Читать книгу Self-Service Data Analytics and Governance for Managers - Nathan E. Myers - Страница 33

“How did you first learn of the AI components and technologies that are Social Listening?”

I first learned about Social Listening AI technology during a lecture given by a PA-based firm called Monetate back in the Spring of 2015. At the time of the lecture we were still essentially a start-up and we were more focused on foundational steps to build our company. However, AI captivated me from that moment onward, and I began to watch developments in the space much closer. The potential to get inside of our customers’ heads to improve our products and services sounded like a game-changer. As our company grew to a stage where we were ready, we immediately plugged in. Frankly, we knew that to be a trend-setting and best-in-class company, we needed to be on the forefront with cutting edge technology like AI Social Listening. How could we not?

“How have you employed artificial intelligence and applied data science for Social Listening?”

At Lash Affair, anything we can do to isolate and uncover consumer trends and brand sentiment will give us an edge. Our company is growing quickly, and more than ever before, we need our hands directly on the pulse of our customers. We want to be the very best in our industry and want to set the bar very high when it comes to exceeding customer expectations. How do we understand those expectations? Well, of course, we don't have time to devote to personally trolling social media sites for posts related to our industry, products and services, and our brand in particular (although we admit to trying every day). However, we have armed ourselves with a serious data analytics dashboard, which gives us the reach to be able to scour the enormous social media landscape for relevant data points that can help us to understand how we are doing with our customers – and with influencers.

“What specific AI components are employed for Social Listening?”

Web-data capture is instrumental to helping us to cast a wide net based on search criteria we use to define relevance. This technology is important to allowing us to capture an enormous number of target observations. From there, we work with machine learning analysts and consumer intelligence experts to extract and understand the tone of posts. With enough timely observations, we can interpret and even get ahead of sentiment about both our brand and trends in the industry. Once we have relevant data, NLP technology is enlisted to translate the informal vernacular of social media participants in posts. After all, few bloggers use straightforward and easy-to-interpret affirmative statements like, “My brand sentiment for Lash Affair products and services is extremely positive – on the very far right of the brand sentiment continuum.” Instead, we need to be able to analyze a vast number of sentiment observations and classify each as “positive” (+1) or “negative” (–1), or somewhere in between along the spectrum. Imagine an algorithm that assigns numerical values to the series of observed adjectives being used to describe excitement about our brand, recent customer experiences, and customer loyalty to help us gauge prevailing consumer sentiment. By charting sentiment observation values in time-series, we can spot trends. To get at it involves feeding tons of training data through the machine learning classification algorithm, so that it begins to interpret observations as predictably and reliably as if I personally was in the chair, reading each post, and graphing each sentiment observation as it comes in.

“What do you do with the summarized customer sentiment information?”

The obvious benefit is that we can put our own bias aside to listen to what our customers and target market are saying. Where an opportunity to improve is highlighted, we proactively make changes to better meet the needs of our customers. Where we are doing things that are extremely positively received, we do more of it! One way we can directly react is through engagement. Our service provider gives us an added drill-down capability that allows us to hone in on specific observations that appear as outliers, whether positive or negative. We then have the opportunity to respond through an active feedback loop. We can reinforce positive sentiment and respond to or even turn around negative sentiment through this channel. The other key component here is having a highly trained internal team to take swift action when the time comes. Our Google review stats are proof that we understand the importance of keeping consistent excitement around the Lash Affair brand. Data analytics capabilities have given us a giant advantage in getting this done.

Paul Paris, CEO at Lash Affair.

www.lashaffair.com