Page 103 - MYM 2015

When we look at the later age groups, i.e. those 35 years of age and up, what is most striking is that both giving and getting advice starts to decline at age 45 or so and giving advice declines consistently from age 35 onward. Thus, the rise of social media does not seem to have impacted those over the age of 35. This is a most interesting nding and one that deserves greater analysis than is possible in this paper.
From the information above, we can create postulate #3.
Research Postulate #3: getting and giving advice has changed dramatically with the introduction
of social media. It may well be that what is considered traditional “word-of-mouth” is being replaced by social media.
There are many social implications that come from this Postulate, not the least of which is con rmation that younger consumers, while they may move in crowds, appear to be much most in uenced by electronic
than human contact. This too deserves considerable research attention going forward.
C. Online Purchasing
One of the major marketing growth factors over the past decade has been the rapid increase in the volume and frequency of online shopping/purchasing. Online
is commonly de ned as electronic purchasing which occurs either through the internet of via other electronic means. Online volume has grown rapidly, not just
in the U.S. but in other established and emerging countries and economies. Indeed, online shopping in China over the past few years have surged far more rapidly than in other established markets where the concept developed. (McKinsey, 2013) Little seems to be known however; about that generational group that is driving that growth. Given the prevalence of mobile
the time frame of 2007 (before the advent of today’s social media) with the reported use of online purchasing in 2013 by age cohort. The result of that analysis is shown in Exhibit 11.
Two measures are reported, i.e., “Regularly Purchase Online” and “Frequency of Online Purchasing”, i.e., average in weeks. The same age categorizations are used in this analysis as those before.
In terms of Regularly Purchasing Online, three factors are easily identi ed. In almost every age category, increased regular shopping online is reported between 2007 and 2013. The only two exceptions on are males 45 through 65+. Females all reported a greater propensity to regularly shop online among all age groups, with major changes in females in age groups after 35 years. While we can’t attribute that directly
to the growth of social media, it would seem likely it had some impact. What is clear, however, is that the social media boom did not impact younger people any more than it did those the older groups. In fact, if we look at the Yearly Change in Regularly Shopping Online, the results skew much more heavily toward the older groups than the younger. Thus, it does not appear that the growth of social media availability has had much
to do with the growth of online shopping in terms of whether or not it is done regularly by the younger age cohorts, i.e. the Millennials.
If we look at Online Purchasing Frequency, again comparing the age groups in 2007 with the same age groups in 2013, we see that frequency did not grow at all, indeed, it declined. It is not until we reach the 65+ age groups that online purchasing frequency increased. Thus, if social media has increased online shopping either in terms of consistency (regularly purchasing) or frequency, it appears that the millennial generation is not the one primarily creating that impact and the availability of social media does not appear to be a driving factor. This is a most interesting nding and one that is contrary many of the reports and speculation that has appeared in the trade press. (Bolton, et al, 2013)
Forecasting with Generational Data
One of the major values attributed to generational analysis is the ability to forecast future marketing results or returns based on the movement of an age cohort through the various life stages over time. While that seems to be true in some instances, as shown above with media usage, it has proven not to be as effective in other areas, i.e., online shopping. That naturally raises the question: can generational data be used to forecast the attitudes, beliefs, and therefore the behaviors of consumers as they pass through the various age groupings? Many have tried to answer
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Exhibit 11
Online Purchase and purchase Frequency Age and Gender
2007 vs. 2013
Regularly Purchase Online Yearly Purchase Frequency* Yearly Age Gender 2007 2013 Change 2007 2013 Change
18-24 Male Female
Total 25-34 Male
Female
Total 35-44 Male
Female
Total 45-54 Male
Female
Total 55-64 Male
Female
Total 65+ Male
Female
Total Total Male
Female
Total *Average in weeks
22.54 23.25 0.10 2.91 2.71 14.32 19.71 0.77 2.98 2.94 17.54 21.28 0.53 2.95 2.84 29.32 33.58 0.61 3.48 3.29 22.96 33.26 1.47 3.51 3.22 25.19 33.41 1.17 3.50 3.26 28.99 30.89 0.27 3.60 3.68 24.64 34.52 1.41 3.54 3.30 26.07 32.76 0.96 3.56 3.48 26.93 24.48 -0.35 3.95 4.04 24.13 28.85 0.67 4.03 3.58 25.06 26.85 0.26 4.01 3.79 27.17 24.82 -0.34 4.62 4.54 20.6 29.75 1.31 4.76 4.02 23.33 27.43 0.59 4.70 4.27 20.78 18.17 -0.37 4.74 4.90 13.75 20.7 0.99 4.98 5.47 16.94 19.37 0.35 4.87 5.17 25.8 26.02 0.03 3.93 3.80 21.25 27.72 0.92 3.96 3.63 22.96 26.91 0.56 3.95 3.71
-0.03 0.00 -0.02 -0.03 -0.04 -0.03 0.01 -0.03 -0.01 0.01 -0.06 -0.03 -0.01 -0.10 -0.06 0.02 0.07 0.04 -0.02 -0.05 -0.03
media among the younger generations it has been assumed that much of that increase has come from
the activities of the Millennials age group. To test this or those hypotheses, we again used MBI data starting with
marketing