Customers are changing the rules and companies must connect with their customers in new ways. Behaviors are evolving along with changing demographics, and digitization has made it easier for customers to get what they want, when they want it. This cycle of customer behavior disruption is substantially faster and broader than what we have seen in the past.

In addition to convenience, customers want interactions that:

  • Reduce noise, time and steps
  • Increase efficiency and enhance self-service
  • Are smart and intuitive: meaning easy to learn and incorporate into their daily routine

While customers want digital convenience, increased connectivity and sharing, they also demand privacy and security. So the challenge to serve up the right offer/content becomes even more complex.

How should companies use technology to give their customers what they want without it seeming like Big Brother/Big Data is watching their every move?

There is a big difference between what Customers say and what they do. Understanding what they do is key to marketing success. However, technology that allows us to track these behavioral patterns sometimes leaves us feeling a little uneasy and bit “Big Brother-ish.”

Think if it like an internet “cookie.” Interestingly, many companies – specifically retailers – are beginning to take this concept offline and are implementing “cookies” of their own to help optimize the customer experience as well, through their Wi-Fi and Security cameras.

It’s helpful to know how many customers are shopping at any given time, where they go throughout the store, and even what items they pick from the shelves – whether online or offline.

Obviously the idea of being watched when you are shopping isn’t exactly a new one. After all, security cameras have been used for decades. There are benefits to Big Brother watching us while customers shop though, and not just the ones I have already mentioned about better staffing and efficiency for product placements on shelves. When a store is collecting this type of data, it shows a valid effort for delivering a better customer experience.

How do marketers balance personalization and draw conclusions from behavioral patterns and interactions to introduce the customer to new things that they may actually like but wouldn’t necessarily seek on their own?

Analytics play an important role in the marketing engine. Technology has become so advanced that we can see data, customer journey maps as well as patterns that allow us to serve timely and relevant offers during customer interactions.

And, we can use this information to predict which products or services a customer might be interested in down the road. For example, if a couple has recently married, we can use that data to send home buying or even newborn offers within a certain period of time after they wed. It’s using data to determine the natural next customer step that allows businesses to balance personalization with anticipated buying behaviors.

But perhaps the couple isn’t necessarily ready to buy a home or have a baby right away? Using data about the couple’s behaviors, marketers can offer other products or services that they might not necessarily look for but might be interesting to do/buy before they settle down even further.

How do you marry the benefits of mass marketing while keeping things personal?

The modern world can unfortunately be a dehumanizing place. Especially as more big box retailers emerge, it’s becoming more common for marketers to serve more generic offers (coupons, limited time sales) to a larger segment of their customers. But technology is helping the larger organizations create more personalized offerings with learning algorithms. As big data opens up a new world of possibilities, and as technology is getting cheaper and more efficient, companies are able to store and process an amazing array of information about customers and marketers are learning to see them more transparently.

This is obviously more complicated, but simply put, marketers are seeing us less as inert data points on a distribution curve and more as unique sets of patterns that can be identified, analyzed and catered to.