Creating an eCommerce presence doesn’t just mean taking operations online and using the same marketing methods. Upgrading marketing techniques with text analytic tools is needed to remain competitive in a crowded marketplace.
What Are Text Analytics?
Text analytics is the process of taking unstructured user-generated data, such as reviews, social media posts, and customer service interactions and developing actionable consumer insights. A text analytic tool uses machine learning algorithms to discover topics in product categories and put customer comments in context. The result is insights that can drive product development and marketing strategies.
Through AI, text analytics tools develop an almost human-like understanding of the texts and can spot tone and subtleties in unstructured texts. They organize information into categories and analyze customers’ sentiment, even when they are subtle or neutral. Text analytics complements other metrics’ results to give a full picture of how people view brands and products.
Beyond Data Analytics
AI has opened up a new world of data analytics that assign numerical scores such as a Customer Satisfaction Rating (CSAT) and a Net Promoter Score (NPS). These numbers can be useful and provide a basis for comparison, but text analysis is also needed to tell the complete story.
Using only numerical data to measure customer satisfaction and other metrics gives access to information but means being left out of the conversation. Listening to how consumers discuss brands and new products on social media reveals hints behind customer behavior and preferences for some brands and features over others. The combination of text and data analytics is a comprehensive approach to Voice of Customer strategy.
For instance, any company can feel good about a high NPS or Net Promoter Score. This metric quantifies customer loyalty and how likely the customer would be to recommend products to friends. Some companies may stop there and be satisfied that people like their products and are willing to recommend them.
However, the same company could decide to capitalize on successes by upgrading a product with new features, such as a soda that contains other fruit flavors such as mango, to follow up on their success with cherry cola. A strategy that takes only numbers into account would not reveal that what customers liked about the drink was specifically the cherry flavor. The assumption that other fruit-accented colas would do well is an incorrect one.
Incorporating insight from user-generated texts on social media can prevent faulty product launches or tone-deaf developments that can negatively affect NPS down the line. Text analysis provides a “why” to the “what” of data analysis, and both approaches complement each other and contribute to a full picture of a brand.
It’s Not All Black and White
Some data-driven strategies provide a black and white view of a brand’s progress. Numbers are either up or down. Either the majority of people like a product, or they don’t. Text analytics do not just take the most extreme views at either end, the ones who really love or dislike a product, but also those whose views are more nuanced.
Text analysis often highlights customers who phrase reviews with, “I liked the product, but this could be improved.” or “I might not rebuy this item, but I did like this feature.” These comments are precious because they are consumers who are on the fence about a specific item and can be won over. They do not love or hate a feature, but some tweaks and upgrades may be enough to cause them to buy or purchase with more enthusiasm.
Customer Service Insights
Text analysis provides the full story behind customer service interactions. The question to ask is not just “How effective is our customer service?” but “Why are people contacting our representatives? What questions do they need answers for?”
There is no need to read every one of these interactions, which would require a lot of time, but text analysis tools can analyze which words keep recurring and the emotions detected behind these words. For instance, a coffee seller may see the word “bitter” frequently appear in customer service chats and realize there is an issue with the coffee’s flavor.
However, if the word “strong” often appears with positive emotional coloring, that may indicate customers are happy with the coffee’s strength. In addition to tracking how often certain words appear, sentiment analysis can give clues on the connotation of those words and consumer attitudes when using them.
Staying Ahead of Market Trends
Market research is not something that should be undertaken a few times a year but should be ongoing. Trends are happening faster in the world of social media as more people have more conversations about more things. Yesterday’s hot product grew stale quickly. Collecting and analyzing user-generated text is a process that should continue throughout the year, not just before the holiday rush.
Many innovative ideas come from customers. As they suggest improvements in reviews or discuss on social media what they want to see, companies that listen to these conversations benefit. Highlighting certain useful comments can encourage other customers to leave reviews and can foster brand loyalty. Customers reward listening with sales.
Listening to What Customers Say
Consumers can speak volumes, but it is also essential to pay attention to their conversations about brands, features, and products. Text analysis complements data analysis and adds color and context to consumer actions. Listening to consumers can open up the door to innovation and help companies produce the kinds of items people want to buy and brands they can depend on.
