Predictive analytics can be a powerful tool to plan your marketing and other tactics in your business. But what are the disadvantages of predictive analytics? In 2018, back when I was fresh-faced, full of energy, and had an endless well of optimism, I wrote this blog about the issues with predictive analytics.
Now, 3+ years later, when I have permanent circles under my eyes, I never know what day it is, and an ounce of optimism sounds sarcastic, I want to revisit this post to see what’s changed. I want to dig into the disadvantages of predictive analytics.
In 2018, I talked about Black Swan Events. Oh, how little 2018 Katie knew. She did not know that in 18 short months she would be in a global pandemic. Therein lies the definition of a Black Swan Event – it’s something unforeseen.
One of the disadvantages of predictive analytics for marketers almost two years into a global pandemic is that most historical data that pre-dates March 2020 is irrelevant. So much has changed about how we live our lives, how supply and demand operate, and how we view the world, that using that data won’t give you a sense of how to plan your marketing.
How do we overcome the disadvantages of predictive analytics in marketing? How do we set ourselves up for success when using predictive analytics?
Look at more than one data source
Sure, you can look at only your data. However, it might not tell you the full picture of what is going on. You may also consider looking at other publicly available data sources as a gut check. Looking at outside data sources can help color the story of what’s going on within your business. Try thinking beyond the digital channels. Here are some examples:
Data.gov is the US Governments open data. You can do research on topics such as local government and climate issues.
Bureau of Labor Statistics can keep you updated on the job market. If unemployment rates go up, there are certain products and services that will cease to be viewed as necessary.
Federal Reserve Economic Data (FRED) can help you understand economic factors that can help predict consumer behavior.
Forecast shorter periods of time
Given that just about every dataset either spiked or declined in March of 2020, consider shorter time periods for your predictions so that you can remain agile. I would not recommend using a predictive forecast for an entire year’s worth of planning. You can use it as a guide, but know that the data will likely change. A better plan is to look at quarterly, monthly, or even weekly trends if you have the resources to do so. Doing this will allow you to make changes faster as you learn what’s working.
Some trends don’t change
Despite the pandemic, humans are creatures of habit. We still make New Year’s resolutions, we still celebrate holidays, and we still take summer vacations. What’s changed is how we approach these things. As you’re looking at your keywords and forecasting, be sure to include qualifiers such as “virtual”, “remote” and “alternatives”.
Are you looking for some help with your planning and want to see how a predictive forecast fits into your marketing? Give us a shout!
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