XM Knowledge Base
Author: Adam Bunker
Subject Matter Expert: James Skay
What is customer intelligence (CI)?
Customer intelligence (CI) refers to the analytical knowledge businesses gain – and the improvements they can make – by converting customer data into actionable insights. Those insights provide CX professionals with a better idea of their customers’ habits, needs, preferences, and pain points.
Effective customer intelligence combines data from behavioral analysis. heuristic insights, and feedback across owned channels and third-party platforms.
Robust customer intelligence improves CX by closing experience gaps, fine-tuning things like pricing and marketing, and better personalizing offers for each individual customer or audience segment.
Powering a great customer intelligence program relies on gathering a healthy mix of behavioral and transactional customer data, as well as solicited and unsolicited customer feedback, and then acting on the actionable customer insights that these combined inputs generate.
Customer intelligence vs business intelligence
Customer intelligence focuses on experiential, often unstructured data like sentiment and behavior. Business intelligence, on the other hand, is much more transactional; it usually centers on metrics and figures like financial and operational data.
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Why is customer intelligence important for CX?
Let’s first think about the role and responsibility of a customer experience professional. What are you trying to achieve? The simple answer is likely to be a more streamlined journey for each and every customer, right?
That involves removing pain points, closing experience gaps, and fostering positive brand associations.
Customer intelligence (CI) helps businesses:
- Identify and address customer pain points
- Understand customer behaviors, needs, and sentiments
- Increase conversion rates while reducing churn
- Boost satisfaction and long-term loyalty
But to do all that, you need to understand what your business’s customer experience looks like now – and how that compares to last week, month, and year.
To that end, customer intelligence data is a goldmine of information about what’s working and what needs attention. The data you gather and the insights that come as a result will show you what people think, where they’re running into issues, and the factors that determine customer satisfaction and customer loyalty.
Customer intelligence’s role in CX, then, is akin to the role blueprints play in building a skyscraper. You need that foundational knowledge and plan to be able to work with. Track and use the right sources of information, and you’ll be able to spearhead actions that improve every element of the customer experience – and do so on an ongoing basis.
Challenges in implementing customer intelligence
Having a well-rounded customer intelligence program requires ticking a lot of boxes, and treading carefully so as not to upset – or flat-out violate the privacy of – your customers. Here are some of the biggest hurdles to overcome on the way to effective CI:
Organizational buy-in and change management
Buy-in here comes in two flavors. The first is that you’ll need the go-ahead to deploy modern, AI-powered customer experience management software. That means tools capable of sorting the signal from the noise when it comes to collecting and analyzing omnichannel customer data.
Second, your business needs to be ready to change based on what your customer data shows you. Customer intelligence is only useful if you act on what you learn, so businesses need to adopt an approach to change management that permeates every department – without siloing useful insights.
Data privacy and ethics in customer intelligence
Balancing customer privacy concerns with the need for personalization is a key challenge. Transparency about data collection practices and adherence to privacy regulations like GDPR and CCPA are essential.
For example, session replay tools – designed to highlight UI issues on websites and in apps – recreate user journeys algorithmically, rather than recording people’s actual usage. When in doubt, honesty is the best policy; be thorough with your cookie policies and let people know if you record certain calls while explaining that doing so will help you improve.
Learn more about session replay
Compliance considerations
Naturally, any customer intelligence data collection method needs to comply with local laws and legislation in the markets you operate in. Acts like the California Consumer Privacy Act in the US, or GDPR in Europe, for example, necessitate that collecting customer data becomes a fully managed process – one in which businesses must explain and catalog their data collection methods while also obtaining and recording customer consent.
Creating a customer intelligence strategy
With the challenges understood, let’s outline the five key steps you’ll need to take to build an effective customer intelligence strategy:
1. Define clear objectives aligned with business goals
It’s important to understand what you’re trying to achieve with customer intelligence because that will impact where you focus your efforts – especially if time or resources are tight. For example, if your key priority is to improve the efficiency and readability of your website UI, that will affect the kinds of tools to prioritize – like session replay, heatmaps, and user journey analytics.
2. Map customer journeys and identify data sources across all touchpoints
In general, the more sources you can draw from, the better. True omnichannel analytics involves pulling information together from every single customer touchpoint, so it’s best to use an experience management software suite that can draw from as many channels and platforms as possible.
3.Regularly update customer personas to reflect changing behaviors and preferences
What works for one customer type might not for another, so it’s always wise to run regular customer persona exercises to understand your different audience segments. For example, while your customer intelligence data might show that a healthy proportion of younger customers is more receptive to social media marketing on TikTok, that may not be true for older, higher-value customers, or those in different geographical regions.
4. Collect and unify customer intelligence data
It’s impossible to perform meaningful customer intelligence data collection manually; most businesses will simply have far too many interactions, data points, and nuances in customer behaviors.
Instead, you’ll need an experience and customer relationship management software suite that can gather behavioral data, run sentiment analysis from the content of incoming calls, scour through social media platforms, and more – with AI and natural language processing built in for scale and speed. The result of analyzing customer data from a wide array of sources like this will be insights and trends that you’d otherwise miss.
5. Act on insights
Your customer intelligence efforts will no doubt surface a wealth of valuable CX insights. Now comes the most important part of the process: acting on what you’ve learned in order to drive change.
This should be a business-wide process, where relevant insights are shared across teams and action plans are made to tweak the customer experience according to what your CI data tells you.
If customers are dropping off in large numbers before checking out, and you learn that they find a specific element of the process frustrating, you’ll want to make UX changes to streamline things. If you learn that your marketing efforts are falling on deaf ears, you’ll need to go back to the drawing board. If you figure out that people love your products but don’t like the price, then you’ll want to fine-tune your position in the market.
6. Measure, iterate, and repeat
Once you’ve taken action, you’ll need to measure the impact of those changes. That means regularly monitoring specific KPIs, metrics, and inputs so that you can compare today’s customer intelligence data with what went before. As you move forward, you’ll then be able to continue to test and iterate the changes you make as a business – hopefully resulting in an upward trend in the metrics that matter most:
Key customer intelligence metrics and KPIs
Customer satisfaction (CSAT)
CSAT is a standardized measure of overall customer satisfaction. It’s derived from questions that ask how satisfied a customer is with their experience doing business with you, on a scale of 1-5.
Net Promoter Score (NPS)
Similarly, NPS is a standardized, cross-industry score that asks people how likely they are to recommend a business to others – where that likelihood is indicative of an excellent customer experience.
Customer Lifetime Value (CLV)
CLV is calculated by multiplying the amount of money a customer has spent with you by the length of time they’ve been a customer. An upward trend in CLV usually indicates a boost in loyalty.
The future of customer intelligence
Customer intelligence is never a one-and-done process; it leads to actions that require measurement, iteration, and further data gathering. Similarly, the industry around customer intelligence is always moving – with new capabilities and tools landing all the time.
AI and machine learning in customer intelligence
There’s an awful lot of customer data out there – and the sheer volume of it can be daunting. Luckily, modern customer intelligence tools are built to sift through these gargantuan datasets in an instant, thanks largely to the abilities on offer from machine learning and artificial intelligence models.
When AI is imbued with natural language processing capabilities, for example, it can understand the content of every customer call, instant message, email, text, social media post, and review; analyze sentiment, effort, and intent; and provide insights that combine the entire pool of customer opinion.
AI can simultaneously analyze and unify:
- Social media posts
- The content of contact center calls
- Customer emails, IMs, and SM messages
- Online sentiment from third-party review sites
- Behavioral data from users visiting your website
- How transactional data relates to customer interactions
Likewise, AI that understands customer behaviors along the customer journey, as well as input from transactional data and customer preferences, can surface trends and opportunities that would be impossible to spot otherwise.
AI-powered experience management suites are the wave of the future; they’re how CX professionals can make the most of often disparate customer data points to find actionable insights – and use them to build stronger customer relationships.
Predictive analytics and customer behavior forecasting
The next step for AI in customer intelligence and customer data collection is predictive analytics.
Whereas traditional customer intelligence programs have always relied on using historical data to provide a snapshot of how things are now, machine learning algorithms can use that same customer behavior data to predict what your customers might do next.
That might mean identifying at-risk customers well in advance of them churning (helping you intercept with a personalized offer), or it might be predicting buying trends in a given demographic that can help steer your marketing efforts.
Customer intelligence (CI): Key takeaways
Customer intelligence is a customer relationship management strategy that puts behavioral data and customer feedback to work – and that helps CX professionals generate insights for driving positive change.
With an effective CI strategy in place, you can:
- Understand customer preferences and opinions
- Uncover UI and UX inefficiencies and problems
- Discover and fix pain points that would otherwise go unnoticed
- Improve the customer experience and customer journey
- Build stronger marketing plans and more personalized outreach
Customer intelligence is essential for improving customer experiences and driving business outcomes. But doing so effectively requires an experience management software solution that can collect customer data from every channel and touchpoint and connect the dots – with smart, relevant, and even predictive insights. The Qualtrics® XM for Customer Experience® turns billions of customer signals into direct, actionable insights that help businesses build stronger customer relationships.
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