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Customer Intelligence is a methodology that allows the ongoing analysis of customer data that’s harvested from omni-channel conversations and interactions. Unlike transactional tracking methods, Customer Intelligence 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.
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.
Related: Relationship Intelligence Matters
Customer Intelligence is helping Customer Success teams gain a bird’s eye view of their accounts and internal operations for added clarity. This is proving to be a true game changer, especially in complex use cases and companies scaling up fast.
Here are some benefits:
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.
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.
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.
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 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:
All in all, every piece of data, especially customer-related information, can help us analyze the account’s health, potential, and business value in real time. This can help gain insights about performance and better recognize new growth opportunities.
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:
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.
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.
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.
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.
Spoiler alert: It’s not as straightforward as it may seem at first.
Related: Customer Relationship Score
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.
AI-powered customer intelligence has arrived. But don’t take our word for it. Book a demo now!
Together with our content partners, we have authored in-depth guides on several other topics that can also be useful as you explore the world of machine learning.
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