This post introduces the concept of a Service Data Layer and explains why every support team will need one in order to be competitive and make data-driven decisions with their CX, product, and automation strategies.
The term data layer is commonly used by marketers and developers to refer to platforms that collect and structure customer data from web properties, like your website or mobile application, and syndicate that data to other platforms to ensure data quality and consistency. The concept was popularized by the rise of Customer Data Platform (CDP) companies like mParticle, Bloomreach, and Segment and is now an integral part of any marketing stack.
Here at Lang, we’re working to build a dedicated data layer for support organizations. Our strategy is underpinned by the belief that every company should have a unified Service Data Platform, or SDP, that empowers them to understand and analyze what their customers are asking across any channel, and then to act upon this information and make confident decisions that drive improvements to their customer support, product operations, and automation processes. The platform we’ve built automatically collects data from service channels, then cleans, categorizes, and standardizes that data before sending it on to third-party systems and tools such as analytics, customer service, product operations, and automation. Our goal, simply put, is to make customer support a competitive advantage for every company. This blog post goes into what specifically a service data platform is, how it works, and why it is important that every CX team invest in a data strategy today.
What Does a Service Data Platform Do?
The service data platform sits between a support organization’s service desk software (i.e. Zendesk, Salesforce Service Cloud, etc.) and their front-end engagement layer (i.e. chatbots, email, web form submission etc.) and contains all of the topics and reasons why customers are reaching out to a CX team. The data held within the service data platform is collected from every customer support channel ranging from support tickets to surveys to call transcriptions. This data is intelligently structured and categorized and the SDP serves as a source of record for support contact reasons. Once structured, this data can be sent to other systems that rely on these data assets like service desk platforms, CRM, automation tools, and data visualization platforms.
There are four main characteristics of a service data platform:
- Collects and structures customer contact reasons and feedback from all support channels in a single platform.
- Integrates with CX, product, and productivity tools and ensures consistent data quality. A service data platform makes sure each tool receives the data it needs to do its job.
- Establishes a unified data taxonomy within a company, providing cross-functional teams with a single view of customer support topics and feedback.
- Workflows that empower teams to take action based on trends or events driven from the data collected.
Most companies do not have the luxury of having a data layer for support and due to the complexity of building/maintaining one it is typically something that is only leveraged by only the largest organizations with dedicated ML and AI engineers to tackle the issue. Despite this, the need for a dedicated data layer for support has recently surged because customers are now reaching out to companies through more channels than ever before, and support and service volumes have skyrocketed due to the accelerated shift towards digital interactions fueled by the COVID-19 pandemic.
Why is a Service Data Platform Important?
To better understand why a service data platform is important, it is first critical to get a picture of what most support organizations are going through today. Having one enables a whole host of benefits for support teams and adjacent business units that rely on customer data to make informed decisions. The following are conditions that make an SDP mission-critical for any customer-centric company:
Scaling customer support in a personalized way
Customer support typically grows in a one-to-one fashion as a business’ customer base grows. It’s one of the most challenging parts of a business to scale because it entails a lot of manual processes to address customer needs. CX leaders are constantly working a fine balance on how to drive efficiency and savings into their processes through automation while also keeping support interactions personalized, prompt, and pleasant for the end customer.
Analyzing data from different sources and formats
To make things even more complex, customers contact businesses through a variety of channels that are constantly evolving — ranging from chatbots, to email, to calls. Most companies have trouble making sense of this data and struggle to make it actionable due to its siloed and complex nature. Operations and data science teams often put in heroic efforts to gather and analyze feedback data, and far too often large volumes of data coupled with time-consuming analysis leads to companies focusing on the biggest impact issues while smaller or less frequent issues go unnoticed and ignored.
Support issues are constantly evolving
The vast majority of service organizations are reactive due to the simple fact that their businesses move faster than their processes. Launching a new product often entails new and unforeseen issues that arise and leave support teams playing catch up to update their training, processes, and help documentation. Augmenting this complexity is the specific nuance of language itself: there are multiple ways to ask the same question in any language and this often leads to key details being omitted from ticket tagging efforts, preventing proper categorization and triage of support issues. In short, customer behavior changes rapidly and companies rarely have a complete and up-to-date picture of what their customers are asking nor do they know how to act upon this information in a data-driven way.
What are the Benefits of a Service Data Platform?
Greater Organizational Agility
The core purpose of a service data platform is to grant companies with a centralized and reliable tool to collect customer support data as it is generated and having one helps companies analyze their customer trends and make data-informed decisions faster and with more confidence. A service data platform specifically benefits the following areas within an organization:
- Customer Support: Monitor customer channels in real-time and take action on key issues before they blow up. A single view of customer contact reasons leads to a data-driven approach for more effective self-service materials and helps arm agents with more effective training and content to solve customer issues faster. Improve customer retention and uncover revenue opportunities from insights surfaced in your CX data.
- Product Operations Teams: Gather customer feedback in a centralized view to make faster and data informed product strategy decisions.
- The Automation Stack: Leverage insights captured from a single view of all customer contacts across all channels to rapidly implement new and to improve existing intelligent automation tools like chatbots and voice bots. Improve overall ticket deflection strategies and empower support teams to focus on the most complex issues without fear of them burning out on repetitive one-touch issues.
No More Data Silos
As mentioned earlier in this post, the number of support channels has recently exploded. There are now more ways to get in touch with an organization than ever before, ranging from chatbots to social media to email. Omni-channel support strategies, while effective at meeting customers where they are, create silos since rarely do companies have a complete picture of what their customers are asking and the feedback they’re providing across all touchpoints. A service data layer ensures that teams have visibility into every customer touchpoint.
Consistent Data Quality
Consistent and reliable data is high quality data. A service data platform ensures that all content from customer support interactions from all channels is consistently tagged under a unified taxonomy. Every tool that uses this data from this point on is working off of the same taxonomy and playbook, drastically increasing the effectiveness of any tech stack. A service data layer directly takes on the phrase “garbage in, garbage out” when referring to data quality and changes it to “quality in, quality out."
Lang’s Service Data Platform Supercharges CX
Customer support is one of the most important functions for every business. Companies that fail to properly address their customers’ needs will ultimately fail. In a world where change is the only constant, and organizations require increased agility, a reliable and easy-to-scale service data platform elevates companies by granting them full visibility into what their customers are talking about, and empowers them to make data-driven decisions with their CX, product, and automation strategies.