3 reason why elections are hard to predict for humans and machines

3 Reasons Why Elections Are Hard to Predict for Humans and Machines

What if a model said no one will win the election? That model would be rejected immediately, but what if it wasn’t wrong?  What if the learning picked up a signal from historic data that knew the election would be contested and no one wins?  That’s not an unprecedented outcome when a “hanging chad” is a known feature. Perhaps the model predicts the US courts will decide. Plausible. Probably unlikely.  Someone always wins eventually so what’s the ground truth – was the model right or wrong? 

Perhaps the wrong question was asked; are election outcomes really binomial?  Inherently with more than two political parties running, the results aren’t binary- they win or not. Elections can be  truly a multinomial classification; however, we rarely treat elections as multinomial, but rather usually binomial.  Right Mr. Nader?  Right Mrs. Stein?  Thousands of Markov chain simulations are cool on a random walk to the polls but there’s always the drunk leaning on the lamppost. 

What causes bad predictions and why is it difficult for not only humans but machines to predict the outcome?  So many reasons beyond the scope of this blogivation, which we will tell you in Squark Symmetry, but here are a few to get started:

  • Bad data.  People lie.  Citizens do it to pollsters who inform politicians who lie to citizens about the results; it’s an unvirtous cycle, for sure.  A strong empirical indicator that the current US President will win again is when you ask Americans who they will vote for they say Biden, but when you ask the same American who their neighbors will vote for, they say Trump.  This of course varies by geography, but it’s the same trend seen in 2016. 

    I’m told people are cautious to announce their support for Trump or Biden, and don’t want to disclose that they align with a particular politician.  The reasons are as myriad as snowflakes.  With the models ONLY being as good as the data used, predicting election outcomes – within unprecedented times with viral externalities and the potential for foreign interference  –  means new features harder to engineer.  Feature named “Pre-Covid” yes or no may become the “new normal” column for machine learning. 

  • Skewed data.  It is well known candidates increase their positive polling trends directly after a political convention. Post-convention bounces regress to the mean (or to the moon) but still hold perhaps some predictive power.  But are these candidate bounces just dead cat bounces? When trying to predict November are post-convention polling increases mostly meaningless earlier in the year?  Seems that way. The polling data can be skewed by bias around the convention based on who was sampled in the data frame. Coverage error, selection bias, and so on.

  • Overfitting. Which means the results of the analysis match too closely to the data the machine trained on, and thus fails to fit new data in a reliable way.  It’s a problem in prediction, and it’s not always easy to work around.  One way to do it is to early stop models when they begin to degrade and get erroneous. Other methods exist too, but these are complex math that aren’t always obvious or even known to data people. It helps when someone has written the code to do this work for you, like Squark.

There are so many other reasons why election probabilities are just that, probabilities, that may or may not happen.  People fail to clean data; they overclean data, they don’t use enough observations.  Sometimes analysts, despite best intentions, just don’t know what they don’t know. Recently on LinkedIn I saw some “expert” with an agency produce an inflammatory Covid related correlation analysis, and the guy didn’t even make the data stationary.  Doh!

The cool thing about Squark is we know this and a lot more too.  When we invented no code predictive analytics, we created a powerful entire automation, with deep tech sub-automations built it, that help reduce and work around the programmatically solvable issues above and many more. 

We started with a vision to democratize prediction for business users by putting an advanced AI capability into their hands, without coding.  We’ve done it now, and we want it to be affordable and powerful, so everyone can use it, even politicians. That is, if we would actually sell it to them.  But like Jack Nicholas said in that Tom Cruise movie, perhaps they “can’t handle the truth!” 

Please don’t forget to vote wherever you are, whenever you can!

Predictive analytics helps to drive results in uncertain market conditions

Predictive analytics helps drive results in uncertain market conditions

Nowadays, marketers are having to deal with extreme uncertainty due to the impact that Covid-19 has had on the majority of industries. Strategies are reevaluated and plans are pivoted over and over again. With the majority of companies’ marketing efforts now being digital, marketers are facing a huge influx of data collection across multiple channels.

In times of uncertainty such as these, scenario planning is critical to combat the uncertainties of the future. The most crucial ingredient for effectively planning scenarios is knowing how to use your data. When used correctly, data can offer a doorway to current trends, it can help avoid costly errors in decision making, and aids in charting a way forward. The obstacle most companies are facing is that data can be challenging to collect and maintain (we’ve all seen the CRM with dirty data). It’s usually with the help of technical experts such as data scientists, that companies are able to extract insights from their data and create accurate predictions. 

If you don’t have a team of data scientists, not to worry, because no code predictive analytics is bridging that gap and helping marketers understand their data to help craft and design winnings strategies during these uncertain times. 

Here are 3 ways no-code predictive analytics is navigating the ‘COVID’ era:

1. Responding to Ever-Changing Customer Preferences

Marketing departments are seeing major changes in consumer behavior. Changes in the way consumers interact with brands, changes in preferences and purchasing habits and so on. Some of these changes in behavior could be temporary due to current circumstances. Others, however, may reveal themselves to be long-lasting. 

As a result of these constant changes in customer behavior, our data is also changing and adapting. Predictive models created just a few months ago, are no longer accurate and have to be updated manually by data science teams. Updating models could take months which could lead to a never-ending cycle of outdated insights. No code predictive analytics is solving this problem by allowing marketers to generate the most advanced models and predictions in a matter of minutes. This gives teams the advantage of predicting data as fast as it changes. Ultimately, meeting our consumer’s preferences and building loyalty in the nick of time.

2. Allowing for Personalization at each Touchpoint

The customer’s journey has changed due to this current pandemic. It’s become important to meet customers’ new expectations and ways of interactions. However, touch-points throughout the journey still remain to be an opportunity to engage with the customer and align resources more quickly. In order to reach potential customers at various touch-points, marketers will need to use the influx of new data to better personalize messages and offers. By adding predictive analytics, marketers can transform data into insights to essentially personalize services, offers, and products to their audience, outmatching the competition. This helps guide customer interactions throughout the buyer’s journey and lead to more conversions.  

3. Efficient Budgeting

No code predictive analytics is not only useful when trying to predict a future outcome such as, “when is my customer going to buy from me next?” or “which service is my client most likely to buy?” No-code predictive analytics software such as Squark will tell you why these outcomes will occur. By incorporating explainable artificial intelligence (XAI), Squark is going to tell you what the main drivers are for the predictions. This power of explainability can help guide some data-backed attribution modeling. AI decisioning becomes transparent for business users.

For example, you could ask, are more people interacting with our boosted social media posts or should we focus on webinars to help drive sales? If more people in the sales process have been associated with certain programs along their buying journey than using predictive analytics can uncover which would produce the most sales. Knowing how to attribute marketing efforts, budget expenditure can be allocated more wisely to channels that can generate a better return. As you prepare for the next quarter, forecasting necessary budgets has never been easier than doing so with predictive analytics.

These are only a few ways in which no code predictive analytics is helping companies navigate and drive results, even during these uncertain times. Marketers and business leaders can take advantage of the new digital space we are entering by being early adopters. Data doesn’t need to intimidate you and you don’t need an army of data scientists. Early adopters of no code predictive analytics will pave the way and have the advantage over competitors by constantly driving results. Read more about how Squark is helping marketers succeed.

loyalty and customer marketers use predictive analytics

3 Ways Loyalty & Customer Marketers Use Predictive Analytics to Stay Ahead

With everything going on in the world today, businesses find themselves in new and uncharted territory.  Given what industry your business is in, some loyalty and customer marketers have had a very challenging year trying to forecast outcomes and keep retention numbers high despite so many external factors impacting projections. As a result loyalty and customer marketers have taken an interest in predictive analytics to help them address some of their challenges by using their data to craft forward-looking offers, programs, and forecasts.

Relying solely on historical data to predict retention, any upsell or cross sell opportunities, or even your churn number may feel impossible right now.  Luckily, emerging software companies are making these heavy workloads feel a bit lighter.  No code predictive analytic software was invented by Squark and has become a popular tool the second half of this year for those exact reasons.  Your historic data doesn’t currently help you plan because this past year may look like a bunch of scribbles on paper that was crafted by a three year old, lots of ups and downs.

So how do you stay ahead? How do you know which customers will respond to your offers, how are you predicting that outcome?  How do you currently score for loyalty, is that model even accurate right now?  Lastly, the number that we all check daily – churn.  Do you have any data-backed churn indicators?  Or do you know when your team should be reaching to at risk customers before they send that “I’m sorry but…” email to their customer success rep?

Below are only three of the many ways loyalty and customer marketers are applying no code predictive analytics to their day-to-day and planning routines to stay ahead of this rapidly changing business environment.

  1. Identifying which customers will respond
    The simplest way to understand a future customer outcome is to classify them based on likelihoods to do something, like download a white paper or attend a virtual event.  Predictive software such as Squark, can predict any event for which you have data on. This allows you to plan a bit smarter based upon the predictive data outcomes.  You’ll be able to hopefully increase conversions and have more confidence in obtaining your program goals.
  2. Scoring Customer Loyalty
    You may already generate or create a unique customer loyalty score.  Whether using rules in a system like your marketing automation or CRM, or even your own mechanisms and logic.  In that case, Squark can take your scoring model to the next level with predicting drivers and forecasting future loyalty.  Using complex scoring systems from customer lead score, NPS, or a combination of a few systems you can confidently predict who is likely to be retained and what offers will keep them converting.
  3. Predicting Churn
    With historic data about which customers have lapsed or stopped entirely being customers, it is possible to score exactly who will churn and why.  Armed with this knowledge loyalty and customer marketers can ensure at-risk customers are addressed sooner and quickly to mitigate revenue loss.  Knowing who may be at risk you can craft a tailored program just for those accounts to make sure you stay top of mind and open dialogue.  On the other hand, it’s nice to have that information so that you can actively pull them out of other campaigns that may not be suitable for their account at that time.

There are many other use cases where no code predictive analytics can support loyalty and customer marketers.  However, these three examples are being used more and more so that it can take the guessing out of planning and make the most out of every marketer’s time.

With shifting strategies and new priorities, it can be a lot to support and very time consuming. Take advantage of no code analytics to reclaim lost time due to data prep and cleaning, analytics, and projection planning.  Be confident in your programs’ success.  And lastly, software such as this allows you to create programs ahead of the curve so that you’re offering items or services that your customer is likely interested in, creating a happier and healthier client base (and hopefully NPS).

Predictive Analytics Powers Customer Loyalty

3 Ways No Code Predictive Analytics Powers Customer Loyalty

Customer loyalty is where long-term value is created. No code predictive analytics gives marketers a easy to use way to predict what their customers will do next, without coding.