As customers, we amass a large amount of data under our names. We may not realize it, but our store cards, credit cards, Google searches, create a picture of us. What we buy, what we like, what we’re interested in.
All of this information can be used by businesses to help decide how to target us more effectively so we buy their branded product.
How? Some decide by gut instinct but most do so by predictive analytics. So what is it anyway?
According to Webopedia, it is “the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends”. In business, it is used to take current data, paired with historical facts, to understand a specific audience or sector so you can detect risks before they happen or optimize for opportunities to come.
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The technology isn’t brand new. In fact, it’s been helping credit card companies determine whether or not their card holders will pay on time. It’s also being by the US CDC to determine where the next flu outbreak will occur.
However, the predictive analytics has grown up and is now available in comprehensive software packages. So if you’re thinking about buying one, make sure to consider these things before doing so.
1. Get a demo
It may sound silly but some don’t try before they buy. Make sure to take note of how time consuming and complex it is. Will it take more time to learn it than to actually use it? If so, don’t waste your money.
2. Learn the power of the tool
Can it automatically detect and learn about operating systems, application frameworks, your organizations applications, database engines, and networking media? Is it compatible with the latest and greatest technology out there? Consumer product operating systems tend to be a bit more advanced than that of enterprises. Make sure you aren’t buying an outdated tool where your employees can’t use their new toys.
3. Consider the price tag
There are a lot of solutions out there, make sure it is cost effective to your environment after evaluating the aforementioned points. Require that your supplier prove their claims by sharing customer references.
If possible, ask to speak to a customer. Find out what the seller’s customer service is like. Without quality support, your implementation and issues that will come up may make the predictive analytics tool not worth your time.
There is no one solution fits all. Some aspects on the wish list will be missing, but don’t use that excuse as a reason against implementing predictive analytics. In fact, if you need a few reasons – here are 7. However, if it’s time efficient, powerful for what you need, and at the right price, then make the purchase. You won’t regret it!
Image from PredictiveAnalytics.org