Personalizing customer service interactions can help reduce customer effort, increase customer satisfaction, and support service leaders to achieve the holy grail that is high customer loyalty.
Gartner research has even found that 86% of B2B customers expect companies to be well-informed about their personal information during service interactions.
There are a range of customer service capabilities that can be enhanced through personalization, including proactive outreach that prevents unnecessary interactions, connecting reps with the context to suggest an appropriate next best action, or through delivery of personalized experiences within a self-service portal.
For personalized service to be effective, customer service leaders must have a data-driven foundation of customer preferences and values. Organizations that rely on generic and untested organizational assumptions regarding customer preference will find that this can often lead to ineffective or harmful service outcomes.
These assumptions typically fall into two broad categories:
- All customers are treated as a homogeneous group, with little to no segmentation to distinguish the preferences and values from one customer to the next.
- A reliance on broad and untested segmentation — for example, grouping customers by demographic factors that offer little, if any, meaningful insight into true customer behavior and values.
To address these assumptions, customer service leaders can explore segment behavioral analysis, which is a technique that can be used to develop a more refined and accurate understanding of key segment preferences. The analysis combines service team insight alongside customer behavioral data taken from sources such as service channels, product and search. The result is a more measured set of customer segments upon which more effective personalized service techniques can then be designed.
To conduct a segment behavioral analysis service leaders can follow a three step process.
Step 1: Identify Customer Segments
Customer service leaders should start by working with service managers, as well as service and support SMEs, to identify what are already considered to be their key customer segments, as well as the perceived preferences and expectations of each. This doesn’t have to be a data-driven exercise.
The goal at this stage is to simply gather insight from top service employees about potentially important customer segments that can later be validated and refined with data.
It is important to discuss the final list with service representatives, checking that the team agrees that there are meaningful distinctions between each segment identified.
It is also important to consider the data available to segment customers — each unique segment must be measurable across systems, ideally with the use of high-quality data fields to categorize each customer such as contract type, user role, job title, life cycle stage, etc. This will be important in order to move successfully into the quantitative stages of this analysis.
Step 2: Analyze Segment Behavior
The goal at this stage is to perform a data-driven analysis that validates and refines the list of customer segments identified in step one. By applying analytical techniques that consider the behavior and preference of each group, it is likely that new discoveries will be made that either challenge or complement existing service team assumptions.
Customer service leaders should start by creating a list of key behavioral traits they would like to test. These could include things such as common issue type, first channel preference, product usage profile, error frequency, etc. When deciding on the list of traits to analyze, customer service leaders should consider both the availability and quality of the data they have access to, as well as the service objectives they are looking to achieve. Once the list has been created, go segment by segment using available service data to analyze each of the behavioral traits selected against each segment.
Step 3: Develop New Personalized Outcomes
With the analysis completed, customer service leaders must meet again with their service and support SMEs from step one to present their analysis and to discuss the results. This collective group should consider in each instance whether the findings of the analysis matches the initial assumptions captured.
The objective of this discussion is to develop a new and refined set of customer segments by documenting preferences, expectations and values based on the qualitative as well as quantitative elements of the analysis. The team should consider whether the results of the behavioral analysis match their initial assumptions regarding the preferences and expectations of each key segment.
Once complete, share these results throughout the wider service and support team. Doing so can help to break down assumptions regarding segment behavior and preference while promoting a more analytical approach.
By developing a data driven set of customer segments using these three steps, new techniques can be devised that more accurately speak to the preferences and expectations of customer groups as they interact through service channels. Having taken the first steps, customer service leaders can now look to the future and more advanced analytical strategies that will further enhance service capabilities. The most advanced organizations today are able to take personalization one step further by treating the requirements and journey of each individual customer as unique, preempting customer desires through the use of predictive analytics.
