Due to the rise of marketing automation technology, we have entered the “uncanny valley” moment, where computers can generate a near-identical resemblance to a human being to the point where it causes revulsion to those who see it. With this, companies can deliver highly-personalized campaigns which ultimately results in higher ROI on the marketing spend. At the same time, we sometimes feel like we are being watched; for example, you can Google search for a specific pair of boots from, say Timberland, and ads for those boots will appear on any website that offers paid ads. But expecting a machine to generate the perfect personalized experience on its own is a fool’s errand; so we have been systematically testing ideas with real customers and then continuously iterating to achieve meaningful personalization. But now, tools exist to execute this operation more efficiently and fluidly as well as creating a continuous, self-improving cycle of connecting insights to results.
In order to create this self-learning ecosystem, three things must be integrated: Data Discovery, Automated Decision Making, and Content Distribution.
Data discovery involves gathering and combining traditional and behavioral data to uncover meaningful insights about customers, such as preferences, interests and needs. This is no easy task due to the sheer number of data points out there as well as extracting the meaning behind them. As such, companies generally focus on the data that is most readily available. Traditional CRM systems often don’t have the flexibility or scalability to manage these vast amounts of both structured and unstructured data. The systems in demand today are ones that can run the advanced analytics to discover useful and practical insights, and use them to send the appropriate message (e.g. if customer does x, send them y.)
Today, companies are increasingly utilizing a customer data platform (CDP), which integrate first-party data (such as customer-supplied data, purchase history, website/app behavior, marketing response, engagement rates) with third-party data on customer interests and shopping behavior to improve individual targeting.
To put the valuable data into good use, an automated decision making platform is used to calculate propensity scores for each customer. These scores show the probability of an individual responding to a specific offer or engaging with specific content. This is done through two-way communication: automated decision-making processes collect and track customer reactions and turn that information into the focal point for future messaging and offers.
The final touch of personalization is a good content distribution method. Using customer and prospect scores to distribute specific, personalized content across all channels will be crucial to really driving the customer to act in your desired way. For example, airlines can set rules in their automated platforms to make decisions on the lowest cost they can offer for a ticket and reasonably predict which customer segments will respond to the offer over an email, a display ad, or within the mobile app.
For these three “Ds” to operate successfully, companies need to integrate their technology systems to allow their data to flow where it’s needed and to make real-time decisions. This will create a cycle that is self-learning and makes its necessary adjustments. It develops statistical and event-based models based on response data from customer interactions. Predictive marketing analytics then make recommendations on which actions drive the highest conversion rates. Through this automation, the best-fitting content goes to the right customers.