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Next Big Thing: Natural Language Processing

Furniture World Magazine




Natural language processing is being applied to furniture store marketing, competitive positioning, customer service and much more.


Natural Language Processing (NLP)— the analysis of speech and text by computer software—may seem like a tool that’s out of reach for many in our industry. It is a technology, however, that will soon help many furniture retailers to become more efficient and market their products better.

Advantages of NLP

Natural language study has been around for half a century now, but its use beyond basic research is relatively new.

NLP algorithms are used in a number of ways to analyze speech and text. For example, they can analyze the links between languages and help to unravel the intent of written or spoken words. And now, NLP algorithms have been tasked with improving furniture store digital marketing in the following ways.

NLP Helps Understand Customers: Using the wealth of data available through social media, apps and mobile devices, retailers can use NLP to better understand customer intent. It’s a step up from the segmented data—similar data grouped by gender, buyers vs. prospects, etc.—that social networks have been providing for a long time.

NLP Improves SEO and Keyword Targeting: NLP works to improve keyword targeting, the traditional bedrock of content marketing strategy. It identifies popular words from customer reviews and feedback that can be matched against current targeted keywords.

NLP Helps Monitor the Competition: Another advantage NLP data analysis offers retail furniture store operations involves monitoring competitors. It can deliver business intelligence regarding a competitor’s services or products that their customers love. With that information, retailers can come up with better alternatives and update competitive strategies.

In a post-pandemic world, NLP will continue to be a powerful tool used by furniture retailers who need to know what their audience is thinking, talking about, and wants to purchase.

NLP Improves Customer Experiences: In today’s consumer culture, it is standard practice for people to share data in exchange for a quality retail experience. NLP allows businesses to use this data in more meaningful ways. For example, to meet customers’ expectations for retailers to provide the following services:

  • Remember what they’ve previously purchased.
  • Know their preferred channels of engagement.
  • Make suggestions regarding what to buy next depending on past interactions with a brand.

NLP Helps Discover Purchase Intent: People are using the internet at all-time high levels to research and purchase home furnishings. In a post-pandemic world, NLP will continue to be a powerful tool used by furniture retailers who need to know what their audience is thinking, talking about, and wants to purchase.

Gather Customer Feedback: Traditional customer feedback metrics such as Net Promoter Scores (NPS) and Customer Satisfaction (CSAT) scores are too vague to provide a holistic understanding of customers. Today, businesses need actionable information that can lead to improvement. That’s why a customer feedback platform that incorporates NLP is something retailers should investigate. Technologies like automation and data analytics are tools, they’re not the endgame. NLP helps to successfully implement AI and machine learning that require an understanding of system requirements and inputs. And, It helps identify parameters on which systems base output. For example, to look up sales order numbers, organize by customer zip codes, or group them by age and household income ranges.

Beyond Better Marketing

At the root of natural language processing is understanding the intent of human communication. It’s something beyond what conventional marketing is capable of. NLP starts by creating a highly personalized profile for each user to understand them more deeply than through segmentation.

This goes beyond knowing what a person’s preferences are based on past purchasing behaviors. It uses real world data to build communication etiquettes that can even predict how people will react to different ads.

Better Customer Service

NLP algorithms can analyze customer support tickets before a live agent checks them. Say, for example, a customer wants to know if their merchandise is out for delivery or if a retailer carries a certain brand of merchandise. Small and large businesses can implement this next-generation customer experience tool to leverage AI. NLP based interfaces can help service employees understand customers better to guarantee more favorable results. For example, if there are five customer service reps and all are busy, the customers won’t need to wait. An NLP-enabled system can respond to basic questions and perform actions such as emailing a copy of a sales receipt or rescheduling a delivery.

Better Chatbots

Creating better chatbots is another benefit of NLP for digital marketing. NLP algorithms help chatbots learn how to better respond to different types of customers.

NLP, especially in the healthcare and entertainment industries, has allowed businesses to move toward introducing chatbots that can do as good a job as customer reps. These more advanced chatbots strategically fit between live customer communications and static website content thereby reducing outreach and support costs in cases where users can be directed to specific FAQ’s. For example, chatbots can answer questions about the availability of merchandise seen on a website without requiring a customer to wait on hold to speak to a customer service agent.

NLP goes beyond knowing what a person’s preferences are based on past purchasing behaviors. It is trained by data to build communication etiquettes that will even predict how people will react to different ads.

These bots can answer standard queries, help visitors with mundane tasks such as account password reset or route them to various other categories and pages, thereby increasing customer engagement and enhancing the customer experience. Chatbots traditionally learn over time so the more business rules they are taught, the better they will perform.

Automated routing rules ensure that bots distribute support tickets efficiently to the most appropriate agents to handle specialized customer queries, increasing organizational credibility.

These bots can be smart with inbuilt texting etiquette. Custom business rules with sentiment analysis can help to provide better customer service by, for example, knowing what to text and how frequently to text. This ensures that customers will be more receptive to receiving ongoing communications.

More Effective Sentiment Analysis

Several interesting areas of sentiment analysis (analyzing customers’ emotions and language tones) have been developed over the years. These include brand monitoring, product analysis, reviews and competitive research. NLP can help furniture retailers to understand at a deeper level what their customers are saying. They can leverage advanced NLP algorithms to help stores understand and respond to gazillions of stakeholder and target audience conversations. Word choices used in reviews reflect how stores are viewed in the public domain. NLP algorithms can improve a retailer’s understanding of underlying customer and shopper sentiment. They can detect sarcasm and humor in social media and on customer review sites in posts, revealing customer concerns about service, complaints about not finding what they wanted or comments regarding salespeople.

Automated Trend Identification

According to research carried out at Stanford University, NLP-based automated summarization has become more complex. It can now move beyond simplistic media summarizations. This means that companies can effectively use NLP for marketing automation. A retailer’s system can, for example, automatically identify a certain age group with a range of household income in specific zip codes that are buying recliners. Armed with this information the marketing team can generate a direct mailing list to reach these prospects.

NLP algorithms can carry out trend analysis without explicit instructions. This is extremely useful and can lead to the creation of new products and services, or the improvement of old ones. Say for example, the system finds that in a few zip codes, customers only take deliveries on Friday or Saturday. If the delivery team is alerted to this situation, it can focus on offering people in other zip code areas, Friday and Saturday, deliveries to fill up open spots.

Customer feedback
metrics like Net Promoter Scores (NPS) and Customer Satisfaction (CSAT) scores are too vague to provide a holistic understanding of customers.


Amitesh Sinha is a technology consultant based in North America. With over 20 years of experience developing and deploying solutions for retail, Sinha has gained a reputation for home furnishing software solutions, furniture software, POS furniture software, and re-engineering of software with extended features. His company, iConnect offers business technology solutions that integrate with most P.O.S. systems to make them more efficient and user-friendly. For more information about this article or any retail technology question contact Amitesh at 703-471-3964, amitesh@iconnectgroup.com or www.iconnectgroup.com.