Optimization

I came across this article and read the research Microsoft did with Contently’s Document Analytics, thought it was insightful findings.

Here is the part that caught my attentions, quoted as:

Microsoft discovered that landing pages requiring email addresses in exchange for downloadable content were 25% more effective at capturing email leads, but that embedded email forms within freely accessible content were more effective at capturing content reads. Subscribers who entered their emails in embedded forms were 51% more likely to read the entire piece of content, according to a Contently case study.

Here are the images of the difference on where and how the email address was asked by the site.

In the first example, it is a standard deployment you usually come across where you land on the page, provide an email address, and then you get the PDF file.

The second example is the delayed gate, where you download the PDF file or the material of interests, then after few pages in, you’re asked to provide your email address in order to access rest of the material.

As stated in the quote above, it’s fascinating how human psychology plays into different outcome just by asking the email address in different timing.

It might not be surprising to some people, but it is interesting from a standpoint where this may give more ideas to how we A/B test and optimize landing pages to drive results that match certain business outcomes.

It is common to find marketers obsessed with putting out content to capture more traffic and leads, but in many cases, the value of the material is really within the file and what’s written.

A lot of money could have gone into the research, write, and distribute. What good is an email address if people are downloading and not reading it because that’s how end users understand the positioning and value of the companies who provide these materials.

That doesn’t mean every marketing objective is to make people read the entire thing. Capturing user attention and gain volume of quality leads as you get in front of your audience are not a bad marketing objective.  So if you can pull it off with the standard landing page and gating it, then that’s fine.

I guess this opens up more ways for us to test ideas beyond CTA, creative, message, but also “timing” of how we capture leads or reach our audience.

I thought I’d share some interesting read that I came across. Enjoy!

Here is the original article, check out.

source: http://www.mediapost.com/publications/article/280860/contently-launches-document-analytics-for-tracking.html

Integrating audience data is a very powerful way information a marketer could have to plan and reach the right segment of the audience a brand has to go after. As audience data becomes available or able to integrate into tools like web analytics software, companies can now optimize the site experience by understanding three key points here:

1. Type of audience the site is attracting.
2. Type of audience is the e-commerce site converting.
3. Type of audiences the site should attract more due to a better  opportunity to convert.

Google Analytics allows Ad Words integration with retargeting enablement as an option. In that process, you can determine if you want to pull in audience data to Google Analytics. Make sure to check with your privacy policy so that these type of data enablement aren’t violating your privacy policy.

1. Type of audience the site is attracting.

First thing marketers typically do is plan their marketing effort around reaching the audience they need to go after. So the 1st point here is really looking at the traffic to your site by the audience type. I’ll be using data from ‘Affinity Category’ as the main examples.

I love using the advanced segmentation. If you have a site with different sections that potentially attract or reach different audiences, then it is worth segmenting your data to a particular section so audience data is applied more specifically to a subset of website’s data. My examples here are actually using particular segments of sessions based on users who have visited a certain part of a site. The reason I did that is I wanted to be specific to a particular category of business the site is offering. (For example, if your site offers watches and necklace, you could be segmenting your data to sessions that include any pages that belong to watches.)

Here is an example. This data shows a segment based on sessions that hit a certain section of the site. Notice the kinds of affinity categories this site section if attracting. You’ll notice that this data is really different when we sort with different data from sessions.

2. Type of audience the site is converting. (e-commerce site)

If you sort the data by the transaction, you may see that the site is attracting and converting audiences that are different from above. Hence people who are buying could be different from the audience you’re getting the most traffic from. If you’re paying and targeting for an audience who aren’t buying your service or product, then you might want to rethink your paid media strategy.

3. Type of audiences the site should attract more due to a better opportunity to convert.

Again, if you re-sort the data by conversion rate let’s see what we have. It is very interesting to see that we have another different set of audience type on top of the list. Cat lovers…

This tells you where you need to acquire more traffic and the kind of audience you could be targeting more of to drive higher sales or business outcomes. I guess we’ll have to acquire more cat lovers who are local news junkies.

One can argue that the audience data may not be accurate. However, it is what Google AdWords are likely going to be using to help you target your ad, and if it is this data that is being recorded saying the cat lovers are likely going to convert, why not take advantage of it.

What marketers need to be aware is that chasing after an audience that does not fall in the target audience the brand is chasing after is a debate you need to have internally within your company/team. Performance is one thing, but if you’re in a business where audience type matters to your brand and the overall impact of how your brand is perceived, then it is a very important discussion that the entire marketing team needs to be aligned on. So that one department is not chasing after an audience for the sake of conversion, but the debate about how you can strategically attract and convert the right kind of audience for the brand and measure it. In that case, perhaps your goal is to make the all that list we reviewed to have certain audience dominate the top 3.0, but think about it. First, let’s analyze your data.

Digital marketing is changing really fast. What digital marketing had a hard time to measure and integrating data such as the demographic or psychographic data is becoming something of a common practice. Looking at A/B testing tools and where it is going at integrating 3rd party audience data is something to look out for, but it won’t be surprising when you see more of this with web analytics as well.

Bonus

Other interesting ways to use this data is to segment affinity category data and see where this audience land on the site and behave. You might have thought you were reaching that audience on a certain section of your site, but it may turn out you have more work to do to place the right message for the right audience on places you never put many efforts on. Maybe this something I can write more about next time.

As I was checking out the eConsultancy’s survey on ‘Top Priority for Digital Marketers for 2013‘ crossed my mind, and decided to take a look at once again.  We live in a world where real time data is used to enhance user experience, and often called as ‘Personalization’.  Marketers are starting to take advantage of such capabilities and you should take notice of this.

It was interesting to me, especially given that I lead building a Content and Conversion Rate Optimization discipline in 2013, I have more appreciation for this area of marketing practice of ‘targeting and personalization’. Early last year, Targeting & Personalization was a topic that was way far away from where my mind was at.  As for optimization, I applied a simple methodology on building experiments based on setting a goal, plan, set up tests, execute and review, and then working at it over and over to optimize customer experience.  It worked.

As you do as many tests as we did (more than 100 in one year), you start to realize certain things.

Such as realizing to ask questions like “How scalable is manual A/B testing?” or “Do we really have to have change requests for every possible success factors we know that performs?”.

What if we let computers run, push winning content automatically, and reserve human power for planning and instructing the machine on what the goal should be.  Stuff like that.  Big companies like Amazon or Google execute that well, but they have many data scientists and engineers…

So I went to meet ups, participated in data events, reaching out to friends and peers in small to big companies and learned few things:

1. I felt that even what I’ve started was a simple first step into website optimization, we are ahead of the curve when it comes to the adoption of optimization. Probably even more mature than many companies when it actually comes to content site-wide testing.
2. Many of my peers agree the wealth of data we have from website or social is underutilized (see below quote highlighted from eConsultancy report Page 13)
3. Facing challenges on the privacy-related limitations/concerns to the ability to target and execute what is relevant for the end users

eConsultancy’s report Page 13
In a recent edition of the Quarterly Digital Intelligence Briefing, 52% of marketers said that the ability to personalize content was fundamental to their online strategy. However, there is plenty of room for improvement. Some of the most powerful methods of personalization are rarely used;

• Purchase history is only leveraged by 21% of marketing organizations, while 77% of those say it has a “high impact on ROI”.
• Behavioral data is employed by 20% of respondents, but 68% of them report strong ROI.
• Social graph data has the highest ratio of all; only 6% usage, while nearly all of those organizations (88%) giving it high marks for ROI.

Now it is 2014, and we have learned a lot, technology has changed a lot as well. Then I read this article “The rise of context for customizing digital experience” and was pretty excited because it basically confirms some digital marketing practices or fundamentals I was firm about.

To sum up this article it is basically saying (at least in my interpretation) before marketers even obtain personal data, there is enough contextual data that is NOT personal data to do smarter targeting and kick ass in digital marketing. And yes, I think that could support following points (Note: again in my view with heavy emphasis on where I am at in this area of data practice):

• You may not have to modify or break the privacy policies to do content targeting.  At least if you’re planning to use non-personal info add more context to what you have about behavior.
• Targeting and Personalization are NOT ONLY ABOUT enabling retargeting in Paid Ads, instead brands can be more relevant to target and match the message that was put out on Paid Ads using owned content or website. (Seems like a good first step to avoid those creepy ads that keep on following you, even after buying the product…)
• Contextual data could be enhanced to be more powerful with many other data such as Social, 3rd party data like Weather, CRM profile data (using non-personal attributes like customer ID instead of PII), etc.
• You don’t have to build a microsite or add web pages by wasting resources and money, but instead being more targeted and smarter by leveraging technology and place the right message at the right timing.

As the article says at the end — brands don’t have to be invasive to know about consumer’s circumstances, the context of their interaction, what is happening in the environment, to deliver a better experience, but still not knowing actually who they are. That’s what happens every day when we interact with people we don’t know personally.

What is awesome about this is, technology is available in the market for brands to start to capitalize from.

An example of a content when targeted based location, weather, temperature, Paid Search (AdWords) adverts.

In late October, I flew to New York to speak for Clicktale’s first conference on ‘Converting Customer Behavior to Business’. I have to say, it was awesome to be there to be part of the speaker along with Bryan Eisenberg and Seth Godin.

I’d like to share with my blog reader on the high-level stuff that I covered.

1) Idea of “Set Ready, Aim, Fire, and Debrief”

Many marketers get this part wrong.  It is very common to hear from analysts who are struggling to get out of being a reporting analyst that marketers who he/she supports always involve the analytics after they’ve planned or executed the campaign.  In other words, what they are doing is Set Ready -> Fire -> Aim, that is why the analytics person gets involved after the marketers fired off their marketing efforts.  Now, here are what goes into the each point, but it is important that the entire marketing team and analytics are following a discipline of Set Ready -> Aim -> Fire -> Debrief.  Note: notice the difference on the order of Aim and Fire.

Set Ready

• Define your objectives and goals
• Know what success looks like

Aim

• Measure
• Listen to customers
• Plan your strategy
• Build hypothesis with good test plan

Fire

• Execute
• Run tests
• Work towards approximated goal
• Make changes based on the outcome

Debrief

• Review the completed test results
• Validate results post production changes
• Then repeat…

2) Process

• Respect the process
• Build optimization rhythm thru process
• Find the bright spot and repeat

The process is very important for anything in digital marketing, analytics, and optimization.  What’s awesome about digital marketing that makes it very different from traditional media is, you can fail and raise back up fast, and optimize your efforts anytime.  Now, here is a process I use for optimization and I’m sure everyone got something similar which comes in various flavors.  When you put this process with the earlier Set Ready, Aim, Fire, and Debrief, this is what it’ll look like.

3) OGSTM Framework

Don’t forget this.  KPI is key performance indicator and you want to track that critical few.  The GOAL right.  All measurements for the strategies and tactics are metrics to help you achieve and optimize towards the goal.

4) Customer objective != Business Objective

• In many cases, Business objectives are not necessarily aligned with Customer’s objectives.  Check out the example from the table below.
• Your website experience issues could come from not meeting customer objective and goals since it is easy to get focused and locked on the business objectives.

5) Test Test Test

Now you know your process, and you know what problems to solve to hit your target. For every pain points or hypothesis, you have to improve your web site’s experience you’ll need to go test it. A/B testing tools are affordable now, and there is no reason not to test. The more you wait and not test, you’re just leaving money on the table.  Or your competitors are just going to get better than you. Testing is the most important thing you can do to learn and optimize to convert customers for your business.

6) Focus on the KPI and keep on executing your strategies and tactics

It is very important to respect the process and keep on going through it many times and got to do it fast. It is like you having to train your muscles to work for you in a certain way.  This point is like highlighting the importance to train yourself to build that cadence for optimization.

7) Don’t forget to focus on the Bright Spot

• Avoid getting caught on the bad numbers.  For example, when you look at bounce rates for areas to optimize, it is easy to get caught up on those numbers (high bounced pages) and lose the bigger picture.  That’s why you have to keep on analyzing and dig deeper beyond bounce rates.
• Focusing on the positive outcomes and finding the ‘Bright Spot’ may give you more ideas and angles to approach problem-solving.  If your test works and you see great results, then celebrate and try to see if you can repeat that on another part of the website.

8) Don’t underestimate teamwork and collaboration

It is pretty common to hear people ask how big is the analytics team and all.  Obviously, depending on the size of the company and where they’re prioritizing resources will vary, but if some assumptions are put in place that we don’t have a good amount of budget, resources, or tools.  In that case, you don’t or won’t have a big analytics team, what would we analytics person do or can do with such limitations.

If you look up for growth hacker (who uses data to help make a startup grow) and see what they’re doing in start-ups, it’ll be evident that they’re getting stuff done with very limited resources and budget.  They’re able to get stuff done because they work with their team, get engineers involved, they have to work across different groups.  A common thing that big enterprise analytics people should be thinking is that how do you work across departments and make sure you get your stuff done, but scale so that you’re influencing beyond your area and changing how departments talk to each other.

What I mean is, if there is anything you need to do like getting a database set up or getting some creatives for A/B testing, don’t be afraid to get them to work together.  Share the results that you see on your measurement and optimization projects.  Get them excited so that they want to work with you.  This is a pretty good recipe to make your optimization process run faster.

Hope you were able to enjoy the high-level stuff that I shared at the Clicktale conference, and hope that gave you some spark for an idea or something to take back to your work. All the process and effort you put in will definitely help you convert customers for your business.