As the universe of what is 'knowable' about customers is expanding, new machine-learning technologies evolve to help businesses see further and deeper, improving business decision-making. Combining human expertise with machine intelligence can be a powerful tool, since human interpretation alone can miss contextual clues in large data sets. According to data from 451 Research's Voice of the Connected User Landscape (VoCUL): 1H 2017 Corporate Mobility and Digital Transformation survey, 82% of businesses say that machine learning for automated contextual recommendations is important to creating personalized customer experiences.
The 451 Take
It's important that marketing understand the alphabet soup differentiation. MDM, DMP, CRM, CDP and CIPs all offer a variety of benefits, and businesses are still searching for the 'holy grail' solution. It's very difficult to build a CIP from scratch that does more than just house the data but also acts on that data in real time for multiple use cases. Businesses must shift away from 'he who holds the most data wins' attitudes. It's important to plan for all potential intelligent business application use cases of a customer 360 throughout the customer journey. Advanced machine learning that can take action on signals with real-time decision-making for 'in the moment' execution across both physical and digital experiences is essential.
Additionally, ensuring that a company is compliant with the GDPR will mean combing all customer data to account for a variety of factors, including where and how data is stored, and ensuring that businesses always have the most current information. Since complying with the GDPR can be a massive cost undertaking, having a single, real-time customer view can motivate a business to turn it into a profit-making activity instead.
Harnessing the power of data is essential for businesses as they capitalize on improving the customer experience. The pressure to deliver differentiated and consistent customer experiences is essential because multiple industries are facing considerable disruption. There will be a seismic shift in growth strategies for business and consumers to subscribe to services, rather than buy products. The result will be a stronger focus on loyalty and relationships that build upon consistent, positive interactions with customers.
It's about aggregating and connecting insights to deliver a consistent and dynamic experience across the customer journey. The goal is to achieve competitive differentiation by viewing the world through the eyes of the customer and modifying interactions of the experience accordingly. However, it's difficult to meet increasing customer demands at scale. With such a large volume of data, insights can be hidden, making it challenging to target and serve micro-segments effectively. Identifying and acting on true signals from the 'noise' of all the available data is an enormous challenge.
As a result, a new approach is demanded to understand the customer's intent through advanced machine-learning algorithms and artificial intelligence (AI). The ability to capture, analyze, understand and act upon information, including the ability to recognize patterns, comprehend ideas, plan, predict, problem solve, identify actions and make decisions is needed.
However, past approaches by companies that used combinations of CRM systems, master data management (MDM) and data lakes to create a single source of truth have all struggled to live up to the expectations of front-line business users in areas such as marketing, customer care and digital commerce. Looking ahead, however, the new requirement will be investment in customer intelligence platforms (CIPs) that do more than consolidate a single view of the customer: they add a layer of data governance, synthesis and identity, which powers a dynamic customer graph to fulfill the vision of contextual experiences.
Understanding the new customer 360 for intelligent orchestration
Improving customer experience demands an approach that considers all the tools, processes and data across the customer journey. This complex process usually involves dynamically maintaining a single source of truth about each customer to drive personalized experiences based on individual preferences and behaviors. Businesses to date have primarily invested in systems of record to serve this purpose, such as legacy CRM and ERP. While these systems are critical for managing internal operational processes, they are typically not effective for consolidating customer information at the pace of business change today. Structured data from operational data stores now provides only a small slice of the overall data needed to improve customer experience.
IT departments previously invested in MDM and data warehousing technologies to consolidate information associated with customer profiles. The emergence of additional unstructured data, however, further relegated traditional CRM, MDM and other systems to just another silo. Businesses need to incorporate the exploding growth of unstructured data from IoT sensors, social data, behavioral data, location data and even third-party data to truly have a single source of the truth. In response, digital marketers and agencies have adopted data management platforms (DMPs) enabling companies to target campaigns to anonymous audiences across third-party ad networks and exchanges. Another category evolved over time, called customer data platforms (CDPs), aimed at creating unified profiles of customers from multiple sources of data regarding both known and unknown individuals.
Looking across all these players, figure 1 below highlights the key differences in the industry between CRM, MDM, DMP, CDP and CIP.
Figure 1: Key differences in the alphabet soup
MDM major players with expertise in customer management including Informatica, Oracle, SAP and IBM are first an IT tool used for primarily managing structured data for known customers. However, many MDM vendors still don't factor in all unstructured customer data, resulting in providing less-than-ideal applicability for critical line-of-business customer experience use cases. There are also other MDM providers that offer more in-depth expertise for creating a 360-degree view of the customer such as Semarchy, Stibo Systems, Reltio and Riversand Technologies that usually use a combination of modern data processing and analytics technologies such as Apache Cassandra, Apache Spark, Apache Hadoop and elements of in-memory data processing engines. Even with the advent of data lakes, it is still an IT strategy that doesn't necessarily contain the proper tools for synthesizing customer data and providing the necessary customer matching, resulting in also lower effectiveness rates for a broad range of customer experience use cases.
A DMP is a repository that enables the creation of target audiences based on both in-depth first- and third-party audience data and helps companies to target campaigns to those audiences across third-party ad networks and exchanges. DMPs such as Neustar, Lotame, IgnitionOne and Cxense evolved over time to improve advertising effectiveness by collecting and integrating first-party data so that vendors can transition into a larger role regarding reporting on customer engagement and journeys, which attracted SAP, Oracle, Adobe and Salesforce to acquire and enter the market. While it helps with targeting of digital advertising campaigns, it primarily houses non-PII (personally identifiable) data for targeting unknown users. Nearly all companies in this space were built to run display campaigns on cookie-based targeting. Many vendors will face challenges in expanding into cross-channel tools, particularly as the definition of cross-channel extends beyond mobile and desktop.
CDPs such as AgileOne, Amperity. Amplero, Zaius, BlueConic and Lytics on the other hand store information that is primarily PII data. Lytics was one of the earliest promoters of the CDP category that differentiated itself from CRM and DMPs with the capabilities for managing both structured and unstructured data to improve targeting of known and unknown customers for marketers. These vendors provide SaaS products that offer no-code aggregation of customer data scattered in dozens of different silos into one centralized hub, then create richer customer profiles that dynamically build over time. CDPs help marketers with identity resolution and machine-learning algorithms for targeting and personalization using intent, preference and behavioral data about individuals across devices and channels to allow marketers to act on information in real time to deliver personalized content and experiences to customers.
With the growth of unstructured data, requirements for GDPR and advances in machine-learning algorithms, we expect a few of these vendors to broaden use cases and incrementally transition toward referring to themselves as a CIP (illustrated in figure 2), which drives contextual experiences across not just marketing but also a broader set of line-of-business use cases for intelligently orchestrating contextual experiences. As data grows, human-driven investigation of that data becomes less effective and errors become more prevalent. Combining human expertise with unbiased machine intelligence delivers a powerful combination that almost every business can benefit from.
Aspiring CIP vendors with broader approaches include AllSight, RedPoint Global, Lytics and Treasure Data. AllSight was first to adopt attributes of a customer intelligence platform with its deep roots in master data management, broader use cases and expertise in synthesizing data and adopting advanced machine-learning techniques. Lytics also uses advanced machine-learning data management and can orchestrate in real time across the customer journey.
The advances in predictive machine-learning intelligence build on a variety of algorithms to achieve real-time one-to-one capability (ideally in fewer than 20 milliseconds). It does not replace a CRM or even MDM strategy, but augments its advanced data governance, synthesis and identity, which power a dynamic customer graph to fulfil the vision of contextual experiences. CIPs are not just about the data, but also the potential for delivery of dynamic rich media content, including images, videos and voice using advanced techniques such as neural networks, genetic algorithms and computer vision for self-learning improvements. As the user interacts with the system, it is able to continuously train to ensure a better understanding of the context of the situation. A CIP can also be used as a stand-alone product with pre-built dynamic user interfaces for specific use cases or deployed as a headless architecture, which can then enhance existing applications for sales, marketing, customer care and commerce.
Figure 2: Customer intelligence platform needed for driving contextual experiences
Automated reasoning helps to make inferences and enrichments on each customer profile, and also helps line-of-business users predict the customer's future actions such as churn, propensity to buy, proximity and location. It provides a deeper understanding of individual customer journeys and unique interactions, combined with transactions, to accurately understand and improve customer experience. Outlook
The industry is evolving quickly, as seen in figure 3 below. Leading CRM vendors such as Salesforce, SAP and Oracle will not sit back and cede control to a new vendor such as a CIP or even a CDP. As a result, all these players will expand with critical platform advances for the dawn of the new customer 360. CDPs are already adding advanced machine-learning capabilities and will broaden use cases and applicability beyond marketing. DMPs are transitioning away from just ad tech to include broader cross-channel marketing measurement. The last stand-alone DMPs such as Lotame and IgnitionOne along with many CDPs will be targets for either other CD's as a consolidation strategy or CRM vendors looking to advance the platform for a common unified data strategy.
Figure 3: Industry expected to mature and move toward a CIP
Sheryl Kingstone leads 451 Research’s coverage for Customer Experience & Commerce, which covers the many aspects of how customer experience is a catalyst for digital transformation. She oversees the company’s coverage of a variety of customer experience software markets spanning ad tech, marketing, sales, commerce and service.
As a Senior Research Associate in 451 Research’s Information Security Channel, Patrick Daly covers emerging technologies in Internet of Things (IoT) security. His research focuses on different industrial disciplines of IoT security, including the protection of critical infrastructure, transportation and medical devices. In addition, Patrick’s coverage spans technological domains, including security for IoT devices, applications, platforms and networks.
Keith Dawson is a principal analyst in 451 Research's Customer Experience & Commerce practice, primarily covering marketing technology. Keith has been covering the intersection of communications and enterprise software for 25 years, mainly looking at how to influence and optimize the customer experience.