Customers talk to support teams more than they talk to any other part of the business.
Despite this, support teams are often the least informed when it comes to customer-analytics data.
As a customer contacting a helpdesk or support centre, how many times have you been asked to confirm contact details several times? When support teams waste time asking basic questions, they are providing generic and reactive customer-service experiences.
Analytics on your customer support can drive increased retention and customer satisfaction, by analysing this crucial touchpoint in depth.
The challenges of customer service analytics
1. Getting data which maps the customer’s journey
Knowing the journey a customer has taken when you speak to them can be a technical challenge. The data has to be aggregated to a user level and presented in a complete and user-friendly way for customer support teams to interpret quickly.
2. Integrating your data into customer service workflows
The ideal state is to respond proactively, before a customer even reaches out to your call center, but that requires a mixture of well-understood data sets and well-implemented automations.
Most helpdesk and customer support center platforms have the facility to plug in data from different data sources to attempt to get a complete picture of the customer’s information.
These views are limited as they rely fully on the available sources, and are dependent on other vendors to:
- develop the integration you need
- aggregate data to the level you need (generally user level, but within this different models enable a clearer picture of different customer behaviors)
- manage the compliance of the data, which is generally being shared with a 3rd-party organization
How to create effective customer service analytics
The truth is that no two support organisations are alike, no two support teams are alike, and no two customer bases are completely alike.
To succeed you must tailor a customer support view that meets your needs, giving exactly the right real-time customer insights to your support team – right where they need them.
Your team needs trustworthy, customized, real-time data that enables them to provide the best service experience to your customers. As their is generally Personal Identifiable Information (PII) involved, compliance is also crucial.
A warehouse-first approach to customer service analytics helps create fully understood data sets which can be used to get the full picture and create the necessary actions – such as real-time responses to customer needs.
In addition, the use of customized behavioral data means you track customer behavior second by second and customize the metrics to your business needs.
Why choose Snowplow for your customer service analytics?
Snowplow lets you generate, enhance and model your customers’ behavioral data in a highly-customizable way. You can get up to 130 metrics out of the box to describe different interactions, offering a level of granularity unsurpassed in the analytics market.
The whole infrastructure of our pipeline is deployed in your own private cloud (“private SaaS“), which really builds compliance into the fabric of our product. In addition, we don’t have access to or at any point store your data, and you can collect detailed information on compliance, such as GDPR context appended to events.
As the warehouse becomes your central source of truth with Snowplow, you can derive any information you need from a singular data set – modeled to the exact altitude you require.
With these behavioral data models, you can surface key metrics for your support team, and then deliver these metrics to the CRM or service desk tool where your team spends their time. Your helpdesk is no longer a patchy operational silo – it can take its rightful place at the heart of your customer journey.
To test this for yourself, try Snowplow for free