When you hear the term “intelligence”, some of the first things that leap to mind include super-secret governmental reconnaissance and AI (artificial intelligence) technology. Although CI (customer intelligence) seems like a completely different story, it bears a little bit of resemblance to the former, in a benign sense, and leverages the power of the latter as machine learning algorithms come into play.
CI meaning boils down to harvesting and analyzing information about customer behavior. It sheds light on the whys and wherefores regarding the events and trends in a company’s consumer ecosystem in order to provide the business with actionable insights for more effective CX strategies. Importantly, this concept isn’t only about collecting the relevant data, but it’s also about harnessing that information to understand the customer’s needs, introduce improvements, and derive better results.
American corporate executive Frank Eliason once wrote, “Truly listening is hearing the needs of the Customer, understanding those needs and making sure the company recognizes the opportunities they present.” CI streamlines the components of this workflow.
How it works
The CI process starts with gathering reference data about a customer, such as their age, marital status, geographic location, and education level. The sources of this information may include satisfaction surveys, forms filled out for loyalty card issuance, as well as social network profiles. Whereas these “raw” details are of paramount importance, they don’t bear much value unless combined with a certain context.
This brings us to customer touchpoints and specific transaction data stemming from them. Businesses can use CEM (customer experience management) tools to record all instances of consumer interaction automatically in real time and retain this information. Every purchase made, each instance of contacting product support – all of these make reference data more meaningful.
With the ubiquitous automation of these workflows in place, gathering feedback “in the field” continues to be an indispensable element of CI done right. Mystery shopping, another popular method, can help gauge the company’s customer service performance and compare it against the competition.
Finally, the client data is subject to extensive analysis, which is the only way to put it to good use. The above-mentioned machine learning can streamline this process and predict the customer’s future needs, thus enabling the business to stay proactive.
Why conduct customer intelligence?
There are several fundamental reasons why CI matters. A brand’s awareness of its audience’s current sentiments and interests is a great thing, but there is so much more that can be achieved by drawing conclusions from this data and making the right decisions at the right time. Here are a few noteworthy benefits of carrying out customer intelligence. CI meaning is about loyalty. In a dynamic business climate, if you fail to stay on top of customer behavior patterns, you will witness the churn rate increase tomorrow. People may switch to a more proactive competitor who can offer them exactly what they need at this point.
To get customers, you need to go from the heart to the brain to the wallet. Indeed, fine-tuning the customer journey based on CI data can have a favorable long-term impact upon your sales. Satisfied customers are more likely to return and recommend your brand to people they know. Furthermore, focusing on the strategies that work while abandoning the ones that don’t is what helps maximize revenue and eliminate redundant spending. All in all, CI contributes to an agile business strategy revolving around the customer.