The Customer Relationship Score, a relatively new concept now being adopted by mature CS teams, is helping businesses understand the strength of their relationships with their customers. An accurate Relationship Score can help recognize sudden sentiment drops, detect engagement level issues, and identify new relationship opportunities to promote account stickiness.
What is a Customer Relationship Score?
The Customer Relationship Score is a measure of the strength of a relationship between two organizations or between two individuals in these organizations. Measuring relationships might sound difficult since people have subjective views about their relationships and different considerations when assessing relationship strength. With the right framework and definitions, the relationship score is a practical and valuable tool in assessing customer health and driving business goals.
We can’t ignore the fact that relationships play a significant role in today’s Customer Success space. Organizations invest heavily in developing and maintaining their customer relationships. While it’s relatively easy to measure business value through revenue or product usage, it’s difficult to measure the impact of relationships. How customers feel (i.e. Customer Sentiment) is an important blindspot that a Relationship Score is able to uncover.
In this post we will discuss why businesses need the Relationship Score, what we should take into account in order to measure it as well as its business impact.
Why Do You Need a Relationship Score?
Why do companies need to define, calculate, and track Relationships on an ongoing basis? Don’t we have enough KPIs?
Here are a few benefits of healthy and strong relationships in the business world:
- Promoting a spirit of partnership vs a cold client-vendor exchange of goods/services.
- Transparency and openness to work through problems and challenges that would otherwise result in customer churn and brand damage.
- Proactive customers who act as brand ambassadors promoting your brand by recommending it to colleagues, friends, and family.
- Early warning of potential risks such as org changes and competition, as well as opportunities such as expansion into new parts of the company.
- There is more and more data demonstrating a deep correlation between strong customer relationships and higher Net Revenue Retention (Watch this webinar).
Now that we have established the importance of relationships, the natural question is how does an organization operate to create, nurture and manage their relationships? The answer is to collect, measure, and monitor relationship data. The Relationship Score defines what data is required and helps align the organization to focus on the same key aspects of relationships in a uniform and comparable way.
What are the Components of a Relationship Score?
As the name suggests, the Relationship Score is an attempt to give the strength of a relationship between two individuals or two organizations a numerical value. This is commonly done by using a rather basic 1-5 scale.
A common representation of the numerical values are:
- 1-2 – No relationship (or just getting introduced).
- 3- Professional, responsive but lacking a deeper connection.
- 4-5 – Strong professional and personal relationship, quick communication dynamics, responsive and on private channels (Whatsapp, text communications), both sides are friendly and see demonstrated value.
While a scale of 1-5 is a nice and easy concept for people to work with, relationships are complex. Let’s take a closer look at the components of the Relationship Score:
1. Customer Sentiment
Human emotions are often hard to read clearly. Subtley written, spoken or visual expressions can be interpreted differently by different observers making customer sentiment a challenging KPI. But one thing is clear: when it comes to extreme emotions most people are able to identify positive sentiment from negative (i.e. someone is happy or sad/upset). Organizations should invest in accurately measuring customer sentiment through a variety of means from surveys to artificial intelligence. Missed early signs of frustration (shorter responses, “cold tone”, repeated requests, slow to respond) can escalate quickly into negative outcomes and customer churn.
2. Communication Channels
Today, people use a variety of communication channels from simple texts, emails, to slack channels and video calls. There are cultural and geographical considerations as well (popular forms of communication in the US are less popular in Europe for example whatsapp vs iMessage). Relationship strength can often correlate to the type of channel two people are using. For example, after a first business meeting, people will exchange emails but as time goes by they may also include personal phone calls and text messages. Therefore the type of channel as well as the content (how verbose the interaction) are important in assessing relationship strength.
3. Customer Engagement / Responsiveness
Engagement levels can vary – some are always on their emails, others take a few days to reply. That’s why engagement KPIs need to be enforced and tracked on an ongoing basis to identify sudden increases or drops in engagement that could reflect on the relationship. For example, a sudden and unexplained drop in engagement from a customer who is usually engaged is a possible early warning signal that something in the relationship is not going well.
Response Times can also be factored into the Customer Engagement calculations since they show how locked in users really are. Once again, this should be used with a pinch of caution. For example, people can take vacations, time off or move on from their current position.
4. Open Items
Customers can communicate in different channels. These days this is required to provide customers with a superior service experience. However, from a CS perspective that could mean a lot of scattered clues and cues that a Customer Success manager has to follow. Open items could be unanswered emails, tickets, or messages that can contribute to negative sentiment. You can also have open feature requests that are causing frustration, even if the ongoing communication is pleasant and friendly.
5. Key Stakeholder Relationships
Today’s usage patterns are getting extremely complex. You can have multiple stakeholders in the same company who are using the same product – some champions, others not. You need to track three things on an ongoing basis:
- The status of the stakeholders you are in touch with.
- Are your connections the right people to be connected with?
- Are you taking the required actions to nurture these relationships?
- How long have you been in a relationship with this stakeholder?
The existence and health of Key relationships are important in order to assess the relationship between two companies. For example a customer with multiple strong relationships (decision makers, executives etc) has a stronger overall relationship than a customer with only one relationship. Relationships between companies can exist over many years and outlast individual relationships.
Related: Top 5 Customer Success Pro Tips
The Relationship Score impact on Mature Customer Success groups
Harvesting relationship data and then analyzing, benchmarking, and compressing them into a simplified scale is a complex endeavor. Interestingly, most organizations still rely on human assessment, which is often based on gut feelings. This is both time consuming and inaccurate (think about the challenge of admitting you have a bad relationship or forgetting a recent trend).
Furthermore, humans are very good at assessing the present. But they are often not equally skilled at looking back and analyzing trends. Also, benchmarking or comparing different instances objectively is a hard feat to achieve manually.
That’s why a blend of technology, data science and Artificial Intelligence (AI) is now needed to collect, analyze, and predict relationship scores:
- Leverage technology to collect big data – It’s now possible to collect and store digital engagements (both meta-data and content) via modern API’s.
- Use data science to augment human capabilities – clean, normalize benchmark extract patterns and identify anomalies and correlations.
- Predict using Machine Learning – Train machine learning models to predict the relationship strength based on the data collected and analyzed.
The next challenge we face is to figure out how to combine the various components (features) in a way that results in an accurate prediction of the relationship – this will be discussed in a separate blog. Once you have established the Relationship Score, the magic happens. Leaders can manage and track which customer accounts have strong relationships, and detect at-risk accounts where urgent action is required to prevent churn.
Relationship Score FAQs
- What’s the benefit of using AI to track Relationship Score?
Some of the Relationship Score ingredients are impossible to track with the naked human eye.
- Do I need AI-generated insights for my Relationship Score?
Yes. The number of variables is constantly growing and you simply can’t do everything manually anymore, especially when your company is scaling up fast.
- What other customer success metrics can I use with Relationship Scores?
Net Revenue Rate (NRR) and North Star Metric go well with Relationship Scores.