It is a new day in customer satisfaction. Both the Internet of Things (IoT) and artificial intelligence (AI) according to industry analyst IDC are slated to usher in the best technologies for customer engagement and happiness. Research supports the importance of continued efforts to increase customer satisfaction. However, companies are in a transition phase in which the ability to process and use the data gathered in meaningful ways may still present a problem. Below are a few guidelines on the art of analytics.
1. Well-Defined Metrics
Any good analytics team are going to define meaningful metrics that present a consistent snapshot of trends over a period of time. For example, a camera company wanting more information on how many customers are buying online versus in-store could easily define the metrics allowing comparison. Don’t add too many contemporaneous factors in, as things can get confusing here and may need professional tweaking. It is one thing to know in-store versus online camera brand purchases, it is entirely another to guage this with time of purchase patterns, seasonal data, etc. While this information can contribute valuable information to your sales strategy, it’s not for the beginning analyst unless the data is in the hands of professionals. But better metrics are a result of many case studies coming out now.
2. Thoughtful Automation
Context is difficult for AI. The amount of data available right now is overwhelming and humans aren’t the only ones having trouble. Machine learning is growing in popularity as companies continue to see its importance in developing a competitive edge. However AI also automates many tasks that are not only repetitive but becoming increasingly less lucrative for the today’s professionals. 3-D printing and machine cutting are examples of how thoughtful automation is improving product quality. In terms of context, however, AI is getting more and more accurate with every year. Facebook’s DeepFace technology is now 97% accurate. The emotional intelligence of today’s chatbots and voice responders is as developed as ever, but we’re not quite there yet.
3. A Personal Touch
It is clear that AI is advancing in leaps and bounds. Studies report that consumers interaction with enterprises will occur without interaction with a human being. Customer service is likely to depend less on the problem-solving of human beings and more on the ability of AI to automate resolution processes. The trend can be seen today with an ever increasing trend towards live customer service agents becoming a choice. Granted, consumers tend to choose these agents over robots. Companies now want to figure out how to make dealing with chatbots more personal and feel just as responsive to customer enquiries as human beings.
4. Increased Visibilty
Businesses should be visible to customers, but also among other businesses. As companies identify what is working within businesses successfully employing AI, they are more likely to follow suit, creating an evolving if not similar standard for how data can act within the marketplace. Thos companies that experiment with customer engagement now, in the beginning stages of AI’s advancement, will likely set the standard for the businesses of tomorrow. Proctor and Gamble’s new shelfpoint software is changing the game in how businesses can use facial recognition the determine customer reaction to displays.
5. Shorter Research and Development
AI has the capability of gathering and storing information in real time. The only real need for companies savvy with their data acquisition will be analysis and use of said data. There is an exponentially lengthy time spent on product development as of today. These times can be cut shorter with data gathered in real-time. Of course, the advantage of data isn’t available in all markets as of yet, and this may present a challenge to countries just developing. Countries such as Africa and India are prime markets for the development of data tools which decrease the amount of investment needed for B2B e-commerce of consumer goods, for example.