“Customer 360” connects marketing, sales, commerce, and technology with a single view of customer data, i.e., single version of the truth (SVT). Business can use “SVT” in terms of data and analytics to provide customers with personalized experiences across their (lifetime) journey, proactively. From budgets to back-office, from marketing to monetizing, Customer 360-based insights provide critical metrics, measures, and key performance indicators (KPIs). From an operational standpoint, Customer 360-based insights are commonly referred to as “Know Your Customer (KYC).”
As customers become more and more digitally savvy, businesses and marketers are collecting and analyzing large scale datasets. Furthermore, driven by the pandemic, customer interaction through the digital channel has tremendously increased. All these increased interactions are driving the need to deliver a more personalized customer experience, which means we should be able to offer the right experience and help them better improve their relationship with their clients. As a result, boosting customer satisfaction becomes an essential key driver for businesses. It’s also important to identify the right tools at the right time and when there is a need how to engage these tools and accelerators. These challenges can be separated into three distinct areas: (1) For online customers, there is a demand for near real-time business insights and proactive incentive offers. (2) As customer data collections steps up, how granular, and how much data needs to be collected to get the changing customer behaviors, (3) How can analytics support the expanding regulatory, privacy and compliance requirements?
Hence customer 360 analytics is paramount not only to capture key customer behavior, but also to predict market demand and supply, as well as to offer personalized, seasonal incentives and discounts. According to McKinsey and Gartner, due to rapidly accelerating technology advances and increasing data literacy, by 2025-26 customer 360 analytics will be in the driver seat for the customer 360 metrices. The real value of customer 360 will be the ability to look for data and analytics-driven answers while still collecting datasets, even if the data sets are incomplete. The top three questions for data-driven enterprises include:
Q1: How to accelerate Customer 360 analytics using alternative, innovative and in some cases, if possible, real-time processing, even if the datasets are incomplete?
A1: The customer data needs to be processed and the analytics need to be delivered in near real time.
i. Today companies often tradeoff between speed and computational intensity.
ii. According to McKinsey, by 2025-26, use of more powerful large-scale in-database analytic data tools will be so pervasive that there will be no tradeoff between speed of data collection, computation, and operationalization.
Q2: How to leverage faster and self-service analytics through flexible data access?
A2: There is a need to enable flexible data access and ready-to-use of near real time data.
i. Today: Even if maximum datasets are unstructured/semi-structured most usable data is still organized/converted to structured using manual/bespoke processes which is time consuming, not scalable and error prone.
ii. By 2025-26: Data practitioners leverage time series, graph, and NoSQL databases to expedite unstructured and semi-structured data processing. It will enable customer 360 data platforms to be continually updates with the online/near real time datasets.
Q3: How to operationalize customer 360 analytics using up-to-date data, analytic tools, and accelerators?
A3: Data needs to be the driver within the operating model while catering the changing data and analytic tools and accelerators.
i. Today the Integrated datasets have “no true owners”. Data duplication across siloed costly environments, makes it difficult for users across different organizations to arrive at well-defined business insights.
ii. By 2025-26, the use of dataops and modelops aligned with embedded data security, data/model catalogs, and governance will accelerate to prepare integrated data from wide range of new data sources. It would facilitate to pick and use most up-to-date analytics algorithms if the trained model and model scoring through a common model exchange format.
Teradata introduced ClearScape Analytics which includes the most up to date in-database analytic platform, enabling end-to-end data preparation, modeling, and operationalization. It includes (1) in-database analytics libraries and statistical functions to prepare the data in-database, thus minimizing the data movement; (2) it provides extensive range of option to languages, and links to other platforms (using an open analytic framework) to democratize and provide the analytics modelers and data scientists to use their tool of choice; finally, (3) from an operationalization standpoint, ClearScape Analytics provides ModelOps environment for model cataloging, operationalization, governance over and above supporting the similar regulatory and compliance needs.