One area that companies are investing heavily in is providing an “extreme customer experience” to the “segment of one.” What does this mean for analytics? Traditionally, large enterprises split customers into multiple segments based on customer attributes that were then used to identify and classify customers. These segments included their location, their current and potential spend, and which products and options they chose when they became a customer.
Marketers use these segments to determine which products they would market to which customers. Likewise, customer support applies different levels of service to each customer segment, and operations measures the profitability of each segment separately.
This is both highly frustrating to customers and an incredibly inefficient use of resources.
- Every customer is different. They feel frustrated when their individual needs aren’t met, and their expectations about how they’re treated as customers are rising.
- A one-size-fits-all approach doesn’t take into account the emerging customer acquisition and support channels that provide the potential to reduce the cost of service and market much more effectively. This includes mobiles applications, social networks, and the internet of things.
- Because the cost of customer communication is plummeting, customers are inundated with content. They’re choosing to delete, unfollow, and unsubscribe from content that doesn’t speak to them.
These same trends are opportunities. Companies are collecting far more information than ever before and the technology exists to leverage this at scale. They no longer need to treat customers as being pure segments. They can market to them personally, understand their likes and preferences, and give them services, all of which turns them into fans and advocates.
So how do we use data to connect to the segment of one?
- Make the segment of one a corporate mandate. Communicate and service each customer as if it were a personal connection.
- Rethink how your digital front office assets (including digital marketing, customer service and online) interact with customers to support this mandate.
- Build a team of data scientists and data analysts to move from guesswork to data-driven decision making.
- Build your customer communication around their analysis and deploy their work into every front office application. Measure and monitor the return on investment (ROI) from each initiative.
Done properly, this will result in happier customers and higher net promoter scores. It also means that the data companies are collecting results in visible ROI, which improves their bottom line.
We would love to hear your thoughts on how the segment of one will drive your data strategy. Contact us or comment here to let us know.
For more on predictive analytics, join us on the Analytics from SAP blog every Thursday. You can also find the previous series posts here.