AI and data analytics – the power duo for better customer experience

It’s no doubt that the more you know about your customers, the better you can serve them. Traditionally, this was a mammoth task and took long hours of an analyst’s time. Today it seems a lot easier to gather information about your customers.

And even if this was made possible in the not so recent years, customer data and insights were limited to large businesses and companies because it was an expensive affair. Small businesses couldn’t afford this kind of information. But AI changes all of that.

AI makes it easy to process a lot of data in a short time and offers insights. Hence many companies have started to implement artificial intelligence to make sure that their customers enjoy a great experience and can make informed decisions at any given point in the customer journey. Forbes recently reported that nearly 70% of enterprises use artificial intelligence and machine learning to enhance customer satisfaction by more than 10%.

Data analytics that is powered by artificial intelligence (AI) can provide companies useful insights in a matter of seconds which might otherwise take a human analyst’s hours on end. To top it, the data might be ridden with human bias and error. Does this mean, AI will give data analysts a run for their jobs? No. AI will play a prominent role in helping data analysts connect the dots in an unbiased and error-free manner.

Since AI can quickly help you gather insights from vast amounts of data, you can make faster adjustments to your business process and engage with your customers in a more personalized way throughout their journey.

Let’s take a look at how you can power up data analytics using AI to help drive revenue and also help your data analysts ace their job.

Artificial intelligence or human intelligence – which is better?

To be honest, this is an ongoing debate. No matter which side you take, there is never a winner.

Smart automation is becoming commonplace in various businesses, processes, and industries across the globe. If not now, companies are planning to implement AI into their systems in the near future. To top this, data shows that businesses that are driven by AI will see a growth of up to 3.9 trillion USD in the next two years. Parallelly, more than 40% of a data scientist’s tasks will be automated.

However, it is important to establish early on that AI is not here to take our jobs. It’s a tool – one that helps us do our jobs better. It helps us save time by automating manual tasks that don’t require cognitive capabilities and creativity.

AI for customer analytics

With the advent of AI in customer analytics, brands have the opportunity to glean insights into their customer base which would otherwise have not been possible. The beauty of the technology lies in its speed to analyze hundreds of individual customer data to highlight insights such as their interest, preferences, etc. This enables marketers to design more successful and targeted marketing campaigns.

“The scale of its potential. Customer experience—that is to say, the ways customers engage with a brand—is the responsibility of more than just the marketing department. AI offers up multiple components and avenues for marketers, CIOs, and data scientists to understand, explore, and work through together to enrich the customer experience.

In an environment where brands are always looking for the best way to optimize the customer journey through design, it is crucial to look at how AI can be orchestrated across customer touchpoints. The insights from data are more mature and data models far more accurate. This is allowing brands to allocate their resources more intelligently and productively.”

— Pramod Sudhindra, a partner in advisory services at EY
Anomaly detection

Anomaly detection analyses the information and pinpoints towards anything out of the standard operations or expectations. It could help brands predict whether a particular campaign succeeds beyond its goals if a video will go viral or spur interest within the audience if the audience was engaging with the content.

By using anomaly detection, one can deduce what worked in their favor and what didn’t. It can assist you to identify features that lead a prospective client to become a customer or those features that caused them to steer away. This helps bridge the gap between customers and organizations where the latter can understand their customers’ behavioral patterns and eliminate hurdles that push them away.

Behavioral patterns offer insights

Companies have begun to scale the facility of the last word duo of AI and predictive analytics. Humans tend to form choices that supported a group’s behavioral pattern and not always logic. We often purchase an equivalent item, prefer a selective range of brands, behave in similar ways, and act on similar intuitions.

Predictive analytics has propelled the AI market by bringing customer intelligence the power to travel beyond the understanding of the historical data. it’s producing useful insights that delve into what happened and suggest what might be done to enhance a particular scenario. Leading solutions in the market are infused with cutting-edge, innovative algorithms that will solve most intractable problems and make the simplest decisions possible.

Closing thoughts

AI is specifically what marketers and any customer-facing team need. Advances in AI allow you to segment content and products for customers. It supports the analysis and understanding of their purchasing habits. But personalization isn’t effective enough. Customer-facing teams should use the right insights and tools to personalize interactions with customers and increase brand loyalty.

AI can provide personalized customer experiences by predicting customer behavior to influence current business models, and help change marketing campaigns.