Kris

Great analysis goes further to explain WHY

April 11, 2015

A lot of the data analysis revolves around the business questions that people bring to you. Horrible questions are like when analytics people gets a simple data pull type of requests, but no background information or explanation of why they would be needing a data. Hopefully, that is the not the case for you, and we hope to believe in a perfect world where all marketers and business folks are asking great business questions.

Typically, marketers are trying to achieve something out of their marketing efforts, and we (analysts) are trying to meet their needs by providing data and insights back. A lot of times what’s lacking in this analysis or the challenges from the marketers lack the awareness of probing into the ‘why’ factor in the change in the data trend.

What does this mean?

For example, when paid search traffic goes down, a typical analysis would look at the traffic, ad spends, it’s associating pages, the list of keywords, conversion rate, etc. Usually, great analysts go far more because awesome managers, executives, and business people ask challenging questions. That means, showing ad spends going down or conversions going down is not just enough, they’ll ask “why” several times.

Heavey web analytics users will find over time that just using Google Analytics alone will not give you the answers you’re looking for. Here are few ideas to use help you get better underlining reasons to why things go up or down in traffic.

Segmentations, filters, drill down and add columns

Use macro data like Google Trends, Webmaster Tools, etc.

Look at competitor’s data

Use surveys

Perform A/B testing

Segmentations, filters, drill down, and add columns

As an exampleHere is a sample view of the detail filters and segmentations you can build our of google analytics. Segment generation is a pretty powerful feature as you can create a rule that applies to your entire reports and compare. The segment applied is sticky so you don’t have to always have to think about adding when you switch between reports. Much other analytics tools has a similar feature, but it is something you have to take advantage of.

ga-traffic-segment-042015

The segment could be a cumbersome feature especially if you want to play around to quickly find something that sticks out. The secondary dimension helps you quickly add a column, so you can sort and compare by different date range. Typically that could give you something interesting that explains more details in your data trend.

ga-traffic-2nd-dimension-042015

Here is a basic filter feature within google analytics, but it is pretty helpful when you want to exclude or include certain data sets so that you can narrow down to particular data that may explain the cause of the traffic changes

ga-traffic-filter-042015

Use macro data like Google Trends, Webmaster Tools, etc.

Checking out data on Google Trends and many other market data could give you a broader sense of what could be happening.

A hypothetical example here: Despite allergies, queries are going up peaking at April/May, yet the search on major allergy relief pills brands are flat? What’s happening here? Is something taking away your search/interests share away? Are natural remedies growing? Definitely a good context of data if you want to look at a more higher level explaining traffic trends.

google-trends-allergies-042015

Look at competitor’s data

Site’s like SpyFu (note: there are many other similar tools out there), allows you to see what’s happening on site’s that you don’t control. Let’s take a peek at Nike golf site. They’re kicking ass in branded terms in Organic Keywords, no surprises here. But it looks like in Paid Keywords, you’ll find ‘Callaway gold drivers’ as one of term Nike is bidding on.

It is pretty common for brands to buy competitor’s terms to steal traffic or interests. Idea is to watch out of potential changes in competitor’s marketing strategies because that could explain changes in your traffic or business performance.

Use surveys

I’m a big fan of Qualaroo because it gives me that flexibility to whip out a quick survey to place on a page and control the survey introduction timing. It helps us ask site visitors directly and answer potential questions that quantitative data that Google Analytics couldn’t answer.

I won’t write much about this because it is pretty no-brainer, but this is some powerful stuff if you ask the right questions. I like using the open text questions and take advantage of that initial insights to help me structure better questions next time.

voc-qualaroo-042015

Perform A/B testing

The reason why we test is because “We don’t know what the outcome is going to be”. You can spend in a meeting room all day, but at the end of the day, you are not going to know if it is going to work. Go test it out! Tools like Optimizely will allow you to whip out some basic A/B testing on the site.

If setting up A/B testing and putting that JS code is too much for you, then try tools like UsabilityHub.com. You can basically test your idea during your conception phase by asking users to perform some action to get data. You can link your existing page and ask users to perform certain tasks. See if your site sucks or does its job.

userbilityhub-jira-example-042015

How to Select the Right Digital Marketing Analytics Tools

March 27, 2015

While a lot of us are working hard with digital marketing data to find that actionable insight, let’s not forget the importance of selecting the right digital marketing analytics tools.

I’ve talked to many people, including marketers, marketing managers, CXO, etc. Many times, I come across about the discussions around tools.

Even cases where people talk data before it is even collected. This situation is what I refer to as:

Marketers firing before they aim their strategies to the target.

I come across this situation a lot from many marketers. People talk about collecting data, or even tools or data vendors to purchase too many times. Even blaming the tools for lack of data discipline.

It is very important to really start the planning, and define your business’s objectives and goals. So that you have a clear guidance on what tools you need to solve your digital marketing problem.s

Maybe even before that, you have company’s vision and mission, that many employees are trying to contribute towards. Let’s make sure to recognize all of that.

In marketing, you have to have a plan and a goal. As you may know, Google Analytics and many other tools do well in providing click, event, traffic data on digital properties, but it is not until someone makes sense of it to bother digging deeper into it to find those “insights”.

This is why I like to refer to this process that I use to frequently approach optimizing the digital experience and run much more a/b tests while transforming the team into a data driven organization.

Just as if you’d ask a professional photographer, “what camera are you using to take that nice photo?” What about his/her skills?!.

In a digital data world, I hear marketers talk about tools and data collection, but not enough to the action or the strategies built around the hypothesis generated from the analytics.

Something is really missing here. Sorry, data is not going to solve problems, it is you or the people who need to solve the problem will. With a massive amount of marketing technologies out there, marketers are saying that they’re not getting the full benefits of the data.

By the way, I’m not making this up. According to Econsultancy’s survey on marketer’s top data challenge shows, the key theme is turning data into actionable insights.

I’m glad, happy, and proud to say that I have had excellent leaders who managed me, and it is such a blessing to have their guidance.

When I moved from Japan to California in 2006, I knew data landscape was changing and disruption was happening in the marketing space with the internet.

It was going to change how marketing and eCommerce adjust the course of the business, just like how traditional business analytics have done for businesses.

While data have become widely available and accessible, it has given a more fuzzy feel for marketers that they can wear multiple hats to do everything including (not limited to) sales, A/B testing, analytics, data management, media buying, etc. This is dangerous. When resources and expertises spread thin, especially while the direction is not clear, data is pretty meaningless.

Brainstorming and data discoveries are fun exercises. Yes, it is fun, but at the end of the day, we’ve all got jobs to help business perform. So at some point you really have to select the critical few must have data points that differentiate ‘great’ from ‘good’ to know data.

So, the tools and data, all of that good stuff should come from good planning and requirements,

I wrote this in my previous blog post “Converting Customer Behavior to Business”, but a lot of people follow the process of “Fire -> Aim”.

Instead of “Ready -> Aim -> Fire -> Debrief”. So if you really want to focus on the data, tools, that will bring insights to help your business perform, you have to know what you’re measuring for.

People need to understand what is the problem we’re trying to solve for the business.

One manager has mentioned to me (not in exact words..) “People working within constraints, even that means working very few resources, budget, or tools, people actually can learn quickly about what is missing and required to drive performance”.

That meant to me it is, there are very important critical few that you should be focused on and solve that problem. Same goes with digital marketing analytics tools selection.

My basic suggestion that I recommend approaching selecting tool is following these steps:

Map and understand your business model. How is your customer is going to engage with your business?
Design your marketing funnel
Select your marketing technology tools

For #3, I would look into the solutions around:

Quantitative data
Qualitative data
A/B testing
Capture, manage, known user information (CRM or Email Service Provider)
Data Manager (e.g. Tag Manager, automation tools, productivity tools)

These are super key to start with.

That is because when you have constraints you have to focus on the critical few to act quick and deliver results while really feeling the exposed pain points. No fancy big data stuff here.

Quantitative Data

You want basic analytics around measuring your funnel. Conversion rate, revenue, traffic, AOV, engagements, etc.

Qualitative Data

Quantitative by itself does not give you all the insights, you have to listen to your customers and have the capabilities to track and gain feedback from your customers.

A/B testing

Doesn’t have to be a tool, but idea is to track and use your analytics tools to measure your tests so that you can iterate on changes to improve your business performance.

User Information (CRM)

You need to be able to understand your customer by their name, email, address, profile, etc. Typical web analytics tools aren’t designed to help you manage your customer data. So you’ll need a tool to help you manage that.

Data Manager // Productivity Tools

Tools acting in silos aren’t very helpful. There are tools out there that will help you track, scale your resource by automating many tasks you could be avoiding. Like tag manager, automation tools like IFTTT or Zapier.

Only with many options, time, budget, and all that happy stuff, people will tend to lose sight, explore, and wonder off from the core problems.

To me, that made a lot of sense. In fact, I had to go through painful business transitions during the bad market conditions. Learned that focusing on the critical few helps you find the important things to work on and fix fast.

And when you have few goals with a very high bar to hit, then it creates a sense of urgency. Stressful, but a good one. And it helps you prioritize and be selective about the tools you’ll need to solve the problem.

I think this is why Growth Hacking mindset is really interesting as fundamentally you’re collaborating with peers under the constraints of resources or budget. Then you really have to grow and perform, otherwise, startup’s fund goes dry.

So you end up picking that one or two KPI, putting heads down and optimize the crap out of it by looking at the important things that matter to gain insights, to then act on it.

Here is a great article about consumer insights on Huffington post “What Are Consumer Insights and How Do They Impact Marketing Effectiveness”. Article’s concept shows that it’s key to getting insights out of data, because a lot of time when analyzing data in web analytics tools, performing A/B testing reading the results, we as analysts are trying to find that story, and the “why” factors on why things have turned out a certain way.

Point is, consumer insights, having that mindset to think like a customer is important, so you’re not just saying things like “traffic is up because we spend more money on Google’ Paid Search Ads”. So go beyond measuring traffic and conversion rate.

My favorite line of text from that article.

“Consumer insights provide understanding that leads to marketing on a more direct and personal level.”

We’re all obsessed with data and tools, but let’s really try to focus on the “why” factor and follow a process that makes sense.

See if that helps you set the right expectations and be part of the fun journey in finding that “insights”, and add awesome tools to your portfolio of capabilities after understanding what it takes to solve your business problems.

Enabling Mobile App Tracking for Analytics

March 21, 2015

Enabling tracking

One of exciting project I’m working on involves enabling mobile analytics capabilities on mobile native apps. Generally speaking, good news is that many tools that we’ve been using in web analytics world support mobile analytics tracking. I’m sure through recent years of mobile growth, I’m sure these companies have worked really hard to bridge the gaps to make marketers data needs reflected in the complex work of mobile app development.

The concept of slapping a tag or pixel on mobile app is quite different in mobile app. You’ll here a lot of the word ‘SDK’ or software development kit. That is because pretty much all the 3rd party analtyics tracker would need to be deployed into the mobile app’s code.

Looks like this.

Google Analytics Mobile SDK example

Source: Tetsuya Shiraishi – Google Analytics Framework

This google’s article on best practices for mobile app analytics is actually informative. I believe it could apply to most of the 3rd party analytics tracking, too.

Track different apps in separate properties.
Track different platforms of an app in different properties.
Track app editions based on feature similarities.
Track different app versions in the same property.

So if you have two different apps and have each in both Google Play and Apple App Store, then it’ll look like this in Google Analytics:

GA property 1: App A & iOS
GA property 2: App A & Android
GA property 3: App B & iOS
GA property 4: App B & Android

Great news for those who have been using Tag Manager. Yes, many tag manager systems support mobile app deployment, hence you can use tag manager to deploy varoius 3rd party trackers without having engineers to code gazillion SDKs. Make sure to check with your vendor and read through the documentations.

You also want to make sure that the tags you plan to install into tag manager can talk with the data objects defined within tag manager, sometimes called as container tag and associated universal data objects.

Campaign Tracking

Event though you might see tag manager saving many of the pain points, that is really not the end of the mobile analytics tracking saga. Especially, some tools are optimized to worked in certain ways, so that the app store data could be integrated with the analytics tools.

Google Analytics for example, in order to get the Google Play or Apple App Store data to talk with Google Analytics, you’ll need to make sure that Google Analytics is set up with proper store identifier ID. That’ll give you valuable information like app installs per campaign sources, where without this set up you’ll lose the source attribution data when users land on the app store landing page.

Here is a good video that describes this process.

Considerations

Although I highlighted Google Analytics in examples here, it is important to understand your business needs and priorities. At the end of the day, most marketers want to know which and how each marketing stategies or tactics are working, so they can make better decisions to improve the marketing spend. That said, various tools are positioned to do well in different areas:

Tracking campaign performance measuring against the installs
In app usage and engagement. (Typically event tracking based tools do well in this)
A/B testing
Campaign attribution analysis
Mobile app platform performance (or server side performance)

Think through and discuss with your team on the needs. You’ll soon learn that to get all the needs out, there are probably going to require many SDKs implemented than what you may have initially thought.

A lot is happening in the mobile app tracking for marketing, so feel free to share your experience here.

Great writings/discussions on mobile analytics

http://www.kaushik.net/avinash/mobile-site-app-analytics-reports-metrics-how-to/
http://www.quora.com/What-does-Mixpanel-do-that-Google-Analytics-is-incapable-of-doing
http://www.analyticshero.com/2013/07/24/web-analytics-vs-mobile-analytics-whats-the-difference/
http://www.kaushik.net/avinash/mobile-marketing-customer-data-acquisition-behavior-targeting/

Key behaviors in marketing analytics career I learned

March 8, 2015

I’d like to share some of my lessons learned from my career working with data in marketing, focusing around the things that I tried which I believe added value to the business.

Share what you have learned

In this field of analytics and marketing, it is pretty common to find people who have learned things keep those knowledge and insights for themselves as if that belongs to that person. This is not unique to people in analytics, but that is a pretty lame move in general. Your team and people working in the company are trying to perform, and analytics are important function of this entire business process. The biggest part of analytics people’s job is to support solving business problems through data, evangelize data and insights, support the business move forward achieving it’s goals. Without sharing the data and insights, there is no evangelizing.

Also when you assume what you have shared is being translated to others, don’t assume they have shared with others. Many companies have great internal resources and capabilities that allows employees to share what’s going on. If not, then I’m sorry to hear that. In tools like Jive, it is important to create your own area and start publishing your learnings. You can always reference that source, and communicate to the wider audience. This is obviously not the only way, but the idea is try and find ways to share more than that one point of contact, boss, CMO, etc. What good it is to not share your awesome work and let it sit and decay.

Be curious, build a habit to find answers and apply

Every job, great jobs come with opportunities to learn. Sometimes it may come in a form of unexpected things you have to deal with, or things encountered through new projects, or even come by terminologies encountered through work place, experts, school, and peers. Never be shy to ask, there are no dumb questions. There is Google for us to search wealth of knowledge online as well, and we should take every advantage of it. Up until this point, I hope people do search for their own answers when encountered with questions that you want answers to. Curiosity in a dictionary states the meaning ‘a strong desire to know or learn something’, and for me, this is one of the must have behavior in analytics expert.

Another key thing is to ‘apply’. One of the common characteristics I see in successful people in any industry is that they don’t just consume things, but they apply their learnings and practice what is in their mind to help drive tangible or real impact. Example could be… don’t just talk about tools and wish it get’s purchased or implemented, find ways to bring in a proof of concept deal, and prove the value of the tool.

Communicate and be clear

Communicate your findings, but reserve for the insights. Don’t communicate for the sake of communication. When you communicate, you want to make sure you’re respecting every moment of people’s valuable time. What that means is, be clear as much as possible. Whatever you find through data, you don’t want that to be miss interpreted or taken action in a wrong way from what you originally recommended. If you have found something interesting, but not necessarily gives an answer to the business question, steer the communication to drive testing, or excite others to ‘test it out’. A/B testing tools are so cheap now a days and it is no longer something we should be wait for. Clear communication needs to have a direction, and expected action or outcome that is lead by you.

Always be clear on the problem you’re trying to help solve.

Unless you have some spare time and just playing around with data, don’t waste your time solving and analyzing problems your team, department, and company is not solving for. If a data practice you’re involved isn’t involved in the problem business is not trying to solve, that is a super low priority thing to work on. When analyzing data and concluding your findings, make sure to go back, and ask if the finding helps solve the core business problem or not.

It doesn’t help to be confused on the problem business is trying to solve either. If you’re trying to analyze data for some problem that is way off from the real problem, then that’ll be a huge waste of time. Make sure the problem is clear and you really understand it.

In a knut shell…

I might have learned the hard way to understand the key behavior points listed here (which is not limited to), but hope you find this list of key acumens required in marketing analytics helpful.

My apologies if this came out to be me puking out things many people already know, but I thought it was important for me to put this out and see if this helps for others. Being in this field of data for over 12 years, there is definitely more to it, but I guess I’ll stop here and write more in near future.

Design thinking process and data driven optimization

November 9, 2014

As a data person who looks at data to help marketers understand what’s happening on their website or digital marketing programs, it is part of our work to recommend the business on possible next steps and actions to improve the business outcomes.

Going to analytics related conferences and seeing many charts and slides from various experts, it is very common for us to come across data analytics and optimization process diagram.

Usually, those work out really well. It helps us data experts gain a sense of structured process on how to make our data work for our business.

Optimization Process 2013 Nov

In reality, in the Optimization phase, analytics folks would need to work with web designers, UX expert, engineers, etc. These experts are likely going to work or have different thinking process than analytics experts. I won’t be able to cover the entire subject around design thinking process, but I think it would be very interesting to understand how designers think around solving problems.

I won’t be able to cover the entire subject around design thinking process, but I think it would be very interesting to understand how designers think around solving problems.

A typical scenario of data person handing over insights and recommendation to a designer would end up in some ideation or brainstorming session between analytics person and designer to come up with the test version within the A/B testing practice. If the designer has some knowledge around data and how to optimize, then that is great. It makes many conversations very easy, and great designer would be able to digest the analytical findings and run their designer’s thinking process to come up with some great test design.

If the designer has some knowledge around data and how to optimize, then that is great. It makes many conversations very easy, and great designer would be able to digest the analytical findings and run their designer’s thinking process to come up with some great test design.

This is just my limited view on things, but when I attend A/B testing type meet up or conferences around CRO, I feel like the key designers aren’t there participating at the event. Usually, it is some data expert from marketing division or someone who manages the Optimization team, but not so much of actual producer type of designer.

I feel like in modern marketing, all roles should be involved with data and be part of the optimization process. They should be proactively engaged and participate.

I always wondered how the test designs are generated and introduced as something that may work better than the control. A lot of designers are very opinionated and with full of ideas. It is always interesting to hear what designers articulate and explain their design.

I think the first area of things we analytics people should start to look into is the studies behind the design thinking process.

I’m sure there are many kinds of methodologies, so I’ll just introduce what I found from Stanford University’s d school site and good old Wikipedia. I think by checking out some of these thinking, and be informed would be a great starting point to make collaboration between right brain people and left brain people even better.

Some references to understand design thinking process

Design thinking process links:
http://dschool.stanford.edu/redesigningtheater/the-design-thinking-process/
http://dschool.stanford.edu/our-point-of-view/#design-thinking
http://en.wikipedia.org/wiki/Design_thinking

After checking out some of these sources, let me try to understand the what’s being quoted from one of the sites.

“The Design Thinking process first defines the problem and then implements the solutions, always with the needs of the user demographic at the core of concept development. This process focuses on need finding, understanding, creating, thinking, and doing. At the core of this process is a bias towards action and creation: by creating and testing something, you can continue to learn and improve upon your initial ideas.”

Let’s digest this a bit…

“The Design Thinking process first defines the problem and then implements the solutions, always with the needs of the user demographic at the core of concept development.”

Ok, this is great!! Because data people can help prioritize what the problem is from the data that marketers and designers need to focus on.

“At the core of this process is a bias towards action and creation: by creating and testing something, you can continue to learn and improve upon your initial ideas.”

Awesome!! Isn’t this what the CRO (conversion rate optimization), and data people are trying to do as well? To look at data, create a hypothesis to test, and TEST TEST TEST !!

So it looks like in Stanford’s design thinking process, it consists of these 5 steps:

EMPATHIZE: Work to fully understand the experience of the user for whom you are designing. Do this through observation, interaction, and immersing yourself in their experiences.

DEFINE: Process and synthesize the findings from your empathy work in order to form a user point of view that you will address with your design.

IDEATE: Explore a wide variety of possible solutions through generating a large quantity of diverse possible solutions, allowing you to step beyond the obvious and explore a range of ideas.

PROTOTYPE: Transform your ideas into a physical form so that you can experience and interact with them and, in the process, learn and develop more empathy.

TEST: Try out high-resolution products and use observations and feedback to refine prototypes, learn more about the user, and refine your original point of view.

This is great to know. It looks like to me, the connection with data people and design people need to happen a lot around the ‘Empathize’ and ‘Define’ phase. That is where we may different processes in coming up with a hypothesis.

No wonder designers don’t understand what data people say because designer’s ‘Empathize’ phase could totally be in ‘Optimize’ phase in my earlier diagram, where we’ve defined the hypothesis already. When designers seem to create hypothesis in the ‘Define’ phase.

I may be wrong on some details as many people and processes are different from the various business environment but hopefully, you get the idea.

For me, as a data person, who wants to be a leader in my space, would need to understand how your peers think and solve the problem. This is key to successful collaboration and solving problems together.