How do your customers feel about your brand? Do you really know? To truly understand their perceptions, you must perform sentiment analysis. Sentiment analysis is a type of data mining that uses text analysis, natural language processing (NLP), computational linguistics and other machine learning insights to analyze customer opinions towards brands, products, people, and ideas.
Businesses looking to optimize their offerings and brand image turn to sentiment analysis to get it done. It’s applied by combining metrics and data from various touchpoints; and StarfishETL makes those connections possible by integrating data between all different types of applications. Here are a few examples of how teams can facilitate sentiment analysis using StarfishETL.
#1 Integrating Call Center Software with a Sentiment Analysis API
Call centers are rich with data that can be applied to sentiment analysis. Speech-to-text programs can convert conversations and look for semantic meaning. Historical analysis of chats can reveal whether an individual is mostly satisfied or dissatisfied with their service. Plus, patterns of negative interactions can help organizations recognize a clear tipping point for customer churn.
This is why many businesses choose to integrate their call center software with a sentiment analysis API and feed those insights into another system, like CRM. Proper data connections (enabled through iPaaS) make that analysis digestible and easy to act on, so the call center can make real changes towards improving the customer experience.
#2 Combining AI with Social Media
Marketing teams rely on social media channels to target and engage the right demographics. In fact, social media advertising accounted for 33% of all digital advertising spending in 2022. It’s the second biggest market for digital ads behind search advertising. So, wouldn’t it be helpful to be able to reflect back on those campaigns on a deeper level?
That’s exactly the intention of the brands integrating their social media campaigns with AI. They use the AI to interpret what comments and hashtags around their campaigns mean about the intentions of the audience. What do those patterns reveal?
StarfishETL makes bringing these tools together painless by reading data from the AI and social media APIs to connect them. This makes the AI-social media connection seamless and specific to the results the organization is looking to achieve.
#3 Learning From and Acting on Email Sentiment
As one of the most used communication channels, email has great potential for a role in sentiment analysis. Sales teams use email to respond about demos, pricing information, and other requests scooped up via a company’s website and forms. Now, say your business starts tracking certain keywords across these emails to look for sentiment patterns.
Combine the email with a sentiment analysis API that could use if-then rules to trigger an action or notify a fellow team member via the CRM when specific keywords are mentioned. However, if your CRM solution comes with sentiment analysis tools built in, you could simply connect the email to the CRM to trigger those same types of actions. An integration would gather insights from the email to be pulled into a CRM workflow which would then analyze the sentiment and move the data through to the logical next steps in the process.
These are just a few of the ways you can tie-in sentiment analysis through APIs and integrated solutions. StarfishETL supports those tie-ins via its powerful iPaaS. An iPaaS (integration platform as a service) is highly flexible and adaptable. It’s meant to connect all kinds of applications and APIs, which makes it a great option for seamlessly weaving sentiment analysis into your business. For more information on iPaaS, view our web page.