5 Good Reasons to have a Business Intelligence Analytics dashboard

5 Good Reasons to have a Business Intelligence Analytics dashboard

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As shared previously in another article about Big Data Analytics, we’ve got tons of information available for us marketing manager / analyst… to take in account to make good business decisions.
Thus we need to be selective in the data we look at, each data should have a purpose, even if we have access to quantity lets aim for quality. A business intelligence analytics dashboard is one step towards this objective: consider qualitative, selective and intelligent data to make easier data-driven decision.

As for a car where the dashboard is the part of a car which contains some of the controls used for driving and the devices for measuring speed and distance, a dashboard from a business point of view is a document showing in a single view the most important KPI (key performance indicator metrics) that need to be measured and monitored to drive your business. A dashboard is not only displaying some of your metrics, the challenge is to display them in a intelligent way : telling a story with your metrics that will help making data driven business decision looking backward and forward.

I am lucky enough today in my new role at MRM Worldwide to work with companies that consider a business intelligence dashboard as a must-have or companies getting to it and part of my job is to help them to build it efficiently.
Let’s deep dive here : Why would your business need a BI dashboard ?

1. BI Analytics Dashboard: What are we talking about?

A dashboard is kind of a reporting variety. A dashboard is a web based tool that deliver selective and relevant KPIs in real time to targeted user. Those KPIs have to be displayed in a way simplistic way – KISS, keep it simple, s… – that include visual guidance as gauge, traffic lights… and the guidance should also come from the ability to intelligently analyze historical and real-time data to model the future.

To elaborate a little more on my last item, each chart/graph should be accompanied with a title such as “What question this chart is answering to”, a platform that send plain English emails to give some alerts or status update and directions in the actions to be taken, a platform that has to be explained and built with the users.

2. It’s 100% tied to your business objectives

That’s the spirit of it.
You don’t really need all the metrics that your analytics and else platforms are allowing you to see. For instance, when every morning I am looking at my blog web analytics reports and I just want to answer few questions : are my loyal readers still there, what about the new ones ? new comments, new shares, how far did they read my articles, did they subscribe, when and where did they drop ? (this is a very ego-killing job but that’s part of the game).
So I do use Google Analytics ability to set my custom dashboard, however I still need to open all my others tools (Clicktale, Feedburner, Bitly…) to get the whole picture and evaluate it versus the expected outcomes I had in mind.

I’m not running my blog as a company, however if I was, using a business intelligence analytics dashboard will allow “my” company to automate comparison with real metrics VS expected outcomes or target and help me increase my operational efficiency. A marketing dashboard offers you the possibility, amoung others stuff, to follow your completion rate towards your company goals and act accordingly. For instance, knowing your completion rate you could use some predictive analytics (i.e. forecasting) to adjust either your goal or your campaign effort (spend, segment…) in order to meet your goals. A dashboard is not going to do the marketer job however all historical data could help to set up some alerts in case your campaign is heading in the wrong direction : run a SEO audit, spend more on this channel, test a new promotional call-to-action…

As a real life example, have a look at the data below from TNS Digital Life : The role of the consumer voice. It does make sense to compare your own site data to TNS data from a benchmark point of view, you’ll easily see then where are your strength and weaknesses:

3. It’s customizable & (B) Intelligent!

It has to be customized to your audience or there is no point having a dashboard.
Your CEO will not look at the same KPI as your Marketing Director, Manager or Regional Director or CMO… however everyone need to see the Big Picture, working in silos is not efficient and it’s frustrating (just my point of view). As you may be using segmentation and targeting for your campaign online or your website content – the same rule apply here : adapt the content to your audience. And as it’s web-based tool, customization could be endless thanks to segmentation, behavioral targeting, widgets for instance and mobility : a dashboard as to be mobile friendly as well. That’s for the customization part. What about “Intelligent”.
What is Business Intelligence ? What is BI when it comes to digital marketing?
From my point of view, it’s when you reach the point of telling a story through your data. To tell a story, your data have to be meaningful :

Well, I think the visual speak for himself, you should fell more guided with the right-hand side version of the visit’s metric. One the right version, you will be displaying the data but also evaluating it to your goal, you will use predictive analytics to measure your chance of success and finally use a green light to let your user know that it’s on track.

According to Gartner, Business intelligence (BI) is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.

 

4. It’s a time savior.

Better use your time to analyse, look for pattern and trends, take decison than building the tool to help you make those decisions.
As a marketing manager, I guess remembering your Facebook Insight, Twitter, Google Analytics, Google Webmaster tools, Google Adwords, Bing AdCenter, Doubleclick, Customer Feedback tool, TweetReach, Mailchimp, Clicktale, Salesforce… and so on accounts, logging to each of them pulling data in your excel file and wrap them up together to get an overall view is not your priority, right? Even though Google made a great job of integrating each of his service into Google Analytics, we are not all Google-dependant some use multiple analytics tools : SiteCatalyst, Webtrends… and your CRM, Emailing, Feedback… platforms are not Google either.
So time is money and if you want to be able to catch the whole story & be close as possible to your customer journey, you may want to consider a dashboard.

5. Because it’s fancylicious 😉

Firstly you would be able to sound very intelligent, I can’t help thinking that Business Intelligence is a very cool word.
Anyway, working for data don’t have to be boring some even that it’s sexy: … I do LOVE excel spreadsheets and plain and simple table with black figures on a white background – always very efficient but I love infographics as well, so why not having both when it’s possible and relevant. To perform efficient data analysis, data visualization matters a lot. And there is a lot of magnificent dashboard that maybe will make you love data, find here my pearltrees about data visualization.

Thanks for reading! If you liked this post, spread the love…

When Analytics is the King, Segmentation & Targeting are Queens

When Analytics is the King, Segmentation & Targeting are Queens

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Now officially, it’s been more than 1 year between Omniture and me, things are getting pretty serious! Since 5 years we’ve been turning around each other – it has to be said Omniture consultants are pretty tenacious – last year I finally gave up !

Time to take a look back…
Like in every relationship, at the beginning you cannot hold yourself to compare the new to your ex. My ex analytics companion was Google Analytics since a long time, so I compared every features available or not. No need to reinvent the wheel, there are already plenty of great posts blog which compare Google Analytics & Site Catalyst, please refer to them if you need a feature by feature comparison:
– SiteCatalyst and Google Analytics comparison, conceptually speaking: Part 1 and Part 2.
From my point of view, I’ll just say that both have more or less the same basic metrics that you need but GA is much more easier to handle at first because GA is more user friendly and there is almost no set up when SiteCatalyst needs a lot configuration from the beginning which will ask for ressource in your team. Beyond the basic metrics on both tool, you’ll need time to customize it: eCommerce tracking, Campaign tracking, Social Media, Segmentation… all those features are not native in the tool and more or less easy to set up.
SiteCatalyst may be a pain in the ass to set up (excuse my French) but it’s more powerful, you can do a lot of things that you cannot in GA & it doesn’t come alone ! I’ll come back to that in a minute.
Finally, Google Analytics is FREE when SiteCatalyst is not.

However, I am here to talk about Adobe products : Site Catalyst, Discover & a little about Test & target, as to be honest I tend to use a lot more of Discover those days than Site Catalyst – essentially because it’s much more flexible to handle a large amount of data and I don’t need to call Adobe consultants to enable the functions that I need (as correlations, subrelations…).
Just a quick note to explain why I choose to speak only about Adobe products in this blog post – no bribe here – it’s just that lately I have been working exclusively with them and I get to know them and grew fond of some features. Plus, I passed my User certification & I am currently on a training for Implementation ! So why not use all this cool stuff I learned and put them into a post. 🙂

So let’s say that, as an analyst we want to Get useful data & Turn them into actionable insights to be able to Suggest, Recommend, Test in order to Optimize our website towards our Business Goals.

How specific features of Adobe products can help to do this in a upper level way?

My favorite first: SAINT Classification

SAINT Classification is useful to give sense to various unique items & allocate each item multiple categories. To be more specific, thinking about traffic channel driver performance, imagine that you are using a tracking code for each of your online advertising campaign, creative… such as on your landing url will look like www.monsite.com?cid=AA00112233 (‘cid’ being the default container for external campaign tracking) and your referring domain is wwww.referrer.com. Site Catalyst will be able to identify traffic coming from the referring domain and if you enabled it, Site Catalyst will be able to identify traffic from cid=AA00112233 (thanks to the GetQueryParam plugin), you could even breakdown Referring Domains by Tracking codes. However seeing metrics associated to AA00112233 is not really user-friendly for an analyst, except if you know by heart what creative, message and so on the code is referring to.
That’s when knowing about SAINT Classification become useful as you can import a reference file to Site Catalyst that will classify each of your tracking codes into comprehensive categories such as Campaign Name, Creative, Message and so on. You will be able to breakdown every category by every category thanks to Classification hierarchy. See below for classification examples:

Using SAINT Classification in this case, will help you to get useful data about your marketing effort performance : learn how a specific channel, campaign, placement, creative, message… is working and most of all leading your visitors to engage & convert on your website.

Second one, will be Processing rules, a bit nerdy but useful

This one is pretty handy as it happens that once your implementation is done, you realized that you have forget something and your IT guys are not available or it means waiting for too long…
You have to be certified to use it as it’s kind of dangerous tool. So I did take the exam just because I love danger, ahahah… Anyway, let’s take a real life example: copy an eVar into a prop or vice versa. This one is very handy as if I want to know how many times someone is searching for a specific keywords and if this search turned into an order, I would use an s.prop5 to count the volume of searches and an eVar5 to know if this search converted. Imagine you forget to set up the s.prop, you can then use processing rules to tell Site Catalyst that eVar5 = s.prop5 for instance.

Segmentation

According to eMarketer, an obvious trend for 2013 is fragmentation : “The expansion in the number of media channels has fragmented audiences” and well obviously you will need to identify and understand how your various type of audience is interacting with your website. I strongly believe in segmentation for better understanding of your audience & marketing decision making. Segmentation to analyze your marketing effort and especially to understand your different type of visitor. I am not afraid to be redundant, but this is one of the key to optimization.
A first step will be to consider the “how”, how am I going to collect the data from my different channels – well the point above already answer to part of this in details & you shall know as well that Site Catalyst identify automatically some of your sources (regarding search you do need to enable it first in your report suite manager), if you want to have a more detailed view use s.campaign and finally use SAINT Classification to categorize your campaigns from a business owner point of view.
The magic about that and segmentation in Site Catalyst 15 is that as your data are captured and collected you can use any of these as a dimension to filter your report and create segment!
Site Catalyst already set some out of the box segment for you:
they are quite helpful but you can go even into deeper details such as : look into customer behavior for visits from natural branded searches, behavior for visits coming from a blue banner, behavior for visits coming from a specific referring domain, from a specific cross-channel campaign and so on… Possibilities are endless, the pitfall here is to know what are the segments you need to look at, do some exploration but too much granularity will make you loose the advantage of segmentation.

Test & Target

Test & Target is another tool from Adobe, not directly related to Site Catalyst except if you use the plugin to connect both, which I highly recommend. Why? Because as its name points out Test & Target is a tool to Target your content and well it make sense to me to rely on your segmentation strategy to target content to the right user.
Aside from the targeting feature, Test & Target allows you to do AB or MVT Testing on your content : every kind of content such as text, image, landing page… and the testing can be used in the same time as the targeting. For instance, you can send an eDM to your database, this email will have a call-to-action a specific landing page – you can decide to have various landing page depending on the audience: if it’s a fist time visitors display this content or this one (AB Testing) or if it’s a returning visit display this or that message…
Have a look below of how far you can segment, test and target:

Elephants and Analytics Blog – TnT Illustration

This will allow you to test, test and optimize your suggestions that came from your data observations or even your gut feeling.
As a stand alone tool, from what I experienced I was not really happy with the tool, however I was conscious that it was a powerful tool, a difficult setup and a lot of followup if you want your business owners to really get involved and use it. Practice is from my point of view the best ally of this tool but not every one have the time or the patience to it.

Last and not the least: Reading

For those who know me, I’m a compulsive reader… I read as much as I can, daily, some time work related and hopefully most of the time it’s not work related.
However here are work related best reading resources I read religiously :
– Adobe Digital Marketing Blog
– Web Analytics Demystified
– and the rest are on Twitter, some name you should follow: @usujason, @erictpeterson, @tim_ash, @johnlovett, @benjamingaines and so on…

I am just ending Day 4 of Site Catalyst Implementation training (hopefully I will be able soon to implement Site Catalyst by myself…), and there is so much more I could tell now (form analysis, cross-channel analysis, context data…) but let’s keep that for another article and after some practice first !

Big Data Analytics, few definitions & thoughts to cut your teeth on…

Big Data Analytics, few definitions & thoughts to cut your teeth on…

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What is Big Data Analytics ? How is it related to user experience, business decision making… ? How much big is BIG? What about real-time analytics ?

 

Big Data According to Gartner, “Big data in general is defined as high volume, velocity and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.” Still blurry to me…
What’s all the fuss about?

Big Data, quick definition & history:

First of all, it’s not as if I was totally out of date, the buzzword, concept or whatever is still pretty new – that’s a relief! As you can see below “big data analytics” searches and interest is still growing over time and appeared approximately one year ago.

However it would not be accurate to say that it’s a new concept, it’s more like a reaction to how we treat and receive information nowadays compared to few years before. Technology and Digital media’s evolution enabled a lot of new possibilities and Big Data analytics is addressing them.

Professional, Analyst or IT people will speak about Big Data analytics when there is too much data to analyze them in a standard way and that you don’t even know how much of those data are useful or not, when there is so much various data sources that it’s becoming really complex to be able to get useful insight from them and finally when your business requires to be listening to your customer in a more immediate way. In summary 3 components: Variety, Velocity and Volume (I’ll come back to this 3 keywords later on this post).

From a Business oriented point of view, Big Data Analytics is today a necessity for companies which have access to a tremendous amount of information about their prospect/client/customer… but do not take advantage of it because they are overwhelmed by those data. Today most companies will try to understand those data but at-rest, they will produce reports quarterly or monthly and rely on those reports to adjust their marketing mix / communication towards their customers. It’s always good to look backward to take decision but it’s even better to take a decision looking backward and forward. Twitter data source is one of the best example to illustrate how powerful Big Data Analytics can be: you can’t be listening to your customer sentiment 3 months after the launch of a new product and considering the amount of tweets it could represent your datawarehouse would not support that. So you would take 20% of those data and analyze them 1 month or so after – which has to be done. But you could also, analyze in real time how your customer sentiment towards this supposed launch is trending.
IBM Cognos Real-time Monitoring does a really good job about this: (I am sure other do as well, it’s just that Big Data University – that I attended online – is IBM sponsored, easier for me to get all my source in the same place 🙂

What defines Big Data : Variety, Velocity and Volume

The origin:
2001 Note Reasearch from Doug Laney (Research vice president for Gartner Research)- 3-D Data Management: Controlling Data Volume, Velocity and Variety

Variety

        because Big Data is referring to

multiple and disparate data sources

        as long as they can be stored on a computer and “digitalized”: Facebook status, tweets, images, camera feeds, credit-card transactions, consumer purchasing histories, climate information, GPS location etc.

Velocity

        because Big Data is referring to

data

        at-rest but more especially

in motion

        , and that’s where you can and would take advantage of it: the more quickly you can see a threat, analyze an information about your customer, detect a new trend, the better you can make a competitive advantage of it.

Volume

        because Big Data is BIG, I mean referring to huge volume of data. How big is BIG then… According to IBM we are talking about

terabytes—even petabytes—of information

        (e.g. Walmart handles more than

1 million customer transactions every hour

      , which is imported into databases estimated to contain more than 2.5 petabytes of data). And now technology enable us to stop sampling to analyze data and use every data we have!

Will Big Data rescue us all…

Big Data is the answer! But what’s the question?

1 or 2 things I would keep in mind when speaking about Big Data:

        Big Data Analytics at-rest and in motion, will not replace classic Data Analytics. It’s just a “super” layer and a new feature to answer new challenges as helping businesses find direction within the noise. The challenge is still the same

find useful data to analyze and act about it

        . Have a look in here, Deloitte’s ebook “

Big data matters- except when it doesn’t

      ” elaborate on advantage of both Small Data Analytics & Big Data Analytics : 2 complementary disciplines (p8 & 9).

Big Data analytics in a digital marketer / BI / analyst… everyday life

Big Data Analytics is often referred as a challenge for company & analyst.
Indeed, it’s a pretty huge challenge to be able to dig out relevant insights within enormously huge amount of data available and .
Few links to help in our everyday life:
– Big Data University: it’s indeed promoted by IBM but there is a lot of stuff very interesting to get a first impression of what is Big Data & you can even get a nice certificate if you take some online courses.
– A list of vendors and tools (free or not) to address multiple Big Data challenges: analyze things like tweets, payments, and check-ins for online publishers and web companies, create gorgeous charts and maps for free & pretty easily if you know how to use Excel for a start (e.g. Tableau which is a very cool tool), storing and processing gargantuan volumes of data…
– Social Media dedicated Big Data platforms: Sysomos, Brandwatch…

Thanks for reading me so far 🙂
Feel free to share your thought, freebies and else!

How NOT to “I want to do this, so I’ll find data to support my goal”

How NOT to “I want to do this, so I’ll find data to support my goal”

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As a webmarketer and/or analyst, we sometimes persuade ourselves by a GREAT marketing idea:

  • ~ Develop a new design for a landing page, a product page, a checkout…
  • ~ Spend more budget on a channel
  • ~ Implement a new payment method
  • ~ Add more testimonials or security seals
  • ~ Use a one-page ajax based form
  • ~ Use video on a landing page
  • …

Tempting huh…

Sometimes you come across an idea, an idea that from your point of view will enhance your user experience, the lead generation, the checkout conversion rate, the engagement onsite… And sometimes, people don’t get it. Because they think it’s too fancy, time-consuming, not goal-oriented or whatever reasons to turn down your proposal. Then you either give up your idea because people counter-arguments convince you or you can get stubborn and decide to show them by any cost.
From this starting point, you will dig hard into the data to pull any insight which confirm your feeling and resent any data that do not get along with your way. You will try to prove that you have a data-driven proposal.
It’s hard to tell when the cost is too high. When the data that you are presenting are no longer objective but just proving you right.

How to avoid this?
Here are 5 tips, I try to use when I’m wondering if I am crossing the line or when I get the deeply sceptical look from my colleagues:

1. Ask your users via online surveys

To stay one step ahead, we need to be proactive and not rely only on what our users show us they want. It can also be the other way. If we are talking about a major idea, why not asking you end users what they think about ? A simple quick online survey for example. If you get a massive yes from your end users, it will be much easier to convince your boss/clients/colleagues.
Online surveys are not cost-free but using a platform like SurveyMonkey or SurveyGizmo can save you a lot of money.

 

2. Test it first !

You can either decide to test it live or test only your mockups (Verify is a nice tool for mockup, design testing). Either way, it’s unlikely that changing or adding something new without any testing first will be a success. Imagine you are working on a landing page new template, with Test and Target or Google Content Experiments (ex. Google Website Optimizer) you can easily decide to set an AB Test only for 30% of your traffic- with Omniture Test and Target you can easily target further (returning visitor only, chrome visitor only, exclude Sunday visitors…)

3. Within the data: look for insights to prove you right & wrong.

By looking for reasons to do or not to do it, you may find some unexpected insights to specify you idea, reorient it or even cancel it. You could realise that your idea is workable only for a specific target or that it could alter the current flow of your users…

4. Ask your peers (but maybe not from your company)

Consult your peers, ask them if they already come across this idea and how they deal with. Look for online case study about this feature, technology, new channel… you are thinking about. Check for information about deployment, return on investment, survey… It’s possible that someone had this idea before and if they had it’s comforting to dig more about this!

5. Implement a framework for your campaign, new feature… with predefined metrics & KPI


If you know from even before you think of a new idea how-to measure it and stick to this framework (e.g a framework for a social media campaign), you will not be tempted to create from scratch metrics & KPI that will only prove you right.
Fore sure, each case will deserve some specific metrics but most of the time you have unavoidable core business metrics.

You may not need or have the resources to do it all but going through this process should help you to stay objective when presenting the data that will convince or not you client/boss/colleagues to push this idea on the top priority list.

In a ideal world this would not be happening, as what we “marketers/analysts” want to do is what our end-users, customers want to see. Nothing to fight for there but in the real world we want to do crazilicious, funny stuff not always user-oriented sometimes tech-oriented to content our geeky side, sometimes pushy business oriented to content our wallet, sometimes trendy-oriented to content our early-adopters side…
Sad but true, sometimes users want “boring” stuff as long as it is effective and useful for them that should work BUT hopefully sometimes users don’t know that your idea is actually what they are looking for.

What about you ? How do you deal with that, do you have any tips to avoid pulling data to prove you right?

Social media campaign, Tips to an easy framework to start measurement

Social media campaign, Tips to an easy framework to start measurement

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Few weeks ago, I have been questioned about my process & habits when confronted to analyse a social media campaign – a viral video campaign to be more specific. I never tried to put this out on a paper before, but here it is! Sure, I’m not the first to write about this as I found out when surfing. Here are 2 sources I found deeply interesting: Avinash post about social media metrics & the report from Web analytics demystified & Altimeter.
And here is my framework, I followed my campaign analytics measurement conception. Which is :

      1. If I do not want to miss any factors and fully evaluate the success of my campaign, the customer journey: “Reach, Acquisition, Conversion & Retention” is the natural and more accurate path to follow.
      2. What kind of questions I want to be able to answer to?
      3. And from that point, Define my metrics & KPI.

The questions above are social campaign oriented but the sections could be used for any digital campaign for my point of view.

Reach

Does it reach an audience and convey satisfaction?
Reach is your ability to catch visitors attention. Were you able to reach anyone, catch their attention? This could be also how much awareness did you arise. Did you create a real interest for them?
– number of searches (on your campaign), impressions, unique visitor on your landing page(s): facebook page, youtube channel, linkedin page…

Acquisition

Does it convey the Key messages to the targeted audience?
Acquisition is the logical step behind Reach. When you reached those visitors, did you caught their attention enough to stay and look further, get them to land on the page you were expecting them to. Did you get the right audience, the one you were targeting? You will use a list of metrics for this section but most importantly you need to segment to be able to measure success on this step!
– bounce rate, time spent, page views per visits, returning visits… > breakdown per social media channel & per target.

Conversion

Does it go viral & trigger actions?
In this 3rd section, you will measure visitors actions and check how close to your goals they get? Did they submit your lead form, did they share/like/comment your videos, did they subscribe to something, did they write about you… Which channel was the most efficient, which audience targeted was the most active?…
– conversion rate, number of share/like/comment… > breakdown per social media channel & per target.

Retention

Was it worth it? | Will it engage user over time?
Finally, you will want to know: how is the ROI of your campaign? Did that campaign bring you new users that will stay, existing clients that will engage more, did you gain in SEO… ? You will want to track this overtime as the effect of your campaign will hopefully stay overtime.

With these 4 sections, I think we will be covering the most of it. What’s your thoughts on this? Do you use another methodology for this?