If only there were a simple answer to the question “How many times should I mail my customers and prospects?” But because marketers have so many influences and factors to juggle, no single answer will work for every company. On the other hand, three main components of contact management strategy can, if not properly in place, prevent even the most ambitious cataloger from achieving maximum return on investment. Smart use of best practices within those components can lead to improved response, savings, and ROI.
Improving upon the typical
One national catalog marketer recently conducted an evaluation to determine how often to mail to its prospects and customers. Great idea — but how the cataloger conducted the evaluation wasn’t so great. And the company’s process was far from atypical.
As part of the evaluation, the cataloger took all the campaigns from a previous season and put them into a mainframe merge/purge process to add up the number of contacts for each customer throughout the year. This compiled data, though, did not reflect changes in people’s addresses over time. Given that about 20% of the nation’s population moves every year, it’s hard to put together an accurate picture of all of the contacts unless you apply an address-change program such as NCOA to capture those moves.
Loaded with such a large volume of campaigns, the database system slowed down and became tedious to work with. And the catalog company analyzed each campaign without considering the previous one, making it difficult to track any long-term ramifications.
In the end, the cataloger had enough information to determine the optimum number of contacts within a mailing season. But its infrastructure made implementation too complicated.
The cataloger could have made effective use of its data, come to a valid decision regarding the ideal number of contacts, and implemented an effective contract approach if it had built the process upon the three cornerstones of a successful contact management strategy:
A properly tested strategy makes it possible to measure the effects of different levels of contacts and will allow you to properly segment your customer file to yield measurable results. Yet many catalogers could do a better job in planning, setting up, and testing a comprehensive contact management strategy. For example, some marketers don’t create a hold-out sample, a baseline group to be used for comparison purposes. Others don’t create a statistically valid sample to compare this group to. Some don’t even capture contact information!
Many catalogers have a contact management strategy in mind but lack the database systems to support the strategy. In other words, they have great ideas but no way to execute them. The ideal database systems have four attributes:
- flexibility — they are adaptable to changes in the marketplace and provide access to actionable data in a variety of ways.
- scalability — they can grow as a business expands to include millions of prospects and customers.
- real-time capability — they offer the ability to evaluate information quickly and make immediate mailing decisions.
- analytical capability — they allow for OLAP (online analytical processing) reporting, ad hoc queries and analysis, and campaign management to optimize offers, channels, and contact strategies over time.
- Data integration
Your various content channels — catalogs, Websites, package inserts, telemarketing — must be integrated so that you can determine not only how each customer has been contacted but also how each responded across the channels.
Doing it right
Today many catalogers are departing from the standard project-based operating procedures and finding success with new, process-based approaches that take their goals and strategies into consideration and allow them to continue to evaluate the number of contacts for all customers and prospects.
Take the case of another cataloger, which hoped to identify whether there was a point in time when it was either overcontacting (mailing too frequently) or undercontacting (insufficiently mailing) customers. Ultimately, the cataloger hoped to obtain better results and optimize contacts with customers.
At first the cataloger did not have the infrastructure in place to support this type of evaluation. The company had to rely on other factors, such as segmentation approaches, to determine whether a person should receive a catalog.
But the cataloger switched to a relational database, in which different types of information — specifics on customer/prospect contacts, purchase history, geographical and demographical data, model scores — could be stored and easily accessed. The relational database made it possible to track customer and prospect activity, such as how many catalogs and which versions they received. With the relational environment, it became possible to compare this information to the response rates and identify the influences of contacts over time.
The cataloger could now see that customers who were receiving the sixth and seventh catalogs were twice as likely to respond as those receiving their first or second book. Sending six or seven catalogs in a short period of time would be too costly and impractical, of course. But using the relational database, the cataloger tried to determine the proper number and best sequences of contacts. First it ran the normal segmentation/selection strategy to determine who should receive the catalogs. Then the company added, each month, the names of customers who had just received their fifth and sixth catalog to the group that was to be mailed — even if those customers didn’t fit within the segmentation model.
By adding these customers to the monthly mailings, the cataloger gained millions of dollars a year in sales. The company also created a continual process of identifying customers based on multiple factors, including the number of previous contacts.
Another cataloger decided to compare the response rates of top segments of customers who received two catalogs over a two-month timeframe against the response of customers who did not receive a second catalog within the same two-month period. Through the analytical process, the cataloger was able to set up control and test groups and to manage the offers and contacts delivered to those groups.
The cataloger found that the first group’s response was above average. But the second group’s response — even without receiving a catalog — while somewhat lower, was still profitable. The comparison helped the cataloger determine that it would be more valuable to spend its marketing budget on mailing to prospects and expanding the prospect universe rather than on sending customers more than one mailing every two months.
Keep in mind that these contact strategies take time and can not be implemented quickly. Creating and carrying out a successful contact management program requires thoughtful evaluation and consideration. But if devising an optimal contact strategy were simple, then everyone would be doing it — giving advanced thinkers less of a competitive edge!
Dan Wells is vice president of the Catalog Retail Services Group, Merkle Direct Marketing, a Lanham, MD-based provider of database marketing services.
CRM Alphabet Soup
Don’t know your BPM from your BPR? You’re not alone. The customer relationship management industry seems to have spawned a frightening number of difficult-to-decipher acronyms. To help you better comprehend technical articles or speak with CRM pros, Catalog Age presents this cheat sheet:
ACRM analytical customer relationship management; the analytical components of CRM, such as data marts and data support tools, used to provide customer segmentation, profitability analysis, event monitoring, and predictive modeling, among other benefits.
BI business intelligence; the analysis of customer data to reveal hidden sales and service opportunities.
BPM business process management; the process of shepherding a project through a multistep process.
BPR business process reengineering or business process redesign; a strategy that combines changes in business processes and management systems to improve performance and productivity.
EDM electronic direct marketing; marketing through CRM software (which provides information regarding the selection of prospects and customers) and the Internet (generally via e-mail).
EMA enterprise marketing automation; automating the marketing process, including the campaign planning and analysis functions.
ERM enterprise relationship management; a broad (enterprise-wide) strategy that improves the management and flow of a company’s back-office operations.
ERP enterprise resource planning; similar to ERM, although more specifically focused on operational planning and resources.
OCRM operational customer relationship management; automating customer touch points such as sales, marketing, and customer service via connected delivery channels and integration of the front and back offices.
SFA sales force automation; a part of the CRM solution that enables the sales department to better manage its resources and sales cycle; may include territory management, forecasting, and lead management.
Sources: 4C Consulting; CRM Association