The Gist
- Key AI technologies. AI marketing technology like NLP, NLG and NLU enhances customer interactions and improves service efficiency.
- Data generation benefits. Synthetic data generation helps enrich customer profiles and provides better insights for AI and machine learning models in marketing strategies.
- Customer journey optimization. AI marketing technology enables personalized customer journeys, improving acquisition costs and lifetime value through data-driven insights.
Artificial Intelligence (AI) is transforming marketing at an unprecedented pace.
However, not all AI investments are created equal. As AI continues to evolve, certain areas stand out as the most promising for significant returns on investment.
In this article, we’ll explore three key AI marketing technology investments that can significantly impact digital marketing strategies:
Using AI Marketing Technology for Language Processing: NLP, NLG and NLU
Natural language processing (NLP), natural language generation (NLG) and natural language understanding (NLU) form a powerful trifecta of AI technologies that organizations can implement to drive better service and support. This helps improve CX and build long-term customer loyalty and trust.
- NLP helps systems process and interpret language in text or audio form.
- NLU enables these systems to comprehend the intent behind user queries and requests.
- NLG generates contextually relevant responses.
A brand that implements this trio of AI marketing technology can process text and audio strings coming out of chatbots or call centers (NLP), understand what is being requested in those strings (NLU) and then generate an appropriate response (NLG).
These technologies can drastically lower customer service costs, when the underlying models are trained and implemented correctly. However, if these technologies are not trained and deployed correctly, it can result in auto chatbots or IVR systems providing inappropriate results and responses. Not only can this lead to poor customer experience but it can also result in regulatory and financial implications.
Related Article: 5 Actionable Ways to Integrate AI Into Martech Processes
Leveraging AI Technology for Synthetic Data: SDG
As part of generative AI, synthetic data generation (SDG) is the ability to generate data that is synthetic in nature to round out customer profiles or data sets. This process is vital for developing accurate and effective AI and machine learning (ML) models.
For AI and ML models to be run effectively by organizations, the model input data must be complete and of good quality. Organizations can use SDG to fill gaps in existing data sets and improve model output scores. In turn, this provides CX teams (i.e., sales, service and support) with better insights to act upon. These can include propensity, forecasting, demand, optimization and even content generation models. A common challenge with SDG is providing the proper parameters to generate data that is high quality and relevant, but that can be overcome with proper setup and implementation in the beginning phases of the generation process.
An example of synthetic data in action is its use for look-alike modeling. By creating artificial data that mimics actual customer data in its features, structures and attributes, brands can identify potential new audiences that “look like” existing, successful customer segments, and they can then target them.
Additionally, it can be used for testing and optimization. For example, insurers can use SDG to model pricing outcomes. By creating synthetic data that resembles historical policy and claims information to train pricing models, insurers can assess how different pricing strategies would perform without using sensitive personal information from customers.
Optimizing Customer Journeys With Technology: AI-Based CJO
AI-based optimization and customer routing technologies (i.e., customer journey optimization) are used to improve key outcomes like customer acquisition costs and customer lifetime value. This technology focuses on guiding customers through personalized paths to conversion, rather than relying on generic, brand-defined routes. This is achieved by analyzing historical and real-time customer data. AI can identify patterns and predict the most effective pathways.
All of this is rooted in reinforcement learning. A challenge to implementing this technology is having access to the proper historical data and the proper reinforcement learning technology in play to enable AI-based customer journey orchestration (CJO).
An example of CJO is when reinforcement learning can be applied to compare a consumer’s abandoned shopping cart with parallel patterns of other customer journeys that resulted in conversion. Using the latest next-best-action tactics to achieve optimal success for both micro and macro goals will lead to higher conversion rates.
This type of AI marketing technology isn’t being used widely by organizations currently, but I fully expect that it will be common in the next five to 10 years across most analytically advanced martech tools.
Related Article: Customer Journey Mapping: A How-To Guide
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How AI Marketing Technology Fits in Your Strategy
Investing in AI marketing technology like NLP/NLG/NLU, synthetic data generation and AI-based customer journey optimization can offer substantial returns for marketing departments. By leveraging these tools, organizations can enhance customer interactions, optimize data utilization and improve overall marketing effectiveness.
As AI continues to evolve, staying ahead of these trends and investing wisely in these areas will be crucial to maintain a competitive edge and achieve long-term success in the marketplace.
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