CIS 434 Module 7 Implementing the Marketing Program Example

Who Writes the River Report at 9 p.m.? An Implementation Plan for a Small Fly Shop's Internet Marketing Program

Student Name

Business Administration Program, Aspen University

CIS 434: Internet Marketing

Instructor Name

Month Day, Year

Who Writes the River Report at 9 p.m.? An Implementation Plan for a Small Fly Shop's Internet Marketing Program

Rattlesnake Creek Fly Shop has designed a marketing program that depends on guides writing river reports, a tying bench packing kits for distant anglers, expo booths in two cities and e-mails signed by guides. Every one of those tasks lands on people who already have full days. The owner works seventy hours a week in season, and the guides are on the water from dawn. This paper plans how the program will actually be carried out. Rattlesnake Creek, its staff and all figures are fictional and written for this course.

Roles and Hours

The coordinator is the program's most important addition. Without one person whose job is to keep the pieces moving, river reports would stop the first busy week in July. At twenty hours a week for fifty weeks, the role costs about $26,000 a year, paid from the margin on roughly 300 additional kits or twenty additional trips.

Systems and How They Connect

The shop already uses a booking calendar for trips, a point-of-sale system in the store and a basic website. The plan adds a kit shop on the website and an e-mail tool. The critical requirement is that these share information. A kit shown as available online must actually be on the shelf, a booked client must appear in the e-mail tool with the guide's name and trip date, and a store purchase by a past client must be recorded against that client.

Gallino and Moreno (2014) studied a large retailer of home furnishings that began offering customers the option to buy online and pick up in a store, which required showing reliable, store-level inventory information on its website. After the change, the retailer's online sales in areas near its stores fell, while store sales and traffic rose. The authors attributed much of the shift to the information itself: customers who could see that an item was in stock nearby chose to go and buy it there, often buying more in the process. Accurate inventory information, in other words, changed where and how customers bought.

For the shop, the lesson runs both ways. Showing that a kit can be picked up at the store will draw regional anglers in, where staff can sell them a trip. But showing a kit as in stock when it is not would damage trust at exactly the moment Module 4 identified as most fragile. The plan therefore links the kit shop to the store's inventory, with each kit's components counted once a week by the bench lead.

The Paid Search Decision

The coordinator has been urged by a website vendor to buy search ads on the shop's name. Blake et al. (2015) ran a large field experiment at eBay, switching off its search ads on brand terms and, separately, on other keywords in randomly chosen areas. With the brand-name ads off, shoppers simply clicked eBay's unpaid listing just below, and the visits the ads had seemed to deliver kept coming, which meant those ads had been paying for traffic eBay would have received anyway. Ads on other keywords had measurable effects mainly on new and infrequent users, while frequent users who already knew eBay were barely affected, and overall returns were far lower than the company's own reports had suggested.

The shop will therefore buy no ads on its own name. It will test a small set of trip-planning search terms, such as questions about fishing the Bitterroot in September, with a cap of $400 a month from March to June, the months when first-time visitors plan. The test is aimed at exactly the newcomers Blake and colleagues found ads could reach, and Module 8 explains how its effect will be measured.

Using Customer Data

Fader et al. (2005) showed that three simple measures of past behavior, how recently a customer bought, how often and how much they spent, could be combined in a probability model to predict each customer's future purchases and value. Their approach turns a list of past transactions into a ranking of which customers are worth the most attention.

The shop has about 1,600 past guided clients and kit buyers. The coordinator will score each by recency, frequency and spending and sort them into three groups. The top group, about 200 people, receives a personal winter note from their own guide and an early booking offer. The middle group receives the guide-signed newsletter. The bottom group receives only the yearly expo announcement. Guides' limited time thus goes to the clients most likely to return.

Timeline

Risk Register

Conclusion

The program will happen only if specific people have the time and tools to do specific tasks. A part-time coordinator, guides dictating short reports and linked systems make the plan workable for a shop of this size. Gallino and Moreno show why accurate inventory online matters, Blake, Nosko and Tadelis why the shop should not pay for clicks on its own name, and Fader, Hardie and Lee how to spend scarce guide time on the clients most likely to return.

References

Blake, T., Nosko, C., & Tadelis, S. (2015). Consumer heterogeneity and paid search effectiveness: A large-scale field experiment. Econometrica, 83(1), 155-174. https://doi.org/10.3982/ECTA12423

Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4), 415-430. https://doi.org/10.1509/jmkr.2005.42.4.415

Gallino, S., & Moreno, A. (2014). Integration of online and offline channels in retail: The impact of sharing reliable inventory availability information. Management Science, 60(6), 1434-1451. https://doi.org/10.1287/mnsc.2014.1951