# The Democratization of Data and Growth of Marketing Analytics

- Link: https://chatterblast.com/blog/the-democratization-of-data-and-growth-of-marketing-analytics/
- Published: 2021-11-09T18:51:28-05:00
- Author: Joe Mineo

_Advertising and analytics have changed drastically in the past few years, especially
in 2020. This series, “The State of Advertising and Analytics,” explores the evolution
of media, advertising and privacy, the rapid growth of data analytics in marketing,
and the future of marketing and analytics, providing useful tips and value along
the way. Read the [first](https://chatterblast.com/the-evolution-of-media-during-the-covid-19-pandemic/)
and [second](https://chatterblast.com/the-future-of-privacy-in-digital-advertising/)
chapters. _

## ***

Ten years ago, **marketing analytics** were a “nice-to-have” that no one understood
except tech nerds, and trying to get buy-in from stakeholders was nearly impossible.“
We’ve always done it this way,” or “TV/radio/out-of-home has always worked, why 
change?” were the mantras. Cue the mobile revolution, and suddenly everyone has 
woken up to a world where if you’re not learning from your web analytics, you’re
falling behind.

In the third blog of this series, we’re going to take a look at the three stages
of data analytics, how data collection has changed over time, and how companies 
can win by leveraging marketing analytics.

## You’ll shoot your eye out, kid!

Marketing analytics are nothing new. **Nielsen**, a company hell-bent on going [down with the ship](https://deadline.com/2021/09/nielsen-accreditation-national-ratings-yanked-media-rating-council-latest-twist-measurement-drama-1234825622/),
has been analyzing and measuring audiences for over 30 years, providing well-paying
marketing teams with insights across demographics, industries and geographies. Although
most of us weren’t around for it, who can forget good old pre-television radio, 
where marketing messages were woven into serial storylines that led people to buy
all sorts of products, eventually having them mail-in proof of purchase? 

In the cult classic movie _A Christmas Story_, **Ovaltine** asked people to buy 
the product, send back proof, and in exchange, Ovaltine mailed back a prize. The
marketing people at Ovaltine would compile these addresses and bombard homes with
more marketing, knowing those homes had persuadable kids ready to buy more products.

##### _“A Christmas Story,” or should I say, “A Product Placement Story,” was a prime example of analog marketing analytics.⏎  ⏎ Smart brands got that kid hooked on **everything.**

This phase in what we’ll call the “data journey” is called **descriptive analytics**,
which tells someone what happened through the information they’ve gathered. It’s
often good enough to help move the sales revenue needle, and it’s pretty much the
only form of analytics that existed before computers became popular.

Computers have been around for fifty years, so why now, and why is it important?
Well, everything has changed. Data isn’t held by big companies like Nielsen anymore.**
Facebook**, **Google**, heck, even the average college student is collecting data
on spreadsheets and generating insights to help improve their test scores or athletic
abilities. 

Data has become democratized, and the gatekeepers have been removed. People don’t
need a master’s degree in computer science to build charts in order to understand
how ads are performing, and people certainly don’t need to shell out cash to big-
name media conglomerates for long commitments to place and run an effective marketing
campaign. 

Time is important. Marketing dollars are important. Efficiency is important, and
all three of these concepts can finally converge now that marketing data is free
and accessible to everyone.

## Marketing data old enough to buy itself a drink

Let’s talk about the rapid maturation of data in the short time it’s been free.

As mentioned previously, measurement and data centered around volume and [vanity](https://chatterblast.com/the-great-vanity-debate-does-audience-size-matter/)
in the past, and casting a wide net. The “buckshot” approach, hoping one ad would
eventually hit, worked for a long time, but it hooked marketers onto vanity metrics
to help them pat themselves on the back when things weren’t going well. Reaching
a million households means nothing if you can’t drive consistent revenue — just 
ask **[pets.com](https://en.wikipedia.org/wiki/Pets.com)** and their darling sock
puppet.

##### _Sure, Super Bowl ads reach a lot of people, but can you get those people to buy your stuff?_

Enter conversion-based marketing data. Marketers went from hoping people would go
to their websites and logging visitor counts at the bottom, to tracking clicks through
digital ads and measuring click-through rates, to tracking “thank you” page views
and learning exactly how many people purchased goods, all in a matter of 10 years.

The terms “multi-touch attribution” (MTA) and “media mix modeling” (MMM) sprung 
up seemingly overnight, as marketers began to have enough data to evaluate every
touchpoint and channel along the consumer journey. There are now loads of different
ways to evaluate the success of a campaign. If a company is only looking at impressions
or reach, it’s like they are only eating the icing on a rich, decadent cake filled
with their favorite ingredients.

Most companies stop here. They’ve set up their audience size, impressions, clicks,“
thank you” pages, and mix of channels. They’re seeing page views, getting results,
and they can sleep well at night knowing that for every dollar they put in, they’re
getting a certain amount back. 

However, with the rapid maturation of data also comes the rapid escalation of the
amount of data available. **[Moore’s Law](https://www.intel.com/content/www/us/en/silicon-innovations/moores-law-technology.html)**—
a 1965 prediction that computing power, information generation, and data would grow
exponentially — has led to massive leaps. More data means more analysis, and more
analysis leads to whole operations built around that data. Those operations generate
more data, and ultimately, sales revenue for companies. The cake we mentioned before?
Now it has data layers.

## A three-tiered data layer cake

In order to put all that data to good use, marketers can now integrate their favorite
marketing platforms with data connectors (like **[Supermetrics](http://supermetrics.com)**,**
[FiveTran](http://fivetran.com)**, and **[Airbyte](http://airbyte.io)**), store 
that data in repositories called data warehouses (like **[BigQuery](http://cloud.google.com/bigquery)**,**
[RedShift](http://aws.amazon.com/redshift)** and **[PostgreSQL](http://postgresql.org)**)
or data lakes (like **[Azure](http://azure.microsoft.com/en-us/solutions/data-lake/)**,**
[Snowflake](http://snowflake.com)** and **[Delta Lake](http://delta.io)**), and 
create machine learning models, training them to help reach goals with tools like**
[TensorFlow](http://tensorflow.org)**, **[SageMaker](http://aws.amazon.com/sagemaker)**
and **[MindsDB](http://mindsdb.com)**. 

There’s a platform for every problem, including open-source companies dedicated 
to helping those with lots of patience build a marketing data stack of their own.**
Predictive analytics**, which makes use of these data pipelines to make predictions
based on what’s already happened, is the next progression in a company’s data journey.

Some applications might involve identifying which users prefer to watch videos compared
to users who like still images, which can help make creative decisions for campaigns.
Models can also be trained to predict behavior, like how many conversions a campaign
might see in a given month, or how many it would get with an additional $100.

##### _Your business could be like 󠀁[Paul the Psychic Octopus](https://knowyourmeme.com/memes/paul-the-octopus)󠁿, hinging on loads of concrete data, which is way more than just tasty mussels._

The final step in the data journey is **prescriptive analytics**, where your data
tells you what you need to do to achieve a certain result, based on previous descriptive
data and assumptions about predictive data. At the moment, this is where the industry
as a whole has paused. 

Humans are still the best prescriptors, which is why we go to doctors and don’t 
trust WebMD to diagnose our issues. As multitudes of open-source projects move the
data analytics industry forward, the next decade is sure to usher in marketers and
advertisers leveraging reliable prescriptive analytics in their decision-making 
processes — and doing so without breaking the bank. 

To bring you back from scary, confusing, data-speak land, here’s the final word:
Patience. 

Rome wasn’t built in a day, and the behemoths we know as [FAANG](https://www.investopedia.com/terms/f/faang-stocks.asp)
didn’t come to be overnight. Whether you’re just starting out or have been in business
for decades, start building a descriptive analytics practice by gathering as much
data as possible and applying some math formulas to supercharge your findings. 

Predictive analytics will come with time, and your prescription for more conversions
isn’t too far off in the future. To fill that gap, agencies like ours are always
innovating and iterating to help clients progress in the journey, and we’re always
just a few taps or clicks away to [get the ball rolling](https://chatterblast.com/contact).