Targeted Marketing: Lessons from an Agri-Tourism Enterprise

Introduction

Extension specialists throughout the country are frequently asked by farmers and other business owners about marketing issues, regardless of their area of expertise. From beef to blueberries and environmental education to tourism, specialists are challenged by marketing questions yet are rarely trained to address such questions. This is not new; the need for marketing programs and the lack of trained personnel to answer questions is a recurring topic in Extension publications past and present (e.g., Larson, 1997; Muhammadm, Tegegne, & Ekanem, 2004; Weinschrott, 1985).

In an effort to find answers, University of Vermont Extension teamed up with the University of Vermont School of Business Administration and the Vermont Tourism Data Center. Working together, we designed a research project that examines the question: "How can businesses with limited resources, such as family farms, make use of marketing research to reach new customers?"

In the first phase of the project, we worked with Amelia and Chris Darrow of Olallie Daylily Gardens in South Newfane, Vermont. The Darrows grow high-quality daylilies and other perennials on their family farm, and they use a variety of direct marketing methods. They want more people to visit their farm and purchase products while they visit, but they also want to grow their catalog and Internet mail-order business. They teamed up with the University of Vermont to see how research could help them understand and expand their customer base in a cost-effective way.

Background

The well-known four P's of marketing (product, price, place, and promotion) are still essential for success (Parmerlee, 2000); however, several more P's have been added, including partnerships, publicity, and positioning, just to name a few (Comen, 2003). The list of P's goes on and on, but even knowing all the P's doesn't guarantee success in today's competitive marketplaces, with consumers suffering from information overload (Godin, 2003). To reach customers, your product must be remarkable, and your marketing must be precisely targeted (Peppers & Rogers, 1996).

Understanding how to target customers was the subject of a research study conducted by the Vermont Department of Tourism and Marketing and the Vermont Tourism Data Center (Noordewier, 2003). The study profiled visitors to Vermont and purchasers of Vermont-made products. These studies used a national "PRIZM" classification system developed by Claritas, Inc. to better understand who comes to Vermont and who should be targeted for statewide marketing efforts. This system used zip codes to classify customers into a variety of demographic groupings (Noordewier, 2003).

While PRIZM codes technically identify neighborhood types, in practice they can be thought of as representing distinct household segments with particular consumer behavior, demographic, and lifestyle characteristics. A 2002 study of Vermont visitors formed four macro segments consisting of household types with relatively similar profiles. The groups were formed by clustering together similar PRIZM codes. Forming macro clusters is useful for marketing because it makes it easier to target largely similar household types.

The 2002 study of Vermont visitors identified four distinct types or groups of households traveling to Vermont in disproportionate numbers. To determine whether a specific PRIZM profile travels to Vermont in disproportionate numbers, the percent of Vermont visitors categorized in the profile is divided by the percent of the U.S. population categorized in that profile. For example, PRIZM code number 42, New Eco-topians, accounted for 1.00% of the U.S. population in 2002 and 1.62% of Vermont visitors. Dividing 1.62% by 1.00% gives us an index of 162 and a 62% greater likelihood of making one or more trips to Vermont than the average U.S. household.

In the 2002 Vermont visitor study, the definitions and composition of the four target groups (i.e., visitors disproportionately prone to travel to Vermont) were reported as follows (Table 1, Noordewier 2003).

1. Metro Achievers

These households are among the most educated and affluent in the country, with the heads tending to be the elite in their professions or businesses. The families in the clusters comprising this target market live overwhelmingly in the suburbs of major metropolitan cities or "second" cities. In 2002, Metro Achievers represented 20% of households making one or more trips to Vermont. With the clusters comprising the Metro Achievers target market accounting for 14% of all households nationally, such households exhibited a 47% greater likelihood of making one or more trips to Vermont than the average U.S. household. Given their affluence, the propensity of Metro Achievers households to travel to Vermont makes them a highly desirable target market.

2. Small Town Movers

These are households that live in neighborhoods well outside the metropolitan beltways, in some cases in relatively remote country towns and villages. However, while residing in less densely populated areas (in some cases perhaps a deliberate choice to exchange a high profile, high-income urban job and lifestyle for a less complex and stressful way of life), many of the individuals in the clusters comprising this target are well educated, and incomes are relatively high. These households tend to be among the most influential in their communities. In 2002, Small Town Movers represented 13% of the households making one or more trips to Vermont. Since the clusters comprising the Small Town Movers target market accounted for 7% of all households nationally, these households were 79% more likely to make one or more trips to Vermont than the average U.S. household.

3. Small Town Rustic

These households tend to be located in less populated towns and villages, and generally have lower incomes and/or educational attainment compared to Small Town Movers. In 2002, Small Town Rustic households represented 13% of the households making one or more trips to Vermont. With the clusters comprising this target market accounting for 10% of all households nationally, Small Town Rustic households exhibited a 23% greater likelihood of traveling to Vermont than the average U.S. household.

4. New Eco-topia

New Eco-topia households represent a unique target market. These households tend to live in sparsely populated areas and have moderate incomes. The educational profile of this cluster is heterogeneous, ranging from high school to college graduate. The socio-economic ranking of New Eco-topia households places them between Small Town Movers and Small Town Rustics. In 2002, these households, which represented 2% of the households making one or more trips to Vermont, exhibited a high propensity to travel to Vermont, with a 62% greater likelihood than the average U.S. household.

Table 1.
PRIZM-Based Target Markets for Vermont

PRIZM Cluster Number	Cluster Name	2002 U.S. Percent	2002 Vermont Percent	2002 Vermont Index
	Metro Achievers	13.60%	19.98%	147
4	Pools & Patios	1.80%	3.51%	195
7	Money & Brains	1.00%	1.57%	157
2	Winner's Circle	2.20%	3.58%	163
19	New Empty Nests	2.30%	3.44%	150
5	Kids & Cul-de-Sacs	3.10%	3.80%	123
11	Second City Elite	1.90%	2.56%	135
3	Executive Suites	1.30%	1.52%	117
	Small Town Movers	7.40%	13.21%	179
14	Country Squires	1.50%	2.95%	197
16	Big Fish, Small Pond	1.40%	2.28%	163
15	God's Country	2.90%	4.91%	169
17	Greenbelt Families	1.60%	3.07%	192
	Small Town Rustic	10.20%	12.51%	123
52	Golden Ponds	1.60%	2.01%	126
37	New Homesteaders	1.70%	1.94%	114
41	Big Sky Families	1.60%	2.38%	149
43	River City, USA	1.80%	2.08%	116
58	Blue Highways	1.80%	1.98%	110
39	Red, White & Blues	1.70%	2.12%	125
	New Eco-topia	1.00%	1.62%	162
42	New Eco-topia	1.00%	1.62%	162
Note that the percentages do not add to 100 because only the top 18 PRIZM profiles are listed here, out of the 62 total PRIZM profiles identified by Claritas, Inc. The 18 PRIZM profiles reported here represent those most likely to visit Vermont and buy Vermont products. They do not represent all visitors to Vermont.

 

Based on this prior research on Vermont visitors (Noordewier, 2003) and Olallie Daylily Gardens' interest in marketing using mailings, we designed a study that examines the effectiveness of PRIZM coding as well as current marketing methods already in use by Olallie Daylily Gardens.

Research Methods

The study compared marketing methods and assessed their usefulness in bringing in new business. Olallie Daylily Gardens previously used ads (magazine, newspaper, and radio), a Web site, rack cards, and postcard and catalog mailings as their primary means of marketing. With the help of the University of Vermont, Olallie also used PRIZM coded mailings and a control mailing to a random sample.

The first step of the research was to learn more about Olallie's current customers. Olallie sent Claritas, Inc. a list of over 9,000 household addresses from their database of customers. Results revealed that New Eco-topians made up over 40% of the drive-market customer base. No one PRIZM code dominated the mail-order customer list.

The next step was to send a mailing to a sample of potential customers based on their PRIZM category and a random sample as a control. We purchased 10,000 mailing labels (names and addresses) from Claritas, with half of the mailing labels targeted to reach potential mail-order market customers. The remaining 5,000 mailing labels were selected to reach drive-market customer targets. The exact breakdown of the mailing list was as follows.

• South-Central Vermont, excluding zip 05301, (17% or 1700 addresses) of which 850 addresses are randomly selected (no PRIZM), balance of 850 addresses would be PRIZM code #42 (New Eco-topians)

• Southwestern New Hampshire (8%) 400 random, balance PRIZM code #42

• Northern Massachusetts (17%) 850 random, balance PRIZM code #42

• Southwestern Connecticut (8%) 400 random, balance PRIZM code #42

• Minnesota (13%) 650 random, balance with an even distribution of PRIZM codes #42, 41, 15, 1, 2, 14 and 37

• Wisconsin (13%) 650 random, balance with an even distribution of PRIZM codes #42, 41, 15, 1, 2, 14 and 37

• Michigan (12%) 600 random, balance with an even distribution of PRIZM codes #42, 41, 15, 1, 2, 14 and 37

• Illinois (12%) 600 random, balance with an even distribution of PRIZM codes #42, 41, 15, 1, 2, 14 and 37

The mailing labels were affixed to postcards, and the postcards were marked with codes so we could keep track of responses. Olallie's employees were trained to ask how new customers heard about the farm and check postcards for specific codes. The mailings were conducted during the summer of 2003.

Research Results

During the winter, the Darrows counted the tally of responses for the 2003 season, which runs from May to September. For bringing in new catalog requests, advertising in Fine Gardening Magazine was responsible for the greatest response, leading to 407 catalog requests (Table 2). Web searches were second, with 270 catalog requests. In third place was an advertisement in Horticulture Magazine, with 123 catalog requests. Word-of-mouth came next with 100 catalog requests. Other forms of advertising ranked lower, including the mailings.

Word of mouth was the most effective way to bring in new farm visits (Table 2). Advertising in a local magazine ranked second, and Web searches ranked third. In contrast to catalog requests, posters, signs, maps, and book listings were effective means of generating farm visits. Similar to catalog requests, mailings generated a low percentage of farm visits.

Table 2.
Marketing Methods Used by Olallie Daylily Gardens and Percentage of Catalog Requests and Farm Visits

Marketing Method	Catalog Requests n = 1092	Farm Visits n = 201
Horticulture Magazine	11.3%	2.0%
Fine Gardening Magazine	37.3%	4.0%
Local Magazines	4.6%	16.9%
Newspaper Ads	0.7%	6.5%
Brochures	0.1%	5.5%
Radio and TV	0.5%	2.0%
Map/Book Listing	0.3%	9.0%
Sign/Poster	0.0%	9.0%
Web Search	24.7%	12.4%
Web Link	4.6%	2.0%
Word of Mouth	9.2%	23.9%
Mailings	1.0%	2.0%
Other	5.9%	5.0%

 

Implications

The low response to the mailings combined with high responses to other kinds of targeted marketing methods led us to develop the Niche Products hypothesis (Figure 1). At the bottom of the pyramid are products with broad appeal, such as credit cards. Mass mailings are an effective way to reach new credit card customers for generic cards. Moving up the pyramid, products become more specialized, appealing only to select customers. For example, specialized credit cards target specific markets (e.g., the L.L. Bean credit card for L.L. Bean customers). Products such as those produced by Olallie Daylily Gardens--high-quality, field-grown, hand-dug daylilies--are toward the top of the pyramid, requiring finely targeted marketing techniques such as word of mouth and advertisements in magazines geared toward a select audience. PRIZM code classifications are an improvement on mailings to random samples, but, as we learned from the research, they are not finely targeted enough for such a specialized product as high-quality daylilies.

Conclusions

The next phase of the research project is to fill in the blank spaces on the pyramid and improve Extension's understanding of the marketing methods that work best for different products. Beef, for example, can fit in many places on the pyramid depending on how it is produced, packaged, branded, and marketed. No-name hamburger is toward the bottom of the pyramid, while grass-fed, hormone-free, premium-priced tenderloin raised in Vermont is toward the top of the pyramid. Extension specialists can use this pyramid to help them and their audience make informed decisions about appropriate marketing methods, keeping in mind that the more specialized a product, the more targeted the marketing methods need to be.

Figure 1.
Specialized Products Need Specialized Marketing

Acknowledgements

The research was supported by the Vermont Tourism Data Center (a partnership between the University of Vermont Rubenstein School of Environment and Natural Resources and the Vermont Department of Tourism and Marketing), University of Vermont Extension, and Olallie Daylily Gardens. I'm grateful to Amelia Darrow, Gary Deziel, Tom Noordewier, and Wendy Wilson for helpful comments on earlier drafts.

References

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