Brands have had to update their marketing playbooks with insights and data that help people make better decisions in the pandemic and post-pandemic environment. Innovative companies are placing their bets on customer intelligence, especially because the marketing environment has completely evolved over the past two years.
Modern marketing relies heavily on tracking consumer behaviour and making sense of the massive amounts of data that are being gathered, but traditional methods of gathering customer intelligence are becoming challenging due to data privacy concerns and the impending end of the cookie era. On the other hand, consumers are quite active on social media, on e-commerce platforms, and in user forums. They share really useful information about their thoughts on a product or their experience with a company.
The amount of content that needs to be analyzed and the nature of the content involved prevent automation efforts, so these sources of information are not fully utilized. For example, most text analytics tools struggle to extract actionable insights from short, highly variable texts accurately.
Also Read: Six Stages to Develop a Customer Intelligence (CI) Strategy
Just Sentiment Analysis is Not Enough
A good way to determine whether consumers are satisfied with the new product features or support center is through sentiment analysis. It can help immediately identify pain areas or reassure the marketing team about their strategic course. But for information to be truly useful, they require the answers to questions like, exactly what do their customers like? Why? What benefits do they receive from the product? Businesses can use this kind of information in their upcoming marketing campaigns and reach out to potential customers from a different angle.
Many businesses make an effort to use the data that consumers disclose online for that specific reason.
The vast amount of customer feedback that major brands receive in the form of product reviews and social media posts makes it impossible to analyze all of it manually unless businesses are willing to load their budget with high labor costs.
Structured Vs. Unstructured Content
Why can’t this be automated in the era of intelligent document processing and Robotic Process Automation so that marketers can utilize the data buried in online content at scale? The challenge here is unstructured data. Automation tools are excellent at processing large amounts of structured content.
A prime example of structured content that automation tools can seamlessly process is online forms with text options and pre-defined fields. The issues start when the form has a free text field where users can enter anything. Generally speaking, semi-structured content of this nature can still be correctly processed because the business has access to thousands of examples that will help the software be trained. However, the majority of online content is completely unstructured. This includes product reviews on e-commerce sites, social media posts, and customer service emails. Automation tools have found it difficult to process this type of content because it lacks structure or standardization.
Also Read: Why Customer Intelligence is Crucial for Customer Success
Boosting Customer Intelligence with NLU
Natural Language Understanding (NLU) is a set of Machine Learning and Artificial Intelligence techniques that goes beyond the keyword-based and statistical methods used for text analytics tools and sentiment analysis. The most promising methods create systems that are capable of accurately interpreting various expressions of the same idea by using vector-based, fine-grained representations of words that store all senses and contexts.
Neural networks and deep learning are used by several MarTech platforms to enhance the functionality of virtual assistants and chatbots, for instance. Other NLU methods expand the potential of social media listening by enabling the rapid and minimally invasive extraction of useful insights from online content.
The ability of a brand to foresee market changes and modify its positioning in response to emerging trends is a key indicator of its competitiveness. In the era of pervasive digital presence, understanding what customers think can be challenging. If marketing lacks the tools to understand too much information, it may be counterproductive. Verbatim data can, however, be controlled thanks to recent advancements in AI and Natural Language Understanding, opening the door for real-time, cross-channel customer intelligence.
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