Do You Need AI Powered Social Media Analytics?

Artificial Intelligence and Paid Media

The world of digital tools for business growth used to evolve every decade. Now, the renewal interval is less than a few years, and AI seems to be grabbing all the attention. It has surpassed marketing automation platforms (MAP) and social media management (SMM). In fact, AI will not only replace but enhance these already established directions with new approaches.

The ability to give machines human-like skills is scary and tempting, but in fact, it should be regarded as a simple and useful tool. We are still a long way from fully autonomous intelligence. Yet, marketers can take full advantage of the options offered by AI which include building detailed buyer personas, creating fine-tuned recommendation engines, evaluating competition in real-time and managing heaps of content.

Harnessing the Power of Social Media Analytics

AI is an encompassing term for multiple algorithms which feed on data and try to mimic the reasoning of humans. The approach is iterative, and the machine learns from mistakes and corrective actions, much like children do. Pairing AI with data from social media is a natural approach. The platforms offer massive amounts of data, and the algorithms dig into the information to create actionable patterns. Marketers and managers can use results to improve their strategies and directly impact revenue. We will further discuss a few examples of AI-powered social media analytics.

Competitor Analysis

You can think of AI as an industry spy that gives you feedback in real time. Some algorithms are able to identify which content is sponsored on social media platforms by your competitors. This is almost like having direct access to their strategy. Knowing what they do allows you to either take the same approach or try something completely different.  Track their keywords and learn from their mistakes.

At the same time, you can compare your own content’s performance against the competition. AI can offer you an overview of how you measure on the markets you are trying to conquer and also give you hints. The insights could be related to the type of content used, the promotional budgets or even the response time of the customer service. By comparing your scores to industry leaders, you can also learn from the best and create targets.

Content Management

Content is still king, but selecting between diamonds and garbage is becoming increasingly hard due to volume. Enter the slack bots. You can ask yourself if your ideas have already been implemented by a competitor, in which way, and check if it was successful.

You can also verify if there are knowledge gaps in your competitors’ strategies and fill those whitespaces with relevant content to attract clients.

The work to keep up with scheduling content, creating editorial calendars and maintaining a list of past and future topics can all be left to AI. The significant advantage is that considering emerging content from competitors, the calendars can be dynamically reconfigured to reflect interest changes from clients.

Chatbot Customer Service

The tendency to replace call center agents with chatbots is becoming more apparent every day. This is driven partly by cost concerns and also by the users’ preference to interact with brands via text messages instead of calls or e-mails.

Right now, chatbots are still in their infancy, but through AI’s development, they will become more human-like. Some companies have already implemented text analysis solutions to enhance information extraction, categorization, and semantic search. All these features help chatbots identify what the user intends to say even if they are not speaking in complete phrases.

Buyer Personas

The volume of data created daily on social media is a challenge and an opportunity for marketing professionals. On one side, it could lead to information overload and make specialists wonder what is essential. On the other, correctly interpreted, it can help create very life-like buyer personas, taking client segmentation a step further. These models can be used to identify potential brand ambassadors or target marketing campaigns with excellent accuracy, thus saving money. In fact, studies show a revenue increase of 171% when using AI-generated personas.

Increased Personalization and Recommendations

The buyer personas are just the first step in reaching your ideal audience. Those are still at a general level. The real insights of social media can give you hints about what will trigger individual responses. Learning from Amazon’s tactics to recommend products that fit the user’s general profile combined with their purchase history, your company can replicate this tactic.

The real gain from such a granular level is to be able to create almost real-time campaigns targeted at very specific groups with high success rates. It is nearly mind-reading from the breadcrumbs they leave online.

Privacy Concerns

The latest scandal from Cambridge Analytica shows that mining social media data for insights can cross some privacy borders. Even if the data harvesting methods can’t be classified as hacking, it is still unethical and dangerous to use social media information without getting the consent of those involved.

There is a fine line between serving the clients’ best interest by presenting them with relevant content that also helps brands sell more and using the data for other purposes.

Success Stories

Top organizations have already understood the benefits that AI can bring and how it can be used to get them to a higher level.

The Dutch company KLM is a pioneer. They automated the answers to the most common questions that came through their customer service apps spread over three social media channels, Twitter, Facebook, and WhatsApp. Through semantic analysis, they identified the most frequent requests and created canned answers. Even so, the system does not operate independently, but it is still coordinated by humans who decide the appropriateness of each response. The system learns and becomes better with each interaction.

Another good example comes from LinkedIn who acquired to use their job-matching algorithms for candidates. The goal was to offer better opportunities to job seekers and less ballast to companies in a hurry to hire.