3 areas with the biggest returns on AI marketing technology

The essentials

• Key AI Technologies. AI marketing technologies such as NLP, NLG and NLU improve customer interactions and improve service efficiency.
• Benefits of data generation. Generating synthetic data helps enrich customer profiles and provides better insights into AI and machine learning models in marketing strategies.
• Optimization of the customer journey. 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 equal. As AI continues to evolve, there are certain areas that show the most promise in terms of significant returns on investment.

In this article, we’ll explore three key investments in AI marketing technologies that can have a significant 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 trio of AI technologies that organizations can implement to improve service and support . This helps improve customer experience and build long-term customer loyalty and trust.

• NLP helps systems process and interpret language in the form of text or audio.
• NLU allows these systems to understand the intent behind user queries and requests.
• LNG generates contextually relevant responses.

A brand that implements this trio of AI marketing technologies can process text and audio strings from chatbots or call centers (NLP), understand what is being asked in those strings (NLU), and then generate a appropriate response (NLG).

These technologies can significantly reduce customer service costs, when the underlying models are trained and implemented correctly. However, if these technologies are not trained and deployed properly, automated chatbots or IVR systems can provide inappropriate results and responses. Not only can this lead to poor customer experience but it can also lead to regulatory and financial implications.

Related article: 5 concrete ways to integrate AI into Martech processes

Leveraging AI technology for synthetic data: SDG

In the context of generative AI, synthetic data generation (SDG) is the ability to generate data of a synthetic nature to complement customer profiles or datasets. This process is essential for developing accurate and efficient 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 information to act on. These can include propensity, forecasting, demand, optimization and even content generation models. A common challenge with SDG is providing the appropriate parameters to generate relevant, high-quality data, but that can be overcome with proper configuration and implementation early in the generation process.

An example of synthetic data in action is its use for similar modeling. By creating artificial data that imitates reality customer data in its characteristics, structures and attributes, brands can identify potential new audiences that “look like” existing, high-performing customer segments, and 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 evaluate the performance of different pricing strategies without using customers’ sensitive personal information.

Optimizing customer journeys with technology: AI-powered CJO

AI-based customer optimization and routing technologies (i.e. customer journey optimization) are used to improve key outcomes such as customer acquisition costs and customer lifetime value. This technology aims to guide customers through personalized conversion paths, 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.

This is all rooted in reinforcement learning. A challenge in implementing this technology is having access to the appropriate historical data and appropriate reinforcement learning technology to enable AI-driven 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 models of other customer journeys that resulted in a conversion. Using the latest action tactics to achieve optimal success for both micro and macro goals will result in higher conversion rates.

This type of AI marketing technology is not widely used by organizations currently, but I expect it to be common over the next five to ten years in the most analytics-advanced organizations. martech tools.

Related article: Customer journey mapping: a practical guide