Albert Einstein once said “information is not knowledge” and data without context is just organized information.
In essence, data is just people doing stuff.
The true value of data is far beyond obsessions with key performance metrics.
For most businesses, it’s about extracting insights to create value that has the potential to drive innovation to improve products and services.
In fact, more companies are shifting their focus from traditional business intelligence (BI) to predictive analytics – using historical data to predict future events.
Understand the World of Big Data
To put things in perspective, according to IBM “we create 2.5 quintillion bytes of data – so much that 90% of the data in the world today has been created in the last 2 years alone.”
There is so much data coming in at such a high velocity in all types of complexity that this phenomenal we called big data is now a problem for most businesses.
In fact, there are so many challenges in dealing with big data that it’s often hard to process let alone understand.
This is especially true for any business that engages with digital advertising or online marketing.
This is why it’s crucial to keep business goals in mind along with all the online marketing strategies. As Nassim Taleb, the author of the book Antifragile, stated, “We’re more fooled by noise than ever before, and it’s because of a nasty phenomenon called ‘big data.’ With big data, researchers have taken cherry-picking to a new level. Modern times present too many variables but not enough data for each one. As a result, false connections grow much faster than true information. In short: Big data may provide more information, but it also brings more false information.”
It’s meaningless if we have the means to analyze the data but the data is wrong to start with.
And of course we also need reliable data which is exactly why Samuel Arbesman, the author of The Half-Life of Facts, encourages us to start thinking about long data.
The point is that whether you’re doing marketing or product development, we need reliable data to help us make better decisions.
How to Get Into Big Data Analytics in Online Marketing
Just like you wouldn’t expect a musician to compose a song without a tune, or a restaurant to open without a menu, you can’t expect to develop a strategy or execute a tactic using data without knowing what you want to achieve.
This is at the core of any data-driven performance marketing – makes decision based on analysis to prove or disprove hypothesis.
It’s about running tests, collecting data, analyzing results to find the story the data seeks to tell.
If we’re going to become better in performance marketing, we also need better tools and processes transform big data into smart data.
Here are 7 ways you can get into big data analytics.
1. Focus on Business Objectives
Don’t collect data because you can, collect data because it’s necessary. Identify the core problems that have to do with meeting business objectives.
Speak the right language to the right people as different stakeholders in business have different goals that they focus on.
If you’re focusing on impressions, clicks, CTRs, and CR, and the person you’re dealing with only cares about ROI, CPL, and CPA you’re going to have a hard time communicating your value.
Learn to translate your data into terms that’s tailored for your audience.
2. Understand Business Infrastructure
Realize that you will need to understand technical infrastructure such as web hosting, data warehousing, and how data flows in and out of business infrastructure.
In addition, recognize that every business utilizes a variety of applications behind the technical infrastructure.
So make sure that you have some basic knowledge of how each of those applications work and what other tools are available to help you integrate more useful t data.
3. Take the Data Science Approach
You need a multitude of skills to stay at the top of your game, but most importantly you need to become a data scientist. This means investing in learning more about statics, analysis, experimentation, and data visualization.
These skill sets are now in high demand as big data proliferates.
Data scientist is about performance marketing, you need to be the one leading the charge in research and delivery of business intelligence.
Ensure your data integrity will be tremendous for segmentation and optimization.
4. Integrate the Entire Conversion Journey
In the search engine marketing world, a conversion means either a sale or a lead. KPIs such as CPL (cost-per-lead), CPO (cost-per-sale), AOV (average order value), or even ROI are typically what SEMs deliver on the frontend.
However; few SEMs talks about lifetime value or backend conversion metrics that enables you to get a clear picture on the full conversion funnel.
For example: if your frontend click-to-lead CR (conversion rate) is 10% at a $30 CPA (cost-per-acquisition) but your backend lead-to-sale CR is 20%, your actual click-to-sale CR is actually just 2% which means your CPA is actually $150.
All businesses want to know their true return on their marketing dollars; this is why if you don’t have the backend data integration, the frontend data can be very misleading.
And if you have the right data integration, you can proceed to optimize towards the most important KPI, which often times is NOT the frontend metrics.
This applies to offline data as well since TV, radio, print, or even billboards can drive traffic to your website, it’s important to take those media cost into consideration. And don’t forget about other cost of sales attributes such as call center or cost from other channels.
5. Leverage Web Analytics
Web analytics is a great place to start your data journey. It tells you where people came from, where they clicked, how long they stayed, what pages were visited, and a whole lot more.
Web analytics puts context to your visitors to your site by adding behavioral data that reveals intent. Someone that searched on a branded term will most likely act differently than those that did not. The same applies to the length of the query.
In fact, even Google uses real human raters in addition to its algorithm to rate content because real human experience is what Google’s search engine tries to mimic.
6. Tell a Story via Data Visualization
Human beings are hardwired to pay attention and remember stories more than anything else. And we all know that a picture is worth a thousand words.
So what’s better than translating your data into graphs or diagrams to help you narrate your story?
The idea of you presenting the data is not to confuse your audience but to communicate fully the integrity and the meaning of your analytics so they can understand it, and take action against it.
Storytelling in the context of data visualization depends on how you balance the visual narrative against your target audience’s ability to discover and interpret.
If you’re to produce great data visualization, I highly recommend that you take a look at Edward Segel and Jeffrey Heer’s paper called “Narrative Visualization“, in which they’ve identified three distinct genres of narrative visualization.
7. Start Predictive Analytics
A great example of predictive analytics being deployed can be seen in Google’s Instant Search. It predicts what you’re trying to search before you finish typing to save you 2-5 seconds per search, guide your search, and load search results instantly as you type.
In fact, predictive analytics are what’s powering recommendation engines of companies such as Netflix, Facebook, Amazon, LinkedIn, Match.com, and more!
These predictive analytics are often utilized as conversion optimizing features inside products, such as ad targeting, recommendations, personalizations, and more.
It may sound far beyond our ability to predict the future, but the truth is that predictive analytics is about identifying and exploiting patterns.
The first step is to understand how to leverage techniques in statistics, modeling, and programming.
However; you can start by doing simple projections or forecasting then gradually move into more sophisticated techniques.
You don’t even need anything fancy, just some basic Excel skills will do to get started.
The Take Away: Big data analytics is here to stay.
One of the most fascinating things I get to do at work is to look at data from SMBs to Fortune 50s.
We try prioritize our decisions to spend our client’s investment based on data because it’s what we do – performance marketing.
I can’t stress enough the importance of statistics and its supersets econometrics and data science in solving real life problems.
Great online marketing strategies aren’t just about the tactics on traffic acquisition or conversion rate optimization (CRO); it’s about getting the most out of your marketing dollars.
It requires you to understand the connection between your marketing activities and the broader business objectives.
By integrating rich, relevant business data and powerful analytics, big data allows businesses to quickly assess emerging trends, identify correlations, and take meaningful actions.