An Insider’s Guide to Digital Marketing Analytics

digital marketing analytics
Courtesy of Avinash Kaushik

Forbes proclaimed 2014 the Year of Digital Marketing Analytics, summing up the problem this way:

If most digital marketing programs or campaigns have a weak area, it’s analytics. One recent study identified that the biggest talent and hiring gap in online marketing is in the analytics space. 37% of companies surveyed said that they desperately needed staff with serious data chops.

If you take a look at the image above, courtesy of Avinash Kaushik on Occam’s Razor, you’ll see a similar emphasis on “Big brains” and there just aren’t enough of them going around.

The state of digital marketing analytics today

Well, in 2015 we still find too few analysts trained in digital marketing analytics, especially when it comes to more advanced analytics. What passes for digital marketing analytics is also pretty dismal, amounting to little more than rudimentary vanity metrics.

If you look at interest in digital marketing analytics over time, you find the term first appeared in search in 2011, but searches exploded in 2013. Google forecasts continued steep growth in searches for digital marketing analytics based on the graph below from Google Trends.
trends in digital marketing analytics

So, what do these searches turn up?

A ton of tools, many of which aren’t really analytics tools, but automation tools with a little tracking. For instance, I love SproutSocial for helping share and curate content, but it’s not really an analytics tool. Here’s what you get:

Reports | Sprout Social 2015-01-16 09-55-43

I ask you, how does this data help manage your digital marketing? What insights does it provide?
The same goes for many “analytics” tools provided by the social networks, which are pitifully anemic. A couple of caveats here, however. Google Analytics and Facebook Ads Manager provide very useful, insightful data to help optimize your digital marketing results. I’ve provided detailed directions for setting up and interpreting data from Google Analytics and Facebook’s Ads Manager.

What you need to rock digital marketing analytics?

Surprisingly, the first step is to gain an appreciation of analytics. I find many small and mid-sized companies don’t appreciate how critical digital marketing analytics are for their success. Even some large businesses don’t really get the importance of digital marketing analytics and focus too much on late funnel assessment rather than top of funnel assessments.

Recognize that digital marketing analytics require a budget. Too many businesses try to go cheap here with the notion that money is better spent on other activities. And, in the short run that might be true. Unfortunately, what you’re not seeing in this cost strategy is the opportunity cost of sales you didn’t make because your efforts weren’t optimized. I call this a penny-wise and pound foolish strategy because you’re saving a little money up front to lose a lot of money on the back-end.

kpi and metricsKPIs and ratios

Next, you need to build KPIs (Key Performance Indicators) and metrics from your mission and strategy, focusing on both top of funnel (consumer sentiment, reach, engagement) and bottom of funnel (ROI, conversion, etc) strategies. This is why you need marketers schooled in digital marketing analytics — they understand marketing KPIs.

Set realistic priorities because you can’t focus on every possible KPI at the same time. I recommend selecting a balance between the KPIs at the top, middle, and bottom of the funnel that have the greatest impact on market performance.

Setting goals for these KPIs allows you to develop more meaningful metrics like ratios of expected versus actual. Large ratios demand investigation (and maybe testing to figure out why the ratio was large) while small ratios indicate you met expectations.

Level of analysis

Also, think about level of analysis issues — you want both overviews of how well your strategy is working and insights into segments, such as different social platform performance, performance of different types of content, etc. As an analyst, think about what different users need in terms of level of analysis. For instance, the VP marketing needs an overview, but she might want to deep dive into why some KPIs had high ratios. Meanwhile, your brand managers want to understand the performance of their products and community managers the performance of individual pieces of content. These elements fit within Kaushik’s notion of dimensions that covers performance of individual keywords, campaigns, posts, referring sites, countries, types of visitor, etc.

Data visualization

Visualizing data is critical for easing interpretation. In his TED talk, David McCandless, said this about the importance of data visualization:

By visualizing information, we turn it into a landscape that you can explore with your eyes, a sort of information map. And when you’re lost in information, an information map is kind of useful.

Data visualization not only acts as a short cut for interpreting data, the human eye sees pictures a whole lot better than numbers. Thus, appropriate visualizations allow managers to identify problems quickly so they can fix the problem before it becomes a crisis.

For instance, P&G monitors deliveries using GPS installed in its fleet of trucks using colored digital block — each block representing the value of the customer to P&G and the color representing expected delivery (green for on time, yellow for possible delays, and red for likely delays). When a truck runs into problems (traffic, weather) that threaten delay to a major customer (like Wal-Mart), managers can quickly send replacement shipments from a local distribution center or re-direct shipments from less critical customers or shipments with sufficient lead time.

Translating digital marketing analytics into action

Unfortunately, many firms find their digital marketing analytics programs falling down at this critical step — translating insights into action. In this article, Google quotes poet, Andrew Lang who eloquently said:

He uses statistics as a drunken man uses lampposts—for support rather than illumination

Translating insights into action often means going back to manipulate your data for more nuanced insights;

  • looking for relationships among your data – for instance, you might uncover a relationship between top performing posts and specific keywords used or publication timing
  • looking at trends rather than data points – trends often help you identify meaning in your data such as cyclical trends or when a particular data point stands out from others versus simply representing normal fluctuation
  • turn data into predictive models – don’t stop with viewing data as isolated points and basing forecasts on simple linear extrapolations. Predictive models use historical data to determine the relationship among a set of factors and desired outcomes (like KPIs). Then analysts use these algorithms to predict future KPI performance. You can even play “what-if” games to determine the impact on performance of various actions. This helps determine which changes represent the greatest impact on performance.
  • don’t forget that data analysis is part art and part science. Translating insights into action involves a certain amount of playfulness with the data to discover deeper insights.

Need help?

We welcome the opportunity to show you how we can make your marketing SIZZLE with our data-driven, results-oriented marketing strategies.  Sign up for our FREE newsletter, get the 1st chapter of our book – FREE, or contact us for more information on hiring us.

Hausman and Associates, the publisher of Hausman Marketing Letter, is a full service marketing agency operating at the intersection of marketing and social media.

 

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Rockin’ Google’s Universal Analytics: Step-by-Step Guide

universal analytics
Create a Google Analytics Account

Are you rockin’ Google’s new Universal Analytics?

Even Yoast (creator of my favorite WordPress Analytics plugin) finally supports Universal Analytics, so there’s no excuse. Universal Analytics provide access to enhanced analytics (especially about visitors to your website) and, it’s easy to add to your website or convert from Classic Analytics.

Here’s a step-by-step guide for upgrading your Google account to Universal Analytics.

Google Universal Analytics

1. Create an account

If you don’t already have a Google Analytics account, you’ll need a gmail account. Then simply navigate to the site and click the “Access Analytics” button in the upper right. You’ll get a screen like the one above.

In the upper right corner, select the gmail account you’ll associate with Google Analytics (if you have multiple gmail accounts select the one you want to use from the drop down list or add a new one), then select the “sign up” button.

2. Completing your Analytics account

Answer the questions about your website or mobile app. Google automatically defaults to the new Universal Analytics tracking code.

Finally, select the “get tracking id” at the bottom of the screen.

Converting to Universal Analytics from Classic Analytics

More likely, you’re converting from the existing tracking code (now dubbed Classic Analytics) to the Universal Analytics code.

In that case, skip the previous steps and navigate to the admin option from the menu bar of your Google Analytics account and you’ll see something like this:

universal analytics
Google Analytics Admin Panel

Select the proper account, property name, and all web site data using drop down menus. Now select the “property settings” link and

universal analytics
Demographic and Interests Report

click the button next to enable demographics and interest reports, which will now change to blue and say ON.

Behind the scene, this selection makes a small change in your tracking code that allows Google Analytics to provide additional information about site visitors. So, you’ll need to copy and paste the new tracking code in the head section like you did before unless using a plugin like WordPress SEO (which is SO much easier).

While you’re here anyway, it’s a good time to tidy up your analytics account, connect your AdWords and Webmaster Tools, and set up goals, but that’s a post for another day.

That’s it! You should be all set to get enhanced analytics from Google’s Universal Analytics.

Now, the bigger question is:

How to get the most from Universal Analytics

I’m glad you asked.

Getting the most from Google’s Universal Analytics

I’m sure you access your analytics report every day, but don’t stop at monitoring high-level information such as # visitors, bounce rate, and time on site. Sure, this stuff is important, but, with Universal Analytics, you’re now getting some great stuff you can use to optimize your content to make it convert like crazy.

Let’s start with a little example of using Google’s demographic and interest reports — which you’ll find under the AUDIENCE tab on the left sidebar.

Here’s what a report looks like:

universal analytics
Demographics Report

Sorry, Google’s charts aren’t very attractive. Visualization is really important for interpretation.

I usually export the data to an excel spreadsheet then pretty it up with contrasting colors to make it more appealing and instantly interpretable by my clients.

Interpreting this data, we find slightly more visitors to the site are female and most are under 34. Demographics suggest a certain slant to my content marketing to increase visits among this group that already likes the content.

So, I might use examples that fall within this age group or use more female pronouns or examples in my content. If I were an ecommerce site, I might use younger, female models and feature products preferred by this group.

But, just because these folks visit my site in larger numbers doesn’t necessarily make them more important.

And, Google gives me a way to determine which visitor groups have the largest impact on ROI. If I have goals set, I can plot demographics by conversion rate. If I don’t have specific goals, I can plot valuable behaviors such as time on site or # pageviews/ visit (since these are important in the Google ranking algorithm).

universal analytics
Demographics based on PageViews/ visit

Now my chart looks more like this:

Notice, older demographic groups become equally important as there’s no real distinction between the # of pages viewed between different demographic groups. Without this information, I would have tweaked my content strategy to appeal to a younger demographic and might have lost the stickiness provided by older visitors.

Playing around a little, I found another important difference based on age that only showed up when I plotted visits over time.

universal analytics
Demographics impact over time

I’d always known my visits dip over the weekend (Fri, Sat, and Sun). With Universal Analytics, I now see that much of this dip comes from visitors over 45 and that visits from younger demographic groups is affected less by the day of the week – notice the purple and yellow disappear in the weekend dips.

Knowledge is power. Knowing that consumers over 45 disappear over the weekend, leaving younger consumers, provides opportunities. I can focus content marketing on younger consumers over the weekend or implement a promotion on the weekend that keeps older consumers visiting the site.

I have options I didn’t even know existed.

Universal analytics: Interest reports

Here’s what my interest report looks like:

universal analytics
Universal Analytics Interest Report

Again, I have options based in this information that provide opportunities to explode my ROI.

Adding goals and plotting based on reaching these goals further expands your opportunities to explode ROI.

Setting up cross tabulations

Google Analytics allows you to create cross tabulations of your data based on 2 different variables. Hence, you can select a secondary dimension. Your chart might look something like this if you plot gender with age:

universal analytics
Age by Gender

The arrow in this diagram points to 25-34 year old women, while the blue one next to it shows 18-24 year old women.

We see something interesting in this chart that we didn’t know before — not only are younger visitors more common on my site, but young WOMEN in particular visit my site, much more so than any age of men. The tidbit of information was hidden in other plots and charts because they only looked at gender for the entire visitor group.

Interesting.

Again, knowledge provides an opportunity to tweak my strategy to make my ROI explode.

Have you learned something today?

Have a question of your own?

Let me know in the comments below.

Need Help?

Whether you need a complete content marketing strategy or a complete metrics-driven social media strategy, we can fill your digital marketing funnel. We can help you do your own social media marketing better or do it for you with our community managers, strategists, and account executives. You can request a FREE introductory meeting or sign up for my email newsletter to learn more about social media marketing.

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5 Tips for Creating Landing Pages That CONVERT

For most of you, creating a landing page that converts is critical for success. Whether you’re doing lead generation, building a subscription list, or closing a sale, the quality of your landing page determines your success.

I found this easy infographic from QuickSprout that explains the various elements critical for creating landing pages that convert.

Take a look and let me know what you think in the comments. But, don’t stop there, at the bottom of the infographic I’ve added recommendations on creating landing pages that convert!

increase conversion

 Added recommendations for landing pages that convert

1. Generating images

Unless you have an in-house graphic designer, getting high-resolution images for your landing pages is challenging. I recommend 2 sources: Hubspot and Premise.

Hubspot offers 50 Call to Action button templates free. They’re easily customizable with your own color scheme and fit whatever size you’re looking for. You can even change the text on each template. I find them very easy to use and flexible.

Premise is a landing page creator from the folks who also bring you Genesis — StudioPress. Not only does Premise offer a host of images in various colors, it offers advice on copy. I use Premise on my website.

2. Design/ layout

Design has to do with the layout of the page. My recommendation is to make it chunkable so readers can easily get the information they seek. Include lots of white space, too, as this invites reading. Take a look at my landing page for digital marketing to see and example of what I mean about layout.

This landing page is actually a single page with internal tabs that make it easier to information you need to make a sound decision — courtesy of Premise. I prefer this layout to pages that just seem to go on and on.

3. Influence

QuickSprout’s recommendations include 2 tool of influence — social proof  and authority –, but there are others that help with converting from your landing page.

  • Scarcity — imply something’s limited and more folks want to buy it and they’re likely to buy now rather than waiting until later when they might forget about your product
  • Reciprocity — or tit-for-tat. Give someone something and they feel obligated to give you something back.
  • Likability

4. Benefits not features

Consumers (business or end users) buy products because they solve problems. That means they’re looking for benefits from you and could care less about features. Tell them how your product helps them.

5. TEST

Creating landing pages that convert doesn’t happen by accident — it’s a function of experience and that comes with monitoring analytics and testing. I’ve done this a long time and I still test every major element — headline, image, call to action … Tools exist to make your testing easier, but that’s a post for another time.

Need help?

Whether you need a complete analytics strategy, some help with brand marketing, or some consulting to optimize your existing social media marketing, we can fill your digital marketing funnel. We can help you do your own social media marketing better or do it for you with our community managers, strategists, and account executives. You can request a FREE introductory meeting or sign up for my email newsletter to learn more about social media marketing.

 

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Social Media Analytics Review: Beckon

One of my favorite perks of running this website, is meeting great people and learning about new things in the online/ inbound marketing space — especially new social media analytics tools. A couple of weeks ago, I got a demo of a new tool — Beckon — that has a lot to offer. It’s price tag might be a little steep for small businesses (about $10K per data source), but I think it solves many problems enterprise businesses currently struggle with — disparate data sources, different scales on metrics, and the general mish-mash of data that’s challenging to interpret.

managing big dataThis image clearly demonstrates the main selling point for Beckon — it takes a bunch of unruly data and orders it so analysts can interpret and develop data-driven strategies.

The state of social media analytics

I mean, face it. If you’re trying to create data-driven strategy for your inbound marketing, you’ve got a lot of stuff coming at you every day — Google Analytics, Facebook Insights, Pinterest Analytics, Twitter Analytics ….. Developing data-driven insights from all this data is challenging.

It’s a mess. Right now I export all that data on a weekly basis to a Cognos (IBM) dashboard so I can show each client exactly what’s going on with their social media campaign.

The problem, of course, is I have limited ability to ANALYZE the data across platforms — I’m merely displaying it in 1 place — and adding nice visualizations so clients can see the big picture better. Beckon normalizes the data, so you’re comparing apples to apples and can make informed decisions based on the how well different social channels help you reach your goals based on paid, earned, and owned impressions.

Beckon makes order out of this chaos and generates reports that look more like this:

social media analytics

Armed with these reports, I can now determine which channel helped reach goals for awareness, engagement, and conversion. By creating ratios, I now gain better insights for each channel and each campaign within each channel. I can also see which posts met my goals most effectively and do more effective testing for strategic alternatives to help optimize future campaigns.

Key ratios might be:

  • paid/ earned impressions
  • engagement/ impression
  • open/ click rate in email marketing

Where does the data come from?

Beckon uses data you already get. For instance, if you get your Google Analytics emails to you — and you should — just CC Beckon on the report.

Beckon also derives data from internal sources, like your order system, email client, etc.

One of the nicest features of Beckon is that it can handle such a wide variety of data types: png, PDF, CSV, powerpoint …

I’ll definitely add this to my list of Social Media Analytics Tools.

Need help?

Whether you need a complete analytics strategy, some help with brand marketing, or some consulting to optimize your existing social media marketing, we can fill your digital marketing funnel. We can help you do your own social media marketing better or do it for you with our community managers, strategists, and account executives. You can request a FREE introductory meeting or sign up for my email newsletter to learn more about social media marketing.

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The Secret to Social Media Success is HUMAN

social media successI know I post a lot about social media tactics and the importance of analytics on this site. That’s because they ARE important. But don’t forget that social media marketing is SOCIAL, which means it has to be human — human connections, human stories, human emotions. At it’s most basic, the secret to social media success is HUMAN!

The secret to social media success

Data alone isn’t what makes the marketing needle move for business … True brand intelligence lives at the intersection of mind and heart

That quote from a recent HBR post about sums up the secret to social media success — blending the human elements with strong data analytics. Either one alone is much less powerful than the 2 together.

The graphic shows different types of marketing intelligence, both data-driven analytics and human-driven.

Now, some of you are reading this saying, “Duh, of course. We knew that. It’s not much of a secret.”

I can tell you you’re wrong. It is a BIG secret. Maybe a few examples will help.

The allure of big data

It’s really not new. Brand marketers long believed you could successfully run a business with analytics. In fact, the discipline of business intelligence relies solely on analytics.

WalMart

I remember I once allowed a C-suite level WalMart executive into my class. Her presentation was ostensibly to share WalMart best practices, but the subtle subtext was recruiting newly minted MBAs into the WalMart machine.

Ms. WalMart VP rattled on far too long about how brilliant WalMart is and all the analytics used to optimize merchandising ROI. I was so proud of my students because they asked the hard questions that ferreted out huge failures in WalMart’s strategy.

Students quickly understood WalMart’s obsession for measuring everything and learning nothing. Most decisions were based on simple heuristics. For instance, when asked about how WalMart determines how many Christmas ornaments to buy, the venerable VP answered they just add a fixed percentage to last year’s order.

Really?

Now, I’d recently finished reading the pseudo-autobiography of Sam Walton, Sam Walton: Made In America, which I recommend highly. In the book, Walton clearly attributes much of his success to his habit of randomly visiting stores and just shooting the breeze with customers and employees, even stockroom workers. He even routed through various WalMart stores when heading out for vacation.

Here, not even a generation after his death, Walton’s kids were substituting data for human understanding. And, the thinnest kind of data — descriptive statistics, rather than deeper, predictive analytics. It’s not surprising that some WalMart stores are now seeing declining year-on-year sales and some predict WalMart’s best days are behind it.

P & G

When I taught at Xavier University, many of my MBA students were folks from P&G, Toyota, and GE. Very smart people, but very stupid human beings.

Following the treatise of AG Lafley, former CEO of P&G (now returned to that role), managers aspiring to the C-suite believed data was their ticket to the big penthouse office. However, after the horrific financial crisis of 2008, even Lafley himself lost faith in strategic guidance using data along. While not entirely relinquishing his love of data, Lafley now agrees there’s nothing inherently scientific about data and opening himself up to other types of intelligence, including human intelligence.

When Lafley first assumed leadership of P&G, he was the savior for brands that had lost much of their luster over the years. His focus on innovation, marketing, and strategy using analytics-driven insights was just what the flailing company needed. Lafley’s engineering focus on number crunching worked — until the 2008 economic crisis, when tough economic times revealed the importance of human understanding in supplementing data-driven insights.

Here’s the meat of Lafley’s argument:

Many managers feel they are doomed to weigh the futile rigor of ordinary strategic planning processes against the hit-or-miss creativity of the alternatives. We believe the two can be reconciled to produce creative but realistic strategies. The key is to recognize that conventional strategic planning is not actually scientific. Yes, the scientific method is marked by rigorous analysis, and conventional strategic planning has plenty of that. But also integral to the scientific method are the creation of novel hypotheses and the careful generation of custom-tailored tests of those hypotheses—two elements that conventional strategic planning typically lacks.

Creating social media success

Bridging “scientific” data-driven analysis with more human understandings is challenging. In part, this is an epistemological problem — a fancy word that really just means that people who are good at one type of intelligence often fail to understand the other type of intelligence or lack the skills to develop understandings outside of their particular data-analysis stream.

I don’t think it is impossible o combine the 2 types of intelligence. It’s just challenging and businesses should embrace challenges with such rich potential to improve their market performance.

Text analytics

As an example, I recently spoke at the Text Analytics Summit — you can see my presentation here.

In the presentation, Mark Eduljee, head of social listening at Microsoft, and I made the business case for building deeper understandings from social media posts. Rather than simply condense rich social media utterances to sentiment numbers or using artificial intelligence to form categories comprising the topic of these posts, we argue businesses must use the attitudes, beliefs, emotions, and how connections respond to the post if a brand wants to develop a deep understanding of the potential of the brand and improve that potential, resulting in superior market performance.

Tracking sentiment over time is a gut-check on how well your brand is doing overall, but simple sentiment analysis doesn’t tell you WHY consumers feel better or worse about your brand.

Big data

Big data are seductive. As Lafley argues, numbers contain reality. They appear scientific and scientific management is BETTER. And, certainly running your business based on numbers is MUCH better than running it based on your instincts.

Data is really good for developing certain types of understandings:

  • How many people visited my site and where did they come from?
  • How did people move through my site and what factors impeded their conversion?
  • Which posts did visitors (or Fans or Followers) like best/ least ?
  • Which advertising (Adwords, Facebook promoted posts, Twitter ads, etc) converted the most visitors?

You get the idea. Big data is best at answering how much, how many, which, when, and where types of questions.

Human understandings

Now, you might use big data to build human understandings, but you’re as likely to be wrong as right. That’s because human behavior — which is what you’re inherently measuring when you use big data — isn’t a perfect predictor of attitudes, beliefs, feelings or future behavior. It’s just that — past behavior.

Yet, as a brand, you’re only mildly interested in what happened. You’re primarily interested in what’s going to happen in the future. As a marketing strategist, it’s nice to know that something you did worked, but you really need to know WHY it worked if you hope to reproduce the social media success you experienced — or avoid social media failure.

Human understandings are best for answering questions like:

  • Why did consumers buy a competitor’s brand rather than mine?
  • Why do some consumers complain about my brand?
  • Why do some connections share my content (or engage in other ways) and some don’t?
  • What do consumers need that I’m not giving them right now?
  • If I did X, how would consumers respond?

Need help?

Whether you need a complete analytics strategy, some help with brand marketing, or some consulting to optimize your existing social media marketing, we can fill your digital marketing funnel. We can help you do your own social media marketing better or do it for you with our community managers, strategists, and account executives. You can request a FREE introductory meeting or sign up for my email newsletter to learn more about social media marketing.

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