Creating Value with Big Data Analytics: Making Smarter Marketing Decisions by Verhoef Peter C. & Kooge Edwin & Walk Natasha

Creating Value with Big Data Analytics: Making Smarter Marketing Decisions by Verhoef Peter C. & Kooge Edwin & Walk Natasha

Author:Verhoef, Peter C. & Kooge, Edwin & Walk, Natasha [Verhoef, Peter C.]
Language: eng
Format: azw3
ISBN: 9781317561910
Publisher: Taylor and Francis
Published: 2016-01-07T16:00:00+00:00


Table 4.1.1 Seven classic data analytics

Averages can be misleading: Only looking at the average can be misleading, especially when there is much heterogeneity. Therefore, we strongly recommend not only looking at overarching statistics, but also at the distribution of variables. In this regard, descriptives such as the standard deviation can be useful. Graphically, box plots can be shown. A histogram is also very useful. A histogram can, for example, show that there are two segments of customers when considering a customer feedback metric: haters and lovers. Inventing some numbers for this metric (see Figure 4.1.1), an average might then report a score around 6, and this average is not very informative, as apparently there are very unsatisfied customers (scoring around 4) and very satisfied customers (scoring around 8).

Focus on extremes: To outperform competition an average performance is frequently no longer sufficient. Firms need to delight customers and have extremely well evaluated brands to successfully compete. Marketing metric reporting should therefore go beyond only reporting averages, and also consider extreme responses. This is specifically important for customer feedback metrics, in which top2-boxes or specific transformations such as the net promoter score (NPS) can be very valuable (see also our discussion in Chapter 2.1).

Report trends: Reporting on the current status can be informative, but managers will be more interested in the trend. Is the churn rate going down? Are customers becoming more satisfied? Does our brand image improve? Does the market grow? In that sense, specific trend metrics such as growth rates can be very informative descriptors. Static descriptors then become more dynamic and will raise specific questions (see Figure 4.1.2 for a sales trend).



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