Getting a 360 degree view of the customer--having all their data in one place, including basic contact information; past and present purchasing data; interactions with customer service, and even social media behavior--has become a prime objective for businesses today. Lack of ability to quickly share quality data is one of the biggest problems in getting there.
Data might be required instantly when making major decisions. Though you may have the data, and the infrastructure for sharing it, mapping it to the specific requirements will take some time to build. And, once you start building it, you realize it may not be so easy to share, as different departments have implemented their own systems for reporting and the same KPIs may have different definitions across different departments.
For example, when a customer service process is executed by an agent, it may take some time after the call to document the resolution steps. For customer service, the time taken to document a call is included in the total time taken to complete the service request. But the operations team may only look at the call or chat start and end time as the time to complete the request. Both departments may call it turnaround time, but since their definition differs, their data will be different as well.
As long as departments are reporting on their own data, they only need to agree on the definition within their own departments. However, when they try to create an organization-wide intelligence, it's like a little Tower of Babel, where everybody is speaking a slightly different language and nobody is understanding each other. Each department is optimizing their data strategy for their own users, and not for the organization as a whole.
Disparate data journeys
The problem is that each department started on their data journey at a different point in time. Finance was probably the first team, followed by sales, and then marketing. But today, CEOs are looking not so much for each department to report out on its own data, but to drive the entire organization based on a more holistic understanding of the data. They are also looking for opportunities to implement machine learning and artificial intelligence. Such an organization-wide data strategy requires that your data be cleaner, more accessible, better defined. It's forcing the organization to look at the bigger picture.
This is one of the reasons we chose to partner with Incorta--to help organizations that have implemented departmental data strategies and to bring all their data together to implement an organization-wide strategy. This is the next step towards a mature data strategy.
Think organization wide
It starts with an organization-wide objective. For example, in the initial stages of an e-commerce business, the objective is typically to maximize sales. You give discounts, or you offer free delivery. You test various sales and marketing strategies as well.
As the company matures and you collect more data from these efforts, your objective shifts to maximizing profit. That means optimizing logistics, minimizing discount offers, and maximizing customer loyalty, among other things. This requires every department to be involved, and to align their objectives to the company wide objective, and then to look at the data they need to achieve it. They need to be able to look beyond their departmental data and see how their operations impact other departments.
Then there is Incorta’s ability to pull in external data for analytics and business intelligence. Some of our clients are travel companies that have been hit very hard by the pandemic. Now that people are beginning to travel again, travel companies need to be able to integrate their customer data with external data about health and safety rules where they live.
To bring back their loyal customers, they need to understand where people can travel within their own country, and internationally, and create the right kind of packages, along with marketing campaigns that reassures them that COVID best practices are being followed while they are traveling and at their destination.
Integrating third party data
Many other types of companies can benefit from integrating external data sources with their internal data. With Incorta we can quickly integrate external data, match it with existing data and create different kinds of analysis. Without Incorta, the data modeling could take a lot of time.
Mergers and acquisitions are some of the other use cases for Incorta. Every time you merge or acquire a new company, you also have to merge the data from different systems. Even if you have multiple instances of a system such as Salesforce, they will all be configured differently. Let’s say you have Salesforce data from the US in one system, and from Canada in another system. If you try to combine them, you will no longer be able to tell which is which, without a lot of data modeling. However we can tie them together in Incorta without having to do that.
Gaining a 360-degree view of the customer is a never ending project, as the customer and the market dynamics keep changing. You can't reach 360-degree analytics in a day or in a project. It requires time. It requires maturity. It requires a clear view of the company wide objective, and the ability to change, so that you can incorporate new data, and new ways of looking at the data. It requires a tool such as Incorta that is capable of handling all of these.
Learn more about Incorta's partnership with Kanerika.