Customer Intelligence: Benefits, Challenges, and Best Practices

More About Customer Intelligence

What is Customer Intelligence?

Customer intelligence (CI) is the process of collecting and analyzing customer data to better understand customers and how to interact with them. CI can help companies identify customer needs, behaviors, and preferences, and use that information to improve customer experiences, increase loyalty, and predict customer behaviors.

Customer data can be harvested from omni-channel conversations and interactions. Unlike transactional tracking methods, CI data is derived from all the customer touch points – ranging from customer-vendor communications (written or verbal communication i.e. emails, tickets, calls, meetings etc.), product usage data or service consumption, and more.

Customer intelligence methodologies help companies identify at-risk customer accounts, recognize growth opportunities, segment customer accounts, and apply customer inputs and optimizations for their roadmaps.

This is part of an extensive series of guides about AI technology.

Why Is Customer Intelligence So Crucial Today?

The reason is simple. Product analytics paint an incomplete picture when it comes to gaining visibility into customer accounts. These product-based insights are different from customer activity-based insights, which is what CS teams need today.

By adding Customer Intelligence and Analytics capabilities to supplement product and usage data, CS teams can dissect communications with stakeholders, understand what internal personas are currently engaged with accounts, and uncover churn blind spots. By doing so, Customer-facing teams are creating more and more bridges between the organization and the customers for a more robust book of business.

Customer Intelligence vs. Business Intelligence: What Is the Difference?

Customer Intelligence (CI) and Business Intelligence (BI) are both critical for making informed decisions, but they serve distinct purposes and provide different types of insights.

Customer Intelligence focuses on understanding the behaviors, preferences, and sentiments of individual customers or customer accounts. It leverages data from customer interactions, communications, and feedback to offer a detailed view of customer relationships. CI helps in identifying at-risk customers, growth opportunities, and customer satisfaction trends, making it essential for Customer Success (CS) teams aiming to enhance customer retention and engagement.

Business Intelligence covers a broader spectrum of data analysis across business functions. BI tools analyze operational data, financial reports, and market trends to offer insights that drive overall business strategy and performance.

While BI can provide a macro view of business health, CI dives into the micro-level details of customer interactions and experiences.

Customer Intelligence Benefits

Here are some of the key benefits of customer intelligence:

1. Identifying At-Risk Accounts

Human insights are crucial in recognizing sudden customer sentiment drops, communication frequency issues, and other underlying account risk factors. Recognizing at-risk accounts helps reduce churn and boost key KPIs like NRR. Risks include sudden champion departures, underlying negative sentiment about specific features or services, or ongoing deterioration in CSM-stakeholder communication.

2. Recognizing Growth Opportunities

Besides reducing risky stakeholders and accounts, CS teams can now also leverage this methodology to identify champions, nurture key stakeholders, and create more brand advocates to promote sustainable growth. For example, a stakeholder who the CSM is not in close touch with may talk about an upcoming team expansion or mention a feature that the customer needs, both golden growth opportunities.

3. Customer and Account Segmentation

Every use case is different and is also changing with time as new business or operational requirements come up. Segmenting accounts helps CS teams create a more granular and targeted approach to identify each account’s needs. and unlock customer happiness. This is achieved by creating personalized user journeys based on the actionable insights gained from the Customer Intelligence solution.

4. Crucial Roadmap Inputs and Optimizations

Product analysis tools and marketing solutions can help point roadmaps in the right direction, but none of them provide any insights into customer communication trends and relationship dynamics. Customer Intelligence is helping on that front. For example, if the overwhelming majority of your customers has a specific view about some feature, you can navigate your product in that direction. Besides the obvious business benefits, you can also add more brand advocates to the ranks.

Customer Intelligence Data Sources

Customer intelligence is all about making your customers fall in love with your product. Aside from making them more engaged with the features and loyal to your brand, it also creates more opportunities for advocacy, upselling and cross-selling.

Here are the key sources needed to gain customer intelligence:

Communication data – When people talk about customer data, this is often related to communication data. Emails, chats, tickets, calls and meetings are just a few examples. The amount of information that’s out there is mindblowing and good customer intelligence needs to take all of it into consideration.
Source attributions – Customer and business data can no longer be misattributed. Strong customer intelligence models need to have accurate source attribution capabilities for accurate and up-to-date analysis.
Product usage data – Product friction is one of the leading causes of churn. B2B businesses need to better understand what features are being underutilized and why customers are finding it hard to reach the a-ha moments. Product usage tracking is a crucial component in the customer intelligence machine.
Company’s background – As a B2B business, you are dealing with accounts, not just customers. You need to know about what’s going on in these companies. For example, a merge or buyout can change things significantly.
Segmentation – Segmentation is also a key ingredient in the customer intelligence stew. Besides the obvious segmentation of customers based on their lifetime value, profitability, and preferences, businesses now also need to dig deeper. This involves the integration of their geo-locations, age groups, professions, and psychographic information into the grand scheme of things.
Financial data – Understanding the financial status of your customers and prospects is also a customer intelligence essential.

Customer intelligence data sources Staircase AI

The Customer Intelligence Process

Customer intelligence isn’t easy to get done on the fly. Using an ad-hoc approach often results in incomplete or inaccurate data, not to mention the time and resources that you’ll be wasting in the process.

Here are three key customer intelligence principles:

1. Harvest customer, product, and business data straight from the source

Data is everywhere. Collecting it is one of the biggest challenges businesses are facing today, especially while trying to create an accurate customer intelligence model. A noticeable weak link is the lack of customer data. Only 1% of it actually makes it into CRMs, making many predictions and action items inaccurate. Siloed data is a reality and you have to make sure everything is taken into account.

2. Cleansing and consolidating the collected data

Once you have collected the siloed data from the various channels mentioned in the previous section, you will need to make sure that it’s ready to be processed. This involves the elimination of duplications, standardizing formats, and cleansing everything for the analysis process. Keep in mind that there’s a lot of data and doing all of this manually in real-time is extremely challenging.

3. Crunching and analyzing the data to get real time risks and opportunities

The customer intelligence model should also be able to process the data in real-time (outdated data doesn’t help on the accuracy front) and generate actionable insights that can be used to better understand the accounts. These insights are key to making customer-driven decisions. Modern customer intelligence models should be able to perform ongoing sentiment analysis and customer relationship tracking.

Customer Intelligence Examples

Customized New Customer Journeys

Creating tailored onboarding experiences for new customers helps ensure they quickly grasp the value of your product. With Customer Intelligence, businesses can analyze initial interactions and preferences to design customized onboarding processes.

For example, CI data could indicate that new customers from the tech industry prefer self-service options. In such a case, the onboarding journey can include detailed tutorials, a knowledge base, and automated email sequences with step-by-step guides.

Conversely, if a segment shows a preference for personalized support, providing dedicated account managers or scheduled onboarding calls can enhance their experience, increasing satisfaction and reducing the time to value.

Persona-Based Customer Communications

Different personas within a customer account often have varying needs and communication preferences. Customer Intelligence allows businesses to identify these personas and tailor communications accordingly. For example, senior executives may prefer strategic insights and ROI-focused updates, while operational users might need detailed product usage tips and troubleshooting advice.

By using CI to understand these differences, businesses can send targeted emails, create persona-specific content, and ensure that every interaction is relevant and valuable. This approach improves engagement and builds stronger relationships by showing that the company understands and caters to the needs of each persona.

Activity-Driven Product Recommendations

Customer Intelligence can monitor how customers interact with your product to offer timely and relevant recommendations. For example, if a customer frequently uses a reporting feature but hasn’t explored advanced analytics, CI can trigger suggestions for tutorials or webinars on that feature.

Additionally, if CI detects that a customer is consistently encountering issues with a particular tool, it can prompt proactive support interventions or suggest alternative workflows. This activity-driven approach helps customers get the most out of the product, enhances their overall experience, and opens up opportunities for upselling and cross-selling by introducing them to features they might not be aware of.

Contextual Geo-Targeting

Integrating geographical data with Customer Intelligence allows businesses to provide highly relevant, location-specific content and offers. For example, a customer in a region experiencing specific regulatory changes can receive updates on how your product addresses these new requirements. Similarly, during local events or holidays, targeted promotions or messages can resonate more with the audience.

For example, sending special discounts to customers in a region celebrating a local festival can increase engagement and sales. This level of contextual targeting ensures that communications are relevant and timely, enhancing customer satisfaction and loyalty.

Customer Intelligence: The Main Challenges

Now that we have established the benefits of having a Customer Intelligence solution in place to generate insightful human insights, it’s also important to understand the main challenges that come with its implementation.

Communication Data is Siloed – All the B2B businesses today are using multiple communication channels to engage with their customers. This can include in-app chatbots, ticketing systems, Slack chats, Zoom video calls, phone conversations, and of course email communications. Is your Customer Intelligence solution capable of digesting this fragmented information?
Too Many Xs and Ys – There are often too many blind spots when it comes to company-customer communications. You can have multiple focal points in an account, along with a dozen of internal personas engaged with them at any given time. Tracking these Xs and Ys on an ongoing basis is extremely crucial (and very challenging) when it comes to Customer Intelligence.
One Too Many – One focal point (for example, the CSM) communicating with an account that has multiple stakeholders, has all the visibility. But this CSM doesn’t have the capacity to keep track of everything, nor the ability to constantly share vital data with other personas (for example, CS executives). One person owns all the data and others have no idea what’s going on.

Related: Customer Relationship Score

Best Practices to Implement Customer Intelligence in Your Business

Here are some tips for implementing CI in your business strategy.

Leverage Customer Sentiment

Customer sentiment analysis helps you understand the emotional tone behind customer interactions. By leveraging natural language processing (NLP) algorithms, you can analyze emails, social media posts, and customer support tickets to identify positive, negative, or neutral sentiments.

This real-time analysis allows the business to respond to dissatisfaction, celebrate successes, and continuously improve the customer experience. For example, if sentiment analysis reveals a spike in negative feedback about a new feature, the product team can prioritize improvements, and the support team can reach out to affected customers to address their concerns directly.

Build a Customer-First Organization

Implementing Customer Intelligence requires a cultural shift towards a customer-first mindset across the organization. This means that every department, from sales and marketing to product development and support, should prioritize customer insights in their decision-making processes. Encouraging cross-departmental collaboration ensures that customer feedback is integrated into product enhancements, marketing strategies, and service improvements.

Training programs can help employees understand the value of CI and how to leverage it in their roles. For example, regular workshops and seminars can keep teams updated on the latest CI tools and techniques, fostering a unified approach to customer-centricity.

Compile Quantitative and Qualitative Data

An effective Customer Intelligence strategy combines quantitative data, such as usage metrics and purchase history, with qualitative data, like customer feedback and support interactions. Quantitative data provides measurable insights into customer behavior, highlighting trends and patterns. Qualitative data offers context and deeper understanding through direct customer voices.

Integrating both types of data allows you to gain a comprehensive view of customer experiences and needs. For example, usage metrics might show a drop in feature adoption, while qualitative feedback could reveal that users find it confusing. Addressing both aspects leads to more informed decisions and better customer outcomes.

Protect Customer Privacy

With the increasing importance of data privacy, ensuring that all customer data collected and analyzed complies with regulations is crucial. This involves implementing strong data protection measures and transparency in data handling practices. Many companies must adhere to regulations like the General Data Protection Regulation (GDPR) in Europe or the California Consumer Privacy Act (CCPA) in the United States.

Regular audits and updates to privacy policies can help maintain compliance. Additionally, educating customers about how their data is used and the benefits they receive from sharing their information can build trust and foster long-term relationships.

Invest in Customer Intelligence Software

Investing in a comprehensive Customer Intelligence solution is essential for effectively collecting, analyzing, and utilizing customer data. Look for solutions that offer real-time analytics, seamless integration with existing Customer Relationship Management (CRM) systems, and advanced features such as sentiment analysis and predictive modeling.

The right software can simplify data collection from multiple sources, cleanse and standardize the data, and generate actionable insights. User-friendly interfaces and customization options ensure that different teams can easily access and apply the insights relevant to their functions. Regularly updating and upgrading the software ensures it keeps pace with evolving customer needs and technological advancements.

Staircase AI: Pioneering Customer Intelligence

Although Customer Intelligence is a newly introduced concept that’s still not being used universally, its benefits are being recognized by more and more Revenue executives. Staircase AI is pioneering this growing technology with its customer-centric solution with an AI-powered approach.

Staircase AI analyzes millions of customer signals and turns it into churn alerts and growth opportunities, entirely automatically. With the power of AI you can turn your customers’ voice into a growth engine.

The Customer intelligence process Staircase AI

AI-powered customer intelligence has arrived.
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