AllSight's platform creates a next-generation customer 360 that connects billions of data points across disconnected silos to uncover insights for business users across a variety of use cases. To solve the problem, companies require fresh approaches that consider all the tools, processes and data across the customer journey. AllSight lowers the complexity for companies that require the ability to dynamically maintain a single source of truth about each customer, while creating multiple perspectives (unique business views for different functions, geographies and users) to drive personalized omni-channel experiences based on individual preferences and behaviors.

Businesses to date have primarily invested in systems of record to serve this purpose, such as legacy MDM, 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 fully understand customers and improve customer experience.

The 451 Take

AllSight goes beyond existing master data management (MDM) and customer data platforms to create a customer intelligence platform (CIP) capitalizing on advancements in machine-learning intelligence that build on a variety of algorithms to achieve real-time customer 360. Its strength is its ability to intelligently synthesize data – such as transactional data, survey data, interactions and unstructured information coming from web chats and emails – to understand sentiment, personality, customer insight, customer events and relationship influencers, etc. That view is then turned into prescriptive insight using machine-learning-based algorithms to be employed in a variety of an organization's use cases – either in a headless mode or using its dynamic user interfaces. While new architectures and tools are finally making the customer 360 a reality, incumbent CRM and enterprise business applications are becoming more intelligent, and will fight to maintain control of the customer data.


With its roots in big data and MDM, AllSight was launched in 2013. The current CIP consolidates and transforms fragmented data into an intelligent customer 360 that is used by marketing, sales and customer service, as well as for analytical use cases. Headquartered in Toronto, the company is privately funded with 70 employees. The current product revenue comes from annual subscriptions that range from $250,000 to $1m annually.

AllSight experienced exponential growth in the past year, and continues to ramp up its investment in employees and resources for growth in customer deployments. The company's point of view is that businesses are transforming around the growing importance of intelligent data. Companies need technology partners that have a foundation in not just analytics, but also data management, to synthesize and understand all forms of data throughout the business. Since customer behaviors continuously change and new sources of data emerge, customer systems must continuously learn and evolve to meet the needs of line-of-business users.

Businesses have made investments in transactional systems and MDM since about 15 years ago to create a complete customer profile, but they failed to deliver a complete 'single source of truth.' There are continually new sources of data and applications that add complexity, and aren't easily understood or analyzed. This creates billions of data points – much of it held in different silos across a variety of lines of business – that must be synthesized and made actionable. IT has tried to consolidate and manage all the customer data with existing systems, but it struggles to keep up. This has led to business users acquiring fragments of the customer 360 through shadow IT initiatives.


AllSight Intelligent 360 (Ai360) combines structured and unstructured first-, second- and third-party data. By leveraging a modern architecture that extends open source tools such as Apache Hadoop, Spark, Elastic Search, graph and columnar data stores and AI (including machine-learning algorithms and natural language processing functions), the platform creates a broader understanding of each customer.

It continuously synthesizes, learns, adapts, improves and automates in real time, relating and linking data and creating a dynamic customer graph with a high accuracy. The ability to link all this new data is critical. Synthesis understands and creates context from raw data (fragments and unstructured data) providing demographic, product and interaction history, and a full view of the customer journey. It's based on both columnar and graph technology, supporting the scale and speed needed for today's data volumes. Graph databases allow organizations to traverse and analyze information and insights across customers, organizations and households. AllSight does not replace a CRM or even MDM strategy, but augments these with its advanced data governance, synthesis and identity – which power a dynamic customer graph.

As we have mentioned before, CIPs are not just about the data, but also have 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 it, the system is able to continuously train to ensure a better understanding of the context of the situation.

AllSight synthesizes data that dynamically links customer-customer and data-customers using an optimized mixture of matching techniques. As it ingests and synthesizes more data into the customer 360, a CIP must also become more intelligent in identifying important trends and information for each customer, and better at summarizing the important intelligence for specific business users. Synthesis and reasoning must work in balance to ensure the CIP is usable: as more data is synthesized and the customer 360 becomes deeper and richer, the CIP must get better at summarizing the important intelligence for specific business users.

Automated reasoning helps 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. AllSight can 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. There are three major areas where AllSight recently updated the platform:

  • Advancements in genetic machine-learning algorithms. AllSight uses a variety of machine-learning algorithms with a higher degree of accuracy, based on specific use cases such as data matching, which weights the probabilities of a match or non-match based on the relative weightings of attribute matches, and their individual probability of a match or non-match outcome. Its genetic algorithm is another differentiated capability that applies techniques such as survival of the fittest, crossover, and mutations on large population sizes for many generations, to learn the optimal or near-optimal approach.

  • Enhancements to dynamic user interfaces for marketing and customer service and support. AllSight has a variety of dynamic, humanistic UIs, such as the Customer Interaction Predictor that displays products a customer may be inquiring about, previous interactions, product-level sentiment, and proactive offers and cross-sell incentives. This visibility enables CSRs to improve customer satisfaction by increasing first-call resolution, and providing more relevant offers that shift contact centers from cost centers to service centers.

  • Expansions in pre-built customer-centric use cases. Top areas include marketing, commerce and overall customer-experience use cases for improved micro-segmentation and targeting for real-time individual customer journey optimization, content personalization tailored to individual buyers' journeys, voice of the customer and survey feedback, shopping cart analysis, next-best-action for customer service, and attrition modeling and retention. All initiatives rely on specific components of a CIP, and extend existing software and/or data management investments. AllSight's CIP is particularly well suited to addressing multiple use cases and initiatives due to its ability to create and manage multiple perspectives of the same customer – or unique business views for specific use cases, users, geographic or business unit divisions – enabling different, tailored views of the customer for each user.
Beyond the customer-experience use cases, AllSight is also used to enhance initiatives such as data lakes, MDM and risk and compliance. Its CIP offers unique customer attributes that are not found in many existing IT data lake projects. The biggest challenge is how to make sense of the volume of highly diverse data within those lakes. It is also often challenging to address customer-centric compliance requirements that identify risk exposure, especially relevant with regulations such as GDPR that force organizations to be able to combine all customer data, and identify where and how data is stored so they can comply with the right to be forgotten, and other components of the law.



There are scores of firms that have latched onto the customer data platform as a catchall for data management in marketing contexts. AllSight competes directly with companies like AgilOne, Amplero, Amperity, BlueConic, BlueVenn, Lytics, RedPoint, Quaero, Tealium, Treasure Data and Zaius in this space. There is also NGDATA, Reltio and Evergage, which are the closest to providing a CIP. The customer data platform segment, even as it evolves into intelligence platforms, also faces the squeeze from systems of record in the form of the next generation of CRM and marketing tools, like those from Salesforce, Oracle, Adobe, SAP and Microsoft.

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 less-than-ideal applicability for critical line-of-business customer-experience use cases.

There are also MDM providers that offer more in-depth expertise for creating a 360-degree view of the customer such as Semarchy, Stibo Systems and Riversand Technologies. They generally 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 lower effectiveness rates for a broad range of customer-experience use cases.

Sheryl Kingstone
Research Vice President & General Manager - VOCUL

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.

Jeremy Korn
Research Associate

Jeremy Korn is a Research Associate at 451 Research. He graduated from Brown University with a BA in Biology and East Asian Studies and received a MA in East Asian Studies from Harvard University, where he employed quantitative and qualitative methodologies to study the Chinese film industry.

Aaron Sherrill
Senior Analyst

Aaron Sherrill is a Senior Analyst for 451 Research covering emerging trends, innovation and disruption in the Managed Services and Managed Security Services sectors. Aaron has 20+ years of experience across several industries including serving in IT management for the Federal Bureau of Investigation.

Want to read more? Request a trial now.