1. How does customer segmentation help marketers reach their targeted audiences?
Creating and refining personas based on a combination of demographic, behavioral, social and transactional attributes sets the stage to define an audience dynamically, target selectively, and apply rules-based responses. With an intuitive segmentation interface, marketers can utilize expansive data attribute sets, persona definitions, and lead scores – as well as inclusion/exclusion combinations, lookalikes and complex filters – to create hyper-targeted campaign lists.
2. For segmentation purposes, are there any key advantages to using a customer data platform (CDP) as opposed to a data warehouse or data lake?
A reliable customer data platform will consolidate structured and unstructured data from all digital native, industry and brand channels. Advanced algorithms built within a CDP platform enable it to identify and map multiple identities of audience members, enabling marketers to interact in real time and deliver more personalized communications.
Similar to CDP platforms, data warehouses and data lakes collect data from the same source and with the same structure of information. Unlike CDP platforms, data warehouses and data lakes need to be updated manually and are often scheduled at specific times. This process slows the synchronization of data needed to build a single customer view, and in turn, communications cannot be delivered in real time.
Data synchronization is the cornerstone of a robust customer data platform and serves as the primary source of insights marketers need to drive real-time conversations.
3. How does a CDP resolve customer identities across touchpoints?
Not all CDPs are alike, but a foundational capability of a successful CDP is its ability to resolve and manage the customer’s identity across touchpoints. To create accurate customer personas, a CDP must be comprehensive and consolidate data from a number of touchpoints, including a brand’s internal databases and third-party sources. Using intelligent processes, it will sort through this data to learn as much as possible about the customer and provide the necessary insights needed to make informed marketing decisions. Ensuring a CDP has this capability requires an understanding of its role within the organization itself, as well as the martech structure specifically.
4. What are the advantages of using AI as opposed to traditional manual segmentation?
Sophisticated machine learning and artificial intelligence allows marketers to go beyond traditional manual segmentation. These capabilities enable micro-targeting, boost campaign effectiveness and empower highly insightful benchmarking. With a CDP build on statistical and predictive AI, marketers can take advantage of strategic proactive and reactive capabilities to target audiences with unprecedented precision, engage at the segment-of-one level and gain new insights on campaigns and audiences with linear regression, forecasting and segmentation.
5. How do marketers interpret and apply the data once it is segmented?
Segmentation enables marketers to define lead scoring patterns and rank prospects according to their value and position within the lead funnel using attributes from across profile data, transaction patterns and propensities. It also allows marketers to document the true impact of marketing investments by defining specific targets, controlling audience groups within segments, and specifying custom delivery proportions for every campaign.
From a brand perspective, a customer persona embodies traits around which it can build hypotheses that can then be leveraged for targeting and engagement. Such well-developed personas can contribute directly to increased revenue and lower costs.
6. Is there an industry vertical that is harder to segment? If so, why?
Not all marketers require the same level of segmentation to target their audiences accurately. Different industries may have significantly different needs that involve varying amounts of detail when building out consumer personas. For example, investment firms have very strict regulatory and compliance challenges that limit a marketer’s ability to store and backup shareholder’s data. This makes it more difficult for marketers to segment customer data because there are guidelines on what information can be compiled and leveraged to generate insights.
CTO at Resulticks With more than three decades of experience in the technology and innovation space, Mani’s strong entrepreneurial spirit positions him as a forward-thinking industry leader with a focus on delivering solutions for tomorrow’s problems to both enterprises and technologists. He has demonstrated his expertise in IoT, predictive modeling, and big data solutions across a broad spectrum of industries including manufacturing, logistics, banking, capital markets, insurance, real estate, utilities and telecommunications. In his current role as Chief Technology Officer at Resulticks, he is instrumental in the design of the solution’s architecture, implementation approaches to market, and application within client business environments.