Why is so difficult to predict the future?

Published on — Written by Wonderflow

why is it so hard to predict the future - wonderflow

Predict the future, in many different fields, might be a hard task. If you play with stocks, for example, you might have realized that. Even though economists give you recommendations about best companies to invest, a single video that turns viral from a customer complaining about an experience might affect predictions, lowering prices of these stocks.

In the Customer Feedback Management field, the same thing happens. Even though we might have some control over the internal environment, external events might affect positively or negatively in ways that are hard to predict. Go Daddy, for example, might not be able to predict that one bad service would result in this video from our blog, in which hundreds of people will now have a different perspective about this company.

This is one of the reasons explained by our CEO Riccardo Osti in this video, to answer the question: Why is so difficult to predict the future?

If you like the content, feel free to subscribe to his channel here.

When talking to my clients I often get asked: “does your software have predictive capabilities?”. Then I go online and I see all kinds of tools selling “prediction” as you would sell ham or cheese. Imagine the data scientist asking you “I have cut two hundred extra grams of predictions….should I leave it or I take it away?”

Mmm….no, I would say that this is for sure an interesting HOT topic, but there is still a lot to do from an education standpoint…that’s why I decided to put this short video together.

Please let me know if you like it in the comments, and I can make some more recordings on this topic!

There are two basic kinds of predictions:

intuitive predictions, which rely on experience and intuition, and statistical predictions, which instead rely on data and algorithms.

Today we are talking about the second ones, which are obviously more interesting from a business perspective. The goal is to gain enough evidence, both from a qualitative and quantitative perspective, in order to make decisions and drive investments in the near future.

What I am saying is that predictive technologies usually read and interpret past events trying to understand how things will develop in the future.

There are three main factors that we need to consider to evaluate the reliability of any prediction:

  • One, the quantity of historical data.
  • Two, quality of historical data and
  • Three, the influence of external events
Quantity is key to predict the future.

Most of the predictive technologies are based on statistics and are nothing more than advanced regression analysis. This implies that if you have more data you have a more solid backbone to start with. In some categories, it is easier to collect a large amount of data:   you can think of weather forecasts for example. In some other categories, such as text analysis, the availability of the data is much more limited

Quality is also a key aspect.

The reliability and the accuracy of the dataset that we use to start the prediction is just too important. Let me give you an example. Let’s say that you are a product owner at Samsung and your goal is to improve your products to make sure customers like them better and sell more. You may use text analysis to understand what customers dislike and identify the problems of these products. You could then try to predict how much the satisfaction would increase if you could solve one of those problems.  In this case, in order to make a realistic prediction, you should be able to start with a dataset that has been correctly analyzed.

External events are the deal breaker.

The biggest barrier to generate an accurate prediction comes from the outside. In fact, all the events that are not directly influenced or controlled by us are much more difficult to predict. We have made the example of the product owner, who tries to predict how much he could increase customer satisfaction by improving one element of its product. What would happen if a competitor launches a product with better performances at a lower price point? Well, most probably the prediction would be completely invalidated. And the problem is that predicting these type of external events is extremely difficult, but common at the same time.

So… what’s the secret recipe for good prediction?

Use a lot of historical data, make sure the quality of the analyzed data is high, and most importantly…keep in mind that external events are hard to predict but extremely dangerous

I hope you liked this video. If so, please subscribe to my channel! If you want to see me talking more in-depth about this topic just let me know in the comments.

About Wonderflow

Wonderflow empowers businesses with quick and impactful decision-making because it helps automate and deliver in-depth consumer and competitor insights. All within one place, results are simplified for professionals across any high-UGC organization, and department to access, understand, and share easily. Compared to hiring more analysts, Wonderflow’s AI eliminates the need for human-led setup and analysis, resulting in thousands of structured and unstructured reviews analyzed within a matter of weeks and with up to 50% or more accurate data. The system sources relevant private and public consumer feedback from over 200 channels, including emails, forums, call center logs, chat rooms, social media, and e-commerce. What’s most unique is that its AI is the first ever to help recommend personalized business actions and predict the impact of those actions on key outcomes. Wonderflow is leveraged by high-grade customers like Philips, DHL, Beko, Lavazza, Colgate-Palmolive, GSK, Delonghi, and more.

Start making winning decisions based on customer feedback todayGet a free demo

Other articles you might like:

wonderflow image

Product Development · Sep 12, 2022

Proper Review Analysis: How to Analyze Customer Reviews (+ Examples)

It’s pretty difficult to imagine a world without reviews. Not many of us walk into a shop and make a purchase without browsing at least a handful of online reviews beforehand.  In fact, a study from TrustPilot found that almost 9 out of 10 customers consult online reviews before buying.  Though these reviews are incredibly helpful to consumers, for business owners, trying to use reviews to guide the product development process or improve the customer…

utility cx

Utility & Energy · Aug 09, 2022

More Than Another Bill: Why VoC Programs Improve Utility CX

When did your utility provider last ask you to rate your customer experience (CX) with them? With rising competition, energy costs, customer demands, and regulatory and stakeholder pressure to improve customer services, these key factors drive service providers of water, electricity, and gas to engage deeply with their customers. Old traditional business models must be changed to adapt to the times, and utility brands must prioritize CX more than ever before.  The Utility Customer Experience …

Ron Jacobs Philips Brand Licensing

Interview · Aug 03, 2022

Customer-Centricity in Consumer Electronics: VoC Interview with Ron Jacobs, Philips Brand Licensing product professional

As part of our Voice of the Customer (VoC) interview series, Stefano Pecorari, Client Success Director at Wonderflow, welcomes Ron Jacobs, Brand Licensing product professional at Philips. Philips is a Dutch multinational founded in 1891 as a lighting company and later expanded to medical equipment, consumer electronics, and domestic appliances. The company is headquartered in Eindhoven. Later, it moved to Amsterdam in 1997. Nowadays, Philips is focused mainly on the health tech continuum. Its mission is…