Personalization: You Are Ready, You Just Might Not Know It!
The race to personalization is on -- and brands that are making investments in providing more relevant, individualized experiences to their customers are winning. A study conducted by the Boston Consulting Group found that brands investing in personalization are driving 6-10% revenue increases.
While the concept of personalization sounds complex, getting started doesn’t have to be. You are likely more ready than you know. There are 4 key tools you need in order to begin providing more relevant experiences to your customers.
While you will need to have access to varied content -- for example, product imagery, copy, links --, a content feed is not going to be a one-size-fits-all. Here are a few examples of how the content that feeds a personalization ad unit will differ by industry:
- Retailer: product name, link, price, and image
- Hospital system: service line image, logo, copy, and link
- B2B: white paper/thought leadership content title and link
Myth: I need a completely unique piece of content for each user that I serve.
Truth: While the content you serve needs to be relevant to each user, that does not mean you need an exhaustively long feed in order to execute personalization. For example, a women’s size small Cubs t-shirt is a relevant product for many potential customers.
If you already have a library of content -- imagery, copy, etc. -- Rise can help you turn that content into a personalization feed. If you already have a content feed used for Google PLA’s, dynamic product remarketing, or Facebook DPA’s, then the most difficult box is already checked.
2. Onsite Tagging to Capture Behavior
At Rise, we use our proprietary Rise tag to capture the key actions needed to create meaningful customer experiences. This includes onsite behavior such as categories or products viewed and searched, items placed in shopping cart, and more. These behavioral signals inform the recommendation engine of the right content to serve. Machine learning can compare each individual’s behavior against the behavior of all visitors from the past to recommend content based on that individual’s similarities to other previous audiences. Personalization allows marketers to do more than simply retarget users with items left in their shopping cart. With personalization, the recommendation engine can recognize that users who browse women’s Cubs t-shirts are typically also interested in women’s Cubs hats. While this is an intuitive example, machine learning continually evolves to identify insights and less obvious patterns to give marketers an edge.
3. CRM / Customer Data
The incorporation of CRM and customer data help take personalization to the next level. Incorporating a customer’s offline profile data is key to connecting online behavior. CRM data integration not only enables smarter recommendations based on a complete profile of each customer, it is also the most efficient way to activate personalized media against your entire customer set. Specifically, CRM-driven personalization allows you to speak to users that haven’t visited your web properties recently and aren’t in your remarketing pool.
With technology partners such as LiveRamp, your CRM data can be matched to the individual online cookie of each of your customers. (Note: match rates can vary, depending on how often the cookies of your customer pool change). This means that you can serve ads to an individual from your CRM, identified as “CookieABC,” armed with their demographic information such as gender, age, and location. As an example, if you identify that a male is browsing women’s Cub’s t-shirts, you may personalize messaging to be more gifting-focused.
4. Transaction Data
The next advancement of applying your CRM data is integrating individuals’ transactions history. This means you can finally avoid annoying your customers by serving product ads to users who already bought your product! For some purchases, like a Cub’s t-shirt, it will be less likely that a user would purchase the same item twice in a short period of time. For products with predictable replenishment cycles, such as household supplies or beauty products, you can use your transactions data to intelligently pace out when you push ads to individuals. Transactions data is yet another input that enables more complex strategies and recommendations for complementary products by comparing the data of an individual to the data of all of your customers’ purchase histories.
So, are you now feeling a bit more personalization-ready? It’s likely that the tools you need are already at your disposal, it’s just a matter of bringing them together. This is a high-level overview of personalization -- in future posts, we’ll dive deeper into how machine learning works.
If you’d like to learn more, read about Rise’s Connex® personalization technology, or contact Rise today.