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Online Advertising

Why Online Ads Trump Traditional Advertising

How much online advertising could a company purchase if it had a Super Bowl size ad budget. With a single 30-second spot costing millions, this famous question challenges the ROI of traditional ads while sharpening the focus on the value of targeted and measurable online advertising.

Let’s keep our advertising budget relatively modest.  A full page spread in a magazine can cost upwards of $25k, for one issue, and the ROI is highly debatable.  Now, if you took that traditional advertising budget and spent it on online advertising, what results can you expect?

$25k could buy you:

  • 7 Years of Google Search Advertising – Competitive keywords can cost as much as $5 per click, but even setting a monthly budget of $300 still stretches our budget. Over 7 years, one ad might be viewed hundreds of thousands of times, generating a steady stream of qualified leads to a website.
  • 4 Years of LinkedIn Advertising – With a pay-per-click cost often exceeding $10, LinkedIn returns the favor by being the most targeted form of advertising available for professional social networks. Assuming a monthly budget of $500, 4 years of targeting ads at specific job titles, company size, level of seniority and industry can pay off big.

Considering online advertising? Start with Google and LinkedIn and avoid less targeted forms of online advertising, like banner ads.



Accelerate Into the Curve: 5 Ways to Get More Sales Traction in 2018

Does your marketing and sales momentum slow down at the end of the year?  2018 is just around the bend but don’t be tempted to take your foot off the pedal.  Accelerating into the curve is proven to give drivers the extra traction needed to navigate tight curves.  Do the same with your marketing and give your sales extra momentum heading into the New Year.

2018 will be a year of convergence for digital marketing, bringing together data, automation, and innovative technology that has only recently become available.  And the tools you’ll need will cost you less than your cable bill.

Here are 5 lead generation trends to help you accelerate your marketing into the curve:

#1 Instantly Know Who Visited Your Website

More than 98% of the people who visit your website leave without a trace.  For sales staff, Google Analytics and other web trackers are useless, providing page views, location data, and zero personal information.

For less than you probably spend on Starbucks each month, you can integrate a powerful new tool with your Google Analytics data that will instantly tell you the names of companies visiting your site, plus the closest LinkedIn contacts to you.  Oh, and it can reveal the e-mail addresses of visitors.

Knowing who is visiting your site and what pages they are interested in allows your sales staff to be proactive, rather than waiting on the phone to ring.

#2 Data Driven E-mail Marketing

Do more with your e-mail list than sending an occasional newsletter or one-off campaign.  Unlock the power of automation to turn hit and miss campaigns into a lead generation engine.

Creating a “drip” e-mail campaign is powerful in two important ways.  First, it lets you build out an entire series of e-mails to send to targeted segments of your marketing list, putting e-mail marketing on autopilot.  Second, valuable data is generated as the campaign runs, such as open and click rate, which can guide your sales team in proactively contacting leads.  With the right tool, e-mail automation can even adapt on the fly based on recipient behavior, such as sending more information on a specific product that was clicked don.

#3 Hooking CRM Up With Your Marketing Tools

All too often, your marketing data is scattered across different systems – e-mail, website data, social media, and CRM – without a cohesive view of your audience and marketing performance.

Does your company have a marketing and sales data integration strategy? Integrate marketing and sales tools with a common dataset to give your sales team real-time intelligence on leads, such as instantly knowing which Google ad generated a conversion or understanding the behavior of a specific website visitor.  Armed with information, sales staff are better equipped to leverage opportunities as they evolve.

#4 Precision Advertising Using Social Profile Data

Consider the amount of personal data Facebook collects on its members: age, gender, interests, and location data.  LinkedIn captures even more data points, including job title (past and present), industry, seniority, academic degrees, and the list goes on.

Whether you are selling to consumers or businesses, your potential clients are on Facebook and LinkedIn right now.  Tap into the wealth of data each platform offers advertisers to target exactly the audience you want.  Plus, with features like LinkedIn’s new lead generation forms, it is easier than ever to capture leads.

#5 Training Google Search to Love Your Content

Building a great website without considering SEO is like building a storefront in the middle of the desert and expecting people to know where to go.  You need to put up lots of road signs that tell Google where to send people when they search for your products or services.

SEO is an art and a science, but the best rule of thumb is to write authentic content that does not attempt to manipulate Google, which can penalize you for trying.  Rather, follow a few best practices with how you write your content and build links to it and Google will reward your site with higher rankings on the keywords that matter the most to your business.

Where the Road Meets the Rubber

The pattern here is all about convergence.  Google Analytics data is merging with third party tools that merge with CRM and other data.  Companies that can tap into this ecosystem of data and automation tools are well poised to hit 2018 running and with their lead generation machine on full throttle.

3 Science Experiments for Your Marketing

Love, fear, rage – these are the 3 innate emotions that psychologist John Watson promoted as keys to effective advertising.  The founder of behavioralism, Watson was one of the first social scientists to crossover into marketing when he joined the J. Walter Thompson advertising agency in the 1930’s.  Watson’s defection introduced the concepts of market and demographic research, beginning a long line of social science innovation in marketing.

As someone with a formal education in psychology who crossed-over into marketing, I am constantly on the lookout for signs of social science use and abuse cases.  Although many psychologists spend their days with patients, many more focus on experimental design and research.  As such, an education in the field entails rigorous math and statistical analysis training.

There has been a massive shift over the last decade to performance-driven marketing.  When it comes to promotion, I strongly believe that if a campaign can’t be measured, it’s not worth doing.  From website lead conversion and SEO scoring to e-mail open rate and social media metrics, measurement is transforming what used to be a business of gut feelings into marketing science.  Return on marketing investments is now easier than ever to calculate.

If measuring is like learning to walk, then testing is like running.  What has really been exciting to watch is the introduction of experimental techniques into the field of marketing and advertising.  Compared to the experimental procedures of social science, marketing is still taking baby steps, but it is encouraging to see how AB testing in particular is evolving.

AB tests have become quite common in many digital marketing tools.  AB (or split) tests borrow the concept of a “control group” to determine the effectiveness of a variation.  We’re not talking about the “Pepsi Challenge” here.  True AB tests should follow the scientific method.

I really am going to dive into some real-World examples of AB testing that can provide insight into your marketing efforts, including online advertising, e-mail marketing, and branding, so not much more of a science lesson here.  I will add that statistical analysis is a huge aspect of AB and other types of experiments, requiring much more attention to detail than I plan to go into.  From Analysis of Variance (ANOVA), and Pearson’s Chi-square, statistical analysis requires many calculations and involves concepts like margin of error, degrees of freedom, and probability distributions which my math friends can cover in more detail.

As a marketer, I value the following AB testing methods and encourage others to use them as well.  As someone with statistical analysis training, I can’t help but critique the science, or lack of science, involved.

Online Advertising AB Experiment

Google AdWords has supported AB split tests for years in a primitive form that required users to collect data from AdWords then use other tools for analysis.  Recently, Google introduced Experiments which allows simultaneous running of two ads or groups of ads with automated analysis.  It is most commonly used to test whether a change to an ad will improve clicks vs. the current ad, although there are many more possibilities.

If you’re an AdWords user, start by creating a copy, or Draft, of the Campaign you want to use as the variant group.  Your original Campaign is now your control group.  Modify the variant group according to your hypothesis, e.g., “will changing the ad copy to include certain new keywords make a significant difference to click-through-rate?”  I recommend only changing one variable, like ad copy, since too many changes make final interpretation very tricky.

When your Draft, or variant, group is ready, you will have the option to “convert” the Draft to an Experiment.  The experiment runs nonce you set the end date and give the experimental campaign its own daily budget.  The longer an experiment runs, the larger the sample size will be of impressions and clicks, which makes the test results more reliable.

When an Experiment concludes, Google AdWords provides a little dashboard of the results showing metrics for control and variant groups.  Statistically significant differences are indicated.  Woohoo, that new ad copy really worked!  And, oh, your costs were also significantly different.

Overall, AdWords Experiments have all the hallmarks of sound analysis.  A great tool that can improve your marketing and sales performance.  But what statistical analysis is Google using to determine significance?  Google provides no information about the math behind the test, making Experiments a black box technology.

E-mail Marketing AB Experiment

Many e-mail marketing and automation services abound, such as Constant Contact and HubSpot.  Mail Chimp is one of my favorites for many reasons, not least of which is bang for marketing buck.  Mail Chimp offers some powerful AB testing tools that can improve e-mail marketing performance and success.

Select AB Testing Campaign when creating a new Campaign in Mail Chimp.  A Variables tab will be added along with the normal Campaign configuration tabs.  Under Variables, users can select what to test: Subject Line, E-mail Content, Sender Name, or Send Time.  Selecting Subject Line, for example, allows testing of up to three different subject lines.

When testing two subject lines to determine which is the most effective at getting recipients to open an e-mail, Mail Chimp will randomly select 50% of your mailing list contacts (this can be adjusted) and then send subject line A to 50% and subject line B to 50%.  All these 50%’s can be confusing at first until you understand how Mail Chimp intends to use the rest of your mailing list.  Users can also base success on click through rate or even how much revenue is generated from each group.  Mail Chimp recommends sending at least 5,000 e-mails to each test group to improve test reliability and waiting at least 4 hours between sending to the test group and “winning group.”

The great feature, from a marketing perspective, is that once the AB test completes (based on the metric you set, like open rate), Mail Chimp declares a Winning Group, then automatically sends the best subject line to the rest of your mailing list.  Cool!

What’s confusing is that Mail Chimp uses terms like “variables,” “randomness,” and “sample size,” to describe the test, something that makes it sound like there is statistical analysis involved.  But, at the end of the day, it’s just a Pepsi challenge on a large scale.  And to achieve “reliable” results (don’t mistake this for statistically significant), the AB test campaign would require at least 20,000 e-mail recipients, according to Mail Chimp.  Not too many businesses sport that kind of database.  The simple addition of a chi-squared analysis would make this feature even better, explained in the final example.

Branding AB Experiment

Let’s say you want to know if a new tagline on your company’s home page will be better than your old one.  You could focus group this the old fashioned way, or use the power of crowd sourcing and AB testing through social media.  With a asocial network like Twitter, an answer waits to your burning question regarding a new tagline.

Google “social media AB testing” and you will find several examples of how some marketers would use Twitter to test branding and messaging.  For most, it boils down to tweeting tagline or message A and B then seeing which one gets the most clicks.  Again, this is a Pepsi challenge method.  It kind of works if you don’t think too much about statistics and, of course, it’s not like marketing is rocket science.  I agree that marketing is about solving problems and testing ideas quickly, but also think there is room for improvement.  And maybe a little math won’t hurt.

Here’s an experimental design that is a compromise between pragmatism and sound science.  Start with a hypothesis: is tagline B more effective at click through rate than tagline A?  Place the text of each on the same background image on which it is to be used on your home page, sized for Twitter posts.  Identify 2 Twitter hashtags that are frequented by your target market.  Now tweet tagline A to the first hashtag and tagline B to the second.

Clicks are one component of determining the success of your test.  If tagline A got 100 clicks and tagline B 150, does that mean that B wins?  Not necessarily.  A might have been viewed 200 times and B 500.  You need to take into account sample size for each group, and in this case we’ll use impressions, or the number of time the tagline was viewed, as the sample size.

Unlike AdWords or Mail Chimp testing, you’ll need to do some manual calculations.  And true marketing scientists will need to equip themselves with a test of significance to really understand their social media AB branding test.  Jonathan Weber provides a good roadmap in his article The Only Statistical Significance Test You Need for Web Analytics.  Not going to repeat the math, but if you do it right, you will have gained new marketing superpowers.