My Journey in Analytics

Analytics are a powerful tool. Industries rise and fall with data. At the end of the day, businesses need to begin to utilize analytics at some level to stay competitive, increase customer experience, and build their business.

However businesses are not using analytics to their true potential. As a digital marketer I was never largely interested in analytics or data. My thought process changed when I began to see real examples of how this data could be used to create positive insights for businesses.

At the end of the day, I believe analytics is still intimidating for many. However with training courses like Google offers, analytics is becoming more and more “normalized” in business, especially in digital marketing. I am fortunate to have gotten such a great headstart and peaked interest in this area of digital marketing. 

#Hashtag – FI’s & Social Media

Financial Institutions (FI) across the country are using social media now more than ever to communicate with their customers. FI’s largely are known as impersonal so their entry into the social media realm and activity in that realm is improving their relationships with customers worldwide.

FI’s are doing what you would expect on social media: communicating with customers, using it for marketing/sales messaging and monitoring customer concerns/complaints. However less than 30%, according to the ABA’s State of Social Media in Banking Research Study, of Banks are using social media for competitive analysis or to conduct research.

A wealth of knowledge is available through these platforms about customers. Facebook can even be used to provide predictive analytics. FI’s need to use social media to socialize with customers but also need to be sure to use it as a tool to further their business.  

Can Facebook Provide Predictive Analytics?

As many of you know, my tagline when asked what I do for a living is something along the lines of “play on Facebook all day.” Fortunately for me (and my employer) my job is much more involved than “playing on Facebook.” As my final semester in Grad School winds down, there has been a large focus in analytics and how they are used to make informed decisions, or in this case using predictive analytics.

Looking at the Financial Industry (FI), it’s no surprise that predictive analytics are being used. What may surprise you however are the way FI’s, no matter the size, can gain insights. In the article Predictive Analytics: The Future of Financial Marketing by The Financial Brand, eight “data sources” are laid out as means of gaining information to draw insights.

As data becomes increasingly more available and the need for predictive analytics becomes more important, we as digital marketers must search for the latest ways to gauge our customers. Believe it or not, Facebook or social media in general may help to achieve the data needed for these “new age” data sources below.

Data sources listed in the article include:

• Channel preferences
• Social media insight
• Mobile data
• Consumer ratings and reviews
• Bill payment behavior
• Personal Financial Management
• Geolocation
• Weather and other external elements

While this list may not be impressive to some, you’ll note that almost all of these data sources can be gauged through Facebook, or other social media channels. While Facebook and social media is often noted as something for millennials, it is becoming more and more intrinsic in all digital marketing efforts.

Next time you deem Facebook as a simple social media platform, you may want to take a second look – it may be telling you exactly what you want to know about your customers. 

Prediction: Predictive Analytics are Important

As Financial Institutions (FI’s) become more aware of the mass amount of metrics they have access to, the need to be able to analyze this data increases. Just as big data hits the limelight, “first-movers” have already moved on to bigger and better analytics, found in predictive analytics.

If you’re looking for a way to continue to push forward in terms of data, metrics and analytics – predictive analytics is for you. According to Predictive Analytics: The Future of Financial Marketing by The Financial Brand, here are four marketing trends marketers must take into account:

• A need to generate customer relationship revenue
• Evolving consumer behavior and expectations
• A continued focus on improved operational efficiency
• The need for competitive differentiation through digital engagement

While this may seem like elementary points in advancing in analytics, they are crucial in gaining “good” insights, which demonstrate a proper representation of your customer base or target audience. 

Predictive Analytics: Creepy or Helpful

There are a few “schools of thought” when it comes to predictive analytics. From the digital marketing perspective/data scientist perspective it’s an amazing tool to gain customer perspective. On the other hand, many consumers view predictive analytics as “creepy” or infringing on their privacy.

Today, many digital marketers and data scientists use predictive analytics to better “predict trends, understand customers, improve business performance, drive strategic decision-making, and predict behavior” according to Forbes.

So, what does this really mean? Is this an invasion of privacy, does it improve the consumer experience, or is it used to just drive sales?

The meaning is up to perspective. Many consumers credit great experiences to above average deals and friendly salespeople. While predictive analytics can’t entirely help your in-store experience, it can predict the type of sales you want/look forward to. Is this really a negative?

One Step Forward and Two Steps Back

In a recent article by The Financial Brand, publisher Jim Marous discusses some surprising statistics regarding the financial industry. The persona of most FI’s has begun to change as they, like everyone else, fully immerse themselves in the 21^st Century – part of which is understanding a communicating better with customers.

Along the lines of communication, it’s no surprise that in the 2017 Financial Marketing Trends survey, when asked “Which of the following will be a priority for your organization over the next 12-18 months?” personalization, mobile marketing, cross-channel/cross-device marketing were all selected as “very important” by at least 50% of respondents with content marketing chosen by 49%, all landing the top spots in the list.

What makes this notable, are not what was selected, but the fact that data analytics/capabilities, marketing automation, and predictive modeling/mapping analysis all fell into the bottom half of the list.

FI’s have noted the shifts in marketing from traditional mediums to digital and have rightfully adjusted their strategic goals and objectives. What many of these FI’s have overlooked is the important role data analytics will play in helping them achieve these goals.

Personalization, mobile marketing, cross-channel/cross-device marketing and content marketing are nothing more than “buzz-words” if there are no analytics to back them up. FI’s that can first concentrate on a procedure to gather, analyze and interpret data (if they’re savvy they’ll work with some type of automated platform) are those who will see the greatest impact to not only their strategic goals and objectives, but also their bottom line.

Based on this survey, it seems FI’s have their eyes on the “right” prize… but they may find themselves working backwards if they don’t use data along their journey.

CDs, Not the Only Item of Low Interest in the Financial Industry

In this digital age we live in, of ever-increasing data and analytics it’s no surprise that financial institutions (FI’s) and the financial industry overall are tracking analytics and using data to make informed decisions. However, what you may not expect is, the use of marketing automation platforms to assist in interpreting and making use of this data is extremely low.

According to The Financial Brand’s article, Achieving Advanced Financial Marketing Maturity, by Jim Marous, less than a quarter of FI’s surveyed in the 2017 Financial Marketing Trends survey said they use some type of marketing automation platform. More shockingly, more than half of respondents simply answered “No”, insinuating they had no plans of using any type of platform.

So the question is “why” – why do FI’s have such little interest in data analytics and general marketing automation platforms? Many FI’s, to their credit, have come a long way in “opening up” to the 21^st Century, from a time when they were typically thought of as a large, impenetrable brick building full of “suits”, to today where many customers now stop by to meet with their local advisor to have a conversation rather than for a business transaction.

FI’s are making great strides in becoming human and personable again; perhaps this is a reason why they lag so far behind when it comes to the use of data and automation? Whatever the answer may be, times are changing. While cash is still king, that king is getting younger – if your FI isn’t using some type of platform to interpret data, they will continue to fall behind.

Looking at the Financial Industry

In my next series of blog posts, I will be focusing on financial institutions (FI’s) and the financial industry overall. As someone working within the industry from the data and marketing perspective I find it interesting to see how FI’s are handling this new digital age and data analytics overall.

Unfortunately, it seems that most FI’s are not using the data available to them to the best of their abilities. I’m proud to say at my FI, we are using data to make informed decisions not only about our own customer base but our footprint as well. I find it interested and troublesome how the financial industry is approaching data analytics. I’m hoping to use this newly found research to continue to improve my efforts as well as inspire other FI’s to begin using this wealth of knowledge available in data analytics.

Choosing the Right Graph

In my last post I highlighted Tableau and it’s abilities, as a data visualization tool. As mentioned, much of data these days is how you interpret that data and present it, or using the data to tell a story. According to Tableau, here are 13 graphs you should know of and how to use:

1. Bar chart. Bar charts are one of the most common ways to visualize data. Why? It’s quick to compare information, revealing highs and lows at a glance. Bar charts are especially effective when you have numerical data that splits nicely into different categories so you can quickly see trends within your data.
2. Line chart. Line charts are right up there with bars and pies as one of the most frequently used chart types. Line charts connect individual numeric data points. The result is a simple, straightforward way to visualize a sequence of values. Their primary use is to display trends over a period of time.
3. Pie chart. Pie charts should be used to show relative proportions – or percentages – of information. That’s it. Despite this narrow recommendation for when to use pies, they are made with abandon. As a result, they are the most commonly mis-used chart type. If you are trying to compare data, leave it to bars or stacked bars. Don’t ask your viewer to translate pie wedges into relevant data or compare one pie to another. Key points from your data will be missed and the viewer has to work too hard.
4. Map. When you have any kind of location data – whether it’s postal codes, state abbreviations, country names, or your own custom geocoding – you’ve got to see your data on a map. You wouldn’t leave home to find a new restaurant without a map (or a GPS anyway), would you? So demand the same informative view from your data.
5. Scatter plot. Looking to dig a little deeper into some data, but not quite sure how – or if – different pieces of information relate? Scatter plots are an effective way to give you a sense of trends, concentrations and outliers that will direct you to where you want to focus your investigation efforts further.
6. Gantt chart. Gantt charts excel at illustrating the start and finish dates elements of a project. Hitting deadlines is paramount to a project’s success. Seeing what needs to be accomplished – and by when – is essential to make this happen. This is where a Gantt chart comes in.

To read the other seven charts Tableau is capable of using to display data be sure to read the rest of the article by clicking here.

Tableau Around the Classroom

Now that we know how important it is to visualize data, the way we present it becomes even more crucial in interpreting it. But before I get into different ways to present data, let’s take a look at what other students are saying:

• Hear how Tableau is changing the music industry through data in Naomi Santiago's post, Taking a Look at Tableau's Music Viz Contest.

• Read from Amanda Kunrath's post about the importance of data visualization and the story it tells in, Visual Appealing Data Visualization: It's All About a Good Storytelling.

• Don't forget supply chain management - Tableau is much more than a data visualization tool in Norah's post, Data Analytics Tools - Tableau Software.

Perspective is something that data tools, like Tableau enable us to understand. Perspective is what I've also gained from reading these blog posts and many more. It's intriguing to see how one platform is being used in a variety of ways, from use in different industries to different uses within one organization.

Visualizing Tableau

Tableau seems to have taken as a frontrunner by those who have commented on my previous blog post. With this in mind, I will expand more on the functionality of Tableau in my final two posts as why it is one of the better options when searching for a data mining platform – in comparison to Excel, of course.

Where Tableau gains a large lead on Excel is when it comes to the visualization aspect. Excel and Tableau are both leaders in their own regard for holding mass amounts of data and being able to successfully sort. Tableau has raised the bar by offering, “visualizations [that] are interactive, easy to share, and help everyone in your business get results. Make confident data-driven decisions with a platform that supports your cycle of analytics.”

The more I research about data, the more important its interpretation becomes. More often than not, interpretation of said data relies not on what is spoken by a presenter but rather how it is presented by that presenter. This new age of data visualization is where Tableau leaps lightyears ahead of where Excel is. For this reason, Tableau takes a clear lead when it comes to trying to decide which data mining platform is better suited for insights.

Read more about Tableau at https://www.tableau.com/solutions/customer/tableau-vs-excel#sT6QSgxSpg7182Z2.99

Excelling Headaches

O.K. Grammar folks, before jumping all over my headline, take a second to read the post – spoiler alert (!) it’s a pun on the article.

For someone who has recently gotten into analytics and tracking metrics, Excel is a great platform for keeping track and illustrating this data. However, as many will quickly notice, Excel often accelerates headaches (excelling headaches – get it now?). While Excel holds its own, it has become quite pedestrian compared to its counterparts specifically developed to filter, track and help illustrate data.

Here are four great data analytics tools, defined by Boost Labs, self-proclaimed as “data analysts, innovative coders, and clever designers.”

1. MicroStrategy Analytics Desktop. “MicroStrategy Analytics Desktop is a fast and user-friendly software for visual data analytics. Quick to download and install, this visualization software allows you to get to work quickly. Included sample data and pre-built interactive dashboards further serve to lower the learning curve.”
2. Domo. “Domo offers an online business intelligence tool that has a sleek UI and is specifically designed to allow users to build sophisticated dashboards with no IT involvement. Because the software is accessible online, Domo and the dashboards it creates, are available to your entire organization.”
3. Tableau. “Tableau offers a suite of tools that include an online, desktop and server version. All of these versions provide an easy-to-use drag and drop interface that can help you quickly turn your data into business insights. The online and server versions allow your entire team to build and work with the visualization tool.”
4. QlikView. “The QlikView business discovery platform is one of a few visual analytics tools offered by Qlik. QlikView can’t create the same elegant visualizations that the other tools offer, but the software’s dynamic model means that you can quickly analyze your data in multiple dimensions. In addition, QlikView is able to work off of data in memory instead of off your disk, allowing for real-time operational BI environments (like monitoring financial transactions).”

These four analytics tools are certain to take your spreadsheets to the next level and eliminate much of the challenges presented by working on and with Excel.

Stay tuned for what I define as the best analytics tool in my upcoming post! To read the full article by Boost Labs, click here. 

The Deciding Factor

You’ve heard the salary, you have a full understanding of what a data scientist actually does. However, at the end of the day true success and happiness is often found in doing something that you love. Understanding the qualities or characteristics of a data scientist is essential in knowing whether or not that fits your true calling.

Data Scope Analytics defines six qualities necessary of a great Data Scientist. Here is what they found, a Data Scientist is a:

1. Statistical Thinker
2. Technical Acumen
3. Multi-modal communicator
4. Curious
5. Creative
6. Gritty

Are these qualities, qualities you feel you possess? Are they qualities that you can develop further? If so, the role of data scientist may be a calling. If not, and you possess only a few or none of these skills, do not be discouraged. The use of data and analytics is becoming more and more important in Digital Marketing. While you may not be a data scientist, the more you can incorporate data and analytics into your everyday job, the more value you bring to yourself and company.

So You Wanna Be a Data Scientist?

So you’ve decided you want to become a data scientist – congrats. In my previous post I discussed the skills needed to be a data scientist, at this point obviously you believe you have what it takes. If you’re questioning whether you have those skills or it’s worth refining those skills you may want to rethink that when you see what the average salary of a Data Scientist is.

As the popularity for the position of Data Scientist increases, those who are actually Data Scientists holds steady. According to GlassDoor, the national average salary for a data scientist is $113,436! So before you write-off crunching numbers and gathering business intelligence you may want to consider that figure and you consider a job change or further developing your career!

Defining the Wizard a.k.a. a Data Scientist

Keeping up with the recent theme of “The Wizard of Oz,” in this post I have decided to analyze the “wizard” or a data scientist. Big Data, analytics, metrics, business intelligence are all buzz words we constantly hear.

LinkedIn released their top skillsets for 2017 with statistical analysis and data mining at number two on the list. There is no hiding that data scientists and the skills required are growing in importance – but do we have a full understanding of the skills actually needed to be a data scientist?

Everything You Should Know About Data Science: The Century's Hottest Career written by Laurence Bradford, helps us begin to understand this “wizard,” “man behind the curtain,” or more simply put a data scientist.

According to Bradford, “Gautam Tambay, cofounder and CEO of Springboard, believes that ‘Data is the new oil.’” There are some specific things you should know about these wizards:

1.       Early on most data scientists were only PhD’s our those who completed various higher level education courses. With the amount of data only increasing, there is a short in the supply of these data scientists. Today someone with logical thinking and a passion for analytical insights are beginning to do the job once reserved for those with PhDs.

2.       Being a data scientist isn’t strictly numbers. Niraj Sheth a data scientist at Reddit stated, "Fundamentally, it is as much about people -- the users you're building for and the coworkers you're building it with -- as it is about math and engineering. Having a hybrid background myself has definitely helped me understand which parts of data science to leverage at different times."

3.       Tambay further breaks down being a data scientist into five simple steps:

1.       "First of all, you want to learn to break down problems into its constituents. Every time you think about why something’s happening, create a hypothesis. This can apply day to day. When you’re doing anything with your friends. When you see something happening, [ask] ‘why did that happen?’

2.       "[Second], think about, ‘what data would I need to prove or disprove this hypothesis?’ Think about why this would happen, think about a hypothesis, think about what data you would need to prove or disprove the hypothesis, then go find the data and see if the data confirms your hypothesis.

3.       "[Third], think about how to bridge the gap between this simple hypothesis-driven thinking to actually running large experiments. That’s where you need to learn the statistics, that’s where you need to think about how to clean and wrangle data, because often data is messy.

4.       "[Fourth], you think about how to organize the data into analyzable form, and that’s when you need the tools, whether it’s Python programming or a language like R or some people will just even use SQL and Excel for smaller problems. But that’s when you need the tools to actually analyze and conduct your analysis.

5.       "Finally, you need tools to visualize and present your insights -- data storytelling."

If we’ve learned anything from my past posts and the movie The Wizard Of Oz, it’s that anyone can be “the man behind the curtain.” With the right kind of drive, inquisitive nature, and logical thinking anyone can be your data scientist.

Data will play a significant role in the success of companies over the next decade. This will require us to adapt – especially us as marketers. I look back to Sheth’s comment from above, as marketers we have the people skills or understanding necessary for part one, we need to develop ourselves analytically to achieve part two.

Putting people and analytics at the center of defining a data scientist is a logical fit for a marketer. While analytics and data was not the “attractive” part of marketing that got me interested in this field, I am beginning to become more interested in it as I recognize it as the future of this field. 

Defining the Curtain – 17 Predictions of Big Data

Photo: "The Wizard Of Oz" MGM

In my previous post, I reference the Forbes article, 17 Predictions About The Future Of Big Data Everyone Should Read. Here, Bernard Marr, defines 17 predictions to define the curtain and more importantly, the man behind the curtain (reference to previous blog post).

We know that big data is the culmination of all the data produced in the world, understanding how its analyzed is crucial in how we use this data as marketers. In order to gather our thoughts, we need to understand the direction (we believe) big data is taking.

Here are what I believe to be the five most important predictions:

• Data volumes will continue to grow. We’ve seen an unprecedented increase in the amount of information collected on and provided by consumers. There is no reason to believe that his data collection will slow down.

• Ways to analyze data will improve. Pretty straightforward – this includes improvements in current technology as well as new technology.

• All companies are data businesses now, according to Forrester. More companies will attempt to drive value and revenue from their data.

• Businesses using data will see $430 billion in productivity benefits over their competition not using data by 2020, according to International Institute for Analytics.

• “Fast data” and “actionable data” will replace big data, according to some experts. The argument is that big isn’t necessarily better when it comes to data, and that businesses don’t use a fraction of the data they have access too. Instead, the idea suggests companies should focus on asking the right questions and making use of the data they have — big or otherwise.

While these are only predictions, I believe these to be the most likely of all to happen. Data volumes and what we have the ability to collect is changing and growing by the minute. With every agreement we sign or click, we are allowing companies to continually collect data on us.

The final three bullet points are really the most important, with the last being my favorite. When most people think “big data” it is most often attributed to bigger companies. However companies of every size, small and large businesses have access to data. We need to begin changing the way of thinking, and realize that the data we collect and use must be fast and actionable.

Businesses of all size are driven by data, even if it’s as simple as data and analytics provided by Google. We will begin to see companies not only use this information to draw insights but also to drive revenue. It will be imperative that this information be used to further business. Data can be the exception for a business – making it exceptional or leading to its ultimate demise.

When it comes to data, follow the yellow brick road. But make sure the man behind the curtain, building insights from this data is who you really think he is. In my next post, I’ll discuss some skills your data “wizard” should have.

To read the full article and all 17 predications, click here.

“Pay no attention to the man behind the curtain!”

Photo: "The Wizard Of Oz" MGM

An ever-famous quote, “Pay no attention to the main behind the curtain!” has a nostalgic effect for most who hear or read it; it, of course from The Wonderful Wizard of Oz. But now, this quote may begin to take on new meaning as it relates to big data.

What’s big data? The Advanced Performance Institute, an organization that basically lives and breathes big data, defines big data as, “the term used to describe our ability to make sense of the ever-increasing volumes of data in the world.”

Big Data, its ability to be analyzed and its relation to privacy all seem to be on a crash-course with each other… this eminent interaction looms large in its future. So again, what is big data? Where does it go? Who is the man behind the curtain collecting it?

A recent Forbes article lays out 17 Predictions about the future of Big Data and whether there really is a wizard behind that curtain. Until then, I’ll keep you on the edge of your seat until my next post where expand on these predictions and the one’s I believe are the most key.

Can You Measure ROI of Digital Analytics?

If I may say so myself, I’ve done a pretty decent job setting the stage for this showdown of past vs. future when it comes to measuring the return on investment (ROI) of digital analytics. The former, can best be defined as investment in marketing and investment in the customer.

If you missed out on my first two posts, you can view them here, or to get a better understanding of the Harvard Business Review, case study I am referencing, click here. The HBR Case Study planted the seed, here’s my take:

At the end of the day your ROI should be measured on investment that impacts the bottom dollar – what can you do that will have the greatest impact on sales and profits. Today, this is strictly by the numbers, the future will take us to greater more grey areas in ROI.

Everything we do in our lives today is relationship-oriented. Customers no longer want to simply purchase a brand, from a store, they want to have a relationship with that entity. A sale is no longer a simple profit or “win” for a business but rather the birth of an organism – the birth of a relationship.

Businesses today are called to extreme lengths to meet the wants and needs of their customers through constant interaction and engagement. This change has caused a change in the way we measure success and investment – analytics is no longer the way it used to be.

And that’s, O.K.

The new wave of measuring ROI will be captured in investment in the customer and represented in key performance indicators (KPI), such as:

• ·         Brand Engagement
• ·         Educational Posts
• ·         User-Generated Content

Your accountant will point to your “books” as the most valuable part of your business. 

STOP THAT WAY OF THINKING. 

Businesses don’t go out of business or experience bankruptcy because of a slow year (for the most part). Businesses experience difficulties and failures because of a lack of customers. A way to keep customers coming back, is to build a relationship, foster the organism you’ve created at your first sale. Invest in your customers.

Social Media and Digital Marketing tools are a cost-effective way to keep your customers engaged in your business. Let’s face it, when they’re not in your shop, they’re a target for other businesses.

Using social media and digital marketing channels does not automatically mean that marketers working for you are off the hook. There are several analytical tools that can be used to gauge the successes of marketing campaigns. This new age of measuring ROI does not mean that standard metrics are not useful, such as:

• ·         Number of Followers/Likes/Fans
• ·         Impressions
• ·         Likes, Comments, Shares, Retweets

I’m not here to completely throw hard numbered-metrics out the window. I’m just saying, measuring ROI via investment of the customer requires a more full-bodied approach than ever before.

Trial social media platforms, see what sticks – see where true value is generated from. Successes in the digital channels and properly analyzed metrics will have you blowing by competition in terms of sales and customer satisfaction.

What’s nice about customers is - when you invest in them – they return the favor.

…and Measuring ROI

In my last post, I set the stage for a showdown… the old ways of measuring ROI strictly through numbers and the effect on bottom dollar and this so-called new-age that will turn your thinking upside down. In the Harvard Business Review previously mentioned, the idea of measuring ROI centers on investing in consumer investments, rather than concentrating on marketing investments. Blogs, Facebooks, Twitters – social media in general has taken marketing by storm. The new age of measuring this type of marketing is still developing. However, we can begin to define the calculation of ROI as in customer investments rather than marketing investments.

The future of digital marketing analytics depends entirely on this new way of measuring ROI. Popular metrics are likes, post reach, or shares but these are not the key performance indicators (KPI) that are hard to measure or lead to a positive ROI or investment in the customer. Meaningful KPIs are the concepts of brand engagement, product education, or user generated content. When a business utilizes social media to build these KPIs they are investing in the customer. Investing in the customer this way, is the business hoping to build a customer experience or customer-to-business relationship that will impact sales in the short- and long-term.

With this new idea of ROI in mind, we have some thinking to do. The question remains, what will lead to the most value for your business – investing in marketing investments or consumer investments. I’ll deliver a verdict in my final post of the week.

To Social or Not to Social, and Measuring ROI

To social, or not to social – that’s the question many business owners ask themselves. One side of the aisle says your fifteen-year old nephew can handle a Facebook or Twitter handle, the other has you spending hundreds of dollars a month. The real question, what will provide your business the most value? The harder question – how is that value measured?

In a Harvard Business Review case study, the idea of measuring the return on investment (ROI) of digital analytics is defined simply as, “turning your thinking upside down.” That’s right, not only are the traditional methods of marketing a way of the past, but ways of measuring ROI through analytics and key performance indicators (KPI) are changing.

This new age and the future of digital marketing analytics say good bye to the cold hard numbers – for better or worse. While tracking numbers and being able to allocate marketing-related expenses directly to the bottom line is monumentally important at the end of the day, it is imperative that, that measurement is not the “live or die” factor in measuring your ROI or success when it comes to social media marketing or general digital marketing analytics.

Interest peaked? Let’s see if your fifteen-year old nephew can accomplish that in 200 words or less. Stay tuned for my next post, we’ll go more in-depth on this new age of digital marketing analytics. If numbers are the way of the past – you’ll need to know what the future holds.