|Understanding Cross-Channel Data to Market Smarter|
|Tuesday, November 15 2011|
The most valuable information that marketers can have is data on the brand loyalty and buying behavior of their target audience and how it can be influenced. In today’s multi-channel world, it is even more important to understand these behaviors and preferences across every media touchpoint available. Unifying and analyzing this type of data across the swath of channels and customer interactions remains a challenge to many marketers, but marketers must be prepared to connect the dots and meet this challenge.
Why is connecting these dots so important? Understanding something like the buying habits of a target audience provides marketers with action items to ensure that their customers are getting what they need based on their preferences. It can also help ensure that a brand has the appropriate presence or messaging in each of those channels. Demographic data is consistently used to drive personalization, which can lead to eye-catching and relevant marketing applications. Nevertheless, more complex and descriptive data can help generate precise, targeted messaging that is truly tailored to each person, creating a compelling reason for them to move through the sales funnel.
An example of how cross-channel data has been leveraged to determine buying behaviors has led to a concept known as the Research Online, Purchase Offline (ROPO) effect. This concept came about as websites were gaining a lot of traffic, but online sales were not reflecting the increase in activity. In fact, according to Shop.com, e-commerce websites currently report an average conversion rate of only 2.2%. Live tracking on the Fireclick Index shows comparable numbers. On the other hand, in-store sales were seeing an increase. Further analysis confirmed that consumers were doing their homework online, but completing their purchases in-store. Organizations such as Google and Yahoo! (PDF download) have researched this concept, finding that it is not limited to large purchases such as electronics or personal/recreational vehicles, but also clothing and other consumer retail goods.
In the aforementioned example, understanding and analyzing this behavior across channels can convince marketers to develop stronger online marketing tactics so they complete the buying process online, or otherwise retarget them through online ads when they visit other sites. What about other buying behaviors that can be uncovered when considering data across newer channels and customer segments?
More recently, buying habits have extended to include mobile in the mix. While in stores or on the go, consumers are leveraging mobile devices to compare prices, seek out deals, and obtain more information regarding their potential purchases. Retail mobile applications and mobile-optimized websites extend online research and purchase capabilities, while mobile apps such as RedLaser and ShopSavvy allow bargain-hunting consumers to scan UPCs or other barcodes to find the best price or read product reviews so they can make a more informed purchase. Location information could theoretically be used as another vector to track behaviors and really get inside a purchaser’s head.
There is no doubt that understanding cross-channel behaviors is a daunting, complex task. Even the most skilled businesses face challenges with cracking the code of consumer intents and behaviors. The rewards are substantial, however, especially considering that consumers see hundreds if not thousands of marketing messages on a daily basis. By analyzing cross-channel data, marketers can gain insight and precisely target people to provide compelling offers that shift consumers from interested buyers into brand loyalists.