At ServiceNow, we define analytics as using data to make better, faster decisions to run the company. We use analytics to not only spotlight every corner of our operations, but also to spark growth. We do this by giving our employees data-driven decision-making capabilities. That means they can take action every single day by using data and digital workflows. In order to drive data-driven decisions, we created a user-centric analytics program based on five major elements, which are listed below.
1. Align to our customer journey
ServiceNow puts the customer at the center of everything we do. We created simple, consumer-grade analytics products featuring easy-to-use interfaces that are tied to our customer journey. Dashboards and data had to be intuitive and easy to act on. We focused on three types, depending on the user and the action they wanted to take:
Entity: Data on every customer, contact, campaign, and product, and geared to help us better serve customers and broaden our reach
Persona: Data for every role in the company: sales reps, sales leaders, product managers, and customer success professionals, to improve the end-to-end customer experience
Activity: Operational data for corporate functions, such as quarterly business reviews, performance reviews, and customer success.
This simple dashboard strategy enabled us to create a knowledge graph, like Wikipedia, but for internal data. It links dashboards to one another so users can move seamlessly and stay within context. For example, if an account executive sees a customer contact name in the Customer Dashboard, she can click and jump to the contact’s information directly using Contact Dashboard. No logging in and out or moving between dashboards. We can do this, thanks to the unified enterprise data platform.
2. Integrate analytics into workflows
Insight without action has no value. Digital workflows are the bridge between them. Digital workflows and analytics strengthen each other when they coexist in harmony. Here’s an example: An account executive receives an alert that a customer needs help. If the account executive requires assistance, the workflow routes to the customer success advocate who executes a playbook or descriptive step-by-step workflow to resolve the customer issue. The workflow improves over time because we use AI and machine learning to automatically find patterns to relieve bottlenecks and optimize response paths.
This example illustrates the three areas where analytics plays a major role:
Analytics with workflows: Enables alerts that activate a workflow for a user to act on. For example, prompting an account executive to follow up with a customer before or just after an issue arises, improving customer service.
Analytics in workflows: Applies analytics inside workflow(s) to guide and route a process for optimal completion. For example, if a process is stalled, it will identify the next point of contact the workflow should route to, improving timeliness.
Analytics on workflows: Applies analytics on top of workflows to identify areas for improvement across workflows, driving continuous improvement (also known as process optimization). For example, if a renewal process is being completed by a second team, the workflow will start automatically routing to this team, to improve efficiency.
3. Embrace artificial intelligence and machine learning
To create a consumer-grade user experience, we rely heavily on AI that is embedded directly into dashboards and workflows. That means users are not only alerted to a change but also given prescriptive guidance for what to do next.
By empowering users with recommendations, we can employ best practices that align actions to the customer journey. AI and machine learning help our employees prioritize and ensure that the most optimal action is taken and tracked. That translates to less time spent on manual, redundant tasks and more time devoted to adding value.
4. Deliver meaningful insights and research
We’ve all received data dumps that don’t seem to signify anything important. That’s why an analytics strategy must focus on delivering relevant research and insights, not plain data. Relevancy is derived from understanding what information decision makers need to shape business strategy.
We engage with leaders and executives to address most pressing and strategic business questions with deep research to help shape decisions that drives executive actions. Leaders use this information to develop company-wide priorities and comprehensive go-to-market strategies that align to the customer journey.
5. Power analytics through a single platform
While the decision to house all our data on one platform was a tough one to make, the simplicity of powering all desired capabilities through a single data platform is essential to our analytics success. It gives us one data architecture, one version of truth, and one data source. By using one data platform, we also are optimizing for an entire enterprise instead of one function, with decision-making capabilities built to scale as we grow.
As a data-driven company, we believe that if you can’t measure it, it doesn’t exist. That applies to an Analytics program as well. We wouldn’t call ourselves data people if we didn’t measure our own successes and share them with the company. To make it simple, we use three metrics across all our Analytics initiative:
Usage - Monthly Active Users (#)
Business Impact ($)
When success metrics are as easy as a hashtag (#), smiley face 😊, and dollar sign ($), chance are everyone will remember it. By keeping it simple and relevant, we are bringing analytics into the spotlight in every area of the company. Emoticons included.
To learn more about analytics at ServiceNow, watch our Knowledge 2020 webinar on Analytics & Workflows, Pillar #2 in the Analytics Strategy.
Now on Now is about how we use our own ServiceNow solutions to work faster, smarter, and better. With Now on Now, we’re achieving true end-to-end digital transformation. To learn more, go to the Now on Now website or set up a briefing with our practitioners through your account team.
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