Cover Page

Table of Contents

Part I: Using Excel to Summarize Marketing Data

Chapter 1: Slicing and Dicing Marketing Data with PivotTables

Analyzing Sales at True Colors Hardware

Analyzing Sales at La Petit Bakery

Analyzing How Demographics Affect Sales

Pulling Data from a PivotTable with the GETPIVOTDATA Function



Chapter 2: Using Excel Charts to Summarize Marketing Data

Combination Charts

Using a PivotChart to Summarize Market Research Surveys

Ensuring Charts Update Automatically When New Data is Added

Making Chart Labels Dynamic

Summarizing Monthly Sales-Force Rankings

Using Check Boxes to Control Data in a Chart

Using Sparklines to Summarize Multiple Data Series

Using GETPIVOTDATA to Create the End-of-Week Sales Report



Chapter 3: Using Excel Functions to Summarize Marketing Data

Summarizing Data with a Histogram

Using Statistical Functions to Summarize Marketing Data



Part II: Pricing

Chapter 4: Estimating Demand Curves and Using Solver to Optimize Price

Estimating Linear and Power Demand Curves

Using the Excel Solver to Optimize Price

Pricing Using Subjectively Estimated Demand Curves

Using SolverTable to Price Multiple Products



Chapter 5: Price Bundling

Why Bundle?

Using Evolutionary Solver to Find Optimal Bundle Prices



Chapter 6: Nonlinear Pricing

Demand Curves and Willingness to Pay

Profit Maximizing with Nonlinear Pricing Strategies



Chapter 7: Price Skimming and Sales

Dropping Prices Over Time

Why Have Sales?



Chapter 8: Revenue Management

Estimating Demand for the Bates Motel and Segmenting Customers

Handling Uncertainty

Markdown Pricing



Part III: Forecasting

Chapter 9: Simple Linear Regression and Correlation

Simple Linear Regression

Using Correlations to Summarize Linear Relationships



Chapter 10: Using Multiple Regression to Forecast Sales

Introducing Multiple Linear Regression

Running a Regression with the Data Analysis Add-In

Interpreting the Regression Output

Using Qualitative Independent Variables in Regression

Modeling Interactions and Nonlinearities

Testing Validity of Regression Assumptions


Validation of a Regression



Chapter 11: Forecasting in the Presence of Special Events

Building the Basic Model



Chapter 12: Modeling Trend and Seasonality

Using Moving Averages to Smooth Data and Eliminate Seasonality

An Additive Model with Trends and Seasonality

A Multiplicative Model with Trend and Seasonality



Chapter 13: Ratio to Moving Average Forecasting Method

Using the Ratio to Moving Average Method

Applying the Ratio to Moving Average Method to Monthly Data



Chapter 14: Winter's Method

Parameter Definitions for Winter's Method

Initializing Winter's Method

Estimating the Smoothing Constants

Forecasting Future Months

Mean Absolute Percentage Error (MAPE)



Chapter 15: Using Neural Networks to Forecast Sales

Regression and Neural Nets

Using Neural Networks

Using NeuralTools to Predict Sales

Using NeuralTools to Forecast Airline Miles



Part IV: What do Customers Want?

Chapter 16: Conjoint Analysis

Products, Attributes, and Levels

Full Profile Conjoint Analysis

Using Evolutionary Solver to Generate Product Profiles

Developing a Conjoint Simulator

Examining Other Forms of Conjoint Analysis



Chapter 17: Logistic Regression

Why Logistic Regression Is Necessary

Logistic Regression Model

Maximum Likelihood Estimate of Logistic Regression Model

Using StatTools to Estimate and Test Logistic Regression Hypotheses

Performing a Logistic Regression with Count Data



Chapter 18: Discrete Choice Analysis

Random Utility Theory

Discrete Choice Analysis of Chocolate Preferences

Incorporating Price and Brand Equity into Discrete Choice Analysis

Dynamic Discrete Choice

Independence of Irrelevant Alternatives (IIA) Assumption

Discrete Choice and Price Elasticity


Part V: Customer Value

Chapter 19: Calculating Lifetime Customer Value

Basic Customer Value Template

Measuring Sensitivity Analysis with Two-way Tables

An Explicit Formula for the Multiplier

Varying Margins

DIRECTV, Customer Value, and Friday Night Lights (FNL)

Estimating the Chance a Customer Is Still Active

Going Beyond the Basic Customer Lifetime Value Model


Chapter 20: Using Customer Value to Value a Business

A Primer on Valuation

Using Customer Value to Value a Business

Measuring Sensitivity Analysis with a One-way Table

Using Customer Value to Estimate a Firm's Market Value


Chapter 21: Customer Value, Monte Carlo Simulation, and Marketing Decision Making

A Markov Chain Model of Customer Value

Using Monte Carlo Simulation to Predict Success of a Marketing Initiative


Chapter 22: Allocating Marketing Resources between Customer Acquisition and Retention

Modeling the Relationship between Spending and Customer Acquisition and Retention

Basic Model for Optimizing Retention and Acquisition Spending

An Improvement in the Basic Model


Part VI: Market Segmentation

Chapter 23: Cluster Analysis

Clustering U.S. Cities

Using Conjoint Analysis to Segment a Market


Chapter 24: Collaborative Filtering

User-Based Collaborative Filtering

Item-Based Filtering

Comparing Item- and User-Based Collaborative Filtering

The Netflix Competition


Chapter 25: Using Classification Trees for Segmentation

Introducing Decision Trees

Constructing a Decision Tree

Pruning Trees and CART


Part VII: Forecasting New Product Sales

Chapter 26: Using S Curves to Forecast Sales of a New Product

Examining S Curves

Fitting the Pearl or Logistic Curve

Fitting an S Curve with Seasonality

Fitting the Gompertz Curve

Pearl Curve versus Gompertz Curve


Chapter 27: The Bass Diffusion Model

Introducing the Bass Model

Estimating the Bass Model

Using the Bass Model to Forecast New Product Sales

Deflating Intentions Data

Using the Bass Model to Simulate Sales of a New Product

Modifications of the Bass Model


Chapter 28: Using the Copernican Principle to Predict Duration of Future Sales

Using the Copernican Principle

Simulating Remaining Life of Product


Part VIII: Retailing

Chapter 29: Market Basket Analysis and Lift

Computing Lift for Two Products

Computing Three-Way Lifts

A Data Mining Legend Debunked!

Using Lift to Optimize Store Layout


Chapter 30: RFM Analysis and Optimizing Direct Mail Campaigns

RFM Analysis

An RFM Success Story

Using the Evolutionary Solver to Optimize a Direct Mail Campaign


Chapter 31: Using the SCAN*PRO Model and Its Variants

Introducing the SCAN*PRO Model

Modeling Sales of Snickers Bars

Forecasting Software Sales


Chapter 32: Allocating Retail Space and Sales Resources

Identifying the Sales to Marketing Effort Relationship

Modeling the Marketing Response to Sales Force Effort

Optimizing Allocation of Sales Effort

Using the Gompertz Curve to Allocate Supermarket Shelf Space


Chapter 33: Forecasting Sales from Few Data Points

Predicting Movie Revenues

Modifying the Model to Improve Forecast Accuracy

Using 3 Weeks of Revenue to Forecast Movie Revenues


Part IX: Advertising

Chapter 34: Measuring the Effectiveness of Advertising

The Adstock Model

Another Model for Estimating Ad Effectiveness

Optimizing Advertising: Pulsing versus Continuous Spending


Chapter 35: Media Selection Models

A Linear Media Allocation Model

Quantity Discounts

A Monte Carlo Media Allocation Simulation


Chapter 36: Pay per Click (PPC) Online Advertising

Defining Pay per Click Advertising

Profitability Model for PPC Advertising

Google AdWords Auction

Using Bid Simulator to Optimize Your Bid


Part X: Marketing Research Tools

Chapter 37: Principal Components Analysis (PCA)

Defining PCA

Linear Combinations, Variances, and Covariances

Diving into Principal Components Analysis

Other Applications of PCA


Chapter 38: Multidimensional Scaling (MDS)

Similarity Data

MDS Analysis of U.S. City Distances

MDS Analysis of Breakfast Foods

Finding a Consumer's Ideal Point


Chapter 39: Classification Algorithms: Naive Bayes Classifier and Discriminant Analysis

Conditional Probability

Bayes' Theorem

Naive Bayes Classifier

Linear Discriminant Analysis

Model Validation

The Surprising Virtues of Naive Bayes


Chapter 40: Analysis of Variance: One-way ANOVA

Testing Whether Group Means Are Different

Example of One-way ANOVA

The Role of Variance in ANOVA

Forecasting with One-way ANOVA



Chapter 41: Analysis of Variance: Two-way ANOVA

Introducing Two-way ANOVA

Two-way ANOVA without Replication

Two-way ANOVA with Replication


Part XI: Internet and Social Marketing

Chapter 42: Networks

Measuring the Importance of a Node

Measuring the Importance of a Link

Summarizing Network Structure

Random and Regular Networks

The Rich Get Richer

Klout Score


Chapter 43: The Mathematics Behind The Tipping Point

Network Contagion

A Bass Version of the Tipping Point


Chapter 44: Viral Marketing

Watts' Model

A More Complex Viral Marketing Model


Chapter 45: Text Mining

Text Mining Definitions

Giving Structure to Unstructured Text

Applying Text Mining in Real Life Scenarios



How This Book Is Organized

Who Should Read This Book

Tools You Need

What's on the Website



Part I

Using Excel to Summarize Marketing Data

Chapter 1: Slicing and Dicing Marketing Data with PivotTables

Chapter 2: Using Excel Charts to Summarize Marketing Data

Chapter 3: Using Excel Functions to Summarize Marketing Data

Chapter 1

Slicing and Dicing Marketing Data with PivotTables

In many marketing situations you need to analyze, or “slice and dice,” your data to gain important marketing insights. Excel PivotTables enable you to quickly summarize and describe your data in many different ways. In this chapter you learn how to use PivotTables to perform the following:

Analyzing Sales at True Colors Hardware

To start analyzing sales you first need some data to work with. The data worksheet from the PARETO.xlsx file (available for download on the companion website) contains sales data from two local hardware stores (uptown store owned by Billy Joel and downtown store owned by Petula Clark). Each store sells 10 types of tape, 10 types of adhesive, and 10 types of safety equipment. Figure 1.1 shows a sample of this data.

Figure 1-1: Hardware store data


Throughout this section you will learn to analyze this data using Excel PivotTables to answer the following questions:

Calculating the Percentage of Sales at Each Store

The first step in creating a PivotTable is ensuring you have headings in the first row of your data. Notice that Row 7 of the example data in the data worksheet has the headings Product, Month, Store, and Price. Because these are in place, you can begin creating your PivotTable. To do so, perform the following steps:

1. Place your cursor anywhere in the data cells on the data worksheet, and then click PivotTable in the Tables group on the Insert tab. Excel opens the Create PivotTable dialog box, as shown in Figure 1.2, and correctly guesses that the data is included in the range Y7:AB1333.

Figure 1-2: PivotTable Dialog Box


If you select Use an External Data Source here, you could also refer to a database as a source for a PivotTable. In Exercise 14 at the end of the chapter you can practice creating PivotTables from data in different worksheets or even different workbooks.

2. Click OK and you see the PivotTable Field List, as shown in Figure 1.3.

Figure 1-3: PivotTable Field List

3. Fill in the PivotTable Field List by dragging the PivotTable headings or fields into the boxes or zones. You can choose from the following four zones:

To see the field list, you need to be in a field in the PivotTable. If you do not see the field list, right-click any cell in the PivotTable, and select Show Field List.

Figure 1.4 shows the completed PivotTable Field List and the resulting PivotTable is shown in Figure 1.5 as well as on the FirstorePT worksheet.

Figure 1-4: Completed PivotTable Field List


Figure 1-5: Completed PivotTable


Figure 1.5 shows the downtown store sold $4,985.50 worth of goods, and the uptown store sold $4,606.50 of goods. The total sales are $9592.

If you want a percentage breakdown of the sales by store, you need to change the way Excel displays data in the Values zone. To do this, perform these steps:

1. Right-click in the summarized data in the FirstStorePT worksheet and select Value Field Settings.
2. Select Show Values As and click the drop-down arrow on the right side of the dialog box.
3. Select the % of Column Total option, as shown in Figure 1.6.

Figure 1-6: Obtaining percentage breakdown by Store


Figure 1.7 shows the resulting PivotTable with the new percentage breakdown by Store with 52 percent of the sales in the downtown store and 48 percent in the uptown store. You can also see this in the revenue by store worksheet of the PARETO.xlsx file.

Figure 1-7: Percentage breakdown by Store


If you want a PivotTable to incorporate a different set of data, then under Options, you can select Change Data Source and select the new source data. To have a PivotTable incorporate changes in the original source data, simply right-click and select Refresh. If you are going to add new data below the original data and you want the PivotTable to include the new data when you select Refresh, you should use the Excel Table feature discussed in Chapter 2, “Using Excel Charts to Summarize Marketing Data.”

Summarizing Revenue by Month

You can also use a PivotTable to break down the total revenue by month and calculate the percentage of sales that occur during each month. To accomplish this, perform the following steps:

1. Return to the data worksheet and bring up the PivotTable Field List by choosing Insert PivotTable.
2. Drag the Month field to the Row Labels zone and the Price field to the Values zone. This gives the total sales by month. Because you also want a percentage breakdown of sales by month, drag the Price field again to the Values zone.
3. As shown in Figure 1.8, right-click on the first column in the Values zone and choose Value Field Settings; then choose the % of Column Total option. You now see the percentage monthly breakdown of revenue.

Figure 1-8: Monthly percentage breakdown of Revenue

4. Double-click the Column headings and change them to Percentage of Sales by Month and Total Revenue.
5. Finally, double-click again the Total Revenue Column; select Number Format, and choose the Currency option so the revenue is formatted in dollars.

You can see that $845 worth of goods was sold in January and 8.81 percent of the sales were in January. Because the percentage of sales in each month is approximately 1/12 (8.33 percent), the stores exhibit little seasonality. Part III, “Forecasting Sales of Existing Products,” includes an extensive discussion of how to estimate seasonality and the importance of seasonality in marketing analytics.

Calculating Revenue for Each Product

Another important part of analyzing data includes determining the revenue generated by each product. To determine this for the example data, perform the following steps:

1. Return to the data worksheet and drag the Product field to the Row Labels zone and the Price field to the Values zone.
2. Double-click on the Price column, change the name of the Price column to Revenue, and then reformat the Revenue Column as Currency.
3. Click the drop-down arrow in cell A3 and select Sort A to Z so you can alphabetize the product list and obtain the PivotTable in the products worksheet, as shown in Figure 1.9.

Figure 1-9: Sales by Product


You can now see the revenue that each product generated individually. For example, Adhesive 1 generated $24 worth of revenue.

The Pareto 80–20 Principle

When slicing and dicing data you may encounter a situation in which you want to find which set of products generates a certain percentage of total sales. The well-known Pareto 80–20 Principle states that for most companies 20 percent of their products generate around 80 percent of their sales. Other examples of the Pareto Principle include the following:

To determine a percentage breakdown of sales by product, perform the following steps:

1. Begin with the PivotTable in the products worksheet and click the drop-down arrow in cell A3.
2. Select Value Filters; then choose Top 10…
3. Change the settings, as shown in Figure 1.10, to choose the products generating 80 percent of the revenue.

Figure 1-10: Using Value Filters to select products generating 80% of sales


The resulting PivotTable appears in the Top 80% worksheet (see Figure 1.11) and shows that the six products displayed in Figure 1.11 are the smallest set of products generating at least 80 percent of the revenue. Therefore only 20 percent of the products (6 out of 30) are needed to generate 80 percent of the sales.

Figure 1-11: 6 Products Generate 80% of Revenue


By clicking the funnel you may clear our filters, if desired.

The Report Filter and Slicers

One helpful tool for analyzing data is the Report Filter and the exciting Excel 2010 and 2013 Slicers Feature. Suppose you want to break down sales from the example data by month and store, but you feel showing the list of products in the Row or Column Labels zones would clutter the PivotTable. Instead, you can drag the Month field to the Row Labels zone, the Store field to the Column Labels zone, the Price field to the Value zone, and the Product field to the Report Filter zone. This yields the PivotTable in worksheet Report filter unfiltered, as shown in Figure 1.12.

Figure 1-12: PivotTable used to illustrate Slicers


By clicking the drop-down arrow in the Report Filter, you can display the total revenue by Store and Month for any subset of products. For example, if you select products Safety 1, Safety 7, and Adhesive 8, you can obtain the PivotTable in the Filtered with a slicer worksheet, as shown in Figure 1.13. You see here that during May, sales of these products downtown are $10.00 and uptown are $34.00.

Figure 1-13: PivotTable showing sales for Safety 1, Safety 7 and Adhesive 8


As you can see from Figure 1.13, it is difficult to know which products were used in the PivotTable calculations. The new Slicer feature in Excel 2010 and 2013 (see the Filtered with a slicer worksheet) remedies this problem. To use this tool perform the following steps:

1. Put your cursor in the PivotTable in the Filtered with a Slicer worksheet and select Slicer from the Insert tab.
2. Select Products from the dialog box that appears and you see a Slicer that enables you to select any subset of products (select a product and then hold down the Control Key to select another product) from a single column.
3. Click inside the Slicer and you will see Slicer Tools on the ribbon. After selecting the Buttons section from Slicer Tools change Columns to 5. Now the products show up in five columns (see Figure 1.14).

Figure 1-14: Slicer selection for sales of Safety 1, Safety 7 and Adhesive 8.


A Slicer provides sort of a “dashboard” to filter on subsets of items drawn from a PivotTable field(s). The Slicer in the Filtered with a slicer worksheet makes it obvious that the calculations refer to Safety 1, Safety 7, and Adhesive 8. If you hold down the Control key, you can easily resize a Slicer.

If you double-click in a cell in a PivotTable, Excel drills down to the source data used for that cell's calculations and places the source data in a separate sheet. For example, if in the Report filtered unfiltered worksheet you double-click in the January downtown cell, you can obtain the source data in the worksheet January downtown, as shown in Figure 1.15.

Figure 1-15: Drilling down on January Downtown Sales


You have learned how PivotTables can be used to slice and dice sales data. Judicious use of the Value Fields Settings capability is often the key to performing the needed calculations.

Analyzing Sales at La Petit Bakery

La Petit Bakery sells cakes, pies, cookies, coffee, and smoothies. The owner has hired you to analyze factors affecting sales of these products. With a PivotTable and all the analysis skills you now have, you can quickly describe the important factors affecting sales. This example paves the way for a more detailed analysis in Part III of this book.

The file BakeryData.xlsx contains the data for this example and the file LaPetitBakery.xlsx contains all the completed PivotTables. In the Bakerydata.xlsx workbook you can see the underlying daily sales data recorded for the years 2013–2015. Figure 1.16 shows a subset of this data.

Figure 1-16: Data for La Petit Bakery


A 1 in the weekday column indicates the day was Monday, a 2 indicates Tuesday, and so on. You can obtain these days of the week by entering the formula = WEEKDAY(G5,2) in cell F5 and copying this formula from F5 to the range F6:F1099. The second argument of 2 in the formula ensures that a Monday is recorded as 1, a Tuesday as 2, and so on. In cell E5 you can enter the formula =VLOOKUP(F5,lookday,2) to transform the 1 in the weekday column to the actual word Monday, the 2 to Tuesday, and so on. The second argument lookday in the formula refers to the cell range A6:B12.

To name this range lookday simply select the range and type lookday in the Name box (the box directly to the left of the Function Wizard) and press Enter. Naming a range ensures that Excel knows to use the range lookday in any function or formula containing lookday.

The VLOOKUP function finds the value in cell F5 (2) in the first column of the lookday range and replaces it with the value in the same row and second column of the lookday range (Tuesday.) Copying the formula =VLOOKUP(F5,lookday,2) from E5 to E6:E1099 gives you the day of the week for each observation. For example, on Friday, January 11, 2013, there was no promotion and 74 cakes, 50 pies, 645 cookies, 100 smoothies, and 490 cups of coffee were sold.

Now you will learn how to use PivotTables to summarize how La Petit Bakery's sales are affected by the following:

Summarizing the Effect of the Day of the Week on Bakery Sales

La Petit Bakery wants to know how sales of their products vary with the day of the week. This will help them better plan production of their products.

In the day of week worksheet you can create a PivotTable that summarizes the average daily number of each product sold on each day of the week (see Figure 1.17). To create this PivotTable, perform the following steps:

1. Drag the daywk field to the Row Labels zone and drag each product to the Values zone.
2. Double-click each product, and change the summary measure to Average. You'll see, for example, that an average of 96.5 cakes was sold on Sunday.

Figure 1-17: Daily breakdown of Product Sales


As the saying (originally attributed to Confucius) goes, “A picture is worth a thousand words.” If you click in a PivotTable and go up to the Options tab and select PivotChart, you can choose any of Excel's chart options to summarize the data (Chapter 2, “Using Excel Charts to Summarize Marketing Data,” discusses Excel charting further). Figure 1.17 (see the Daily Breakdown worksheet) shows the first Line option chart type. To change this, right-click any series in a chart. The example chart here shows that all products sell more on the weekend than during the week. In the lower left corner of the chart, you can filter to show data for any subset of weekdays you want.

Analyzing Product Seasonality

If product sales are approximately the same during each month, they do not exhibit seasonality. If, however, product sales are noticeably higher (or lower) than average during certain quarters, the product exhibits seasonality. From a marketing standpoint, you must determine the presence and magnitude of seasonality to more efficiently plan advertising, promotions, and manufacturing decisions and investments. Some real-life illustrations of seasonality include the following:

To determine if La Petit Bakery products exhibit seasonality, you can perform the following steps:

1. Begin with your cursor anywhere in the data in the BakeryData.xlsx workbook. From the Insert tab select PivotTable and the PivotTable Field List will appear. Drag the Date field to the Row Labels zone and as before, drag each product to the Values zone and again change the entries in the Values zone to average sales for each product.
2. At first you see sales for every day, but you actually want to group sales by month. To do this, put the cursor on any date, right-click, and choose Group.
3. To group the daily sales into monthly buckets, choose Months from the dialog box, as shown in Figure 1.18.

Figure 1-18: Grouping data by Month

4. Now select PivotChart from the Options tab. After selecting the first Line chart option you obtain the PivotChart and PivotTable, as shown in the monthly breakdown worksheet of the LaPetitBakery.xlsx file (see Figure 1.19).

Figure 1-19: Monthly breakdown of Bakery Sales


This chart makes it clear that smoothie sales spike upward in the summer, but sales of other products exhibit little seasonality. In Part III of this book you can find an extensive discussion of how to estimate seasonality.

Given the strong seasonality and corresponding uptick in smoothie sales, the bakery can probably “trim” advertising and promotions expenditures for smoothies between April and August. On the other hand, to match the increased demand for smoothies, the bakery may want to guarantee the availability and delivery of the ingredients needed for making its smoothies. Similarly, if the increased demand places stress on the bakery's capability to serve its customers, it may consider hiring extra workers during the summer months.

If you right-click any month and select Ungroup, you can undo the grouping and return to a display of the daily sales data.

Analyzing the Trend in Bakery Sales

The owners of La Petit Bakery want to know if sales are improving. Looking at a graph of each product's sales by month will not answer this question if seasonality is present. For example, has lower sales every January than the previous month due to Christmas. A better way to analyze this type of trend is to compute and chart average daily sales for each year. To perform this analysis, complete the following steps:

1. Put your cursor inside the data in the Data worksheet of the BakeryData.xlsx file, create a PivotTable, and drag each product to the Values zone and again change the method of summary from Sum to Average.
2. Then drag the Date field to the Row Labels zone, place the cursor on any date, and right-click Group and choose Years. You see a monthly summary of average daily sales for each Month and Year.
3. Drag the Date field away from the Row Labels zone and you are left with the summary of product sales by year, as shown in Figure 1.20 (see the work in worksheet by Year).

Figure 1-20: Summary of Sales by Year

4. As you did before, create a Line PivotChart. The chart shows sales for each product are trending upward, which is good news for the client.

The annual growth rates for products vary between 1.5 percent and 4.9 percent. Cake sales have grown at the fastest rate, but represent a small part of overall sales. Cookies and coffee sales have grown more slowly, but represent a much larger percentage of revenues.

Analyzing the Effect of Promotions on Sales

To get a quick idea of how promotions affect sales, you can determine average sales for each product on the days you have promotions and compare the average on days with promotions to the days without promotions. To perform these computations keep the same fields in the Value zone as before, and drag the Promotion field to the Row Labels zone. After creating a line PivotChart, you can obtain the results, as shown in Figure 1.21 (see the promotion worksheet).

Figure 1-21: Effect of Promotion on Sales


The chart makes it clear that sales are higher when you have a promotion. Before concluding, however, that promotions increase sales, you must “adjust” for other factors that might affect sales. For example, if all smoothie promotions occurred during summer days, seasonality would make the average sales of smoothies on promotions higher than days without promotions, even if promotions had no real effect on sales. Additional considerations for using promotions include costs; if the costs of the promotions outweigh the benefits, then the promotion should not be undertaken. The marketing analyst must be careful in computing the benefits of a promotion. If the promotion yields new customers, then the long-run value of the new customers (see Part V) should be included in the benefit of the promotion. In Parts VIII and IX of this book you can learn how to perform more rigorous analysis to determine how marketing decisions such as promotions, price changes, and advertising influence product sales.

Analyzing How Demographics Affect Sales

Before the marketing analyst recommends where to advertise a product (see Part IX), she needs to understand the type of person who is likely to purchase the product. For example, a heavy metal fan magazine is unlikely to appeal to retirees, so advertising this product on a television show that appeals to retirees (such as Golden Girls) would be an inefficient use of the advertising budget. In this section you will learn how to use PivotTables to describe the demographic of people who purchase a product.

Take a look at the data worksheet in the espn.xlsx file. This has demographic information on a randomly chosen sample of 1,024 subscribers to ESPN: The Magazine. Figure 1.22 shows a sample of this data. For example, the first listed subscriber is a 72-year-old male living in a rural location with an annual family income of $72,000.

Figure 1-22: Demographic data for ESPN: The Magazine


Analyzing the Age of Subscribers

One of the most useful pieces of demographic information is age. To describe the age of subscribers, you can perform the following steps:

1. Create a PivotTable by dragging the Age field to the Row Labels zone and the Age field to the Values zone.
2. Unfortunately, Excel assumes that you want to calculate the Sum of Ages. Double-click the Sum of Ages heading, and change this to Count of Age.
3. Use Value Field settings with the % of Column Total setting to show a percentage breakdown by age.
4. Finally, right-click on any listed age in the PivotTable and select Group. This enables you to group ages in 10-year increments. You can also use the PivotChart feature using the first Column chart option to create a column chart (see Figure 1.23 and the age worksheet) to summarize the age distribution of subscribers.

Figure 1-23: Age distribution of subscribers


You find that most of the magazine's subscribers are in the 18–37 age group. This knowledge can help ESPN find TV shows to advertise that target the right age demographic.

Analyzing the Gender of Subscribers

You can also analyze the gender demographics of ESPN: The Magazine subscribers. This will help the analyst to efficiently spend available ad dollars. After all, if all subscribers are male, you probably do not want to place an ad on Project Runway.

1. In the data worksheet, drag the Gender field to the Column Labels zone and the Gender field to the Values zone.
2. Right-click the data and use Value Field Settings to change the calculations to Show Value As % of Row Total; this enables you to obtain the PivotTable shown in the gender worksheet (see Figure 1.24.)

Figure 1-24: Gender breakdown of subscribers


You find that approximately 80 percent of subscribers are men, so ESPN may not want to advertise on The View!

Describing the Income Distribution of Subscribers

In the Income worksheet (see Figure 1.25) you see a breakdown of the percentage of subscribers in each income group. This can be determined by performing the following steps:

1. In the data worksheet drag the Income field to the Row Labels zone and the Income field to the Values zone.
2. Change the Income field in the Values zone to Count of Income (if it isn't already there) and group incomes in $25,000 increments.
3. Finally, use Value Field Settings → Show Value As → % of Column Total to display the percentage of subscribers in each income bracket.

Figure 1-25: Income distribution of subscribers


You see that a majority of subscribers are in the $54,000–$103,000 range. In addition, more than 85 percent of ESPN: The Magazine subscribers have income levels well above the national median household income, making them an attractive audience for ESPN's additional marketing efforts.

Describing Subscriber Location

Next you will determine the breakdown of ESPN: The Magazine subscribers between suburbs, urban, and rural areas. This will help the analyst recommend the TV stations where ads should be placed.

1. Put your cursor inside the data from the data worksheet and drag the Location field to the Column Labels zone and Value zone.
2. Apply Value Field Settings and choose Show Value As → % of Row Total to obtain the PivotTable, as shown in the Location worksheet and Figure 1.26. You find that 46 percent of subscribers live in the suburbs; 40 percent live in urban areas and 15 percent live in rural areas.

Figure 1-26: Breakdown of Subscriber Location


Constructing a Crosstabs Analysis of Age and Income

Often marketers break down customer demographics simultaneously on two attributes. Such an analysis is called a crosstabs analysis. In the data worksheet you can perform a crosstabs analysis on Age and Income. To do so, perform the following steps:

1. Begin by dragging the Age field to the Column Labels zone and the Income field to the Row Labels and Value Labels zones.
2. Next group ages in 10-year increments and income in $25,000 increments.
3. Finally, use Value Field Settings to change the method of calculation to Show Value As → % of Row Total. The Income and Age worksheet (shown in Figure 1.27) shows the resulting PivotTable, which indicates that 28.13 percent of subscribers in the $54,000 to $78,000 bracket are in the 28–37 age group.

Figure 1-27: Crosstabs Analysis of subscribers


Crosstabs analyses enable companies to identify specific combinations of customer demographics that can be used for a more precise allocation of their marketing investments, such as advertising and promotions expenditures. Crosstabs analyses are also useful to determine where firms should not make investments. For instance, there are hardly any subscribers to ESPN: The Magazine that are 78 or older, or with household incomes above $229,000, so placing ads on TV shows that are heavily watched by wealthy retiree is not recommended.

Pulling Data from a PivotTable with the GETPIVOTDATA Function

Often a marketing analyst wants to pull data from a PivotTable to use as source information for a chart or other analyses. You can achieve this by using the GETPIVOTDATA function. To illustrate the use of the GETPIVOTDATA function, take a second look at the True Colors hardware store data in the products worksheet from the PARETO.xlsx file.

1. With the cursor in any blank cell, type an = sign and point to the cell (B12) containing Adhesive 8 sales.
2. You will now see in the formerly blank cell the formula =GETPIVOTDATA("Price",$A$3,"Product","Adhesive 8"). Check your result against cell E10. This pulls the sales of Adhesive 8 ($42.00) from the PivotTable into cell E10.

This formula always picks out Adhesive 8 sales from the Price field in the PivotTable whose upper left corner is cell A3. Even if the set of products sold changes, this function still pulls Adhesive 8 sales.

In Excel 2010 or 2013 if you want to be able to click in a PivotTable and return a cell reference rather than a GETPIVOTTABLE function, simply choose File → Options, and from the Formulas dialog box uncheck the Use GetPivotData functions for the PivotTable References option (see Figure 1.28).

Figure 1-28: Example of GETPIVOTDATA function


This function is widely used in the corporate world and people who do not know it are at a severe disadvantage in making best use of PivotTables. Chapter 2 covers this topic in greater detail.


In this chapter you learned the following:


1. The Makeup2007.xlsx file (available for download on the companion website) gives sales data for a small makeup company. Each row lists the salesperson, product sold, location of the sale, units sold, and revenue generated. Use this file to perform the following exercises:
a. Summarize the total revenue and units sold by each person of each product.
b. Summarize the percentage of each person's sales that came from each location. Create a PivotChart to summarize this information.
c. Summarize each girl's sales by location and use the Report Filter to change the calculations to include any subset of products.
2. The Station.xlsx file contains data for each family including the family size (large or small), income (high or low), and whether the family bought a station wagon. Use this file to perform the following exercises:
a. Does it appear that family size or income is a more important determinant of station wagon purchases?
b. Compute the percentage of station wagon purchasers that are high or low income.
c. Compute the fraction of station wagon purchasers that come from each of the following four categories: High Income Large Family, High Income Small Family, Low Income Large Family, and Low Income Small Family.
3. The cranberrydata.xlsx file contains data for each quarter in the years 2006–2011 detailing the pounds of cranberries sold by a small grocery store. You also see the store's price and the price charged by the major competitor. Use this file to perform the following exercises:
a. Ignoring price, create a chart that displays the seasonality of cranberry sales.
b. Ignoring price, create a chart that shows whether there is an upward trend in sales.
c. Determine average sales per quarter, breaking it down based on whether your price was higher or lower than the competitor's price.
4. The tapedata.xlsx file contains data for weeks during 2009–2011 for the unit sales of 3M tape, price charged, whether an ad campaign was run that week (1 = ad campaign), and whether the product was displayed on the end of the aisle (1 = end cap). Use this file to perform the following exercises:
a. Does there appear to be an upward trend in sales?
b. Analyze the nature of the monthly seasonality of tape sales.
c. Does an ad campaign appear to increase sales?
d. Does placing the tape in an end-cap display appear to increase sales?
5. The files EAST.xlsx and WEST.xlsx contain information on product sales (products A–H) that you sell in January, February, and March. You want to use a PivotTable to summarize this sales data. The Field List discussed in this chapter does not enable you to create PivotTables on data from multiple ranges. If you hold down the ALT key and let go and hold down the D key and let go of the D key and hold down the P key, you can see the Classic PivotTable Wizard that enables you to select multiple ranges of data to key a PivotTable. Let Excel create a single page field for you and create a PivotTable to summarize total sales in the East and West during each month. Use the Filters so that the PivotTable shows only January and March sales of products A, C, and E.

Chapter 2

Using Excel Charts to Summarize Marketing Data

An important component of using analytics in business is a push towards “visualization.” Marketing analysts often have to sift through mounds of data to draw important conclusions. Many times these conclusions are best represented in a chart or graph. As Confucius said, “a picture is worth a thousand words.” This chapter focuses on developing Excel charting skills to enhance your ability to effectively summarize and present marketing data. This chapter covers the following skills:

Combination Charts

Companies often graph actual monthly sales and targeted monthly sales. If the marketing analyst plots both these series with straight lines it is difficult to differentiate between actual and target sales. For this reason analysts often summarize actual sales versus targeted sales via a combination chart in which the two series are displayed using different formats (such as a line and column graph.) This section explains how to create a similar combination chart.

All work for this chapter is located in the file Chapter2charts.xlsx. In the worksheet Combinations you see actual and target sales for the months January through July. To begin charting each month's actual and target sales, select the range F5:H12 and choose a line chart from the Insert tab. This yields a chart with both actual and target sales displayed as lines. This is the second chart shown in Figure 2.1; however, it is difficult to see the difference between the two lines here. You can avoid this by changing the format of one of the lines to a column graph. To do so, perform the following steps:

1. Select the Target sales series line in the line graph by moving the cursor to any point in this series.
2. Then right-click, and choose Change Series Chart Type…
3. Select the first Column option and you obtain the first chart shown in Figure 2.1.

Figure 2-1: Combination chart


With the first chart, it is now easy to distinguish between the Actual and Target sales for each month. A chart like this with multiple types of graphs is called a combination chart.

You'll likely come across a situation in which you want to graph two series, but two series differ greatly in magnitude, so using the same vertical axis for each series results in one series being barely visible. In this case you need to include a secondary axis for one of the series. When you choose a secondary axis for a series Excel tries to scale the values of the axis to be consistent with the data values for that series. To illustrate the creation of a secondary axis, use the data in the Secondary Axis worksheet. This worksheet gives you monthly revenue and units sold for a company that sells expensive diamonds.

Because diamonds are expensive, naturally the monthly revenue is much larger than the units sold. This makes it difficult to see both revenue and units sold with only a single vertical axis. You can use a secondary axis here to more clearly summarize the monthly units sold and revenue earned. To do so, perform the following steps:

1. In the Secondary Axis worksheet select D7:F16, click the Insert tab, click the Line menu in the Charts section, and select the first Line chart option. You obtain the chart shown in Figure 2.2.

Figure 2-2: Line graph shows need for Secondary Axis

2. You cannot see the units' data though, because it is so small relative to the revenue. To remedy this problem, select the Revenue series line in the graph and right-click.
3. Choose Format Data Series, and select Secondary Axis.
4. Then select any point in the Revenue series, right-click, and change the chart type for the Revenue series to a column graph.

The resulting chart is shown in Figure 2.3, and you can now easily see how closely units and revenue move together. This occurs because Revenue=Average price*units sold, and if the average price during each month is constant, the revenue and units sold series will move in tandem. Your chart indeed indicates that average price is consistent for the charted data.

Figure 2-3: Chart with a secondary axis


Adding Bling to a Column Graph with a Picture of Your Product

While column graphs are quite useful for analyzing data, they tend to grow dull day after day. Every once in a while, perhaps in a big presentation to a potential client, you might want to spice up a column graph here and there. For instance, in a column graph of your company's sales you could add a picture of your company's product to represent sales. Therefore if you sell Ferraris, you could use a .bmp image of a Ferrari in place of a bar to represent sales. To do this, perform the following steps:

1. Create a Column graph using the data in the Picture worksheet. This data shows monthly sales of cars at an L.A. Ferrari dealer.
2. After creating a Column graph, right-click on any column and choose Format Data Series… followed by Fill.
3. Click Picture or texture fill, as shown in Figure 2.4.

Figure 2-4: Dialog box for creating picture graph

4. Next click File below the Insert from query and choose the Ferrari.bmp file (available for download from the companion site).
5. Choose Stack and Scale with 200 units per picture. This ensures each car represents sales of 200 cars.

The resulting chart is shown in Figure 2.5, as well as in the Picture worksheet.

Figure 2-5: Ferrari picture graph


If you choose Stretch in the Fill tab of the Format Data Series dialog, you can ensure that Excel represents each month's sales by a single car whose size is proportional to that month's sales.

Adding Labels or Tables to Your Charts

Often you want to add data labels or a table to a graph to indicate the numerical values being graphed. You can learn to do this by using example data that shows monthly sales of product lines at the True Color Department Store. Refer to the Labels and Tables worksheet for this section's examples.

To begin adding labels to a graph, perform the following steps: