Fundamentals of market response models
1. Fundamentals of market response models
Welcome! To my DataCamp course on Response Models for Marketing Research. My name is Kathrin Gruber - I am an Assistant Professor at the Department of Econometrics, at Erasmus University Rotterdam. In this course, you learn how to construct simple models of market response. The first part of this course starts with response models for sales – price relations and how to extend this relation to account for the effects of marketing activities and dynamics. The second part we head over to response models for customer purchase decisions and how to explain how customers react to marketing activities.2. Marketing mix
To grow your business, you need a plan about how to communicate and sell your product to the customers - a marketing plan. An effective marketing plan combines all the tools you are going to use to communicate the benefits of your product – like Advertising, Public Relation, Word-of-mouth or Price promotions. The key is crafting the right mix between these tools to achieve sales increases and market share goals.3. Market response models
Market response models are widely accepted statistical tools used to optimize advertising mix and promotion tactics. Response models use past data to leverage information on the right marketing mix. Those information is than adopted by marketing professionals to adjust product prices, and to optimize marketing tactics and strategies Response models also enable to predict how customers might respond in the future and therefore allow to test the effectiveness of alternative marketing plans and to plan future marketing activities.4. Retail sales
In the practical example, we are working with a data set named sales.data. The data contains weekly beer retail sales for a single store. In addition to sales - the data also provides information on prices and different advertising and promotion activities. Understanding your data is important to successfully build a meaningful response model. Therefore, we start with having a look at the data structure by using the function structure(). The dataset contains 124 observations and six variables of different data types: OBS indexes the observation week. The numeric variables SALES and PRICE, where SALES is referring to the number of units of beer sold and PRICE is referring to the average unit price within the respective week. DISPLAY, COUPON, and DISPLAYCOUPON are the advertising and promotion activities run by the company and are included as zero-one coded integer-valued variables.5. Understanding sales
To get a first impression about the overall performance in beer volume sales - calculating simple descriptive statistics like mean, minimum and maximum might be sufficient. This can easily be done by using the functions mean(), min() and max() on the SALES variable of the sales dataset. Per week, on average, about 119 units of beer are sold, but with a huge variation, as the minimum value is around 11 and the maximum value is around 1400.6. Let's practice!
Now it’s time to explore some data!Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.