In the dynamic realm of modern marketing, the integration of Artificial Intelligence (AI) has emerged as a crucial asset for businesses seeking to secure a competitive edge. This series of blogs delves into the dynamic intersection of AI and marketing strategies, uncovering how intelligent algorithms and machine learning are reshaping the landscape. From crafting personalized customer experiences to facilitating data-driven decision-making, join us as we explore the transformative power of AI in marketing. These articles provide insights and practical applications, catering to both seasoned marketers and those embarking on their digital journey. By demystifying the role of AI, we aim to shed light on its profound impact on the future of marketing.
MarTech Series – Marketing Technology Insights
In the past eight months, the transformative potential of generative AI has swept across the business world, promising a new era of supercharged processes, increased efficiency, and new heights of innovation. For marketers and Marketing Ops teams, generative AI can drive real-world impact, augmenting their day-to-day tasks, optimizing customer journeys, and creating more engaging and valuable stories for target audiences.
Put simply, its impact is profound. Marketers can now analyze customer feedback by summarizing key concepts from text blocks, suggest next steps for sales teams, and provide data-driven marketing budget proposals to business leaders that ensure maximum ROI — all with remarkable speed and ease. While generative AI won’t replace marketers any time soon, a marketing professional who knows how to leverage AI effectively in their role will replace a marketer who doesn’t. AI is poised to become a linchpin for successful marketing practices, opening up significant opportunities for marketers to elevate how they operate within their roles.
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What exactly does the use of generative AI look like in a marketer’s day-to-day? Below are three outcomes marketing practitioners can achieve by tapping into the power of generative AI.
The rapid acceleration of digital transformation initiatives over the past three years has led to a slew of unintended consequences, including bloated martech stacks, cumbersome manual processes, brittle integrations, and suboptimal workarounds—all of which have left marketing teams struggling to work efficiently. Such issues and process inefficiencies have slowed marketers’ ability to deliver high-quality leads, maintain quick lead response times, and continually enhance the customer journey. In other words, it’s time for a change.
Modern iPaaS solutions with low-code experiences can ease these pain points by enabling marketers to seamlessly integrate the various apps in their tech stack and easily develop custom, automated workflows without needing to rely on IT or engineering teams to code the integrations for them. Adding generative AI as a native part of the user experience completely changes the way marketers interact with their iPaaS solution and allows them to enhance overall business models faster than ever before.
The market is currently experiencing a paradigm shift — similar to when everyone adopted marketing automation platforms. Large language models (LLMs) in combination with low-code integration platforms are a tool every marketing operations professional must master to unlock the ability for their teams to tackle the challenges faced by high-performing marketing teams. Depending on the user’s level of technical expertise, marketers can use LLMs to create sophisticated business processes, automate complex tasks, and build workflows to streamline pre- and post-sales processes.
For instance, with the right automation platform in place, marketers can transform lead lifecycle management by automating processes such as lead routing. If their iPaaS solution has LLM capabilities, a marketer could provide natural language instructions such as “Identify duplicate leads and merge them into a single record” or “Notify the sales team via Slack when a new lead is assigned to them” to effectively streamline the process. Marketers can also improve their segmentation processes by querying the LLM to identify patterns from customer and market data in order to target more relevant audiences. With these capabilities, marketers can create more tailored campaigns and ultimately improve their marketing automation processes.
With planning, the best way to forge ahead is to first evaluate the past. Marketing and sales teams who are looking to optimize campaign investments can use generative AI to easily access and analyze data from former marketing campaigns and glean insights into what worked, what didn’t, and how that can inform future campaigns.
Integration platforms with the proper generative AI features can automate intricate tasks and not only respond to but act on query outputs with no technical knowledge needed. Such platforms can ensure that queries are properly executed and drive the progress of marketing processes forward. For example, a CMO who is seeking to optimize social media investments can make direct queries with a generative AI chat agent to identify the top lead sources for their largest “Closed Won” accounts by revenue and cross-reference those results with the company’s LinkedIn followers.
Additionally, the advanced algorithms within some generative AI tools enable them to exceed the potential of traditional audience targeting and segmentation. These advanced capabilities allow marketers to evaluate patterns in both customer and market data to better identify their ideal target audiences. Marketers can further leverage generative AI to curate personalized content and tailor unique messages for each persona based on their behaviors, preferences, needs and demographics. With the ability to more accurately analyze existing customer patterns, companies can execute more effective lead generation and activation campaigns, which leads to higher engagement and conversion rates and lowers consumer acquisition costs.
Marketers have spent enormous resources over recent years trying to make sense of the quantitative data available to them — whether that be for marketing ROI or customer segmentation. However, there’s a massive amount of qualitative customer data that’s been largely ignored. While we’re already seeing marketers use AI to more rapidly develop messaging based upon actual customer conversations, this is still ad hoc. From customer calls to chat-based interactions and email communications there are several sources for customer data and a treasure trove of qualitative information that AI can help marketers better understand and operationalize. The ability to do this at scale will bring an incredible advantage to the companies that get it right.
It’s inevitable that AI will affect marketers, and generative AI holds enormous potential to maximize operational efficiency, advance 1:1 personalization, and drive marketing excellence. Marketers who fight against the AI tide will quickly find themselves at a competitive disadvantage. At the same time, those who embrace AI and think strategically about how to invest in its capabilities will reap serious benefits in the near future.
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Shane Jackson is Chief Marketing Officer, Tray.io
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