5 Ways to improve your Marketing ROI with Text Analytics

According to IDC, 80% of the world’s data will be unstructured. This unstructured data includes thousands of information produced in surveys with free text, WhatsApp chat, and content shared on social media platforms, emails, and other online platforms. However, if you are a marketer, you would know how important this information is. In marketing, it has clearly moved from the traditional marketing approach to the usage of social media platforms where communication with the public becomes real.   

In this digital era, analyzing marketing data is a tremendous task. As marketing becomes more personalized, regular customer surveys and evaluating the conversations of consumers on online platforms and other media become necessary. To capture all these customer insights and market data scattered across various online and offline platforms can all be collected and used effectively, you require the help of Artificial intelligence (AI). The approach of using Artificial Intelligence in classifying or categorizing text and analyzing the meaning is known as Text Analytics.  

What is text analytics? 

The process of deriving insights from large volume of structured and unstructured text data is known as text analytics. To put it simply, it is the process of obtaining general tags from unstructured text or, in other words, the classification and evaluating text-based datasets. 

It is similar to how your search engine searches the internet after you input a word or phrase in order to locate what you’re looking for. Similarly, in a professional setting, text categorization tools comb through online journals, forums, blogs, chat logs, social media, and more to offer organized insights on your subject and support your market research. 

How does it create value for your marketing campaign? 

Unstructured text data, such as user-generated content gleaned from social media sites, is widely used in marketing research. Analyzing the text units by topic or their stated attitude towards companies and items is a frequent effort in making these data relevant for analysis. Analyzing such microblog postings and product reviews helps in understanding customers’ attitudes towards your product or the brand.  

Brands may mine consumer and media narratives for useful information in each of the following categories by documenting the conversations: 

Add value to marketing campaigns using Text Analytics 

There are several advantages for companies in being able to extract customer and market insight from unstructured data. Brands may use the power of text analytics to enhance their consumer analytics, marketing initiatives, and ultimately, their impact within their market if they have the correct tools. Here are some key aspects that highlight the importance of text data to yield better returns from the marketing campaigns you plan.  

1.     Peep into your customers’ minds: Text analytics can provide reliable insights into customer preferences, needs, and pain points. By evaluating customer reviews, feedback, and social media mentions, businesses can identify trends, improve customer satisfaction, and tailor their products or services accordingly.  

It accurately categorizes and deciphers the meaning of customer remarks posted on social media. It even uses visual processing to extract information and sentiment from misspellings, sarcasm, emoticons, and brand logos. For example, ‘gud’ for good, ‘gr8’ for great, etc.  

2.     Know what your customers feel: Customers’ reactions towards your campaign or any other marketing activities are reflected in sentiment. The sentiment analytics offered through Text Analytics helps in providing sentiment-based insights (such as likes, dislikes, and positive or negative behaviors) and how they connect to your brand. Analyzing the sentiment behind text data can help marketing professionals to prepare more personalized campaigns for the right set of customers. Understanding customer sentiment can aid in building loyal customers, getting more responses for the marketing campaign and strategizing better campaigns.  

3.     Understand your market well: Research done on your target market, customers, and competition is crucial for building a successful marketing campaign. Text analytics can be used to study market trends, competitors, and emerging technologies. By staying informed about industry developments, businesses can better anticipate customer needs and make strategic decisions to maintain a competitive edge. 

4.     Your content is the king:  Personalization is a key aspect of marketing. Customers prefer brands that communicate with them personally and resonate with their needs. According to a PwC report, a single bad response from the brand keeps the customers away from the company. Text analytics can help businesses fine-tune their content strategies by creating more personalized messaging.  This information can be used to create engaging content for the target audience and drives traffic to websites and social media platforms. 

5.    Real-time and Scalable measurement of your campaign: During a crisis, it is frequently necessary to quickly get an understanding of customer perception. You can track brand mentions and keywords in real-time using text classification, which enables your decision-makers to respond rapidly. When brand mentions becomes soaring, your AI won’t panic since you can still access rapid results. 

Wrapping up:  

Automated analysis of target audiences into cohorts may make the lives of marketers easier as marketing becomes more focused every day. Based on the way consumers communicate about a product or brand online, marketers may keep track of them and categorize them. With the help of Text analytics, marketing professionals can easily recognize supporters and opponents of the campaign. Consequently, brands are improved to better serve groups. 

To understand where AI may fit into the company DNA, a successful approach entails identifying areas for improvement, creating clear targets, and guaranteeing a continual process for progress. A good AI system needs clean data to function. The answer becomes better the more data AI is given. Therefore, businesses with more data can better understand their consumers.