Brand marketers have long relied on creativity, emotional appeal, and data to build engagement strategies that target key audience segments through various channels. But with today’s consumers calling the shots on how and when they want to engage, marketers have to rely on data more than ever to understand individual consumers’ behaviors and deliver appropriate real-time interactions. Being able to do this at scale has become essential to a brand’s long-term success.
This isn’t hyperbole. Corporate revenues are increasingly tied to how well brands engage consumers, with Frost & Sullivan recently reporting that organizations globally lose $300 billion every year because of bad customer experiences. Many brands understand this, and 89 percent of companies expect to compete mostly on the basis of customer experience in the coming years, according to Gartner. The companies that succeed stand to benefit from a revenue shift of some $800 billion, according to Boston Consulting Group, which will accrue to the top 15 percent of companies who get personalization and customer experience right.
Getting the data component right isn’t always easy. Collecting, connecting, and preparing the data foundation is just the beginning. Integrating streaming or batch data consistently and effectively as you continue engagement is key to staying relevant and “personal.”
The modern connected customer generates a lot of data. Northeastern University research pegged the amount of data generated every day at 2.5 exabytes, which is equal to 90 years of high-definition video. Not all the data consumers create is equally valuable to a brand’s engagement efforts, but its existence in such volume is one of the reasons driving the need for automation as a key part of any engagement strategy.
Marketing technology is critical to effectively engaging customers in the channel and interaction moment of their choice. Research shows that consumers will switch to a competitor if they think companies can’t provide the relevant interactions they want. That’s why marketers are turning to technologies like customer data platforms (CDPs) to reinforce their consumer engagement programs.
CDPs can deliver a unified customer profile that is the essential “truth” about your customer at any point in time. The unified customer profile combines every piece of information you have about an individual customer, enabling a holistic viewpoint of their personal information, behaviors, interaction history, and even channel preferences. Having this information accessible enables contextually relevant real-time marketing that can dramatically improve customer engagement and add rocket fuel to your marketing results.
A pertinent example is RedPoint’s client Xanterra Parks & Resorts. Xanterra had multiple properties marketing to distinct lists with no sense of how much customer cross-over they had. They consolidated their data from multiple properties into a central location and were able to action that information into cross-property campaigns through multiple channels. The resulting efficiencies meant that Xanterra experienced triple-digit performance improvements in many marketing campaigns – in one case achieving 3,600 percent revenue growth while reducing the number of times they contacted customers by 13 percent. Think about this for a moment: Xanterra drove quadruple-digit revenue growth while doing less work than before. That is the power of putting data – specifically cleansed and unified data – at the center of your customer engagement strategy.
If data and a unified customer profile are key components driving relevant customer engagement, then machine learning is the engine that fuels personalization at scale. Using machine learning to orchestrate messaging more appropriate to a “segment of one” is the next tool marketers need in their arsenal to arm business success.
Machine learning algorithms enable marketers to automatically score and suggest appropriate audiences and content far faster than current manual segmentation practices. The analytical models inherent in these systems, such as propensity models, empower marketers to add only those customers that possess certain characteristics into a specific campaign. These models reduce the amount of work required to provide personalized interactions and provide the necessary insight to succeed at individualizing responses to customers.
With greater insight into customer behavior, you can make more effective decisions on which offers to provide no matter what channel the customer is using to connect with you. If interactions are more effectively personalized through machine learning and deeper understand of customer behaviors, brands can more efficiently close the customer engagement gap, and develop more loyal customers.
By putting customer data at the center of any engagement strategy and leveraging intelligent automation tools to provide the right offer at the right time, brands reap the benefits of hyper-personalized, interactions with consumers.