You’ve probably heard about the power of predictive analytics and how it can practically save the world. Although there’s plenty of hyperbole to go around, there’s no denying the impact of this business analytics software. The right predictive analytics software can be one of the most important tools for a business.
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What Is Predictive Analytics and Why Does It Matter?
Once the domain of data scientists and mathematicians, predictive analytics is now a fast-growing business intelligence tool that lets companies make predictions based on acquired data. Capturing and analyzing data and creating actionable insights are driving businesses to the top of the pile in the data-rich world of 21st-century business.
Don’t yet believe in predictive analytics? Take a look at Amazon, says former Eloqua CMO Brian Kardon. In an interview with Heinz Marketing, Kardon explained the power of predictive analytics solutions by pointing to Amazon’s success: “The best example is Amazon. Its recommendations are predictive analytics in action … More than 30 percent of its business comes through its recommendation engine. It is analyzing current and historical data to make predictions about the future … with stunning results.”
Read our in-depth breakdown about predictive analytics for more information.
So, yes, predictive analytics can make a huge difference in a business. But this difference only happens with proper implementation, which isn’t always the case. There are a lot of analytic solutions in the predictive analytics software market, so it can be tough to find the right one. If you’re going to look at how to implement predictive analytics solutions, make sure you know how to do it the right way. Conveniently, we have a list of five ways to do just that:
Predictive Analytics Software Implementation Tips
Tip #1: Separate Your Needs and Your Wants
Before you even think about looking at predictive analytics software vendors, you need to figure out which features you need, and which are nice-to-haves. This helps start your search off on the right foot in two very important ways. First, you can eliminate any vendors right off the bat that doesn’t have one of your needs. Second, you’re less likely to get hooked by some fancy bells and whistles that look nice, but don’t provide real value. For example, your business probably needs statistical analysis capabilities, but does it need artificial intelligence? Although there are a lot of great things AI can do, it may not be worth an investment right away, and what would that look like for implementation? Having a list made in advance helps make decisions like these easier.
Tip #2: Assess Your Data
Even the best analytic solutions can’t make up for bad data. Kissmetrics offers up a couple revealing stats about the benefits of clean data. Businesses save about five percent of their revenue annually and can generate up to 70 percent more revenue based solely on clean data. So before you start getting price quotes from predictive analytics vendors, make sure you have the right data for predictive analytics software.
Some questions to ask yourself (or your IT department) include:
- Do you have enough data?
- Is this quality data?
- Is this the right kind of data we need?
- And if you don’t have enough high-quality data, can you use external data to supplement what you have?
Tip #3: Create a Success Timeline
Ali Rahim at Information Builders suggests identifying the criteria you’ll use to assess your predictive analytics software. Use those criteria to make a timeline of small, incremental goals to aim for. Setting incremental goals rather than one large goal helps avoid the frustration of not seeing a massive change overnight. By doing so, your employees know what the goals are and can make decisions with them in mind. Enterprise software expert and Forbes writer Louis Columbus says “The best selection processes are anchored in specific business goals, defining exactly what the expected contribution from the application is.” This also simplifies the evaluation process following implementation. All you have to do is compare your current ROI to what your goal was.
Tip #4: Get Everyone on Board
One of the biggest reasons that software implementations fail is because employees don’t fully embrace them. To make sure this doesn’t happen, you need to get everyone on board with using predictive business analytics ahead of time. Even if they’re hesitant at first, show them the data and what it can do to help your growth. This involves making sure every user actually understands the software and its insights for decision-making. By doing so, your business as a whole works cohesively, as everyone uses the same methods to achieve the same goal.
Tip #5: Make a Change Management Plan
As we detailed in 52 Software Selection Tips, change management is one of the most important steps in implementation. Change management is the process of overseeing any sort of change, especially within an agency. It informs how we prepare people to anticipate, execute and adopt changes within a business. Ultimately, change management produces a structured approach to change, hopefully reducing friction as a result.
After you’ve selected a vendor and nailed down a contract, it’s time for the transition. Changing your processes to accommodate big data and predictive models doesn’t have to be mind-numbing. Executing a transition like this is never easy, but it can be pretty seamless with proper planning. A large part of change management involves the training of all of your users. Starting training before the transition begins helps iron out any wrinkles and makes sure that all your users are ready to go from day one.
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Top 5 Predictive Analytics Software
These five steps are necessary for proper implementation, but they’re ultimately useless without a good predictive analytics software vendor. But we’re not just looking at good vendors — we’re looking at the best predictive analytics software vendors. Taken straight from our Business Analytics Software Leaderboard, here are the top five predictive analytics software: