It is well established that a positive customer experience is essential when creating a strong brand. The effectiveness of the strategy, however, varies — and it is important to measure the effectiveness of any customer satisfaction strategy through data and analytics.
Customer Intelligence (CI) collection is the practice of gathering insights about clientele through a variety of data sources that range from advanced digital platforms to customer feedback surveys.
This feedback is then analyzed to provide insight into what customers like and dislike and how to further optimize interactions to increase engagement, brand loyalty, and ensure a satisfactory brand experience.
Big Data, Big Engagement
The most basic form of customer intelligence is reference data, which includes the enduring facts of customers such as names, physical addresses, and job titles.
Reference data is just a starting point in CI. It changes slowly, if at all, over long periods of time. In contrast, transaction data is the constantly changing stream of information regarding daily, weekly, and monthly interactions customers or prospects have with you, such as their purchases and customer service inquiries.
Aside from both reference and transaction data, there is qualitative data that can capture more subjective elements of a customer’s experience such as satisfaction levels and brand associations..
The data sources for reference, transaction, and qualitative information are vast and various, including but not limited to:
A major digital source of customer intelligence data is the analytics engine that is built into practically all digital marketing platforms. Click-through rates, in particular, are a closely scrutinized performance indicator.
When a new offering is lighting up with clicks, for example, it could mean that you have an extremely well optimized landing page and marketing campaign or signal that a particular offering is an especially good fit for your customer’s needs and interests. Needs are exposed this way – be it through testing multiple offerings, experimenting with marketing approaches, or analyzing customer behaviors.
Social media is another fairly recent and quickly expanding source of customer intelligence, albeit one somewhat fraught with privacy concerns. Facebook, for example, found itself in hot water recently over its data security practices. The company effectively diminished public concern through significantly strengthening its privacy protections.
As far as data collection goes, however, Facebook is unmatched. Facebook’s Audience Insights, can look at your customer list and fill in information that it might be lacking, such as age, gender, or even relationship status.
This data can be a gold mine for marketers who wish to engage their perceived interests. In essence, the information gathered online is applied to what you do everywhere. In this way, social networks can provide a very cost effective means for acquiring data that would have once required far greater expense and risk to test.
In recent years speech analysis of customer interactions has grown in popularity and usefulness. You may have noticed that when you dial into many call centers you are greeted with a disclaimer, notifying you that your conversation may be recorded for quality assurance purposes..
Far more commonly, automatic speech recognition systems swiftly parse massive numbers of calls to determine basic information such as the primary purpose of the communication and even the emotional character of the speakers. A high volume of angry calls can signal that your customer relationships are fraying and an adjustment is warranted.
Customer Relationship Management Systems
Though innovative digital tools have greatly improved over traditional modes of information gathering, the classic image of a marketer standing on the street asking passerbys if they would like to take a short survey hasn’t faded away entirely. Yet, modern, digital systems are performing that job at a scale and speed previously only dreamed of.
Any number of platforms can provide extensive software solutions to collect, track, and analyze customer data. The latest industry trend is the use of machine learning and artificial intelligence to convert that data into predictive models that don’t just tell you how your customers are acting and feeling today, but how they might respond in the future.
One thing to keep in mind about CRM and other digital systems, however, is that while they are certainly feature-packed — they can track every click, email, and interaction unfailingly — they don’t see everything. A customer that walks into a store, is underwhelmed, and walks right back out is effectively invisible to digital trackers.
There was no transaction to record, but make no mistake, valuable information for your brand can be gleaned from that occurrence. The simple act of an employee politely inquiring as to why the person is leaving or asking them to fill out a quick survey (often in exchange for a small promotional offer) can reveal insights that a traditional CRM can miss. That’s why a CRM should be considered merely a component of a broader customer intelligence strategy.
Finally, retailers, both online and off, take further steps to see if their careful plans and detailed models are working in reality the way they are on paper.
For example, one method physical retailers use to ensure an optimal in-store customer experience is “mystery shoppers,” or market researchers posing as actual customers. These covert agents go through the entire shopping process and secretly record their experience to study the quality of service at a target location.
If customer intelligence data indicates that the shopping experience is pleasant and the customers are finding the sales representatives helpful, but mystery shoppers come back with a very different story, perhaps your current CI testing isn’t probing deeply enough.
There’s so much valuable information to be discovered about your customers. Our job titles, education levels, or even our lifestyles don’t define us, but the are undeniable clues about our preferences, tastes, and habits. Understanding your customers’ most basic characteristics and needs fosters relationships that are more personalized and, consequently, more meaningful and lasting.
Don’t guess blindly about what your customers are thinking and feeling. Start finding out!