Back in 2008, Darren Herman, made what is now known as one of the most cutting edge data-driven market test in the history of advertising and marketing. He decided to run 27 different marketing offers through Web ads for Vespa, the scooter company (Clifford, 2009).
He packaged the different marketing offers differently. Some ads were rectangular, some square, some static and some were dynamic. He also worded the texts differently. Some dangled the benefit, some the direct offer, some contained a pricing cut, some concentrated on branding and gave away free items such as T-shirts.
The goal was to determine the best motivation for customers that would make them purchase the car or, at the very least, show some interest on the product. They determined that the price discount is the best motivation. The $0 down offer resulted to 71 percent more direct responses than all of the other marketing offers they’ve launched in the past. He also went further by getting looking at where the customers came from, what they do, and their gender (Clifford, 2009).
Mr. Herman used the data to convince Vespa that the $0 down is the most effective marketing offer and it is what should carried by the advertising agency.
From the glory days of the glamorous Madison avenue, marketing has always been about creative images and catchy taglines but the current utilities available to businesses are allowing the collection of data to predict behaviour, determine climate of marketing environment and discover the most effective business strategy that to increase branding and revenues (Brennan, 2008).
The acceptability of Starbucks card could easily be accurately determined through the examination of internal and external marketing environment using data. The external environment must examine six main areas: Demographics, Economic, Environmental, Technology, Technoloy, Political and Cultural.
Starbucks is one of the very few companies who have the opportunity to establish an intimate relationship with its consumers (See figure 2 to see how Starbucks uses its IT). For one, the place of business itself allows the consumers to spend hours on end in their coffee shops. While many businesses struggle to get the attention of their customers for thirty seconds, every Starbucks store has a loyal consumer base.
The one effective strategy is to design a data-centered market analysis. Currently, Starbucks revenue is retail oriented. Each customer usually buys one cup. The Starbucks card is obviously geared towards regular customers for them to even have a reason to have a card exclusive to Starbucks. They could easily analyse data sales by determining which areas have the most numbers customers that make the most number of purchases in a month. This would easily drill down the areas where it will be economically viable for Starbucks make initial investments.
Once that is determined, Starbucks will need to find out what card features would make it attractive to customers. Traditional strategies such as surveys could work as an initial study to come up with a list of possible features to put in the card. It would even be a significant to do a qualitative analysis to determine if there are other similar programs that are implemented in the market by by direct and secondary competitors. The survey and competitive analysis would give Starbucks some insights on how the market is responding to specific marketing programs. See figure 1 to see how data would allow a business to respond better to market demands.
After Starbucks chooses the areas that have the highest regular customers, they could give away the card for free to anyone interested. They should, however, collect data which will include customer gender, income, profession, frequency of visit, and their chosen feature. The frequency of visit could be crossed check to their actual sales records.
Based on the initial test, a one-month market exposure would allow Starbucks to determine how the features will increase sales and whether the operational and capital expense in relation to the revenue would be worth rolling it on a national level.
A data-dependent marketing environment auditing process could drastically change the way marketing strategies are planned.
Barriers to Marketing Planning
Starbucks is a relatively young company and the brand image it has projected through the years has almost never changed. It has also been very loyal in its core product, coffee. This same consistency may also be the number one barrier to any market planning.
All company innovations have been based on beverages. Thus, any other form of marketing, sales and advertising will only result to insights on how the market reacts to different beverage innovation and limited insights on how the market reacts to other marketing programs especially on customer retention programs.
Base studies are very important in establishing trends and analysing possible market reaction to certain marketing programs (Hatton, 2000). For a company like Starbucks that have such a legacy and a long standing relationship with the market, marketing insights would have been extremely helpful in designing future marketing programs.
There is also shortage on intelligent data collection, data monitoring and data interpretation. One of the greatest barriers in data collection is privacy issue however, some of the biggest companies have already devised ways on how they can collect data from consumers without invading the privacy of customers. Starbucks could have very well utilized data collection systems and processes in order to develop a higher level of familiarity with their customers without compromising customer privacy.
Right now, the lack of systematic data collection and interpretation is a major barrier in a data driven marketing plan (Jabber, 2010). Starbucks is now forced to first collect the data before even beginning to design a marketing plan.
There is also a great imbalance on market distribution. More than 75% of the company’s stores are in the USA (DataMonitor 2010). This means that the business risk is also highly concentrated on the U.S. A program like the card is not a high risk business venture but it will still require capital expenses. However, it has an international brand recognition. Blythe (2006) emphasized the importance of consistency in branding and marketing for a global brand because any program that is launched in the U.S. will be heard of in other countries and expected to be rolled out.
This is specifically problematic provided many expansions are failing. Japanese operations, for example, are experiencing some decline in sales and many stores are closing. This development would hurt productivity of any marketing program.
There is a long standing issue on employee efficiency. Employee efficiency is, perhaps, the most important contributor in company productivity (Hollensen, 2003). As of 2010, Starbucks reports one of the lowest employee efficiency (Sullivan, 2003). They recorded a $71,544 revenue per employee. The industry average is $110,841 (DataMonitor, 2010). They have a $5,294 income per employee compared to the $9,500 per employee average (DataMonitor, 2010). Such inefficient employee performance specifically affects how marketing programs could eventually affect the business.
Starbucks should start setting up an intelligent data collection, monitoring and analysis system. A three-month trend would not be as accurate as a 12-month trend but it would still greatly help in understanding market behaviour and predicting market performance of new products.
Data collection should cover basic demographics such as gender, location, and purchase trend. This includes the average spend a single consumer makes, location of the purchase and time of day. Once these basic demographics are in place, it would be easier for Starbucks to trace how each consumer reacts to different marketing stimulus and, eventually, predict general market response.
The company should also take a second look at employee programs and how to maximize employee productivity. This would involve other departments that are not traditionally into marketing programs. Human Resources, for example, would be in the best position to review and determine what is causing low productivity. Whether it is inefficient sales process or dissatisfaction with company benefits and privileges, the determination of what’s causing low productivity would help in maximizing effectiveness of marketing programs.
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