With this information in hand, we recommend marketers ask themselves one question: How can we deliver a personalized shopping experience at scale?
Fortunately, this article by the Harvard Business Review offers a solution: “[The] best way to achieve meaningful personalization is by systematically testing ideas with real customers, then rapidly iterating.” And in order to do so, the authors of this article recommend integrating three things (we’ll focus on the first two):
- Data discovery, which is about sourcing and combining traditional and behavioral data to uncover meaningful insights about customers.
- Automated decision making, which uses the data to find prospects and create a score that defines the probability a prospect will respond to a targeted message.
- Content distribution, which will use these probability scores to trigger personalized ads and landing pages.
Right now, there are a plethora of tools that allow you to discover data and help you make marketing decisions at different parts of the customer journey at the various touchpoints a brand may own (i.e., e-mail, ecommerce, POS, search, etc). These tools do let you test and iterate different messages at different touchpoints, but problems arise when there is overlap across the different channels. Successful marketers today realize there is a need to aggregate data, because as the article quoted above says, “The goal is to create a learning ecosystem, one that connects insights to outcomes as part of a continuous, self-improving cycle.”
There are three benefits to having one ecosystem where all the data discovery is integrated.
First, you can segment your customers across all your owned touchpoints so there is no need to try to connect individual customers across the various touchpoints manually. An automated ecosystem gives you a place where you can see and understand your consumers across their entire shopping experience, from awareness to purchase. Therefore, your marketing decisions are no longer made in a vacuum.
Second, when you plan to test and iterate various pieces of content, you can see how that message reverberates across all of your consumer touchpoints. Therefore, you can measure various marketing messages to see how they lead directly to sales. For example, if you tweet out a message to your followers, you don’t have to just use the clickthrough rate to measure the success of that tweet. With all your data in one place, you can see how that tweet lead to an increased number of shoppers or an increased size of cart.