My favorite bartenders always remember my name. They know what I like and how to talk to me. I expect that from them and, in return, I frequent the bars where they work. There’s nothing worse than being treated like a total stranger at a bar that I’ve been visiting for years.
E-commerce is no different. Companies have data — knowledge about my preferences from past experience — and they use it to provide me better, more personal service. That is the definition of “contextual relevancy” in the age of automated marketing.
Using data to build healthy customer relations is not new. Developing a rapport with your customers and making an effort to understand their needs is as old as commerce itself. We have come to accept that our personal customer data is being collected and analyzed, and we expect (even demand) companies use that data to add value to our experience. Using data to deliver a better experience to a loyal customer is a very noble enterprise.
What has changed in recent times is the volume of data collected and the level of human interaction possible based on that data. Companies are now able to aggregate data from multiple points, which if used properly can make a big difference in the overall customer experience.
When a company has earned the trust of its customers — like my bartender has earned mine — most customers are comfortable with their data being used in many different ways that benefit both sides of the relationship. There are many examples from which to choose:
All advertising used to rely on volume. The larger the reach, the better your marketing channel was. This meant that loud, monotonic ads would be served up as widely as possible in the hopes that a small percentage would reach the right customers and they would actually make a purchase.
With more advanced customer relations databases comes the potential that each message can be tailored to ensure that it has value to each customer who reads it. This greatly increases the open rate of emails and the retention of information in video ads. It also lowers the cost of advertising by skipping over those who would not respond well to the advertisement.
By applying models to historic selection data for a large volume of consumers, we are able to display content that is more valuable to customers. Screen real estate being a precious and finite resource, only allows us to show each customer a few items. Knowing what to show them used to be a gamble. Now we know what they like. Being able to show each customer something different based on their known preferences means that we can promote the entire catalog simultaneously, a luxury that was never possible under any other medium.
Once a customer confirms that they prefer one product over another, that customer can be compared to thousands of other customers who have that same behavior. This will highlight other products that the customer is likely to prefer, which can be served on screen. Customer purchase data has been used to forecast demand for a long time, but using that data to predict products that a customer will likely prefer in the split-second it takes to load a page is very new.
Data can tell us exactly where we are losing the hearts and minds of our customers. Each product has its own journey as the customer moves from first awareness to purchase decision to becoming a loyal, repeat customer. Understanding how and where customers jump off the product bandwagon helps inform and design the experience.
The average time it takes for a customer to unsubscribe from a list, for instance, or abandon their cart, or uninstall an app or close an account is valuable data that can be used to understand customer loyalty.
When making decisions about how to frame a customer experience, failure to understand what customers value is like having a sailboat with no wind. Customer data can be used to understand how customers (or broader customer segments of like-minded people) differ in their perceptions of value. Such data can drive creative content that is more connected to consumers in order to provide them with the most value.
A data-driven segmentation can be built from existing customer data or from data collected passively via social media sources around the web. Major social media platforms contain troves of knowledge about how your customers think about your brands, what they find valuable in general and how they talk about the products they love (or hate as the case may be). We can listen in to these conversations and use that knowledge to tailor content that is more relevant to our audiences.
The Final Word
Starting from the position that the data should profit the company more than the customer benefits neither. In the end, it is important to ask the most critical question: How does the data deliver a better experience for my customer? Once your customers believe that you want to use their information to provide the best experience possible, they will be open to sharing it and more welcoming to your automated marketing as a result.