As more marketers embrace big data, they must look to creative data strategies that simplify segmentation and audience building for targeted ad campaigns.
Marketers face increasing challenges these days as they attempt to manipulate data to define their brands’ high-value audiences. Staring down terabytes of customer and third-party data can quickly intimidate even the most confident marketing professionals.
Implementing a more rigidly-defined data strategy can make this process a lot less daunting. Additionally, with a little outside-the-box insight, one can discover segmentation strategies that offer vast advantages over more standard methods.
Therefore, to improve your targeting efforts and reduce the stress of your marketing staff, consider using the following best practices.
Cross-Sell Instead of Resell
A common mistake when setting up segmentation parameters by action is to include types of actions that separate a real lead from a fake lead. Customers who receive targeted ads based on an item they purchased, even though they later returned it, provide a common example. Another example is customers who are targeted for big-ticket items that are usually one-time purchases, despite them having already purchased it.
Both problems can be solved by integrating point of sale data within your segmentation profiles. As long as online activity can be attributed to the correct person, customer relationship management systems can learn to incorporate in-person activities into the customer’s audience profile. Therefore, instead of receiving ads for the exact same 50-inch 4K TV they just bought, they should receive ads for complementary purchases, such as sound bars or Blu-ray players.
Look at the Big Picture
Consumer data has been expanding at an alarming clip in size, complexity and velocity. Many firms attempt to shrug this issue of scale off by collecting as much as they can and broadly segmenting consumers without looking at the big picture. As a strategy for a pilot program, this approach could work, but on a large scale, it spells trouble.
A solution is machine learning, which uses complex algorithms that dissect an extensive set of data points, resulting in comprehensive, contextual information about consumers for marketers. For example, as opposed to just figuring out what products certain consumers bought, machine learning platforms will calculate why and how purchases were bought by using many specific data points that cater to the big picture.
Machine learning technology factors customer segmentation and marketing automation data into an easily accessible database, allowing users to retrieve more accurate insights. If retail brands want to target individual shoppers who last purchased an item that was significantly discounted, they could use results from machine learning to send shoppers customized discounts for their next purchase.
A potential outcome of cross-selling and looking at the whole picture could be that marketers truly realize that traditional demographics like age and income may not inform accurate segmentation as much as other factors such as the circumstances of why an item was purchased, which must become known before fully embracing a segmentation strategy. Otherwise, the organization ends up capturing and storing data inefficiently, resulting in stressed-out marketers. Therefore, experiment with small groups to obtain proof-of-concept, but let proven patterns govern strategies in the meantime to avoid needless, costly and time-consuming complexity.