Customer Intelligence: Benefits, Challenges, and Best Practices

Customer Intelligence (CI) is a relatively new concept that allows Revenue Leaders and teams to connect the dots in an increasingly complex ecosystem. As more stakeholders enter the decision-making game, and sales cycles get longer, leveraging customer data to improve business outcomes becomes critical. Let’s take a closer look at this upcoming methodology. 

What is Customer Intelligence?

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 Benefits

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:

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. 

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.

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: 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. 

Spoiler alert: It’s not as straightforward as it may seem at first.

• 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

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.

AI-powered customer intelligence has arrived.
But don’t take our word for it. Book a demo now!

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