4 Ways Your Newborn Business Can Leverage Predictive Modeling
4 Ways Your Newborn Business Can Leverage Predictive Modeling
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4 Ways Your Newborn Business Can Leverage Predictive Modeling

Predictive modeling may be a novel term for some. It’s a term that you will hear a lot when discussing all things related to big data and business intelligence. So, what exactly is predictive modeling and how can it be leveraged by SMBs and startups?

Predictive Modeling Defined

It should be noted that predictive modeling should not be confused with predictive analytics. The two terms are often used interchangeably, though both have slightly different meanings. Predictive analytics is the process of identifying trends based on patterns deciphered from big data. Predictive modeling is a method used in predictive analytics and includes data mining solutions for determining likely future scenarios.

Don’t get too caught up in how these two terms are similar and different. Just know that predictive modeling is a valuable asset across all industries and internal team departments.

So, what are some of the best predictive modeling practices? Here are four methods that are useful for established businesses and startups alike.

  1. Incorporate Real-Time Data

Data quality is everything. You should be using data from the real-world, and not data acquired from simulations and tests.

This idea was implemented by Australian electric distribution company Meridian Energy. The company previously used a predictive maintenance system to predict the failure rate of electrical equipment like circuit breakers, generators, and industrial batteries. The data the company was using was obtained from simulated, laboratory tests. The results failed to translate to the failure and reliability rate of actual electrical equipment in use in the real world. This prompted the company to switch over to real-world data using a machine learning-based BI solution.

The lesson here? Data should be from your actual customers and their actual behaviors. The more recent the better. Data from simulated tests, from a competitor’s, and so forth may not translate to how outcomes actually play out within your demographic consumers.

  1. Predictive Modeling Shouldn’t Override Human Judgement

Predictive models provide great insight that influence the decision-making process. However, the data results shouldn’t be the end-all, be all. Use your own industry experience combined with the data analytics to form final decisions.

You may, for example, use predictive modeling analytics to determine an optimal price range for a new product. Data for the analytics may include customer purchase history of similar products, sales fluctuations when similar products lowered or raised in price, etc.

The analytics may come up with an optimal suggested price range. You don’t have to go with that exact price just because it was what the analytics churned out after all the number crunching. If your personal experience tells you that the price should be lowered or raised based on real-time interaction with consumers, then don’t hesitate to do so.

  1. Use Unstructured Data

Predictive analytics may not be as reliable if you only use a single data source. Not only do you need multiple data sets but they must also be of high-quality and specific towards the trend you’re trying to determine.

Polling results from political elections illustrate this point. Sometimes, the voting results swing one way when the polls suggest it would swing in the other direction. This is often attributed to poor data. In this instance, data may be acquired disproportionately from a single demographic that is more likely to lean one way. In addition, those surveyed may say they’ll vote one way due to fear of backlash for support of the other candidate.

This is the shortcoming of structured data. Numbers alone don’t always tell the whole story. Modern BI tools nowadays can gather unstructured data from sources like email and social media that reveal a more accurate prediction of what is really in the minds of consumers.

  1. Be Cautious of “Vanity Metrics”

There is a colloquial jargon in the BI industry that has been termed “vanity metrics.” What is this? It’s basically any metrics that don’t contribute to the information you’re trying to acquire. The number of Facebook likes on a post is a good example of a vanity metric. Sure, it makes you feel good to get a lot of thumbs up, but the popularity of a single social media post probably doesn’t tell you much of anything if your goal is to increase, say, the sign-up rate of a new rewards program.

This is why metrics need to be specific and directly related to your goal. In other words, if you’re trying to increase a product site’s click-through rate, then it’s probably a wasted effort to examine the metrics of a campaign hashtag use from an unrelated product.

Predictive modeling doesn’t have to be complex, but you do have to go beyond the premise of simple and broad data sets. In the day and age when big data is everywhere, it only makes sense to put the data to good use.

Lucy Boyle (@BoyleLucy2), is a full-time mother, blogger and freelance business consultant, interested in finance, business, home gardening and mental health.

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