“Not everything that counts can be counted, and not everything that can be counted counts.”
– Albert Einstein

Many marketers/analysts draw conclusions based on the data and tools available to them. Now how I am so sure about that? Because I often see them saying something like: “do you have data to back up this claim?”

While it is not bad to be data-driven, it is worse to be data-blind.

Data-blindness occurs when someone can’t see anything beyond the data available to him/her.

When you take all of your business and marketing decisions based on the data and tools available to you, you are doing so with the belief that they provide complete insight.

Here is the bummer:

All of the data and tools available to you stop working outside the digital realm.

For example, a speaking gig in an industry conference can generate a lot of leads for your business (in the short term or long term) but this is something which you can’t measure through your analytics tools. You don’t have any data to back up this claim.

But just because you don’t have any data to back up the effectiveness of the speaking gig, it doesn’t mean that it is a useless marketing activity. The same goes for social media campaigns.

The majority of analytics tools are good only in measuring the short term impact of a marketing activity i.e. someone visited the website from social media and made the purchase within 30 to 90 days. They fall flat on their face when it comes to measuring the long term impact.

For example, let’s say your analytics software is constantly telling you that Facebook campaigns are not generating any sales or they are not assisting in any conversions so you should stop the campaign and move on. But what if that Facebook campaign is generating sales and is assisting in conversions in the form of direct traffic.

If you blindly rely on the data and tools available to you, you will not be able to see the complete picture. You will most likely remain busy looking at short term picture instead of focusing on long term results.

We have this tendency to measure what is easy to measure and ignore all other factors which can’t be measured and which still impact the business bottomline. We have this tendency to blindly rely on one or two metrics like conversion rate and statistical significance instead of looking at the big picture to define success and failure.

For example, It is not that hard to increase your website conversion rate in a few seconds. All you have to do is just pause all of your paid marketing campaigns. But will that generate more sales for your business?… No.

What that will generate more sales is an increase in average order value and/or ecommerce transactions.

Similarly we have the tendency to declare success and failure based on statistical significance results alone. But just because a result is statistically significant, doesn’t always mean that it is practically meaningful.

Statistical significance will only tell you whether something works or not. But it is not going to tell you how well it works in a range of contexts’

One of the biggest drawbacks of statistical significance is that it conflates effect size and sample size. Because of this it can’t accurately quantify the size of the difference between two groups (i.e. experimental and control groups).

For that, you need to calculate and rely on the effect size (or size of the effect)

Because of all these implications, whatever you do under conversion optimization must dramatically increase conversion volume (sales, leads). An increase in conversion rate is secondary and should not be your main aim.

This is yet another reason why I never say or do conversion rate optimization. We are not here to optimize the conversion rate. We are here to increase sales and decrease acquisition costs.

Related Post: 2 Powerful Reasons you should STOP doing CRO Right NOW

Not knowing how to use data/metrics effectively is also a type of data-blindness. 

When data is not used intelligently, it can result in making bad decisions with high confidence.

Needless to say, a great understanding of business triumphs over every other metric known to mankind, including your favourite conversion rate. This great understanding is what separates data-driven marketers from data-smart marketers.

Once you are data-smart, you would automatically know what data needs to be tracked, and when, what to look at, what should be overlooked and where to look at in any analytics reports. You will get a clear sense of what your analytics tools and KPIs cannot do as what they can and where you should trade-off.

Other articles on Maths and Stats in Web Analytics

• Beginners Guide to Maths and Stats behind Web Analytics
• How to Analyze and Report above AVERAGE
• What Matters more: Conversion Volume or Conversion Rate – Case Study
• The little known details about hypothesis in conversion optimization
• Is your conversion Rate Statistically Significant?
• Calculated Metrics in Google Analytics – Complete Guide
• Here is Why Conversion Volume Optimization is better than CRO
• Bare Minimum Statistics for Web Analytics
• Understanding A/B Testing Statistics to get REAL Lift in Conversions
• 10 Techniques to Migrate from Data-Driven to Data-Smart Marketing
• The Guaranteed way to Sell Conversion Optimization to your Client
• SEO ROI Analysis – How to do ROI calculations for SEO