Send Messages Your Customers Expect
According to a recent study by Peppers and Rogers Group, personalized and relevant direct marketing communications can garner a 48 percent increase in repeat orders, 25 percent greater average order value and 32 percent increase in overall revenue. However, customers are hardly cheering about the marketing communications they receive.
Yankelovich's State of Consumer Trust Report found that a mere 14 percent of consumers think their personal information is used to generate relevant communications to them. And only 9 percent think that sharing personal information lets companies get to know them and provide products that fit their lifestyle. In short, consumers don't think there is any benefit to sharing personal information with companies they do business with.
Yet virtually all U.S. consumers have shared personal information, with 75 percent providing details like e-mail address and home phone number and nearly 50 percent sharing interests, hobbies and income.
Therein lies the problem. Even with data collected behind the scenes, few companies deliver on the promise of personalization: truly making communications relevant to specific customers. Customer relevance factors (CRFs) are critical to connect with customers in a way that shows you understand their motivations and objectives so you can meet your objectives (increased response, revenue, etc.). CRFs include consumer motivations and attitudes; life stage; previous purchases and timing.
For instance, 69 percent of retail customers report that timing is the main reason they open a particular piece of direct mail. But what is relevant to one customer may be irrelevant to another. Identifying CRFs is a two-step process requiring both creativity and analytics - right and left brain activities - to put a face on your customers.
Step 1: Segment your customers. This involves grouping customers where members are as similar as possible to one another and as dissimilar as possible to members of other groups. This article will not discuss the fundamentals of segmenting. Suffice it to say, a combination of approaches is usually best, such as: RFM, geography, demographics, product characteristics, psychological attributes and socioeconomic status. Groups should be descriptive, manageable and accessible. Of course, it does you no good to segment customers using traits not widely available on your database or procurable through outside data enhancement sources.
Step 2: Understand customer motivations. This involves learning more about customer purchase and behavior patterns through analysis and/or primary research. Depending on budget and timing, you may want to use something as informal as an Internet poll or as formal as focus groups or phone surveys. The aim is to learn about the different motivations of particular customer segments to buy and use your product.
The Bowling Proprietor's Association of America, the trade group for bowling centers, long held that a center's best customers were its league players. These were the most frequent visitors, with players committing to a 35-week tournament schedule. This translated to an average of $262.50 in annual revenue per customer for a center.
But after segmenting customers into league and "open" or social players and conducting focus groups, a different picture appeared. Social players played far less often but were more likely to spend money on food, drinks, rental shoes and video games. This yielded annual per-customer revenue of $360 and also a much higher lifetime value. The BPAA found that a center's best customers were social players whose CRF was fun versus league players motivated by competition and improving their game.
You don't always have to conduct primary research to learn how customers interact. Frequently, transactional data help identify customer behavior patterns. Examples:
* Market basket analysis: Identifies the combinations of items that customers are most likely to buy over time and determines the probability that a customer will buy a particular item based on the purchase of other items. One East Coast grocer found that customers who bought fresh vegetables, yogurt and deli items also were most likely to buy fresh fruit. In developing a promotion for fresh fruit, one segment contained customers who had bought vegetables, yogurt and deli items but not fruit.
* Purchase cycle analysis: Helps you understand the effect of time of year on purchases. Some things are obvious (snow shovels in winter) but others are subtler and best identified through data analysis. Proflowers.com found it had two customer segments: those motivated by a holiday event (i.e., St. Valentine's Day) and those motivated by an emotional event (sympathy, love).
* Life stage analysis: Determines which products are more likely to be used during particular stages of a customer's life. As a marketer, you should know which of your products are most appealing to customers at various life stages. When Wachovia Bank revamped its Privileges best-customer program, it developed a value-added newsletter. Articles were written for pre-retirement and post-retirement customers as well as small business owners. These customers were all part of an elite group - the top 2 percent of customers - but their needs were different. In one issue, customers 66 and older received an article on becoming a venture capitalist while 65-and-under customers read about the value of opening a Roth IRA for children. And small business owners got advice on hiring older workers.
After you identify CRFs, drive that learning into strategy and creative. The logic imparted by left-brain thinkers should fuel right-brain thinkers' creative juices. This requires collaboration among analysts, strategists and creatives to ensure that CRFs are translated into day-to-day strategy and communications that resonate with customers. Make customers feel that your organization is a place that not only knows their name, but also respects what matters to them.