In my view, one of the best ways organizations can approach a given problem space is by leveraging the myriad of data they collect every day. Data analytics comes to mind, of course — crunching a sea of data to find correlations and insights we can use to make a process better. How then do we decide what to do with those insights? You develop and train machine learning (ML) models to make more accurate, unbiased decisions based on the available data. Then you apply artificial intelligence (AI) to suggest the best way to act on those decisions to improve the chance of a successful outcome.
One of the most visible targets of transformation initiatives is to improve the customer experience. The internet has removed geographical distance as a barrier between you and your competitors, so a company’s online presence is more important than ever. That’s why everyone is rushing to provide ever more-engaging online experiences, to hold a prospective customer’s interest.
Numerous companies provide website plugins to track a visitor’s clicks and actions, analyze them to intuit intention, and determine, for example, what content, advertisement, or offer to display next. Going beyond that, today’s most successful e-commerce sites also use AI/ML to personalize each shopper’s experience, like the order and presentation that will most likely result in another click or a purchase.
AI can anticipate with near certainty—based on past and present action, search patterns, profiles, external demographics and more—what a customer wants to see now and will do next. If successful, your website visitors will come to feel at-home, excited, and perhaps even brand loyalists. They’ll buy more and return more often.
There is nothing wrong with applying analytics, AI, and ML to create a more innovative and engaging customer experience. Not doing so can put you behind your competition. It’s all about building customer loyalty and boosting revenue.
No matter how important customer experience is, however, it is a mistake to believe it is the only operational area that can (and should) be transformed using technologies like these. After all, today’s enterprise amasses data about more than just customers and orders. Your company, product, and delivery must broadly innovate — and all these happen on the backend. The efficiency of your internal operations — your support team, supply chain, production, inventory, quality control, human resources, and so on — can all benefit from applying AI and ML technologies. Consider just a few of many possible examples.
The possibilities for internal process improvement across the enterprise are endless.
Read the full article in Business Insider.