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Thought Leadership

DCS not only understands how at-scale Decisioning benefits organisations, but puts the principles of Decisioning into practice. We believe it’s imperative that we invest in innovation to explore new areas of Decisioning that can add value to our Clients and Prospects, rather than just re-iterate what we know.

As a consequence, we’ll provide thoughts and ideas around how to enhance your decisioning solutions, whether that be 1:1 at-scale Customer Engagement, Credit Risk Management, or specific areas, such as decision data management, seamless conversations across channels, or devop cost efficiencies.

Below are our latest articles:

Credit Risk Decisioning

22nd October 2024

Summary
Credit Risk Decisioning
Credit Risk Decisioning systems are crucial for evaluating the creditworthiness of borrowers. By leveraging data like credit history and repayment capacity, these systems ensure responsible lending and are integral to the credit lifecycle. DCS’s Credit Decisioning product and consulting services are focused on making the credit decision processes smoother, smarter, and more scalable. We have merged our credit and lending experience with Pega’s advanced decisioning technology. We help to modernize and streamline the way in which credit decisioning teams operate and function.
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Navigating Noise of Decisioning Data

11th October 2024

Summary
How to Navigate through the Noise of Decisioning data
The article explores how businesses can navigate the overwhelming amount of data in decision-making processes for personalised customer engagements. With the shift from constrained data to a surplus of information, companies face challenges in efficiently utilising this data without compromising agility or customer experience. DCS highlights the need to refine data, focusing on its value rather than volume, and urges marketers to adapt to a more data-driven, always-on approach. DCS outlines strategies for effective decisioning, including developing a balanced decisioning framework, exploring new data opportunities, and integrating decision-making systems to enhance customer interactions. By leveraging AI and automation, businesses can turn data noise into meaningful, scalable customer success.
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Hyper-Personalisation:
Removing the Bottlenecks

1st October 2024

Summary
Hyper-personalisation: Removing the Bottleneck
Hyper-personalisation in customer engagement has elevated consumer expectations, creating significant challenges for businesses striving to deliver individualised experiences at scale. This article explores the evolution from traditional segmentation to advanced hyper-personalisation, examining the complexities and bottlenecks that arise as businesses attempt to meet diverse customer needs in real-time. The piece discusses how hyper-personalisation impacts various aspects of business operations, from content creation to automated decision-making, and highlights the need for a cohesive strategy that integrates data management, data science, and marketing efforts. It emphasises the importance of transparency and collaboration across departments to effectively manage and optimise personalised interactions.
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Owned and Paid Engagement - A Decisioning Balancing Act

20th September 2024

Summary
Owned and Paid Engagement – A Decisioning balancing act
This article highlights the need for aligning owned (e.g., websites, email) and paid (e.g., ads, social media) media to create seamless customer journeys. Historically, separate teams have led to siloed operations, inconsistent messaging, and wasted ad spend. To solve this, businesses should integrate both channels using AI-driven decisioning engines, enabling real-time, personalised engagement across touchpoints. By synchronising data and leveraging AI, companies can improve customer experience, reduce inefficiencies, and increase conversions. DCS helps businesses achieve this balance, driving optimised customer engagement and business growth.
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Simplifying the complex landscape of at-scale

1st September 2024

Summary
Simplifying the complex landscape of at-scale
The article discusses the complexities of large-scale decisioning solutions for personalised 1:1 customer engagement. It highlights the challenges businesses face when choosing the right decisioning tool to manage millions of customer interactions and billions of decisions in real time. DCS emphasises the importance of a well-defined decisioning process that balances speed, real-time insights, and multiple business objectives. The article explores key aspects of decisioning, including handling quantitative and qualitative data, optimising utility functions, and fostering collaboration across business units. DCS advocates for automated, scalable decisioning tools that enhance both customer experiences and business outcomes.
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What does 3rd Party Deprecation do for Decisioning?

1st May 2024

Summary
What does 3rd Party Cookie Deprecation mean for decisioning?
3rd Party Cookies have acted as a great cross-platform means of tracking and understanding customer journeys across the web over time. Local browsers have provided that centralised source of cookie data which can be tapped into by multiple parties to understand each customers profile and behaviour. This data has underpinned both acquisition and marketing use cases. The deprecation of 3rd party cookies could therefore leave a capability hole in your underlying Adtech and Martech stacks. But there is a remedy, and it centres around improving the capture and use of … 1st Party Data. This is the data you capture for your own customers and prospects in your own touchpoints, such as your website and app. Persistence of this data is now in your centralised repositories, as opposed to remotely on device browsers, allowing a robust customer profile to be established over time, up to real time.
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More Thought Leadership articles

Succes Stories

British Telecom - Customer Decisioning Alliance

Alfam - Credit Risk Decisioning Platform

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"Credit Risk Decisioning"

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"Hyper-personalisation: Removing the Bottlenecks"

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"Owned and Paid Engagement - A Decisioning Balancing Act​"

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"Simplifying the complex landscape of at-scale Decisioning"

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"How to Navigate through the Noise of Decisioning data"

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