Wilson Raj
Global Director, Customer Intelligence, SAS

Advanced Analytics are influencing the adoption of automation technologies for marketing and sales. AI and machine learning applications play a pivotal role in making these analytics relevant and powerful for decision-making. Wilson Raj, Global Director, Customer Intelligence at SAS, about their platform and how it delivers Enterprise success.

Tell us about your role at SAS and the team/technology you handle.

I’m the product marketing leader and global evangelist for SAS’s solutions in customer experience and marketing, and digital transformation. I collaborate with customers, industry influencers, partners, and SAS product teams to establish, evolve and evangelize SAS’ analytics-powered marketing solutions.

How do you see trends in Advanced Analytics further influencing the adoption of automation technologies for marketing and sales?

We’re seeing advanced analytics trends such as the growth of AI and AI apps, open source computing for innovation, the emergence of cloud for data and analytics. The convergence of all these trends is overwhelming for organizations. They want simplicity. They want more automation.

Automation of analytics is at the heart of automation technologies for marketing and sales.

We’re automating our marketing technology by uniting the analytical environment—from disparate data for customer experience functions within the enterprise—organizations can fast-track the value delivered from analytics in the marketing and sales department.

For example, we can automatically synchronize varied data sources (channels, media, interactions, online/offline, location, POS, etc.) for a holistic view of a prospect’s or customer’s digital interactions.

Or, we use sophisticated analytical decision engines that integrate digital customer data, content, and channels to automate and orchestrate two-way, interactive customer experiences.

The upshot: increased customer profitability, streamlined operations, reduced churn and stronger brand affinity.

What are the core tenets of your Customer Intelligence? Which markets and industries are best suited to benefit from this enterprise intelligence solution?

SAS Customer Intelligence elevates an organization’s customer experience with real-time, contextualized interactions supported by advanced analytics such as machine learning and AI.

In our view, brands that build the most effective, real-time customer experiences are those that master three interrelated tenets—through analytics and insights:

Unified customer insights: This capability unifies a company’s customer data from online and offline channels to extract customer insights and shape the customer experience.

Proactive analytics (with machine learning and AI): These purpose-built data collection and analytics capabilities incorporate insights on customers, marketing programs, and related customer-impacting functions such as service, operations, and support.

Contextual interactions: This capability involves using real-time decisioning on where a customer is in a journey digitally (browsing product reviews) or physically (entering a retail outlet), drawing her into subsequent actions the brand wants her to pursue.

These tenets apply to any industry—from banking, retail, telcos, government, utilities, and healthcare—and also to the mid-market.

With analytics, brands can see the world as their prospects and customers do—and shape customer experience in real-time accordingly. The reward: higher brand preference, revenue and cost improvements and an enduring competitive advantage

Tell us more about SAS Viya and how does it make analytics more accessible and comprehensible?

For organizations to thrive, you need a modern analytics platform that’s designed to generate insights from your data in any computing environment. To this end, our SAS Platform supports every phase of the analytics life cycle – from data, to discovery, to deployment.

SAS Viya is part of the SAS Platform and helps organizations conquer all kinds of analytical challenges. SAS Viya is a cloud-enabled, in-memory analytics engine that provides quick, accurate and reliable analytical insights.

SAS Viya delivers accessible analytics via three pillars:

Power: Faster processing for huge amounts of data and the most complex analytics, including machine learning, deep learning and artificial intelligence. You can solve analytics problems of any size or complexity, and rapidly deploy results for fast results and maximum return on investment.

Openness: A standardized code base supports programming in SAS and other languages, like Python, R, Java and Lua. This empowers the organization to rapidly collaborate on innovative analytics with a that accommodates a wide range of analytics talent.

Flexibility: Support for cloud, on-site, or hybrid environments. It deploys seamlessly to any infrastruc­ture or application ecosystem. You gain efficiencies from a seamless, governed environment that supports the entire analytics life cycle from data preparation to discovery to deployment.

In short, SAS Viya can address the complex analytical challenges of today, while effortlessly scaling for the future.

How should businesses unlock the value of their First-part and Third-party data? What does their integration actually deliver for enterprise success?

An emerging answer to this issue is the customer data platform (CDP), which is the modern version of a customer data warehouse—though it’s more flexible and interconnected. CDPs integrate first-party data, including customer-supplied data, website or app behavior, purchase history, marketing campaign and engagement data, with third-party data on customer interests and shopping behavior, to improve individual targeting.

Although marketers and customer insights professionals typically “own the CDP initiative,” CDPs must also factor in other functions that affect customer experience—such as service, customer support, operations, and others.

Today, a CDP may evolve from a CRM system, a marketing campaign database, a third-party data management platform (DMP) or even an ad-focused audience platform. Whatever the starting point marketers must rationalize a customer data hub that allows addresses the fragmented data, decisioning, and delivery of interactions inherent in each of those starting points.

As mentioned earlier, a unifying analytics lifecycle (from data to discovery to deployment) can truly unlock that inherent value.

How do you work with AI/machine learning technologies at SAS?

AI is deeply integrated in the SAS Platform and certainly from a SAS Customer Intelligence perspective.

Here are two areas.

AI/machine learning makes marketers more efficient. Manual tasks burden marketers and siphon time away from their strategic responsibilities.

With SAS, we can automate complex, resource intensive, recurring, but vital tasks such as data integration, segment and rule creation, and experiment design. For example, SAS Customer Intelligence can test copy/content in digital channels, and autonomously select, generate and test content based on real-time contexts and segments for each campaign.

SAS also enables marketers to make smarter decisions. Limited data access and insights often throttle marketer’s efforts to deliver personalized, relevant interactions.

With AI/ML, SAS Customer Intelligence has self-learning capabilities that ensure insights are applied almost instantly to campaigns and interactions. For instance, marketers can automate the creation and testing of variations in segmentation and treatment that are far more granular than traditional targeting approaches.

We also apply AI in SAS Customer Intelligence to help optimize the customer journey in real-time and make marketing planning more agile and accurate.

Thanks for chatting with us, Wilson.

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