INBOX INSIGHTS, January 5, 2022: Disadvantages of Predictive Analytics, SEO A/B Testing

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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.

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.

The disadvantage of predictive analytics 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 this disadvantage that is throwing off our analysis?

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.

Despite the pandemic, humans are creatures of habit. We still make New Year’s resolutions, we still celebrate holidays, 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!

– Katie Robbert, CEO

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Data Diaries - Interesting Data We Found

In this week’s Data Diaries, we want to know the impact of an SEO change we made. Back in November, we switched out the Yoast SEO plugin with a different plugin, the RankMath plugin. The challenge with this sort of marketing technology change is that you can’t do any kind of A/B testing – you can’t show a different website to Google and other search engines part of the time and show a differently-optimized version other times.

So, how do we evaluate whether a piece of software is working or not when we can’t do a controlled test? We have to rely on statistical methods that simulate a retroactive A/B test. Here’s how they work, roughly.

We have a series of days called treatment days – days when our change is in effect, like every day since November 29, 2021 when we installed and deployed the new software.

We also have a series of days called control days – days when the change was not in effect, which was every day before November 29, 2021.

We gather up our data for all those days; in this case, we’ll use data from Google Search Console.

When we use statistical software like the R programming language to perform a technique called propensity score matching, what the software does is look at our treatment days and try to match them as closely as possible with similar control days. For example, we’d want the software to compare similar days of the week; there’s no sense in comparing a Sunday to a Tuesday.

This technique also allows us to account for other things going on, which is the most critical part. For example, if we were running Twitter ads during our treatment period, we’d want to compare the treatment period to control period days when we were also running Twitter ads.

With all that in mind, what does it look like when we examine our analysis of our Google Search Console data with the new WordPress plugin versus the old one?

Propensity Score Matches

What we see here are a series of outcomes; the second column shows us the mean (average) for days of our treatment, while the third column shows us our control days. The fourth column shows us the differences. Let’s run through them:

  • For days using the new plugin, we see a 16% increase in the number of search impressions. This is really important – impressions means Google sees our site as relevant to what searchers were looking for and displayed us in the search results.
  • We also see a 10% increase in overall clicks on our search results. Again, good news.
  • We see no difference in the distance metric – this is a statistical measure to show us how different our control days are from our treatment days. If it were a really big number, the test might not be statistically valid.
  • We see no difference in clickthrough rates; this makes logical sense because even though our clicks increased, so did our impressions.
  • We see no difference in our ranking positions. This is more of a function of having relevant content than it is search optimization, something that a change in marketing technology would not impact substantially.
  • Finally, we see no difference in the numerical assignment for day of week, nor should we.

This analysis tells us that there is a meaningful difference for using the new plugin over the old one. With more than a month of data under our belts, I feel confident that the change is worth keeping.

What’s the key takeaway, the So What? moment? It’s not which plugin to use – it’s that it is possible in some circumstances to conduct an A/B test retroactively if you have the right data and the right tools. Study this method and apply it to your marketing (or ask us to do it for you, I guess).

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