Which sectors are paving the way in this approach to customer interaction? Could you give an example?
From experience, the banking and financial services sectors are further ahead of the rest, followed very closely by retail, largely because the data science industry within these sectors is a lot more mature. Their desire to understand customers and what they are doing when they are not transacting with them is one of the main reasons I think banks, insurance carriers and other financial services providers and retailers have such advanced data analytics capabilities.
For example, say a bank had a customer that wanted to attend a skydiving event in New Zealand—that customer would begin by searching for hotels, flights and related activities in the area. Their online browsing data could then send a signal to their bank; if the bank is able to derive insights from this data, they could reach out with a recommended personal loan to help the customer in their upcoming trip.
Along with the loan, the bank could offer the customer a concierge service to help them plan their vacation, or a travel insurance and a Forex card with no foreign transaction fees. And if this information reached the customer when they were at the very beginning of their decision journey, the bank might win them over for this and any other future trips.
How about B2B organizations—are they also able to utilize new data to better understand and connect to their customers?
Data services are still extremely useful in a business context—one of the main differences between these services for B2C and B2B organizations, is related to the types of data they take into account and the insights that are derived.
For example, a B2C business (as in the banking example above) may require data services to focus on online browsing history, social media activity, and wealth and financial data to derive customer insights. In the case of a B2B enterprise on the other hand, data services may need to draw insights from firmographics, online presence, growth trends, credit and business health information, and technology stack data.
Take a platform provider for example, whose main source of revenue is advertising for other businesses. To understand who would want to advertise on their platform, the company would need to have a 360-degree view of the market—the types of businesses, their online presence, their target customer base, their online spend and the type of key words under which they appear in search.
A data service provider would be able to give this organization the type of information they are looking for, and even make predictions about the type of customers that might be looking to advertise through its platform.
What are the potential benefits that come from allying with an established data services provider?
Using complex algorithms and leveraging the power of AI/ML, data services providers can consolidate the signals a customer is sending to predict the likelihood of a customer purchasing a product or service. They can then use these insights as a lead to be shared across a number of different organizations—giving more robust oversight to customer behaviors and actions.
For example, a customer might be looking to buy a car. At the beginning of their journey they might download a few mobile applications, search for cars online, look into auto loans and insurance, and visit local dealerships to test drive cars. At that stage, the customer is in the market for not only a car, but for insurance and a loan too.
This data—mobile, browsing and geolocation data—can be used by a data service provider to send companies in the car market, as well as banks and insurers, a number of signals so that they can reach out to the potential customer at that ZMOT.