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A Case For Using The Internet To Track
Offline, Organic Word Of Mouth
By
Karen Kraft
A few days ago, one of my coworkers mentioned she was going to try out Super
Suppers that night. When I asked her what she was talking about, she explained
that Super Suppers is a company that provides locations where you can assemble
entrées for six meals within an hour. After you’ve assembled the
meals—each in its own pan with cooking directions and serving
suggestions—you take the meals home, put them in the freezer, and
they’re ready for your family with minimal prep time.
It sounded amazing. I checked out the website, saw there appeared to be good
value, and the menu seemed delicious. The next day I followed up with her and
learned the food is quite good. She even said her 16-year-old son enjoys going
along to help assemble the meals.
As a result of her experience, I’ve shown the website to my husband (who
has already forwarded it to some of his coworkers), talked about the company
with my boss (who’s a very busy mom—their target audience), brought
it up at a baby shower, and am so excited about trying it that I’ve
chosen to include this example in this article.
Now, imagine you are the Chief Evangelist for Super Suppers. How would you ever
find out about the spark that was created when my friend told me about the
company? If I decided to try Super Suppers, I would have checked the box on the
website registration page that said I heard about the program from a
friend/relative/acquaintance. However, if I decided not to use the program, you
as the Chief Evangelist would never have known about the wonderful amount of
positive word of mouth that was created when my coworker mentioned, in passing,
she had an appointment a few nights ago. The original WOMUnit she created has
been relayed to at least 20 people (none of whom found it through blogs, online
reviews, message boards, or an amplified campaign).
A company like Super Suppers (small and relatively new) can probably rely on
increased sales during a time of low/no advertising spending because of
word-of-mouth activity. However, imagine you are the Chief Evangelist for a
well-known, long-standing brand with a large advertising budget. How could you
capture and track offline, organic word-of-mouth promotion of your brand? This
seems like it could be a daunting task—almost like trying to catch a fly
with chopsticks.
Traditional marketing research can help companies capture and track at least
some of these episodes that are getting missed by online tracking methods. By
screening everyday consumers, researchers can identify participants in
word-of-mouth activities related to a brand and category, and interview them to
capture and characterize episodes of offline, organic word of mouth. Yet, a
potential barrier to this type of tracking is the number of consumers that must
be screened to identify readable samples of senders (consumers who have
distributed a WOMUnit) and receivers (consumers who have received a WOMUnit)
for tracking purposes.
To determine the incidence of word-of-mouth senders and receivers in several
categories, Decision Analyst Inc. conducted a screening of 2,044 consumers. In
this research, a nationally representative sample of members of American
Consumer Opinion® Online was screened—a sample consisting of men and
women ages 18 to 65. Sample members were invited to complete an online survey
about their word-of-mouth activity related to 54 product and service categories
in the past 30 days.
The following is a summary of the top (easiest to find) and bottom (hardest to
find) categories for past 30-day, word-of-mouth senders and receivers.
Top-Five
Categories For Finding
Past 30-Day Senders |
Incidence %
|
|
|
42.4 |
| 2. |
Movies and television programming |
|
24.9 |
| 3. |
Cell phones/Cell phone service providers |
|
16.2 |
|
15.7 |
| 5. |
Music (CDs, radio, MP3s, etc.) |
|
15.6 |
Bottom-Five
Categories For Finding
Past 30-Day Senders |
Incidence % |
| 50. |
Durable baby care products (such as car seats, strollers, playpens,
etc.) |
|
2.6 |
|
2.6 |
| 52. |
Durable pet products (such as carriers, beds, toys, etc.) |
|
2.3 |
| 53. |
Recreational vehicles (such as boats, motor homes/trailers, off-road
vehicles, etc.) |
|
2.3 |
| 54. |
Home security products and services |
|
1.6 |
Top
Five Categories For Finding
Past 30-Day Receivers |
Incidence % |
|
36.6 |
| 2. |
Movies and television programming |
|
21.9 |
| 3. |
Music (CDs, radio, MP3s, etc.) |
|
13.6 |
| 4. |
Cell phones/Cell phone service providers |
|
11.8 |
|
11.4 |
| 5. |
Over-the-counter medicines (such as pain relievers, cold or allergy
remedies, antacids, etc.) |
|
11.4 |
| Bottom-Five
Categories For Finding
Past 30-Day Receivers |
Incidence % |
| 50. |
Durable baby care products (such as car seats, strollers, playpens,
etc.) |
|
1.9 |
| 51. |
Durable pet products (such as carriers, beds, toys, etc.) |
|
1.9 |
|
1.8 |
| 53. |
Recreational vehicles (such as boats, motor homes/trailers, off-road
vehicles, etc.) |
|
1.7 |
| 54. |
Home security products and services |
|
1.0 |
Thus, according to these findings, it would be easiest to find senders and
receivers of word-of-mouth information about restaurants, and hardest to find
senders and receivers of word-of-mouth information about home security products
and services.
Now, assume a company wanted to interview 150 senders and 150 receivers in each
wave of a word-of-mouth tracking program. How many potential respondents must
be contacted to complete the required number of interviews? To calculate the
number of contacts needed, the following formula should be used:
Using the Restaurant Category as an example—if a company wanted to
interview 150 senders of word-of-mouth information about restaurants, 353
consumers (150/42.4 percent) would need to be screened. However, not all
consumers are available or would agree to be interviewed for marketing research
purposes. So, regardless of methodology—mall-intercept, telephone, or
Internet panel—assume that one in four consumers (25 percent) would be
willing to be screened. This factor of 25 percent would be the response rate.
Modifying the “number of contacts” formula to include the response
rate will result in the “amount of sample” formula as follows:
Going back to the Restaurant category example, a company would now need to have
1,415 consumers available in its sample for potential screening (150/42.4
percent/25 percent). When we apply the same formula to the hardest-to-find
category, Home Security Products, a total of 37,500 potential respondents must
be included in the sample to complete 150 interviews among senders in this
category.
Telephone and mall-intercept research have both been experiencing declining
response rates in the past several years. Caller ID and call blocking are
allowing consumers to avoid even answering the phone when a research company
calls. Additionally, the “Do Not Call” list, while technically not
applicable to survey researchers, has heightened consumer sensitivity to
unwanted calls at home. Meanwhile, more and more shopping malls are no longer
leasing space to researchers or are placing heavy restrictions on interviewer
movement within the malls, which is affecting both response rates and the
composition of the pool of potential respondents.
Researchers with large Internet-based panels, however, have the ability to pull
a representative sample of records from databases of millions of consumers who
have agreed to be contacted for research purposes. Since this type of screening
is all completed online, it occurs in a fraction of the time it would take to
try to contact the same number of potential respondents either in-person or on
the telephone. Thus, the Internet has become not only a very strong sample
source, but also the most time- and cost-effective method of finding consumers
willing to participate in survey research.
Once screened online, senders and receivers can then be interviewed in-depth
about their recent word-of-mouth activities. Using both open and closed-ended
questions, researchers can elicit descriptions of word-of-mouth episodes in a
manner that allows WOMUnits, participants, venues, and actions to be profiled
and analyzed. These key metrics can then be quantified and tracked over time.
Word-of-mouth tracking can either stand alone or be incorporated into a
brand’s regular tracking program. Incorporating this type of tracking
into traditional brand tracking can have several benefits:
- Unaided and aided levels of word-of-mouth activity can be tracked and analyzed
side by side with traditional brand tracking measurements, such as brand and
advertising awareness.
- Word-of-mouth levels can be measured and tracked among the general population,
and supplemental interviews can be completed among both senders and receivers
to obtain and quantify more detailed word-of-mouth information.
- Word-of-mouth measures can be included in customer segmentations to create
detailed profiles of senders and receivers to assist in identifying the
characteristics of high word-of-mouth potential participants to recruit for
word-of-mouth campaigns.
Additionally, since online research is conducted among panelists, those
identified as senders and receivers can be re-contacted for further research
such as:
By including senders and receivers in these types of innovation, product and
advertising research, companies can identify breakthrough concepts, products
and advertising that have high word-of-mouth potential. This can be done using
regular quantitative research, or by recruiting panelists to online qualitative
or ethnographic studies.
Now, again imagine that you are the Chief Evangelist for Super Suppers. If you
implement an online word-of-mouth tracking program, you still may not capture
the particular word-of-mouth experience I described earlier. But you would
dramatically increase your probability of capturing similar offline, organic
word-of-mouth episodes. With the ability to access large numbers of consumers
through their panels, online researchers can help you stop trying to catch
flies with chopsticks and begin shooting fish in barrels.
Copyright © 2005 by Decision Analyst, Inc.
This article may not be copied, published, or used in any way without written
permission of Decision Analyst.
About the Author
Karen Kraft (kkraft@decisionanalyst.com)
is Research Consultant at Dallas-Fort Worth based Decision Analyst. She may
be reached at 1-800-262-5974 or 1-817-640-6166.
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