Achieve Customer 360 with graph visualization

15th May, 2018

From retail to healthcare, and banking to government, organizations around the world are seeking Customer 360 – a single, definitive view of the customer. Through a better, more data-driven understanding of the customer, they’re able to deliver personalized, engaging experiences, and drive significant bottom-line results.

Using graph technology with their data, businesses can build a unified picture of their customers, to better understand them and to predict their future behaviors. That’s the topic we’re going to explore in this blog post.

What is Customer 360?

Customer 360 provides a holistic view of the customer by integrating the information an organization already holds on them, such as demographics, buying behavior and history, across many channels. This data is used to uncover insights that maximize the value delivered.

Data silos pose a big challenge for many organizations. Customer touchpoint data – website activity, emails, transaction logs, social posts, reviews, etc. – are collected and stored in disparate systems and platforms. Graph visualization tools, like KeyLines and ReGraph, make it possible to pull this information into a single consolidated view.

Achieve a single consolidated view of all your data using our graph visualization technology
Achieve a single consolidated view of all your data using our graph visualization technology

This holistic view leads to faster and better decisions – more data points lead to a more complete understanding of the customer’s needs resulting in more reliable decision-making.

How can graph visualization help you achieve Customer 360?

There are two approaches when it comes to Customer 360 visualization – our toolkits can help you with both:

The global approach/view
This effectively visualizes large amounts of customer data providing you with the ability to find high-level trends and patterns. It’s often used to make product and/or organizational level decisions, for example: to improve ROI and/or reduce churn. For this you need a high-performance visualization engine with strong analysis features to handle the large volumes of data.

The local approach/view
This is frequently used by customer-facing staff who need to understand customer data at an individual account/person level. They need a quick overview of relatively fewer data points to make a fast and reliable decision about the customer – are they loyal? Are they likely to leave? Do they have a history of complaining? etc. Here the visualization needs to provide clarity to give the user fast insight.

Example of global vs. local view in KeyLines, one of our SDKs
Example of global vs. local view in KeyLines, one of our SDKs

By using our graph technology, you can consolidate and visualize all your customer data to:

  • Improve loyalty: Visualizing customer touchpoints as a graph can reveal buying trends, identify areas for improvement and ultimately lead to increased customer loyalty.
  • Decrease churn: Using social network visualization, high-risk customers can be identified by looking at the social network of a churning customer. For example, if a close connection of a customer (identified by volume of interactions) has recently left your services, that customer’s churn propensity will increase. Visualizing this information can help you come up with strategies to retain this customer. We go into more detail in this blog.
  • Improve customer service: With graph visualization, you can provide your customer-facing staff with an ‘at a glance’ view of the customer, helping them improve customer service.

Organizations across many industries are pursuing the Customer 360 ideal. In the example below, we’ll take a closer look at a fictional healthcare example.

Graph visualization and Customer 360 in action

The fictitious dataset below represents part of a city’s healthcare system. We’ve color-coded nodes to make them easy to identify:

  • Blue nodes are medical facilities
  • Green nodes are doctors
  • Orange nodes are patients

We’ve styled the links too. The light blue links represent patient visits or doctor referrals, and the red glyphs on links represent a complaint made by a patient against a provider. Links are weighted by volume of visits and referrals.

Using graph visualization and some of the features provided in our toolkits we can begin to uncover some useful insight.

Part of a city’s healthcare system
Part of a city’s healthcare system

1. Get a global and local view with combos

Using combos – our powerful node-grouping functionality – we can group the doctors by the facilities they work in, instantly reducing some clutter.

Doctors in a healthcare system grouped by their facility
Doctors in a healthcare system grouped by their facility

This provides us with a clearer high-level view of the connections between patient and facility, which we can drill into further.

In this scenario, combos help the user switch from a high-level view (global approach) to a drilled down view (local approach) giving trend insight to one audience and individual node-level insight to another audience.

Focusing on Dr Huynh to get a drilled down view
Focusing on Dr Huynh to get a drilled down view

2. Visualize dynamic networks with the time bar

Using the time bar, we can explore how data evolves and behaves over time.

Using the time bar to explore the network
Using the time bar to explore the network

We can bring to the foreground only the appointments that occurred in a particular month and “ghost” the rest of the data.

3. Reduce network noise with filtering

Another powerful feature provided in our toolkits is the ability to filter your data. It helps users filter out the noise and focus on the data they need to understand.

Focusing on one of the patients, Allison Estes, we’ve eliminated all data that isn’t connected to her. Now, we can focus on Allison and the doctors she’s had appointments with. In the side panel we can see additional information about the patient, including her date of birth and contact details. This useful contextual information is pulled from a separate records database to establish a 360-degree view of a customer. Because KeyLines and ReGraph are SDKs, you can easily incorporate it into a wider UI that pulls in further detail and context.

Using filtering to focus on Allison Estes
Using filtering to focus on Allison Estes

We can see that Jose Garcia has received a few complaints with three of them coming from one patient, Rachel Jackson. By focusing on Rachel, we can see that she’s also complained about other doctors too.

Using filtering to focus on Rachel Jackson
Using filtering to focus on Rachel Jackson

It could be that this patient has a tendency to complain or there may be a quality issue associated with these doctors. This is a starting point an analyst can use for further investigation.

Power Customer 360 with our technology

Achieving Customer 360 can be challenging, but with a unified graph view it’s achievable. In this blog we’ve outlined three features – combos, the time bar and filtering. There are many more features to explore in our toolkits.

Get a step closer to obtaining a 360-degree view of your customers – request a free trial here.

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