Modern retailing means collecting enough data on customers so you can offer her a truly personalized buying experience. Both research and real-world experience have made it clear that cookie-cutter customer experiences will not lead to strong enough sales growth no matter how great the product is. The new Golden Rule of marketing is: “Use personalization to create an ideal customer experience, because customers are much more likely to buy when shopping is enjoyable.”
While terms like “digital transformation” are firmly entrenched in the zeitgeist of the marketing world, its true expression as a means of taking prospects further down the sales funnel from awareness to consideration is far more nebulous. The ability for marketers to gather and use data to create meaningful, personalized brand experiences has traditionally been at the expense of context.
Artificial intelligence (AI) is transforming customer service across industries. Thirty-eight percent of enterprises currently use AI in some form, and use is expected to grow to 62 percent by 2020. In response, Microsoft has integrated machine learning and intelligent data analysis into their Dynamics 365 platform. These powerful tools are designed to revolutionize the way your customer service representatives connect with and serve your target audience.
Over the last few years, a great deal of media attention has centered around big data. Companies are amassing data at an incredible rate, thanks to online shopping, social networks and an ever-increasing number of apps. Marketers have more opportunities to really get to know their customers and find engaging ways to deliver personalized messages. However, the sheer volume of data makes it difficult to extract meaningful, actionable insights without having some excellent tools at your disposal.
While big data was receiving most of the attention in recent years, developers were quietly making major strides in the world of artificial intelligence. A long-time staple in science fiction, machines that could learn and initiate independent actions seemed impossible 50 years ago. That was before Alexa and Siri, two of the most popular voice-powered digital assistants available today. That was before Google developed DeepMind, a neural network that goes beyond predefined algorithms to make connections and derive meanings. That was before Netflix and Pandora could learn what users like and suggest appropriate choices. All of these examples leverage machine learning to find true meaning in big data.