How to Get Into Big Data Analytics

by Eric Tsai

How to Get into Big Data Analytics

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 IBMwe 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 important to maintain focus on business objectives in addition to all the online marketing tactics because like the author of the book Antifragile, Nassim Taleb wrote, “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 brought cherry-picking to an industrial level. Modernity provides too many variables, but too little data per variable. So the spurious relationships grow much, much faster than real information. In other words: Big data may mean more information, but it also means 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.

Learn to Extract Marketing Insights from Data

by Eric Tsai

Learn to Extract Marketing Insights from Data
In working with many smart business people and analysts in the past few months, I came to appreciate the ease of accessing web analytics.

Who knew that math and data would become a main revenue driving force for businesses big and small?

Every business is fast becoming a data-generating machine.

From upstream to downstream, data rewards us with actionable insights to make profitable decisions via controlled experiments allowing us to advance our business models.

And yet, this is just the beginning as the number of people connected to the web continues to grow, so too does the vast amount of information about those individuals.

According to McKinsey Global Institute, “collecting, storing, and mining big data for insights can create significant value for the world economy, enhancing the productivity and competitiveness of companies and the public sector and creating a substantial economic surplus for consumers.”

 Big data: The next frontier for innovation, competition, and productivity

Collect and Analyzing Data for ROI

We can’t mine data we don’t have, so now is the time to focus on data gathering.

Simply put, data will produce new value for businesses whether it’s setting up web analytics, collecting email addresses or compiling transaction data, the ability to turn data into actionable insights equals the ability to make money on the social web.

In addition data is the foundation for business return on investment (ROI) that enables predictive analysis to explore highly targeted and optimized marketing campaigns.

ROI-centric businesses focus on maximizing the lifetime value of a customer, which in many cases refers to customer retention and the cost of sale.

That means leveraging weighted algorithms and attribution models to target and re-target the “next-best” opportunity.

The key is to put data in context and “translate” them into meaningful key performance indicators (KPIs).

For example, a controversial topic that I often come across is the concept of social media ROI.

In reality, social media analytics and engagement data do not have a transparent cause-and-effect ROI so analyst Jeremiah Owyang of Altimeter Group came up with a simple formula to look at social media ROI.

Social Media ROI

By focusing on business goals, he recommended companies to develop a standardized way to measure first based on objectives, a fundamental starting point to put ROI in context.

An important aspect of reporting ROI is to put data in perspective for everyone involve. It’s indicative that social media ROI requires mapping the right data to the right role because different data sets mean different ways of measuring, segmenting and analyzing.

The Increasingly Social Search: Social Media Data

Although search engines such as Google, Bing and Yahoo have provided tremendous insights into customer behavior, the rise of actionable social media data is adding fuel to the explosive growth of digital information.

Now that Search engines are integrating social signals into their algorithms, social is going to play an important role to increase efficiency in targeting.

Facebook, LinkedIn and Twitter can help generate even more granular, multifaceted customer segmentation from profiles, posts, click histories, and usage logs by identifying influencers and leaders within social groups, as well as their followers and outliers.

Not only can Social media distribute marketing messages out faster, they allow companies to gain deeper insight into customer behavior in much more detailed than it has ever been.

In addition, social media enters into early majority phase of adoption according to a recent survey by Regus, more firms are using social media to engage with existing customers than a year ago, with the following highlights:

  • 50% of businesses in the U.S. use websites such as Twitter to engage, connect with and inform existing customers
  • In the U.S., 55% of firms encourage their employees to join social networks such as Linkedin and Xing
  • 38% of U.S. companies dedicate up to 20 percent of their marketing budget to business social networking activity
  • Globally, the survey reported a seven percent increase in the proportion of businesses successfully recruiting new customers through social networks such as Facebook

Geolocation: Adding Space and Time to Data

Local data is one of the most valuable forms of data because it can put local business in touch with potential customers while they’re in the vicinity of the business.

Do you wonder why all of a sudden people are “checking in” on Foursquare, Yelp, Facebook and Twitter?

According to IBM Engineer Jeff Jonas, “With roughly 600 billion data transactions from cellular phones on a daily basis, adding space and time to traditional data objects can help predict where someone will be on a given day and time with up to 87 percent accuracy, for example. Adding space-time works because, oddly enough, of physics.”

Watch live streaming video from gigaombigdata at livestream.com

The Take Away

The bottom line is that it is about giving youself the highest chance of marketing success by targeting customers that fit your business model.

Needless to say that it is important to collect the right data (context counts!), but the intrinsic value is in your ability to extract actionable insights beyond trends and patterns that reveal profitable opportunities.

The only question that remains for you is this – what data are you collecting, why, and how does that fit into the big picture?

I recently gave an interview to Adobe’s CMO.com about this topic.
Check it out: McKinsey Report Calls For New Generation Of Web Marketing Analysts