Ocsico: how NLP can help revamp customer experience

NLP to build effective strategies

Natural language processing (NLP) is a perfect AI-based technology for processing customer requests and optimising product search results. In simple words, the technology is about using computers to ‘understand’ human words. NLP is the computer science that has many fields of application.

NLP uses consumer data to come up with data-based solutions. It is used to analyse words and the surrounding context that can reverse any meaning. At the core of the process, there is the deep learning technology and artificial neural networks fuelling an algorithm to process input in the most ‘smart’ way.

The algorithm takes a string, cuts it into chunks, and translates each chunk from a natural ‘human’ language to the artificial ‘computer’ language.

Depending on the task, it returns the result that can be used to support one or another decision in developing a customer-centric marketing strategy.

NLP for customer feedback

To comprehend consumers’ expectations and foresee their needs is a top priority in the service sector. Customer services benefit greatly from applying NLP. The reason is that AI allows far more precise simulation of customer behaviour in one or another situation.          

1. Speech recognition

Many services providers record, transcribe and analyse customer calls. The statistical method of analysing customer data allows generating a most relevant response to ensure customer satisfaction. The technology is actively used in such systems as Siri and Skype translator, or by Facebook. Another popular implementation of speech recognition technology is chat-bots.

2. Personalised bots

NLP’s potential in recognising customer’s intent is growing in leaps and bound. According to Gartner, by the year 2020, 55% of all large enterprises will focus on employing chat-bots rather than on e-commerce application development. For instance, a chat-bot of a fashion online store can help consumers chose an outfit based on purchase desires. The chat-bot needs some consumer information, such as:

•   Style preferences
•   Price category
•   Likes/dislikes to an outfit

Here is the visualization of H&M chat-bot at work:

By leveraging the information about various customer’s preferences, the chat-bot generates a link to the web sore, provides more suggestions and the option of sharing an outfit via social networks.

3. Sentiment analysis

Is everybody able to gauge the emotions of other people well?

Probably not. The sentiment analysis technology is tailored to handle such tasks. A plethora of data is hidden in unstructured texts, i.e., forums, comments, blog posts, social media, etc. The technology helps monitor what consumers think and share about a brand or a provider’s company.

Today, consumers tend to make their choice based on star ratings and independent product reviews. It places the paramount importance on monitoring brand mentions or product ratings on the web.

The following is a simple example of the way the NLP algorithm analyzes text data:

Sentiment analysis assists in figuring out the attitude and emotional state rooted in a message. It automatically assesses the polarity and mood of a comment, and the algorithm tags a comment as positive/neutral/negative/compound, happy/angry/sad, etc.

4.     Cost-effective approach

Whatever the task is aimed at adjusting customer experience, tech-based acceleration can provide significant assistance. Implementation of technologies ensures the highest quality of services, which result in getting extra profit.

If consumers feel that their needs always come first, they return and multiply the lines of loyal customers. Technologies also allow saving on support costs and facilitate the process of analysing data and responding in the most appropriate way. And that is exactly what consumers want.

Concluding thoughts

AI lends a helping hand to navigate retail and e-commerce businesses in line with the needs and expectations of the target audience.

While computer scientists go on working over AI-driven technologies, NLP can be already successfully employed to measure online engagement, monitor the quality of responses an app or a website provides to customers’ queries, fine-tune the search results, and so on and so on.

The key objective is to provide the customer experience as perfect as possible, which can most likely result in a high rating by consumers and increased conversions.

Katrine Spirina is a technology journalist with software development company Ocsico covering trends in web and mobile development, business intelligence, IoT, data, fleet management and e-commerce.

Katrine Spirina can be reached at [email protected]