WHAT IS NLP?
WHAT CAN IT DO FOR POST-COVID DIGITAL MARKETING?
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
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.
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
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
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
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.
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, firstname.lastname@example.org or www.iconnectgroup.com.