For marketing teams and marketing technology companies, finding and growing valuable customers is the key to constructing a profitable business. Large marketing firms for years have recognised that personalised customer experience and streamlined operations are the big factors for marketing success.
Research says organisations which utilise AI for marketing are reaping the benefits of increased customer satisfaction and loyalty while improving operations. The rise of omnichannel marketing automation is giving them a much-needed boost as they strategise to scale and optimise their customer engagement and intelligence in real-time. But what is marketing automation?
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It basically refers to a broad range of automation and analytic tools for marketing, which are purpose-built for marketing firms and departments to more efficiently market on various online channels (web, mobile, email, social media, etc.), gain intelligent insights and automate repetitive tasks.
To know about marketing automation and how AI/ML can help companies in omnichannel customer engagements, we connected with Subramanian Gopalaratnam, CTO, Resulticks, a marketing automation company.
Resulticks has embedded advanced AI and machine learning models on its platform to achieve real-time, omnichannel marketing. Here are the edited excerpts from the interaction –
AIM: What do you think is the importance of using AI in marketing automation?
Subramanian Gopalaratnam: Due to the proliferation of devices and channels, today’s digitally-savvy consumer expects a seamless, highly personalised, and continuously evolving customer journey right from the first encounter to research to purchase and beyond. The days of cookie-cutter communication and spray-and-pray targeting are long gone. There are vast volumes of diverse data that brands must capture, unify, analyse, and translate into meaningful, contextual engagement at any given moment.
AI and ML, embedded into integrated marketing platforms, help marketers study the dizzying array of customer activities across channels to derive patterns and insights from optimising personalisation and contextualising interactions at scale.
AIM: Can you elaborate with a few specific examples/use cases on how AI can support marketing units?
Subramanian Gopalaratnam: AI and ML can bring value for channel and send time optimisation where you predict the best channel and time for communicating with a particular individual. Using AI/ML, one can predict the next-best content, offer, product recommendation, or interaction at the individual level for optimal impact.
You can derive insights on how to add value to a customer’s journey based on their activities and profile such as propensities, content consumption and purchases. A company can facilitate seamless omnichannel switches to continue an ongoing conversation or customer journey, and finally, dynamically determine the most relevant interactions in context and based on AI/ML algorithms.
AIM: What is the unique value proposition Resulticks bring to customers?
Subramanian Gopalaratnam: AI and ML, like all of Resulticks’ diverse modules, are part of an integrated platform built to enable personalised, omnichannel, continuously optimised, measurable customer engagement. The platform has been developed with the technology to allow the algorithms to process and operate on top of rapidly increasing volumes of data so brands can receive the maximum value.
AIM: How are your customers deploying and finding value using your services? Please elaborate with a few examples.
Subramanian Gopalaratnam: Thanks to Resulticks’ AI and ML models, our clients have been able to maintain a focus on the profile completeness of their audience and receive actionable insights for data augmentation. They can enhance segmentation efforts with capabilities such as AI-driven look-alike targeting, and optimise the channel, time, content, offer, and so on of every communication for maximum impact. They also acquire robust ML-based benchmarking capability to compare marketing efforts with, for example, industry and own benchmarks to make projections.
AIM: What according to you are the main technology components for an organisation which wants to advance in AI/ML?
Subramanian Gopalaratnam: For acquiring sufficient AI and ML capabilities, an organisation’s technology strategy should include: –
- A Continuous Implementation/Continuous Deployment (CI/CD) pipeline to support the model deployment.
- A data pipeline that can not only support batch and stream processing but manage the ingestion, transformation, and loading of data to support AI and ML models.
- A services platform that is deployment-agnostic, which means the models in operation should be supported across pure cloud and on-premises deployment.
- Integration components that enable connectivity to all relevant source systems where data should be integrated and published.
AIM: You also use blockchain for your platform. Why did you decide to leverage it, and what value can blockchain bring to marketing?
Subramanian Gopalaratnam: When it comes to marketing, there is a growing need to keep a singular view of the individual customer as well as their interactions across the ecosystem of partners, with minimal reconciliation efforts required. This is something that can only be achieved with a blockchain, one that shares the insights across the ecosystem, from the brand to partners and even customers.
By introducing the marketing data blockchain into the picture, you can reduce the need for handling reconciliations, thus minimising costs and fraud risks alike, and provide customers with an accurate record of how their data has been used. Using blockchain, we are also facilitating efforts to achieve and maintain a consistent understanding of customers across the ecosystem, and achieve streamlined benchmarking that gives valuable insights.