1 Implications for Marketing Mix Decisions

Marketing was originally seen as a toolbox to assist in selling products, with marketers mixing the ingredients of the toolbox to achieve the best possible sales results (Dolnicar and Ring 2014). In the early days of marketing, Borden (1964) postulated that marketers have at their disposal 12 ingredients: product planning, packaging, physical handling, distribution channels, pricing, personal selling, branding, display, advertising, promotions, servicing, fact finding and analysis. Many versions of this marketing mix have since been proposed, but most commonly the marketing mix is understood as consisting of the 4Ps: Product, Price, Promotion and Place (McCarthy 1960).

Market segmentation does not stand independently as a marketing strategy. Rather, it goes hand in hand with the other areas of strategic marketing, most importantly: positioning and competition. In fact, the segmentation process is frequently seen as part of what is referred to as the segmentation-targeting-positioning (STP) approach (Lilien and Rangaswamy 2003). The segmentation-targeting-positioning approach postulates a sequential process. The process starts with market segmentation (the extraction, profiling and description of segments), followed by targeting (the assessment of segments and selection of a target segment), and finally positioning (the measures an organisation can take to ensure that their product is perceived as distinctly different from competing products, and in line with segment needs).

Viewing market segmentation as the first step in the segmentation-targeting-positioning approach is useful because it ensures that segmentation is not seen as independent from other strategic decisions. It is important, however, not to adhere too strictly to the sequential nature of the segmentation-targeting-positioning process. It may well be necessary to move back and forward from the segmentation to the targeting step, before being in the position of making a long-term commitment to one or a small number of target segments.

Figure 11.1 illustrates how the target segment decision – which has to be integrated with other strategic areas such as competition and positioning – affects the development of the marketing mix. For reasons of simplicity, the traditional 4Ps model of the marketing mix including Product, Price, Place and Promotion serves as the basis of this discussion. Be it twelve or four, each one of those aspects needs to be thoroughly reviewed once the target segment or the target segments have been selected.

Fig. 11.1
figure 1

How the target segment decision affects marketing mix development

To best ensure maximising on the benefits of a market segmentation strategy, it is important to customise the marketing mix to the target segment (see also the layers of market segmentation in Fig. 2.1 discussed on pages 11–12). The selection of one or more specific target segments may require the design of new, or the modification or re-branding of existing products (Product), changes to prices or discount structures (Price), the selection of suitable distribution channels (Place), and the development of new communication messages and promotion strategies that are attractive to the target segment (Promotion).

One option available to the organisation is to structure the entire market segmentation analysis around one of the 4Ps. This affects the choice of segmentation variables. If, for example, the segmentation analysis is undertaken to inform pricing decisions, price sensitivity, deal proneness, and price sensitivity represent suitable segmentation variables (Lilien and Rangaswamy 2003).

If the market segmentation analysis is conducted to inform advertising decisions, benefits sought, lifestyle segmentation variables, and psychographic segmentation variables are particularly useful, as is a combination of all of those (Lilien and Rangaswamy 2003).

If the market segmentation analysis is conducted for the purpose of informing distribution decisions, store loyalty, store patronage, and benefits sought when selecting a store may represent valuable segmentation variables (Lilien and Rangaswamy 2003). Typically, however, market segmentation analysis is not conducted in view of one of the 4Ps specifically. Rather, insights gained from the detailed description of the target segment resulting from Step 7 guide the organisation in how to develop or adjust the marketing mix to best cater for the target segment chosen.

2 Product

One of the key decisions an organisation needs to make when developing the product dimension of the marketing mix, is to specify the product in view of customer needs. Often this does not imply designing an entirely new product, but rather modifying an existing one. Other marketing mix decisions that fall under the product dimension are: naming the product, packaging it, offering or not offering warranties, and after sales support services.

The market segments obtained for the Australian vacation activities data set (see Appendix C.3) using biclustering (profiled in Fig. 7.37) present a good opportunity for illustrating how product design or modification is driven by target segment selection. Imagine, for example, being a destination with a very rich cultural heritage. And imagine having chosen to target segment 3. The key characteristics of segment 3 members in terms of vacation activities are that they engage much more than the average tourist in visiting museums, monuments and gardens (see the bicluster membership plot in Fig. 7.37). They also like to do scenic walks and visit markets. They share both of these traits with some of the other market segments. Like most other segments, they like to relax, eat out, shop and engage in sightseeing.

In terms of the product targeted at this market segment, possible product measures may include developing a new product. For example, a museums, monuments & much, much more product (accompanied by an activities pass) that helps members of this segment to locate activities they are interested in, and points to the existence of these offers at the destination during the vacation planning process. Another opportunity for targeting this segment is that of proactively making gardens at the destination an attraction in their own right.

3 Price

Typical decisions an organisation needs to make when developing the price dimension of the marketing mix include setting the price for a product, and deciding on discounts to be offered.

Sticking to the example of the destination that wishes to market to segment 3 (which has emerged from a biclustering analysis of the Australian vacation activities data set), we load the bicluster solution obtained in Sect. 7.4.1:

R> load("ausact-bic.RData")

To be able to compare members of segment 3 to tourists not belonging to segment 3, we construct a binary vector containing this information from the bicluster solution. We first extract which rows (respondents) and columns (activities) are contained in a segment using:

R> library("biclust") R> bcn <- biclusternumber(ausact.bic)

We use this information to construct a vector containing the segment membership for each consumer.

First we initialise a vector cl12 containing only missing values (NAs) with the length equal to the number of consumers. Then we loop through the different clusters extracted by the biclustering algorithm, and assign the rows (respondents) contained in this cluster the corresponding cluster number in cl12.

R> data("ausActiv", package = "MSA") R> cl12 <- rep(NA, nrow(ausActiv)) R> for (k in seq_along(bcn)) { +   cl12[bcn[[k]]$Rows] <- k +  }

The resulting segment membership vector contains numbers 1 to 12 because biclustering extracted 12 clusters. It also contains missing values because biclustering does not assign all consumers to a cluster. We obtain the number of consumers assigned to each segment, and the number of consumers not assigned by tabulating the vector:

R> table(cl12, exclude = NULL)

cl12    1    2    3    4    5    6    7    8   9  10  11  12   50   57   67   73   61   83   52   65  51  53  80  60 <NA>  251

The argument exclude = NULL ensures that NA values are included in the frequency table.

Based on the segment membership vector, we create a binary variable indicating if a consumer is assigned to segment 3 or not. We do this by selecting those as being in segment 3 who are not NA (!is.na(cl12)), and where the segment membership value is equal to 3.

R> cl12.3 <- factor(!is.na(cl12) & cl12 == 3, +   levels = c(FALSE, TRUE), +   labels = c("Not Segment 3", "Segment 3"))

The categories are specified in the second argument levels. Their names are specified in the third argument labels.

Additional information on consumers is available in the data frame ausActivDesc in package MSA. We use the following command to load the data, and create a parallel boxplot of the variable spend per person per day split by membership in segment 3:

R> data("ausActivDesc", package = "MSA") R> boxplot(spendpppd ~ cl12.3, data = ausActivDesc, +   notch = TRUE, varwidth = TRUE, log = "y", +   ylab = "AUD per person per day")

The additional arguments specify that confidence intervals for the median estimates should be included (notch = TRUE), box widths should reflect group sizes (varwidth = TRUE), that the y-axis should be on the log scale because of the right-skewness of the distribution (log = "y"), and that a specific label should be included for the y-axis (ylab).

Figure 11.2 shows the expenditures of segment 3 members on the right, and those of all other consumers on the left. Ideally, we would have information about actual expenditures across a wide range of expenditure categories, or information about price elasticity, or reliable information about the segment’s willingness to pay for a range of products. But the information contained in Fig. 11.2 is still valuable. It illustrates how the price dimension can be used to best possibly harvest the targeted marketing approach.

Fig. 11.2
figure 2

Total expenditures in Australian dollars (AUD) for the last domestic holiday for tourists in segment 3 and all other tourists

As can be seen in Fig. 11.2, members of segment 3 have higher vacation expenditures per person per day than other tourists. This is excellent news for the tourist destination; it does not need to worry about having to offer the museums, monuments & much, much more product at a discounted price. If anything, the insights gained from Fig. 11.2 suggest that there is potential to attach a premium price to this product.

4 Place

The key decision relating to the place dimension of the marketing mix is how to distribute the product to the customers. This includes answering questions such as: should the product be made available for purchase online or offline only or both; should the manufacturer sell directly to customers; or should a wholesaler or a retailer or both be used.

Returning to the example of members of segment 3 and the destination with a rich cultural heritage: the survey upon which the market segmentation analysis was based also asked survey respondents to indicate how they booked their accommodation during their last domestic holiday. Respondents could choose multiple options. This information is place valuable; knowing the booking preferences of members of segment 3 enables the destination to ensure that the museums, monuments & much, much more product is bookable through these very distribution channels.

We can use propBarchart from package flexclust to visualise stated booking behaviour. First we load the package. Then we call function propBarchart() with the following arguments: ausActivDesc contains the data, g = cl12.3 specifies segment membership, and which indicates the columns of the data to be used. We select all columns with column names starting with "book". Function grep based on regular expressions extracts those columns. For more details see the help page of grep. Alternatively, we can use which = startsWith(names(ausActivDesc), "book") instead of which = grep("̂book", names(ausActivDesc)).

R> library("flexclust") R> propBarchart(ausActivDesc, g = cl12.3, +   which = grep("̂book", names(ausActivDesc)), +   layout = c(1, 1), xlab = "percent", xlim = c(-2, 102))

The additional arguments specify: that only one panel should be included in each plot (layout = c(1, 1)), the label for the x-axis (xlab), and the limits for the x-axis (xlim). Figure 11.3 shows the resulting plot for members in segment 3.

Fig. 11.3
figure 3

Hotel booking avenues used for the last domestic holiday by segment 3 and by the average tourist

Figure 11.3 indicates that members of segment 3 differ from other tourists in terms of how they booked their hotel on their last domestic vacation: they book their hotel online much more frequently than the average tourist. This information has clear implications for the place dimension of the marketing mix. There must be an online booking option available for the hotel. It would be of great value to also collect information about the booking of other products, services and activities by members of segment 3 to see if most of their booking activity occurs online, or if their online booking behaviour is limited to the accommodation.

5 Promotion

Typical promotion decisions that need to be made when designing a marketing mix include: developing an advertising message that will resonate with the target market, and identifying the most effective way of communicating this message. Other tools in the promotion category of the marketing mix include public relations, personal selling, and sponsorship.

Looking at segment 3 again: we need to determine the best information sources for reaching members of segment 3 so we can inform them about the museums, monuments & much, much more product. We answer this question by comparing the information sources they used for the last domestic holiday, and by investigating their preferred TV stations.

We obtain a plot comparing the use of the different information sources to choose a destination for their last domestic holiday with the same command as used for Fig. 11.3, except that we use the variables starting with "info":

R> propBarchart(ausActivDesc, g = cl12.3, +   which = grep("̂info", names(ausActivDesc)), +   layout = c(1, 1), xlab = "percent", +   xlim = c(-2, 102))

As Fig. 11.4 indicates, members of segment 3 rely – more frequently than other tourists – on information provided by tourist centres when deciding where to spend their vacation. This is a very distinct preference in terms of information sources. One way to use this insight to design the promotion component of the marketing mix is to have specific information packs on the museums, monuments & much, much more product available both in hard copy in the local tourist information centre at the destination as well as making it available online on the tourist information centre’s web page.

Fig. 11.4
figure 4

Information sources used by segment 3 and by the average tourist.

The mosaic plot in Fig. 11.5 shows TV channel preference. We generate Fig. 11.5 with the command:

Fig. 11.5
figure 5

TV station most frequently watched by segment 3 and all other tourists

R> par(las = 2) R> mosaicplot(table(cl12.3, ausActivDesc$TV.channel), +   shade = TRUE, xlab = "", main = "")

We use par(las = 2) to ensure that axis labels are vertically aligned for the x-axis, and horizontally aligned for the y-axis. This makes it easier to fit the channel names onto the plot.

Figure 11.5 points to another interesting piece of information about segment 3. Its members have a TV channel preference for Channel 7, differentiating them from other tourists. Again, it is this kind of information that enables the destination to develop a media plan ensuring maximum exposure of members of segment 3 to the targeted communication of, for example, a museums, monuments & much, much more product.

6 Step 9 Checklist