical representations of data distribution, grouping observations into bins to reveal frequency. Identify a bell-shaped normal distribution around the mean to guide statistical method selection before tests.

Explore how q-q plots assess whether sample data align with a theoretical distribution such as the normal distribution. Plot theoretical versus sample quantiles to verify distributional assumptions for statistical analyses.

Learn to perform a one-sample t test, compare a sample mean to a hypothesized value, and interpret p-values and one-tailed versus two-tailed approaches as illustrated by Sophie's study.

Explore one-tailed versus two-tailed alternative hypotheses, their directionality, and how their critical regions determine whether we reject the null when assessing mean PPV.

Apply the one sample t test to Sophie’s data, compare the mean pv €6.43 to €6.21, interpret the 0.058 p-value, and conclude no significant difference at alpha 0.05.

Carmen tests a new in-store display vs the old setup using a two-sample t test at 0.05, finding p=0.04 and a significant increase in average sales volume.

Explain the independent variable as the manipulated predictor and the dependent variable as the measured outcome, illustrating cause-and-effect links with examples like sales volume and advertising expenditure.

Apply the Shapiro-Wilk test to assess normality in sales data, using p-values and an alpha of 0.05 to determine if old and new display data follow a normal distribution.

Levene's test assesses equality of variances between two groups, guiding whether a two-sample t-test is appropriate; the example concludes no evidence of unequal variances at 0.05, enabling standard testing.

Use the two-sample t-test to compare means of two independent groups, test null vs. alternative hypotheses, and interpret the p-value; applied to new vs old displays and sales volume.

Assesses assumptions for a two-sample t test, confirms normality and equal variances, and tests whether the new display raises mean sales; finds p = 0.04 and rejects the null.

Evaluate how a more generous return policy affects average order value using a paired samples t-test, revealing AOV rise from $105.45 to $110.81 with p=0.0063, guiding a broader rollout.

Explore the difference between independent and dependent samples, choose the correct tests (two-sample t-test vs paired t-test), and analyze before-and-after comparisons using real-world examples.

Apply the paired samples t test to compare means of related data using pairwise differences, verify assumptions (dependence, random sampling, normality), and interpret p-values to assess significance.

Perform a paired samples t test to compare before and after order values, verify assumptions, and interpret a p-value under 0.05 showing a $5.36 AOV increase (105.45 to 110.81).

Evaluate a new sales incentive with a randomized experiment, showing higher four-week sales in the experimental group and a significant Mann-Whitney U test (p=0.03989, alpha=0.05) for rollout.

Explore parametric and nonparametric tests, their distribution assumptions, data types, and when each offers greater power or robustness in sales and marketing analyses.

Learn how to perform the Mann-Whitney U test, verify assumptions, set hypotheses, and interpret results showing the incentive increased sales from 23.0 to 26.5 units.

Examine a Wilcoxon signed rank test on 30 consumers at 0.05 to show no difference in purchase intent between two package designs, guiding an objective choice.

Apply Wilcoxon signed rank test to paired data and verify assumptions in Python; interpret no median difference between design A and design B at 0.05, note anova and repeated measures.

Examine a case study using the chi-square goodness of fit test to compare observed store footfall with an expected 71 per day and inform weekend staffing adjustments.

Apply the chi-square goodness-of-fit test to compare observed categorical frequencies with an expected distribution, checking hypotheses and p-values at a 0.05 significance level using real-world footfall data.

Demonstrate a chi-square goodness-of-fit test in Python, verify categorical data conditions, and interpret results to conclude footfall consistency at 0.05 while considering alpha adjustments for marketing insights.

Review the essential concepts and techniques of hypothesis testing to empower data-driven decisions in sales and marketing. Recognize that statistics complement critical thinking and guide business strategies amid ongoing analysis.