Predictive analytics. It sounds attractive. But to those not engaged with it, predictive analytics could also sound complicated, expensive and maybe a better fit for someone else. Anything but pragmatic.
But it doesn’t have to be this way. In fact, when done correctly, it can be simple, approachable, valuable to your business and very doable.
What is predictive analytics?
SAS—one of the leading analytics software providers in the world—defines predictive analytics as "the use of data statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data."1
Predictive analytics goes beyond understanding what happened and why; it understands what will happen and what you should do about it.
To be able to predict the future? It's hard to argue with that potential capability! No matter what your business objectives or pain points might be across the customer journey (or sales funnel), predictive analytics can like play a role in improving your business and marketing outcomes.
Whether you're looking to acquire more customers, increase marketing efficiency or improve customer loyalty and retention, predictive analytics could have a positive impact on your goals.
Examples of situations that benefit from predictive models spanning customer lifecycles:
- Insurance companies looking to reduce attrition
- Healthcare providers seeking to reach new target audience for underutilized specialties or services available
- Colleges and universities evaluating the potential of a new program they are developing
- Banks looking to find cross-sell/up-sell opportunities to their existing customer base
All of these use cases lend themselves naturally to the power of predictive analytics. You can likely identify a business objective in your own organization that would benefit from this too.
You may already be using predictive analytics!
If predictive analytics has so much potential and relevance, why isn't it being implemented more often? And why aren't you using it?
You may not realize it, but there's a good chance you—and a lot of other organizations—use predictive analytics through some of the partners you're aligned with already. For example, Facebook offers look-alike modeling as one of its ad targeting strategies. Google provides its own flavor of predictive analytics for both purchasing and churn, as seen below.
So you might be thinking, "Well, sure. It's the kind of thing that trillion-dollar companies can do all day. But what about my company?"
Implement a strategy.
Whether you've been using predictive analytics or a complete novice, you'll benefit from making deliberate choices that help you reach your goal. That's why you should implement a predictive analytics strategy!
You may not have a team of data scientists or expensive analytics technology, but that doesn't have to stop your exploration and implementation of predictive analytics.
Step 1: Identify your business objective
The first step is simply identifying a business objective or pain point that you would like to address. What do you want to understand better? What kind of decisions will be driven by data and what actions do you take?
Example of a typical journey once an objective has been identified:
Step 2: Establish analytics support or resources
Once you've identified your objective—and assuming you do not have in-house data resources—you will need a partner to help with data acquisition and model development.
At Primacy, we know data-driven strategy is the beacon for creative problem-solving. Our team uses data and analytics to fuel thinking and create smarter experiences. And when it comes to predictive modeling, our goal is to ensure it's accessible, affordable, easy to implement and actionable.
Example of a typical model applied to support business objectives:
Step 3: Turn to the data
After identifying your objective and establishing your analytics support/resources, you'll need to gather data. Cleanse and prep the data for analysis, develop a model and then begin execution and refinement.
While any model development process can be complex, we recommend a Crawl, Walk, Run approach. Oftentimes, a lot of impact is realized with simple models based on easily accessible data using everyday software.
It's best to start here and gain some quick wins. Work your way into more complex analytics only as necessary.
You know the benefits of predictive analytics. You have the steps to implement a strategy. All that's left is to consider a practical approach to get started. Just keep these things in mind and you'll be on your way to gaining the benefits of predictive analytics:
Adopt a pragmatic mindset. Don't assume predictive analytics is beyond your reach. Identify a business problem or two, consider how to optimize and keep an open mind.
Find a partner with the same pragmatic mindset. Take time to discuss your situation and objectives with your analytics partner. Come together to agree on an approach that will give you a quick win without excessive time, money or effort.
Get moving! You now have a pragmatic plan to implement predictive analytics.
Our team loves data and analytics.
If you'd like to learn more about predictive analytics and how to incorporate it into your business strategy, we'd love to chat.
1 SAS Institute, Inc. (2021). Predictive Analytics: What it is and why it matters. SAS. Retrieved from https://bit.ly/31JTuHF.