Operational analytics refers to the form of analytics that provides insight into daily decision-making that organizations are faced with in order to maintain effective and efficient consistency in their operations.
Why get stuck having to rely on reports or trust dashboards for the most up-to-date information to react to changes? Companies can get a better understanding of the data that they have at their disposal with operational analytics.
Gain a deeper understanding of how to put your data to work, taking it from the data warehouses where it is being stored to the staff members that can make the most of it with the power of operational analytics.
<h2> 1. Operational Analytics vs. Traditional Analytics </h2>
One of the biggest differences between traditional analytics and operational analytics is that traditional analytics uses data to make sense of business operations, while operational analytics uses data to push business operations.
Traditional analytics makes use of data to understand what’s taking place in your business to inform appropriate decisions over the course of time.
Operational analytics refers to data moving from your data warehouse into other tools, like Hubspot or Intercom, to inform specific activities inside your company, such as sales, marketing, support, or customer success.
<h2> 2. Operational Analytics in Action </h2>
Standard data stacks these days center around a data warehouse that can support both traditional and operational analytics, with this infrastructure making implementing operational analytics an attainable proposition for businesses of practically any size, with these four elements:
- Data Integration: an ETL (extract, load, transform) tool to integrate all of the data sources into your data warehouse.
- Data Storage: a data warehouse to store structured and unstructured data.
- Data Modeling: a data modeling tool to help manage data with a library of data models to make data usable for various purposes.
- Data Activation: a data automation tool to pull your usable data out of your data warehouse, validate it automatically, and deliver it to the tools that need it.
<h2> 3. Why Use Operational Analytics </h2>
Once a company finds itself having the data of customers inside its data warehouse, it is time for operational analytics to offer a way to overcome limitations at scale and allow its data team to take on a more proactive role in using data.
If you don’t use operational analytics, you are stuck having to put pressure on the analytics capabilities of your individual tools. This means that you will be limited by the data you have in that system, and you will have to connect the dots on your own.
If you think that you will be able to use spreadsheets to work around this issue and bypass using operational analytics, once you start scaling up into the area of 5-10 customers, the data will become too much for a spreadsheet to handle.
To make the situation even more sketchy, if you are avoiding the path of operational analytics, you are pushing too much onto your operations staff to have to juggle how those tools will even work together.
You could quite possibly have customer success representatives moving back and forth between dashboards, the help desk, email, and spreadsheets in an attempt to prioritize the next move. You may also have a marketing operations rep managing audiences and lists manually.
But with the assistance that operational analytics brings to the table, all of these daily operations can become automated, saving your team from having to waste energy on these mind-numbing repetitive tasks draining tasks so they can focus their concentration on these more hard-hitting efforts that will raise the level of revenue and pick up the pace with efficiency.
<h2> 4. Customer Success with Operational Analytics </h2>
If a CS (customer success) team is working at a smaller start-up company that is beginning to really grow, they could be using traditional analytics to measure the company’s performance and use this information to plan ahead while using the operational analytics side of things execute and get their plans off the ground.
As the performance of your CS team improves, this stimulates more customer retention and satisfied customers. Overall, CS reps will know which support ticket to prioritize over the less impactful ones.
Remember to empower every team to see the total impact that operational analytics can have by making it possible for them to make decisions that are strategic and backed up by real-time data.
Operational analytics, at its roots, is all about putting an organization’s data to work so that intelligent decisions can be made on behalf of your entire company and so that successful customer experiences keep occurring for years to come.