Should we think of web analytics data as an asset?

Web Analytics data as an asset
Generally speaking, asset is defined as “A resource with economic value that an individual, corporation or country owns or controls with the expectation that it will provide [...]]]> I’d like to get my thoughts together on following question.

Should we think of web analytics data as an asset?

Web Analytics data as an asset
Generally speaking, asset is defined as “A resource with economic value that an individual, corporation or country owns or controls with the expectation that it will provide future benefit.” It certainly sounds like useful data could be an asset. As we all know more and more companies are generating profits and contributing to country’s growth through data business. In my view, this applies to any businesses or industries. Let’s focus on Web Analytics.

Experts in the field of web analytics are well aware of the available quantitative data, highlighting few:
- Traffic to the site and by different acquisition channels
- Traffic to pages
- Actions taken on the site by the visitors
- Custom event parameters tied to user behaviors (i.e. people who came from Organic Search and hit that +1 or Like button)

So any of these worthy of calling it an asset?

Many experts will say, you’ll need to quantify the value of your data. Some will say, go analyze it by seeing what if you have an incorrect data, and review the impact on business. In my view, any data in this erra could be valuable and are worthy of treating like an asset. There would need to be priorities on selecting which data, but recent data storage and infrastructure (even for BIG data) has become a commodity. I believe that good analysts will be able to extract value out of massive amounts of data, but would definitely require good planning around it.

When data is treated like an asset, the first thing that comes to my mind is data warehousing the data. Businesses like to spend money on sales side, but if you look at companies that are making huge dollars from pure digital services, they’re treating user behavior data like the most important thing in their company. Let’s equate that to web data. So why so important??

I’m going to borrow Mark Zuckerberg’s statement in Facebook’s IPO filing –

Simply put: we don’t build services to make money; we make money to build better services. These days I think more and more people want to use services from companies that believe in something beyond simply maximizing profits.

Wow… ok so this is no brainer to me. Building great service means, understanding your consumer/customer, and continue to build better experience by continuing to optimizing the service. Nothing about the sales, but about the upper part of marketing funnel relating to the entire business. These companies championing digital data driving faster revenue growth than any traditional companies are analyzing the crap out of the massive amount of user data, and making investments to drive better engagement, shares, user experience, etc.

They are collecting, processing, and storing non-sales data and ACTING upon it. Great services then yield massive amounts of revenue opportunity. Instead of focusing on sales and review what worked or what didn’t, then try to replicate/apply the learnings, these companies look at consumer’s footprints and other non-sales measures to lead the path to better service or growth.

I can hear people say, “well I got my web analytics data in Google Analytics or in Webtrends or Omniture SiteCatalyst so that’s it right?”.

I say go do your homework and see how long they keep the data, and ask yourself do you really want to keep your company’s asset in someone else’s hands and always have some dependencies around it on third party? Not sure about you, but I’ll say “NO”.

Thankfully, these data companies realize the importance of getting data out of their platform, so most of them provide some form of API interface to query/export data outside of their GUI tool. I love running Webtrends data out of their REST API. Allowing companies to build their own data warehouse gives tremendous amount of leverage to integrate various data and perform valuable analysis beyond “web site” analytics.

Many companies not running a massive .com, but doing simple stuff out of small sites, I would still challenge them to think about the long term big picture, especially if the company is “going” to invest in digital space (could be in a form of online ads, website, CRM, etc.).
A lot of people measure future success by comparing against previous historical data. Some companies plan on historical data and profile data to learn possible opportunities.

These web data could be used in predictive modeling, marketing mix modeling, marketing media planning, CRM/Web Behavior segment targeting, etc.

SO.. web data does seem to be important right?!

3rd-party Analytics Integration (Gigya Documentation)
User profile objects that you can call on custom basis

This object represents [...]]]> So in my previous post I briefly talked about the integration of Social Data with Web Analytics platforms. Here are some more details on the data integration with Gigya.

3rd-party Analytics Integration (Gigya Documentation)
User profile objects that you can call on custom basis

This object represents the current user in the system. The User object may be retrieved with updated information upon a request to a particular method. As you can see, different social platforms provide different kinds of profile info (only when consumers opt-in), so you won’t be getting everything even if users share. Facebook seems to provide the most useful attributes.

What I’ve done is using profile information that are useful and potentially insightful. For example integration some photo thumbnails or photos are less insightful when integrated with Web Analytics. Make sure you plan on what you want to bring in and analyze first.

Some of the interesting things I have in mind to support marketers are…

There are many other application of the data with web analytics, but I’m gonna stop here.

What will be interesting is, if you’re able to export these data into CRM database, and cater your marketing efforts that speaks to the exact audience and their profile, it may lead to a better conversions. Meaning, if consumers give you permission to send email, and they’ve shared their profiel info, you can create email/tweet messages that is customized just for them. That is a very powerful marketing based on data.

Every web analytics tool is unique in how it could be set up to track the events value, so I recommend consulting with your vendor. In Google Analytics Custom Event category and actions could be the part to capture social graph info, but be careful not to pass personal information as it goes against Google’s terms.

On enterprise level analytics platform, it is worth exploring how much additional events you’re expecting to get and the best custom report view to achieve optimal reporting for integrated social graph for insights. As a Webtrends customer, I’m planning to use Webtrends Segments.

Since we can identify unlimited custom events, I’ll apply a standard custom parameters with convention naming that could easily identify the social graph parameters. I would definitely love to hear how other experts were able to leverage social graph and integrate with Web Analytics.

#1) [...]]]> I recently met with an analytics expert in Tokyo and had a discussion about the vendor relationships.
One of the discussions missing from many conferences relating to analytics is how clients build or maintain the relationships, and help web analytics vendors help client’s business to gain more value out of the vendor’s service.

#1) Treat your technical account managers (TAM) like your partner
If you have a dedicated support hours from technical account managers, it is important to have a working relationships that is healthy. Important tracking decisions could be executed well if you communicate with your partner, and explain the details on the business needs.

I’ve heard stories where clients are treating their TAMs like an intern and not leveraging the partner’s expertise and experience/learnings. In most of the cases, TAMs are well experienced and have seen many unique solutions with other clients beside yours. If you treat them like a partner and not an intern, then you can have a healthy dialogue on great solutions that you may have not thought of.

#2) They don’t know your business as much as you do
This was also mentioned at the ‘Accelerate’ conference by Web Analytics Demystified in SF.
Web Analytics vendors are good at what their do, and are the subject matter experts in their solutions. They’re facing hundreds of other clients and your business is not the only one. You should not expect your Web Analytics vendors to know 100% of your internal projects, politics, technical environment, managements, etc.

For example…
You have a fire-drill because the web site went down and important data was lost, and your TAM didn’t pick up the fact that the site was down and he/she can not help you recover data. Don’t expect your TAM to be a super man who can deliver or fix everything. This is totally non-sense.

Another example… Reorganization happend, and the key contact with Web Analytics vendor is gone, new person comes in, don’t expect your web analytics vendor to know everything in between the blank period, and 100% of the previous managements’ projects/tasks.

#3) Set realistic expectations and set priorities
In order to have your web analytics vendors deliver more value and great support, it is important to set priorities straight with your key contacts. 100% of the case, out-of-the-box reports or tag implementations will not deliver to your business expectations or needs. Make sure you have priorities and realistic expectations to have your analytics solution providers to help you get to where you’d like to be with your data infrastructure.

What I do with my TAM is, on a weekly basis we have 30min meeting to go over our task items in Google Docs to collaborate on:
- Priorities (tasks list is numbered in order of priority)
- Status details
- Timeline of expected delivery
- Open questions and items
- Type of support or follow ups
- Business impact

#4) It’s a journey
As I mentioned, out of the box solutions will not cut it in 100% of the cases. Otherwise you can stick with free Google Analytics right? :-)

In addition, it is unrealistic to have your vendor deliver everything from custom reports, tags, segments in one day. It is important to build the road map of the KPIs or metrics to track against the important business questions, tagging implementation, BI integrations, etc.

In fact, digital marketing changes a lot in one year, and expect your data tracking/reporting strategies to change as well. The relationship you have with your partner is an important part of the journey. Some people swap vendors because they’re not building the relationship or managing them right, and probably not even looking at the analytics practice as a journey. Hopefully you don’t fall into that type of management.

#5) Be honest, transparent, and have fun
Same as in marriage or having a girl/boy friend. Honesty is one of the key elements in having a healthy relationship with your partner. I believe it applies to working with any vendors. For web analytics data, it starts from good data planning and collection practice, and it has to go through the process of QA as well as a common launch process. That said, being transparent about your business needs will reflect well on the data collection practice as your vendor may have great ideas or solutions to gain great data.

Lastly, you have to have fun. Bad tensions between you and your support contact is not healthy at all. I’ve learned that having fun in the projects your working on, and sharing the excitements gained through digital metrics will build excitement and confidence in how you and your partners work and grow.

I touched about SoLoMo (Social, Local, Mobile) being the main eco-system on the emerging media and consumer behavior. [...]]]> I covered briefly on mobile analytics in my previous post , but I thought I give it a different view on how we should approach thinking mobile analytics.

I touched about SoLoMo (Social, Local, Mobile) being the main eco-system on the emerging media and consumer behavior. Because successful mobile sites or apps are embracing all these three pilars, it is absolutely important to think deeply about your mobile business’s success factors as well as the environment where the mobile is used.

A lot of us may own a smart phone, so I’m probably preaching to a choir…
Mobile site could in any form of genres and used as:
- Video, music, magazine or any content consumption
- Shopping / eCommerce
- Camera and share photo
- Shop on Amazon.com while in Best Buy
- Check into places and unlock a deal
- Play games
- Cross platform interaction. iPad/iPhone controlled apps on TV

If you think about the data points you could possibly track and tie to different events or data dimensions, it is endless. It definitely has more unique data points or dimensions than the traditional web sites. Depending on the service and number of active users, traditional web site analytics solution may not work. Especially if it is a service requiring real-time data to react to, or provide services that needs to process tera-bytes of data in a day or so.

We could already imagine services like Google, Facebook, News Media companies, or Social Gaming apps may fall into this camp where traditional website anlaytics may not work. They most likely use data managements catered to Big Data.

If you’re a traditional website analytics analyst, and meet some data anlysts who do analytics at (randomly saying…) Zynga, it is very likely that the tools, infrastructures, analytical approach are totally different. That’s not a bad thing, it just means more fun stuff to learn. :)

This difference is already setting the landscape differently in terms of skill sets required to do the job, data tracking environment, reporting practices, etc.

Just keep this in mind, that web analytics is not only about using Omniture, Webtrends, Google Analytics, Unica, Coremetrics, etc.

Shifting gears…

I came across this cool mobile marketing done in South Korea. A number two grocery chain Tesco, wanted to gain share without opening up a new store, became number one in market share by opening up their virtual store front in sub-way station. (I don’t know about now, because I would assume other grocery chain would do the same to compete.)

Source of image: BBC – Shopping by phone at South Korea’s virtual grocery – by Jason Strother

I would imagine the KPIs would not be different from traditional eCommerce. Such as…
- Traffic (traffic after people snap the photo of QR code)
- Transaction
- Revenue
- Average order size

I would imagine, the operational metrics would be very insightful, yet different from traditional website anlaytics.
- Traffic and transactions by Subway (or location)
- Top performing hours by day. My assumption is it works the best during rush hour, but maybe not because people are rushing. hmm
- Customer profile on those who purchased. Is it younger audience? women? men?
- Items performance. Is it the small incremental stuff that people forget to buy at the retail store? or is it the items people would like to carry less?
- Viral effect of promoting such virtual store fronts. I would image, doing some promos or doing such cool digital marketing will yield some buzz. How did that influence the brand?
- Payment preference for mobile transaction.

Wow…

Website analytics have many similar measures and dimension, but what is unique about mobile is the application and the operational aspect of these metrics in online and offline hybrid environment. When that kind of operational metrics are applied different, then obviously that means optimization activities are going to be totally different.

For example…
- Does it work better in non-subway environment? Should they try in air-ports?
- Do bigger graphics on products that work should be the ones they focus on?

This doesn’t mean South Korea is the only country doing cool things with mobile. If we open our eyes, we see a lot of this changes are happening in US, Japan, and many other countries.

Let’s see what you have in mind. Digital mobile marketing is already starting and let’s see how you can apply that website analytics skills to the new era of mobile!!

Not all companies may have mobile optimized site or so called m.site. However, if you get a pretty decent amount of traffic, you should check how fast the mobile segment is growing, perhaps Month-over-Month or Year-over-Year. Traffic [...]]]> What are the key things should we take into consideration when measuring mobile platforms and the user behaviors.

Not all companies may have mobile optimized site or so called m.site. However, if you get a pretty decent amount of traffic, you should check how fast the mobile segment is growing, perhaps Month-over-Month or Year-over-Year. Traffic size may be small, but I bet you anything, most of the case the growth maybe surprising if you haven’t checked it for a long time.

So.. this is where my brain is at and learned from some of the mobile initiatives I came across.

1) Browser based mobile site or Mobile App?
The tagging technology are quite different from mobile website (browser base) Vs. mobile apps like iOS or Android. The metrics and available dimension you want to measure could be different as well.

Typically the objectives and goals for mobile apps differ from the mobile site, and the consumer experience in these two different properties are different as well. While mobile apps tagging may occur by using some type of SDK library in building the app, the mobile browser site are likely to be the traditional javascript tags.

Make sure to involve your engineer and give it a lot of planning.

2) You need to go create the mobile analytics profile for mobile.
What are you waiting for? Digital analytics experts should already be thinking about creating profile to measure the mobile space, and the first easy thing you could do is create a segment of analytics data around mobile browsers or your mobile site (m.site).

3) Identify outcome, and key events
Analytics tools are useless without measuring the site’s contribution to outcome or key events.

The important thing we’ve learned in web site analytics on measuring what worked or not worked against outcomes (downloads, sales, registrations, shares, etc.) need to scale to mobile analytics practice as well.

Events could be tied to some unique mobile interaction events. Could be, but not limited to…
- video view
- check-ins
- sharing (links, image, comments, etc.)
- click-to-call
- screen swype
- promo/coupon redemption at point of sales

4) Prepare for segmentation
As mentioned in my previous post “Measuring ROI using Segmentation” segmentation is the key to getting to an insight and performance analysis.

Mobile Analytics is no different went it comes to segmentation.

The outcomes you defined could the segmentation point. For example, people who registered for newsletter on mobile site Vs. people who didn’t register on the mobile, how are they behaving differently.

Another random example: People who check-in to a store with the app, how is that different behavior different from people who don’t check-in and completed an event X. Was the average order size different?

What else to consider?!

If your site is optimized differently for mobile phone and tablets, then it could be worth looking into separating those two segments out as well.

What are the ways to identify mobile segments?
You can look at the OS or Platform or Screen Size reports and see what makes sense for you to segment the mobile data into the profile in your analytics solution.

If you have a mobile Apps, then it is definitely worth creating a profile for iOS by itself and Android in another profile if you are supporting two different platforms. Make sure you involve the app engineer, because the web analytics vendor may have unique event tags catered to mobile apps.

From reporting stand point…

Even if your objectives and goals mobile/tablets may be the same to your traditional website, there should be unique custom reports catered to mobile/tablets segment. Remember that there are many unique aspects in mobile that traditional website may not offer. (i.e. check-in, screen swype, barcode scan, sharing, using coupons/tickets, etc.)

If you recall the term “SoLoMo”, a term coined by KPCB’s John Doerr. Consumer behavior in the emerging media is revolving around Social, Location, and Mobile.

Recently, Eric Schmidt from Google has mentioned…
“They will change the world. What I’m most excited about is what the next generation of entrepreneurs can do on top of these Cloud platforms. What I do know is that the next generation of these leaders will be something involving mobile, local, social. ” — Eric Schmidt @ 2011 Dreamforce Event

From analytics stand point, we should really pay attention to capturing data and connect the data to build insights around:

• Social: How is mobile platform attributing to social activity that is impacting the business
• Location: Leverage location data to learn and bring insights to build better business strategy around physical location like store, people, services, unique interaction thru mobile, etc.
• Mobile: Champion data around measures that are unique to mobility or mobile platform

Hopefully that gave you some good sense of what to think to get started with mobile analytics. Here some links to popular web anlaytics vendors providing mobile analytics capabilities.

Resources:
Google Analytics for Mobile – Developer’s Guide

Webtrends Mobile Solution

Here is a nice mobile analytics maturity model by Webtrends.

Adobe Omniture Mobile Solution

Site Audit
[...]]]> The field of User Experience (UX) is a very interesting field incorporating various aspects of studies including psychology, anthropology, sociology, computer science, graphic design, industrial design, cognitive science, etc. It is far more deep and complicated than my brief description, but here are the typical outputs from UX practices (via Wikipedia).

Site Audit
Flows and Navigation Maps
User stories or Scenarios
Persona
Site Maps and Content Inventory
Wireframes
Prototypes
Written specifications
Graphic mockups

Some of these outputs seem overlapping with IA (Information Architect) role, but these are pretty important practice in digital world. I want to talk about how analytics and some of the measurement tools could help web site designer make better decisions around improving the consumer experience.

First things first. You need to ask what are you trying to achieve in the analysis. Typical one and probably the biggest one is:

Removing the barrier users are facing so they can complete their task (i.e. add to cart, check-out, form complete, finding information)

In my opinion it is all about the end users experience. You can use the tool in many you want to know or understand things, but at some point, you need to take action against those findings.

There are four types of tools that I’ve used that are particularly useful to measuring and addressing the UX.
- Web Analytics tools (Omniture SiteCatalyst, Webtrends Analytics, Google Analytics, Unica, etc.)
- Heatmap or interaction tracker (CrazyEgg, ClickTale, Mouse Flow, etc.)
- A/B testing tool (Google website optimizer,
- Qualitative measurement tool or Customer satisfaction measurement (Foresee, iPerceptions, NPS, etc.)

Here is an example flow of the usage of these tools. I’m sure there are more creative usage, but just as an example…

1. Web Analytics Tool shows very high bounce rate on a particular page that is getting a lot of traffic

2. Customer satisfaction tool tells the analyst something about the site and that segment of page/category with very bad satisfaction score. Diving into a tool like iPerception tells the analyst that the users can’t find something that they expected to find.

image source: iperceptions.com

3. Analyst goes into the Heatmap tool and looks at ever aspect of the user engagement from mouse over, clicks, browser scrolls, and recordings on a particular segments that engage with that page. Hypotheses are created out of this analytical practice.

image source: crazyegg.com

4. A/B test is performed on that page with better navigation, call to action that speaks to the majority of the page visitors.

Here is a video from Google Website Optimizer

After the A/B test was performed, marketers rolled out the new creative and reduce the bounce rate, and task completion rate increased by X. Satisfaction score went up, and everyone involved are heroes.

It may sound like an expensive practice, but a lot of these technology could be implemented at a relatively cheap cost. The biggest challenge is the process and the resources in place that enables such execution in a rapid fashion. For example…
- Google Analytics is free
- Mouse Flow is free up to certain number of recordings (you can apply the code to just the page you want to analyze). CrazyEgg starts at $9/month for 10,000 visits
- Google Website Optimizer is free doing A/B or MVT testing
- 4Q iperceptions is free and it could be a good start

In a lot of cases, it takes years to even get to this level of data practice. My hope is that many companies are able to take it step by step to the next level and start demonstrating the ROI of each disciplines in web analytics. Yes, there will be middle managements who’s mind sets are stuck in maintenance mode, budget/resource constraints, etc.

I’ve heard great stories from some folks where they were able evangelize the data practices by doing many rounds of POC (proof of concepts) using various applications. Then they’d take that lift in traffic, sales, outcome, downloads, etc. Basically, showing the effort relative to the lift in bottom line. People get excited when they’re involved in that journey and that is the fun part of analytics.

In my view, multi-channel funnel and search engine optimization report are the interesting new reports. I will focus the on the multi-channel report for this post. It [...]]]> Google Analytics made three major changes recently and they are all good.
- Enhanced Path flow report
- Muti-channel funnel
- Search Engine Optimization report

In my view, multi-channel funnel and search engine optimization report are the interesting new reports. I will focus the on the multi-channel report for this post. It is pretty revolutionary as many data analysts always wanted this type of data to understand how much a non-marketing channels like “micro-site” or “product reviews sites” actually contributes to sales. A lot of these type of digital channels may not directly close sales, but could assit sales. So getting that data visibility is really awesome!

The multi-channel funnel report allows marketers to understand the popular combinations of paths users took within digital channels to arrive to your tracked website. It is pretty cool because you can filter the channels by medium, keywords, custom group sets, etc.

I still need to play around with it more, but here some ideas that I have to make use of these data.

1) Look at the proportion of contribution to the bottom line by top contributing channels vs. the long tail part of the channel. It is easy to look at the top 10 and neglect the rest, but as we learned from SEO, the long tail of the keywords or channels may contribute a lot to your bottom line. Looking at the average assist/last attribution ratio on the difference between the top x vs. the rest would be interesting, and see how much you need to focus on optimizing the top x.

2) Keywords pattern as consumer move down the funnel. There are some cases where users consecutively use search engines to visit the site. I think there is a value to look into what keywords users are using and how that keywords change as it move down the funnel. It could indicate something about the patterns about the users and marketers could gain some idea about the consumer journey. For example, if users searched “Furniture”, then in their second visit searched “Sofa”, and finally entered “Blue large sofa” could indicate something about your user and your site’s ability to acquire that type of audience. Learn from it and make use of it.

3) Deep analysis around conversion segments. As you may now, your site may be all about to drive sales, and it could have email registration or some kind of feature to drive leads. How does the user’s channel paths differ when the first interaction is Email or when Email is the last interaction?

If you know the leads you bring into promotional programs are likely to convert more and buy repeatedly, then it is worth exploring to see if there is any marketing opportunities to help consumers go down the marketing funnel.

4) Time lag and path length analysis. One of the report has the capability to understand the time difference (in days) between the channel path. If there is any opportunities you can intercept with an ad to close the sales, then I’m sure this report can give you that hint about the best timing to shoot and ad to make those days shorter. Same goes with the path, if any channel segments are closing the sales in shorter path length, then it is probably worth diving into the data to see what is actually closing it.

Here is video of the details and some screen shots.

Sample screen shot of the multi-channel funnels with different combinations of paths

Sample Time Lag report with segments applied.

As an analyst we all know that we have to spend our time diving deep into the data to connect it to business goals and objectives. However, we end up dealing with many things like tagging, process, QAing, agreement on KPIs, dealing with too [...]]]> Spend your time and focus on bringing insights to the business

As an analyst we all know that we have to spend our time diving deep into the data to connect it to business goals and objectives. However, we end up dealing with many things like tagging, process, QAing, agreement on KPIs, dealing with too many irrelevant metrics, fighting with time to delivery of reports, etc.

You can argue about human communication aspect of the challenges, but in my experience there are many challenges around brining the basic ‘data visibility’ to the key stakeholders from technical stand point. Examples are (not limited to):

• Data reporting environment and availability of data are sub-optimal. Example, clients working with multiple agencies are getting multiple reports in different formats. In most of the cases clients data environment behind firewall is not open to agencies.
• No alignment between different data handlers in what data sources to use. Every data sources are in different locations, and data collection methodologies differences are not well articulated to the end users. (i.e. data handlers could be… clients and agencies or analysts and marketers)
• BI (business intelligence) solutions aren’t well established in the company for whatever reason. Data experts, especially analysts aren’t necessarily an expert in business intelligence.
• Gap between the reality of ‘speed of changes in digital data’ and resources or speed to deploying digital data in traditional BI solutions.

With social media related metrics are booming, and web anlaytics metrics becomes more fragmented, analysts are facing many challenges just to focus on clean data sets to do their analysis.

I feel like a lot of businesses are feeling this pain from the huge disconnect between the speed of the data environment changes and expectations from the analytics reporting and insights expectations. More data does not mean more data visibility or analysts churning out great reports…

Save some time on data cleansing and massaging the data

In my view, data massaging and cleansing are the most time consuming part of the analysis workflow. I’m not saying that we should hate it, but certain tasks are definitely not fun. Like exporting data to excel, switching around different analytics tool to make sure your analysis is backed and supported more than one data point, copying and posting charts/images/data, etc. Analysts should be spending time digging deeper into the meaning behind the outcome.

If you look at data like a water, and knowing that if you built a right pipe lines to have that water come in and out in a way that is ideal to you, then we need to build that pipe. That’s how I see BI, but would expect that to apply in tools that allow analysts to do more robust analysis. Google Analytics does a great job allowing analyst to segment data, create custom reports, and save it into dashboards, but it is not enough. As one of the most important job for analyst is to find correlation and dive into causation analysis, that analytics/report view is just the beginning of it.

My thought and approach of saving some time for analysis is to find your own analytical pattern and approach to data. Based on the assumption that your business has KPIs and goals, and you have those measures covered (perhaps automatically reported nicely in some BI solutions), the next step is to have operational metrics to answer what and why those KPIs changed (went up or down).

So it is in analysts’ best interests to ask, ‘how do I get those operational measures in a way like a water would come out a pipe in automatic fashion so I do not need to do manual work every time I do analysis?’. This question is pretty interesting as when I meet many people in similar fields everybody seems to approach it with different techniques, tools, mindsets, etc. A lot of them are intended to save time or get data in consistent fashion.

Here are some examples practices that you may have came across.

• One practice some people may be doing is scheduling email reports and open it up occasionally for analysis.
• Leverage web services like REST APIs to pull data into excel automatically when needed to refresh. In the past, I’ve built parameter string text files, and when a cell value changes (like dates), then the excel data puller will pull the data based on that changed value in the cell.
• All operational data (pretty much everything) is captured in BI environment. You are lucky if you fall into this camp.
• All operational/detail metrics are saved as favorites or bookmarks in web analytics solutions. However, analysts still go across multiple platforms to do cross analysis.
• Data engineers deal with the heavy lifting. For some magical ways, they’re able to get the data you need anytime. Again, lucky you. You have many supportive resources to help get those detail data.
• Data engineers put a lot of the data you need into some database you could work with (like MySQL, SQL server, etc.). Rest of it is just analyzed by tapping into that DB and query using SQL, or have your data visualization tool connect to that DB directly for further modeling.

Now, I’d like to share you mine. It is definitely not a perfect way, but it is working pretty well for me so far. Especially if you’re running tight on resources for help.

Build technical and marketing business acumen to run a lean analytics practice

I was privileged to learn scripting language during my first four years after college. Recently, I came to learn that with the programming language like Ruby or Python, writing scripts are much more easier than before where I was writing the code in Perl, shell scripts, etc. There are so many libraries to access and parse REST APIs in form of XML or JSON available through enterprise level solutions or even free data online. Also databases like MySQL are free, so putting those data into a storage (even locally) is not a big deal anymore.

In a knut shell the two technical acumens that helped me the most were scripting and database programming. Once the data is stored in the database, you can use Excel or great visualization tools like Tableau, SpotFire, QlikView, etc.

Rapidly churning out data, and charts/graphs are not like the experience we had to go through a decade ago. Once you have these data in a database, then the rest is easy. I’m not saying this because you should “go learn programming”. It is more on the perspective to drive analysts to be creative beyond the common analytics tool’s capabilities, and build the fundamentals around data management where you could potentially be more creative with data.

Also, for analysts to work with technical folks, it is obvious that the analysts will need to articulate what data is needed. Better understanding the underlining data will allows analysts to articulate to the business folks on what is repoortable/not reportable or explain the disclaimers. Definitely a reason why it is very hard to find great digital analytics experts who understand the data from when it was created to all the way down to putting that data into business context.

I really don’t expect all marketing metrics analysts type of role to do tagging, scripting, database programming, but to me, great digital analysts are the ones who could understand and could articulate the whole gamut of digital data from end to end.

With many reporting tools and analytics platforms go into cloud as Paas, and various analytics services are readily accessible as Saas, these are all very beneficial for data analysts in many ways. Understanding these rapidly changing data/services environment, and acting upon these data inputs in snappy fashion are great acumens digital analysts will need to build.

For me, it took about 3 full business days to build 3 prototype dashboards starting from web analytics API to MySQL database to Tableau dashboard accessible through intranet. In traditional BI, in my estimate, it would have taken about 3 weeks to deploy. It wasn’t my programming that enabled this quickly (I wished…), but rather the availability of the technical environment that is so different from years ago.

I highly recommend digital experts to learn and check out.
- Scripting languages
- Database trends
- Application stacks (heroku, dotcloud, amazon, etc)
- Rapid BI tools (i.e. Tableau, SpotFire, QlikView, etc.)