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.
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.
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.
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.
By using our graph technology, you can consolidate and visualize all your customer data to:
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.
The fictitious dataset below represents part of a city’s healthcare system. We’ve color-coded nodes to make them easy to identify:
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.
Using combos – our powerful node-grouping functionality – we can group the doctors by the facilities they work in, instantly reducing some clutter.
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.
Using the time bar, we can explore how data evolves and behaves over time.
We can bring to the foreground only the appointments that occurred in a particular month and “ghost” the rest of the data.
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.
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.
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.
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.