HOT! Trends in Direct Response Marketing
Winter 2009
ØA partner of ours, a leader in online behavioral targeting, has introduced a first-of-its-kind technology that promises to revolutionize customer relationship management and e-mail marketing for Internet retailers. Using this new technology, online marketers and retailers will, for the first time ever, be able to identify and make direct e-mail, phone or postal contact with unregistered and previously unknown online consumers who have demonstrated an interest in a product or service but have abandoned the shopping cart prior to making a purchase.
The CRM systems that facilitate contacting former customers on file who have abandoned shopping carts have proven very successful at reminding people what they left in their carts, but have their limitations as they only identify existing customers. With our new capture technology, businesses can effectively identify unregistered users and target them via acquisition e-mail or postal communication, effectively closing the loop on would-be lost shoppers.
This new capability leverages an industry-leading co-operative database with more than 165 million active permission-based individuals with CAN-SPAM-compliant e-mail addresses with CASS certified postal addresses, which is integrated with the company's proprietary data-matching system. The solution builds on the concept of banner ad re-targeting, the fastest growing method in use for online advertising, and offers users the next generation of this tool to expand re-marketing communication strategies to additional online channels.
ØMobile Messaging Solutions(MMS) are a fast growing area. The capabilities and leveraging of SMS, MMS and WAP on phones are becoming a bigger and broader part of the direct response, social marketing ecosystem. I’m involved in projects that, for example, help recognize and account for visitors to states, cities and attractions that were promoted to but whose response could not be accounted for previously. We are effectively closing the direct response loop in situations where it was difficult, if not impossible, previously.
In retail, MMS is providing a more effective way to capture new customers, enhance marketing database universes and deliver and track incentive promotions. For example, a non-customer store visitor can be given the opportunity to text to participate in special offers while visiting the store. This allows them to be added to a promotional pool or even opt to be added to your database. Or, a customer can go to a web site (or even a mobile WAP site) and be presented with, or actually seek out, special offers and discounts in areas of specific interest. Coupons or validation graphics are immediately sent to their mobile device for presentation at the time and place of sale. This not only increases redemption and store traffic but also increases average sale amounts.
MMS is not only helping our clients build better and richer customer files but also facilitating our understanding of the sales process, providing feed-back on the fruits of promotional efforts, and providing better guidance in designing the marketing programs in the first place.
ØReal Time Data correction and application is allowing for many of the new methodologies we are employing. We can draw upon data files that are far larger and more prolific than anything we have had available in the past. The phone number database contains over 420 million records and includes cell phones and unlisted as well as historical name and phone combinations. There are over 120 million email addresses directly accessible, all containing recent phone and postal information. We can forward or reverse append using both email and/or phone. And of course all the typical demographic data rides along.
Advanced lead generation systems are increasing the success of many of our B to C clients. Success in lead generation requires knowledge of many aspects of the industry and its constituents. Lead generation now plays a significant role in new customer acquisition for many direct marketers including catalogers and other direct mailers. Our ability to examine, correct, append and assess leads at the point of acquisition is lowering acquisition costs, increasing close rates and increasing average order values.
Publisher Lead and Inquiry Aggregation saves money by assuring that all incoming leads are examined in one place before being accepted from the publisher. We are correcting and/or appending contact information including email addresses, phone numbers and postal addresses. This data quality process allows us to detect duplicate leads and referrals quickly. Not only do we see and detect multiple requests and inquiries that may happen during a prospect’s internet shopping session but we can also detect and reject current requests and past customers. The net of this is that our clients don’t pay for customers or leads they already own. They can also interact more intelligently with their customers.
ØConsumer Lead Scoring and quantification will represent one of the largest paradigm shifts in direct marketing. Lead scoring in the B to B space has been popular for some time. Lead Scoring is quickly becoming successful in the B to C space because we can access and append data in near real time, and use that data to make judgments about the lead very quickly. We are using models to categorize and prioritize leads from most channels including Internet sites (co-reg, referral and organic), call centers and to inquiry follow-ups.
Once categorized and prioritized, the sales approach can be adjusted to complement the specifics of the potential customer. For example Web site content can be dynamically presented, sales scripts can be chosen, merchandise options can be customized and call center staff can be assigned according to their sales approach and relationship characteristics.
We also are helping clients reject poor quality or low potential leads before they acquire and pay for them. Lead Scoring not only can categorize and prioritize, it can eliminate. We have found that via our models, we can eliminate 5 to 10% of incoming leads, inquiries and catalog requests that statistically produce no revenue.
ØList Cleaning and Data Quality is a key consideration of every successful marketing program. A new Information Difference Research Study, The State of Data Quality Today, released in July 2009 provides a stark picture of the data quality dilemma still faced today. This study, sponsored by Pitney Bowes Business Insight and Silver Creek Systems, found, in a survey of 193 businesses across Europe and North America, that:
o Fully one-third of respondents rate their data quality as “poor at best” - and only 4% indicated that it was “excellent”
o 42% have made no effort to measure or monitor the quality of their data
o 63% have no idea what poor data quality may be costing them
For DM applications, data quality includes the correctness and presence of address elements such as postal address, email address, home, business and mobile telephone numbers and extends to customer activity and purchase data, demographics, lifestyle characteristics, media preferences and leisure activity data.
ØCustomer profiling and segmentation is essential to success in today’s environment. Profiling is probably the most common first project done for our clients. Today’s increased economic and competitive pressure has made detailed customer awareness and knowledge critically important.
Many clients have historically had profiles built based on demographic characteristics. But today we use a substantially larger array of data to depict behavioral characteristics, lifestyles, life-stages and media preferences. To keep costs manageable and to increase usability, we have developed a very granular household level clustering system. The Smart Clustering system combines multiple descriptive elements to group households by their probability to react similarly to any given offer or opportunity. The system was designed to help ‘clone’ customers for prospecting and to help segment them for best response in reference to a particular product. Similar competitive systems were developed at the household level and are far less granular. Less granular systems (like those supported by catchy cluster names) employ much larger target groups limiting their likelihood of reaching the best households efficiently.
Part of the Smart Clustering system is a network of propensity models that were developed based on Simmons Market Research Bureau questions. There are some 2,200 of these models. They can get pretty specific, reflecting questions such as “Do you enjoy old movies?”, “Have you traveled on American Airlines to Europe in the past 12 months?”, “Have you purchased from the XYZ catalog in the past 6 months”, “Do you read The New York Times daily”, etc. We have actually created vertical lists from these for things such as Home Improvement Enthusiasts, Computer Gamers and Treasure Hunters. All of the models were prepared and based on the Experian National Consumer Household file. Referred to as Smart Targets Plus, the variables are available as individual list selections in concert with factors such as age, income and etc.
We have observed recently that the economy has changed customer groups significantly. Large customer segments that once produced well for our clients are no longer working. We have observed that limiting customer samples for profiling to the past 6 to 12 months produces a very different profile than those done over a longer, more typical 24 month period. This demonstrates that underlying customer characteristics are changing more quickly so more frequent customer profile assessments projects should be considered.