How AI Enhanced Social Listening Can Drive Brand Performance

It’s interesting that the technology originally known for cat videos, silly memes and pictures of people’s lunch could be so important to your brands’ success. But social media can deliver valuable insights that drive growth for savvy marketers. Especially in today’s environment.

Every day an insane amount of information is shared on social media, including conversations about your brands, all in real time. According to one recent estimate, there are nearly 3.5 billion active social media users. On Facebook alone there are 510,000 comments posted and 293,000 statuses updated every minute! Talk about big data.

There’s no way you could manually monitor and analyze all that. Fortunately, you don’t have to. AI can pull out what’s important. And as you’ll see in this blog post, it can help you in several ways.

But first, it’s important to distinguish between social monitoring and social listening. Social monitoring identifies and captures brand mentions across popular platforms such as Facebook, Twitter and others. It’s an important first step, but it doesn’t do any good unless you use that intelligence. That’s where social listening comes in.

With advances in natural language processing, we can perform sentiment analysis on social conversations to understand the prevailing attitude towards pharma brands and products. This allows you to quickly and effectively analyze the data and use it to better target your campaigns. And because AI improves with more data, that content overload is actually a benefit to those with the foresight to put a social listening system in place sooner rather than later.

Here are just a few of the things pharma brands can do with AI-backed social listening:

Spot potential issues early and respond quickly

People share both good and bad experiences on social media. AI and sentiment analysis enables marketers to detect and analyze these more quickly and effectively than ever before. As a result, you can spot potential issues and counteract them before they become too widespread.

For example, Samsung had an issue with a red tint on the screen of its newly-released S8 smartphone model in some areas which they weren’t initially aware of. Thanks to social listening, however, they were able to detect and counteract customer dissatisfaction around this issue before it became it could cause long-term damage to their brand.

Identify Intent and Deliver Tailored Messages

Social listening and sentiment analysis can also be used to spot intent by analyzing the ways that consumers are talking about a product or service. For example, if someone posts, “In the market for a new phone. Samsung S8 anyone? How does it hold up?” or “Borrowed my boyfriend’s iPad and now I’m thinking about getting one…” you know they are in the consideration stage of the sales funnel for those products.

Using this knowledge along with AI, marketers can then target them with advertising or a strategically placed discount. One word of warning though: Marketers should tread lightly with this kind of targeting, or you could risk appearing creepy. Nobody wants to feel like they are being spied on.

Competitive Analyses

Keeping track of how your competitors are perceived in the market is just as important as keeping track of your own brand reputation. And social listening is the perfect way to do that. You can learn what the market is saying about your rivals, what they like about their products, and what they dislike.
Armed with this knowledge, you can identify key differentiators, discover what the market is responding to, and better position your brands in the marketplace. And you can analyze how those changes are perceived by your target audiences based directly on what they are saying.

Spot Key Market Trends

It’s a fast moving world. What worked yesterday doesn’t always work today. And what works today won’t necessarily work tomorrow. To stay ahead of the curve, you need to be able to keep up with the latest market trends.

In the past, this meant chasing after trends, where you’re perpetually behind, or trying to predict the future. Now, with AI-backed social listening, you can spot new trends as they happen from the consumers who are directly shaping or affected by them.

You’ll be able to learn about the hot-button issues your customers care about, for instance. And you can discover what outside factors are affecting consumer attitudes and behaviors.

With so many companies now employing sophisticated AI systems and Augmented Reality that provide a highly personalized and rich experience while providing customers with an immediate response, immediacy has become an integral part of creating a strong customer experience.

Some companies have been creating immediacy options for almost a decade. Amazon created Mayday in 2013. This was a single click video chat solution with a tech support rep 24/7 that allowed real time interaction in which the customer could see the tech support person and the tech support person could see their screen to guide them what to do. The average response time was 9 seconds in 2013.

Currently, outside of AI solutions, there are two main options currently being used for this – increased staff, or digital assistants. Neither of these are ideal. The financial costs of dramatically Increased staff is cost prohibitive, and most digital assistants without AI are very limited and exceptionally frustrating to deal with in my experience. To create a highly performing digital assistant is a costly procedure.

But, there is good news. There are cost effective ways that AI can provide this by plugging into your existing systems. Some of these are even free for a limited amount of interactions (e.g. for up to 500 interactions a month).

The advantage of using an existing AI enabled chatbot, is that the NLP (Natural Language Processing) and deep learning elements have already been developed so the high cost part is done. You are subscribing rather than inventing. And they have all the aspects you would expect in a AI-enabled bot. No scripted responses based on specific text input but actual detection of user intent with their questions. You can configure them easily for your needs and utilize multiple ones for different stages of the customer journey so each one is expert in that aspect of interaction needed at that point in time.

I have found 4 outstanding examples of these – each of them has won Turing prizes – which means that they fooled a panel when the panel had to choose which responses were from humans and which were from bots. The panel could not distinguish between it and a real human. One of these has won the Turing Prize 4 times. I am writing a guide to these and will be identifying different challenges of customer engagement, and how they were overcome by these different specific chatbots. That may help you understand which options suit your needs best. In addition to answering general questions, these are specialized to answering medical questions and can be adapted for your medical affairs call centers and other interactions as well. There are so many high level options available now to do this so the choice depends on what your use is for.

When considering where you could employ these, a useful place to start is the customer journey. Most companies have developed detailed and complex customer journeys and understand how and where and when the customers come in contact with their brand in the various touch points, throughout the customer journey. Consider that and use that knowledge to consider how you could utilize immediacy in each stage of that journey.