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Bridging the Grand Canyon: How Big Data Can Help You Meet Customer Expectations

Authored by ActionIQ Team

When it comes to big data, bridging the chasm between the flood of raw information and customer expectations feels like trying to reunite opposite ends of the Grand Canyon. But why would we continue to collect data if we don’t intend to use it? We can mine the flood of big data to not only provide customers with the service they now expect, but also to provide marketers with unforeseen insight into customer desires and behavior. Let’s take a look at the progression of that experience — with a glimpse of the future.

Old Meets New

From the late 1800s to the early 2000s, the customer experience remained relatively unchanged. Early face-to-face transactions allowed for personal interaction and highly custom experiences. For example, let’s say I run a small coffee shop. I see Joe five times a week and I know what coffee Joe buys, his preferences, his orders, and I can accommodate Joe with specialized service. This builds a foundation for his loyalty to and engagement with my shop.

Unintentional Severing

Then my coffee shop becomes very successful and we grow. I can no longer interact with Joe the same way. I can still provide great service, but I have to build the brand and our service is standardized for all customers — not just Joe. Yet, this is in conflict with today’s customer expectations. People want “the Taylor Swift treatment” when interacting with brands. With the help of big data, I think we can give it to them.

Reuniting the Halves

Now we have online shopping and digital payment processing anywhere, anytime and a confluence of data from personal devices and cloud-based applications. We’re collecting active and passive data at every touch point, and we have unprecedented access to how people interact with brands as well as their behaviors and patterns. It’s through this data that we are starting to be able to simulate Joe’s experience with that small coffee shop.

For example, you’re walking down the street and you’re close to a Starbucks. You really like Starbucks. Through analysis and application of big data, your phone or watch beeps to let you know that you’re near Starbucks and that you have x amount of Starbucks rewards points to use. The data can tell us that on Tuesday afternoons during June, you usually opt for a latte instead of your usual black coffee, especially after you’ve had a meeting with your boss, and we can offer a special suggestion with that information. You can order it immediately from the notice and have the coffee waiting for you when you round the corner. At each edge, big data is a driver.

The Creep Factor

But isn’t that kind of creepy? I can see where in theory it may look like it goes too far, but in practice, it is a seamless interaction that you may appreciate. While gathering and analyzing all this information and providing personalized services that are helpful for both businesses and consumers, there is also an ever-changing fine line between predictive convenience and creepiness.

Value Vs. Voyeurism Threshold

Many people are willing to forego a certain level of privacy as long as the value of the service meets their threshold of acceptance and they were able to maintain a level of control. Essentially, if something makes your life better, you’re willing to make some exchanges or compromises proportionate to what that value is.

Facebook friend tagging, for example, initially bothered some users because the software recognized human faces — adults and kids — and publicly geo-tagged the locations of the photos. A few things happen in response: the value of tagging was realized as more people used it and it was easy and fun, and Facebook responded with increased privacy controls that enabled people to turn off features that they didn’t want, putting them in control of their data. When I worked for Facebook, we expected to see people using the newly introduced privacy controls, but the reality was that very few people wanted to turn it off. People actually prefer to get the value of targeted content.

On the Edge

I know that using big data to fine-tune a cohesive customer interaction is not a new idea, but we are on the precipice — perhaps one side of the Grand Canyon, with schematics to build a bridge. Instead of diving into the river or paddling down it, general practitioners and not just data specialists, will soon be able to walk across.

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