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Best Practices on Implementing Sentiment Analysis Across Marketing Efforts

Sentiment analysis is typically thought of in a social media setting and as a way to monitor how often your brand is brought up on social sites. Yet, sentiment analysis can be a much more robust tool as well as a necessity for local businesses and enterprises in order to determine true customer experiences and business success. Sentiment analysis in terms of the customer experience refers to how customers perceive a brand and how they feel towards the service given, the products offered and the brand as a whole. 

Large enterprises and even local businesses have garnered hundreds of online reviews and social media mentions in the past couple of years as typical word-of-mouth has transferred to be almost entirely online. Those reviews are a gold mine of valuable insights into how successful (or not) each business is. The issue is that only a small amount of organizations currently use a sentiment analysis tool to determine the customer experience of each of their locations. 

The traditional way of gathering insight into how customers feel about a brand, service or product is to conduct surveys to get direct feedback. The issue with this method is that only a very small percent of a business’s customer base will take the time to respond to a survey. According to the American Customer Satisfaction Index, response rates to customer surveys range between 5 and 15 percent. That small group of respondents is predominately found to be either extremely unhappy or extremely happy customers, and not the average or true representation that businesses are searching for.  

How exactly does it work?
sentiment

Sentiment analysis is the process of identifying topics and associating positive or negative sentiment around those topics. The algorithm will break up the content into individual statements and then assign sentiment scores to key topics it finds within the statement. 

In order for the tools and algorithms to understand the context of the reviews and social mention, they use Natural Language Processing (or NLP), which is the ability of a computer program to understand human speech and text. NLP is challenging due to instances of slang, misspellings, poor grammar, emoticons, and more. The industry standard accuracy rate for sentiment analysis tools is around 70-80 percent. For example, look at the word cold. Cold beer at a restaurant has very positive sentiment but nobody wants a cold shower at their hotel. 

Sentiment and Artificial Intelligence

A 70-80 percent accuracy score isn’t ideal for businesses trying to use this information to make informed marketing strategy decisions. Which is why the best sentiment analysis tools have artificial intelligence (AI) and machine learning built into the tool in order to improve the accuracy of the suggested sentiment scores. The tool will score the given topics between very negative up to very positive and use these scores as a means to train the algorithm, which will then classify new text it sees based on the original scores. The tool will progressively learn and become more accurate over time.

3 Insights from Sentiment Analysis:


1. Track fewer topics than one might think
At first, it might seem like the best idea to track topics on every single sector of a business in order to determine the sentiment on everything, but in practice, tracking a multitude of topics can quickly become overwhelming and disorganized. It is best to only track the key topics most pertinent to your business and then make sure that the tool can add subtopics, which are a great way to track variations of a similar topic. For example, Starbucks would track the topic “coffee” and then add sub-topics underneath it such as “latte,” “cappuccino,” and “espresso.” 

2. Be sure to track the historical analysis of topics
Having a tool that can track topics over selected time periods can give great insights into how different topics are gaining popularity and therefore determine up and coming trends in different industries. Based on historical insights, businesses can make changes at the store level and improve the customer experience. 

3. Track campaigns, promotions, and even employee performance

A sentiment analysis tool can be extremely useful in tracking how consumers are perceiving different marketing campaigns or promotions. For example, a BBQ restaurant launches a summer campaign promoting a special on ribs. In order to monitor and manage what customers are saying about the promotion, the BBQ restaurant will add various topics around the promotion. Over time, they can track if consumers are very positive or very negative about the ribs promotion and make changes when necessary. 

Alternatively, a business can add some of their employees’ names as topics to track and whenever a customer mentions an employee, the sentiment analysis tool can determine if that particular employee deserves a raise or not. 

Using a sentiment analysis tool is extremely important to businesses that are looking to track how customers perceive their service, products, and even company cultures. Once a business has implemented a sentiment analysis tool, the next step is to apply those learnings to the rest of the conversation around the business and industry. Businesses can then determine customer trends and be able to cater towards those trends, not only placing themselves as thought leaders in their industries but as well as giving customers exactly what they are looking to purchase. 

About the Author
Collin-Holmes-headshot_2017Collin Holmes, founder and CEO, started Chatmeter in Aug. 2009. Prior to Chatmeter, Mr. Holmes was VP of Product Management and Marketing at V-Enable (now xAD). His extensive experience in the local search industry, both online and mobile, provides a solid foundation for the direction of the company. He has worked in leadership roles at several other startup companies and held other notable positions in product and marketing roles at Akamai Technologies and AT&T Wireless. He earned his MBA from San Diego State University and a BA from UC Riverside.
 

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