personalization

11月 272018
 

Like many b2b and b2c organizations, our corporate website, www.sas.com, is a critical channel for how people learn about SAS and interact with us in the digital space. We have millions of visitors from around the world on a monthly basis looking to learn more about who we are, what [...]

Using SAS at SAS: The power of experimentation using SAS Customer Intelligence 360 was published on Customer Intelligence Blog.

11月 092018
 

In parts one and two of this blog posting series, we introduced machine learning models and the complexity that comes along with their extraordinary predictive abilities. Following this, we defined interpretability within machine learning, made the case for why we need it, and where it applies. In part three of [...]

SAS Customer Intelligence 360: A look inside the black box of machine learning [Part 3] was published on Customer Intelligence Blog.

11月 092018
 

In parts one and two of this blog posting series, we introduced machine learning models and the complexity that comes along with their extraordinary predictive abilities. Following this, we defined interpretability within machine learning, made the case for why we need it, and where it applies. In part three of [...]

SAS Customer Intelligence 360: A look inside the black box of machine learning [Part 3] was published on Customer Intelligence Blog.

11月 052018
 

In part one of this blog posting series, we introduced machine learning models as a multifaceted and evolving topic. The complexity that gives extraordinary predictive abilities also makes these models challenging to understand. They generally don’t provide a clear explanation, and brands experimenting with machine learning are questioning whether they [...]

SAS Customer Intelligence 360: A look inside the black box of machine learning [Part 2] was published on Customer Intelligence Blog.

11月 012018
 

As machine learning takes its place in numerous advances within the marketing ecosystem, the interpretability of these modernized algorithmic approaches grows in importance. According to my SAS peer Ilknur Kaynar Kabul: We are surrounded with applications powered by machine learning, and we’re personally affected by the decisions made by machines [...]

SAS Customer Intelligence 360: A look inside the black box of machine learning [Part 1] was published on Customer Intelligence Blog.

12月 192017
 

If you’ve ever used Amazon or Netflix, you’ve experienced the value of recommendation systems firsthand. These sophisticated systems identify recommendations autonomously for individual users based on past purchases and searches, as well as other behaviors. By supporting an automated cross-selling approach, they empower brands to offer additional products or services [...]

Customer Intelligence 360: The digital shapeshifter of recommendation systems was published on Customer Intelligence Blog.

9月 062017
 

When it comes to the SAS web experience on sas.com, support.sas.com (and more), we have a vision and mission to create experiences that connect people to the things that matter – quickly, easily and enjoyably. You could say that we’re a user-task-focused bunch of web people here, and so whether you’re a web visitor to SAS researching a solution area or evaluating a product, looking for how-to content or applying for a job, our goal is to make sure you complete that task easily.

Using tools like SAS Customer Intelligence 360 helps us do this by allowing us to take the guesswork out creating the most optimized web experiences possible through it's IA, machine learning, omnichannel marketing and analytics and more is all the rage – and for good reason – don’t lose sight of the power and impact of good old fashioned a/b and multivariant testing.

The power of small in a big customer journey

If you think of your website as a product, and think of that product as being comprised of dozens, maybe hundreds of small interactions that users engage with – imagery, video, buttons, content, forms, etc. – then the ability to refine and improve those small interactions for users can have big impact and investment return for the product as a whole. Herein lies the beauty of web testing – the ability (really art and science) of taking these small interactions and testing them to refine and improve the user experience.

So what does this look like in real life, and how to do it with SAS Customer Intelligence 360?

On the top right corner of the sas.com homepage we have a small “sticky” orange call-to-action button for requesting a SAS demo. We like to test this button.

A button test? Yes, I know – it doesn’t get much smaller than this, which is why I affectionately refer to this particular button as “the little button that could.” It’s small but mighty, and by the end of this year, this little button will have helped to generate several hundred demo and pricing requests for sales. That’s good for SAS, but better for our site visitors because we’re helping to easily connect them with a high-value task they’re looking to accomplish during their customer journey.

How do we know this button is mighty? We’ve tested a ton of variations with this little guy measuring CTR and CVR. It started off as a “Contact Us” button, and went through a/b test variations as “Connect with SAS” “How to Buy” “Request Pricing” as we came to realize what users were actually contacting us for. So here we are today with our control as “Request a SAS Demo>"

Setting up a simple a/b test like this literally takes no longer than five minutes in SAS Customer Intelligence 360. Here's how:

  • First, you set up a message or creative.
  • Finally, you create your

    Easy breezy. Activate it, let it run, get statistical significance, declare a winner, optimize, rinse and repeat.

    Now, add segmentation targeting to the mix

    So now let’s take our testing a step further. We’ve tested our button to where we have a strong control, but what if we now refine our testing and run a test targeted to a particular segment, such as a “return user” segment to our website - and test button variations of Request a SAS Demo vs. Explore SAS Services.

    Why do this? The hypothesis is that for new users to our site, requesting a SAS demo is a top task, and our control button is helping users accomplish that. For repeat visitors, who know more about SAS, our solutions and products – maybe they are deeper in the customer journey and doing more repeat research and validation on www.sas.com. If so, what might be more appropriate content for that audience? Maybe it’s our services content. SAS has great services available to SAS users - such as Training, Certification, Consulting, Technical Support, and more. Would this content be more relevant for a return user on the website that's possibly deeper in the research and evaluate phase, or maybe already a customer? Let's find out.

    Setting up this segmentation a/b test is just like I noted above – you create a spot, build the creative, and set up your task. After you have set up the task, you have the option to select a “Target” as part of this task, and for this test, we select "New or Return User" as the criteria from the drop down, and then "Returning" as the value. Then just activate and see what optimization goodness takes place.

    So, how did our test perform?

    I'll share results and what we learn from this test in the upcoming weeks. Regardless of the results though, it's not really about what variation wins, but rather it's about what we learn from simply trying to improve the user experience that allows us to continue to design and build good, effective, optimized user experiences. Tools like SAS Customer Intelligence 360 and it's web testing and targeting capabilities allow us to do that faster and more efficiently than ever.

     

     

     

     

     

     

     

    Using SAS at SAS: SAS Customer Intelligence 360, a/b testing and web optimization was published on Customer Intelligence Blog.

8月 122017
 

Optimization is a core competency for digital marketers. As customer interactions spread across fragmented touch points and consumers demand seamless and relevant experiences, content-oriented marketers have been forced to re-evaluate their strategies for engagement. But the complexity, pace and volume of modern digital marketing easily overwhelms traditional planning and design approaches that rely on historical conventions, myopic single-channel perspectives and sequential act-and-learn iteration.

SAS Customer Intelligence 360 Engage was released last year to address our client needs for a variety of modern marketing challenges. Part of the software's capabilities revolve around:

Regardless of the method, testing is attractive because it is efficient, measurable and serves as a machete cutting through the noise and assumptions associated with delivering effective experiences. The question is: How does a marketer know what to test?

There are so many possibilities. Let's be honest - if it's one thing marketers are good at, it's being creative. Ideas flow out of brainstorming meetings, bright minds flourish with motivation and campaign concepts are born. As a data and analytics geek, I've worked with ad agencies and client-side marketing teams on the importance of connecting the dots between the world of predictive analytics (and more recently machine learning) with the creative process. Take a moment to reflect on the concept of ideation.

Is it feasible to have too many ideas to practically try them all? How do you prioritize? Wouldn't it be awesome if a statistical model could help?

Let's break this down:

  • Predictive analytic or machine learning projects always begin with data. Specifically training data which is fed to algorithms to address an important business question.
  • Ultimately, at the end of this exercise, a recommendation can be made prescriptively to a marketer to take action. This is what we refer to as a hypothesis. It is ready to be tested in-market.
  • This is the connection point between analytics and testing. Just because a statistical model informs us to do something slightly different, it still needs to be tested before we can celebrate.

Here is the really sweet part. The space of visual analytics has matured dramatically. Creative minds dreaming of the next digital experience cannot be held back by hard-to-understand statistical greek. Nor can I condone the idea that if a magical analytic easy-button is accessible in your marketing cloud, one doesn't need to understand what's going on behind the scene.That last sentence is my personal opinion, and feel free to dive into my mind here.

Want a simple example? Of course you do. I'm sitting in a meeting with a bunch of creatives. They are debating on which pages should they run optimization tests on their website. Should it be on one of the top 10 most visited pages? That's an easy web analytic report to run. However, are those the 10 most important pages with respect to a conversion goal? That's where the analyst can step up and help. Here's a snapshot of a gradient boosting machine learning model I built in a few clicks with SAS Visual Data Mining and Machine Learning leveraging sas.com website data collected by SAS Customer Intelligence 360 Discover on what drives conversions.

I know what you're thinking. Cool data viz picture. So what? Take a closer look at this...

The model prioritizes what is important. This is critical, as I have transparently highlighted (with statistical vigor I might add) that site visitor interest in our SAS Customer Intelligence product page is popping as an important predictor in what drives conversions. Now what?

The creative masterminds and I agree we should test various ideas on how to optimize the performance of this important web page. A/B test? Multivariate test? As my SAS colleague Malcolm Lightbody stated:

"Multivariate testing is the way to go when you want to understand how multiple web page elements interact with each other to influence goal conversion rate. A web page is a complex assortment of content and it is intuitive to expect that the whole is greater than the sum of the parts. So, why is MVT less prominent in the web marketer’s toolkit?

One major reason – cost. In terms of traffic and opportunity cost, there is a combinatoric explosion in unique versions of a page as the number of elements and their associated levels increase. For example, a page with four content spots, each of which have four possible creatives, leads to a total of 256 distinct versions of that page to test.

If you want to be confident in the test results, then you need each combination, or variant, to be shown to a reasonable sample size of visitors. In this case, assume this to be 10,000 visitors per variant, leading to 2.56 million visitors for the entire test. That might take 100 or more days on a reasonably busy site. But by that time, not only will the marketer have lost interest – the test results will likely be irrelevant."

SAS Customer Intelligence 360 provides a business-user interface which allows the user to:

  • Set up a multivariate test.
  • Define exclusion and inclusion rules for specific variants.
  • Optimize the design.
  • Place it into production.
  • Examine the results and take action.

Continuing with my story, we decide to set up a test on the sas.com customer intelligence product page with four content spots, and three creatives per spot. This results in 81 total variants and an estimated sample size of 1,073,000 visits to get a significant read at a 90 percent confidence level.

Notice that Optimize button in the image? Let's talk about the amazing special sauce beneath it. Methodical experimentation has many applications for efficient and effective information gathering. To reveal or model relationships between an input, or factor, and an output, or response, the best approach is to deliberately change the former and see whether the latter changes, too. Actively manipulating factors according to a pre-specified design is the best way to gain useful, new understanding.

However, whenever there is more than one factor – that is, in almost all real-world situations – a design that changes just one factor at a time is inefficient. To properly uncover how factors jointly affect the response, marketers have numerous flavors of multivariate test designs to consider. Factorial experimental designs are more common, such as full factorial, fractional factorial, and mixed-level factorial. The challenge here is each method has strict requirements.

This leads to designs that, for example, are not orthogonal or that have irregular design spaces. Over a number of years SAS has developed a solution to this problem. This is contained within the OPTEX procedure, and allows testing of designs for which:

  • Not all combinations of the factor levels are feasible.
  • The region of experimentation is irregularly shaped.
  • Resource limitations restrict the number of experiments that can be performed.
  • There is a nonstandard linear or a nonlinear model.

The OPTEX procedure can generate an efficient experimental design for any of these situations and website (or mobile app) multivariate testing is an ideal candidate because it applies:

  • Constraints on the number of variants that are practical to test.
  • Constraints on required or forbidden combinations of content.

The OPTEX procedure is highly flexible and has many input parameters and options. This means that it can cover different digital marketing scenarios, and it’s use can be tuned as circumstances demand. Customer Intelligence 360 provides the analytic heavy lifting behind the scenes, and the marketer only needs to make choices for business relevant parameters. Watch what happens when I press that Optimize button:

Suddenly that scary sample size of 1,070,000 has reduced to 142,502 visits to perform my test. The immediate benefit is the impractical multivariate test has become feasible. However, if only a subset of the combinations are being shown, how can the marketer understand what would happen for an untested variant? Simple! SAS Customer Intelligence 360 fits a model using the results of the tested variants and uses them to predict the outcomes for untested combinations. In this way, the marketer can simulate the entire multivariate test and draw reliable conclusions in the process.

So you're telling me we can dream big in the creative process and unleash our superpowers? That's right my friends, you can even preview as many variants of the test's recipe as you desire.

The majority of today’s technologies for digital personalization have generally failed to effectively use predictive analytics to offer customers a contextualized digital experience. Many of today’s offerings are based on simple rules-based recommendations, segmentation and targeting that are usually limited to a single customer touch point. Despite some use of predictive techniques, digital experience delivery platforms are behind in incorporating machine learning to contextualize digital customer experiences.

At the end of the day, connecting the dots between data science and testing, no matter which flavor you select, is a method I advocate. The challenge I pose to every marketing analyst reading this:

Can you tell a good enough data story to inspire the creative minded?

How does a marketer know what to test? was published on Customer Intelligence Blog.

1月 282017
 

Digital intelligence is a trending term in the space of digital marketing analytics that needs to be demystified. Let's begin by defining what a digital marketing analytics platform is:

Digital marketing analytics platforms are technology applications used by customer intelligence ninjas to understand and improve consumer experiences. Prospecting, acquiring, and holding on to digital-savvy customers depends on understanding their multidevice behavior, and derived insight fuels marketing optimization strategies. These platforms come in different flavors, from stand-alone niche offerings, to comprehensive end-to-end vehicles performing functions from data collection through analysis and visualization.

However, not every platform is built equally from an analytical perspective. According to Brian Hopkins, a Forrester analyst, firms that excel at using data and analytics to optimize their digital businesses will together generate $1.2 trillion per annum in revenue by 2020. And digital intelligence — the practice of continuously optimizing customer experiences with online and offline data, advanced analytics and prescriptive insights — supports every insights-driven business. Digital intelligence is the antidote to the weaknesses of analytically immature platforms, leaving the world of siloed reporting behind and maturing towards actionable, predictive marketing. Here are a couple of items to consider:

  • Today's device-crazed consumers flirt with brands across a variety of interactions during a customer life cycle. However, most organizations seem to focus on website activity in one bucket, mobile in another, and social in . . . you see where I'm going. Strategic plans often fall short in applying digital intelligence across all channels — including offline interactions like customer support or product development.
  • Powerful digital intelligence uses timely delivery of prescriptive insights to positively influence customer experiences. This requires integration of data, analytics and the systems that interact with the consumer. Yet many teams manually apply analytics and deliver analysis via endless reports and dashboards that look retroactively at past behavior — begging business leaders to question the true value and potential impact of digital analysis.

As consumer behavioral needs and preferences shifts over time, the proportion of digital to non-digital interactions is growing. With the recent release of Customer Intelligence 360, SAS has carefully considered feedback from our customers (and industry analysts) to create technology that supports a modern digital intelligence strategy in guiding an organization to:

  • Enrich your first-party customer data with user level data from web and mobile channels. It's time to graduate from aggregating data for reporting purposes to the collection and retention of granular, customer-level data. It is individual-level data that drives advanced segmentation and continuous optimization of customer interactions through personalization, targeting and recommendations.
  • Keep up with customers through machine learning, data science and advanced analytics. The increasing pace of digital customer interactions requires analytical maturity to optimize marketing and experiences. By enriching first-party customer data with infusions of web and mobile behavior, and more importantly, in the analysis-ready format for sophisticated analytics, 360 Discover invites analysts to use their favorite analytic tool and tear down the limitations of traditional web analytics.
  • Automate targeting, channel orchestration and personalization. Brands struggle with too few resources to support the manual design and data-driven design of customer experiences. Connecting first-party data that encompasses both offline and online attributes with actionable propensity scores and algorithmically-defined segments through digital channel interactions is the agenda. If that sounds mythical, check out a video example of how SAS brings this to life.

The question now is - are you ready? Learn more here of why we are so excited about enabling digital intelligence for our customers, and how this benefits testing, targeting, and optimization of customer experiences.

 

tags: Customer Engagement, customer intelligence, Customer Intelligence 360, customer journey, data science, Digital Intelligence, machine learning, marketing analytics, personalization, predictive analytics, Predictive Personalization, Prescriptive Analytics

Digital intelligence for optimizing customer engagement was published on Customer Intelligence.

11月 042016
 

The digital age has fundamentally changed how brands and organisations interact with consumers. This shift has been a crucial part of the Third Industrial Revolution and helped spark the era of consumers sharing their data with different organisations. But now organisations are heralding the Fourth Industrial Revolution, and data is […]

Analytics: The lifeblood of the Fourth Industrial Revolution was published on SAS Voices.