Regardless of industry, it has become a frequent occurrence that behind every data-driven marketer is an analytical ninja. Together, they formulate recipes in addressing the customer-centric paradigm that considers the different actions that a brand can take for a specific individual, and decides on the “best” one. The goal of [...]
Why does your organization’s website or mobile app exist? What are you hoping to accomplish with your business by being digital? What are the most important priorities for your digital presence? Here are three goals that most organizations share: Sell more stuff. Make marketing more effective. Delight customers. Goals are [...]
Path analysis is an exploration of a chain of consecutive events that a given user or cohort performs during a set period while using a website, online game or mobile app (although other use cases can apply outside of digital analytics). As a subset of behavioral analytics, path analysis is [...]
When data meets geography, use cases revolve around mapping and spatial analytics. But what happens when you combine digital analytics and powerful visualization for customer location analysis? Leveraging data collection mechanisms, SAS 360 Discover captures first-party behavioral information across the entire digital customer experience with a brand’s websites and mobile [...]
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 can you tell if your marketing is working? How can you determine the cost and return of your campaigns? How can you decide what to do next? An effective way to answer these questions is to monitor a set of key performance indicators, or KPIs.
KPIs are the basic statistics that give you a clear idea of how your website (or app) is performing. KPIs vary by predetermined business objectives, and measure progress towards those specific objectives. In the famous words of Avinash Kaushik, KPIs should be:
- Instantly useful.
An example that fits this description, with applicability to profit, nonprofit, and e-commerce business models, would be the almighty conversion rate. In digital analytics this metric is interpreted as the proportion of visitors to a website or app who take action to go beyond a casual content view or site visit, as a result of subtle or direct requests from marketers, advertisers, and content creators.
Although successful conversions can be defined differently based on your use case, it is easy to see why this KPI is uncomplex, relevant, timely, and useful. We can even splinter this metric into two types:
Micro conversion – An indicator that a visitor is moving towards a macro conversion (like progressing through a multi-step sales funnel to eventually make you some money)
Regardless of the conversion type, I have always found that reporting on this KPI is a popular request for analysts from middle management and executives. However, it isn't difficult to anticipate what is coming next from the most important person in your business world:
"How can we improve our conversion rate going forward?"
You can report, slice, dice, and segment away in your web analytics platform, but needles in haystacks are not easily discovered unless we adapt. I know change can be difficult, but allow me to make the case for machine learning and hyperparameters within the discipline of digital analytics. A trendy subject for some, a scary subject for others, but my intent is to lend a practitioner's viewpoint. Analytical decision trees are an excellent way to begin because of their frequent usage within marketing applications, primarily due to their approachability, and ease of interpretation.
Whether your use case is for supervised segmentation, or propensity scoring, this form of predictive analytics can be labeled as machine learning due to algorithm's approach to analyzing data. Have you ever researched how trees actually learn before arriving to a final result? It's beautiful math. However, it doesn't end there. We are living in a moment where more sophisticated machine learning algorithms have emerged that can comparatively increase predictive accuracy, precision, and most importantly – marketing-centric KPIs, while being just as easy to construct.
Using the same data inputs across different analysis types like Forests, Gradient Boosting, and Neural Networks, analysts can compare model fit statistics to determine which approach will have the most meaningful impact on your organization's objectives. Terms like cumulative lift or misclassification may not mean much to you, but they are the keys to selecting the math that best answers how conversion rate can be improved by transparently disclosing accurate views of variable importance.
So is that it? I can just drag and drop my way through the world of visual analytics to optimize against KPIs. Well, there is a tradeoff to discuss here. For some organizations, simply using a machine learning algorithm enabled by an easy-to-use software interface will help improve conversion rate tactics on a mobile app screen experience as compared to not using an analytic method. But an algorithm cannot be expected to perform well as a one size fits all approach for every type of business problem. It is a reasonable question to ask oneself if opportunity is being left on the table to motivate analysts to refine the math to the use case. Learning to improve how an algorithm arrives at a final result should not be scary because it can get a little technical. It's actually quite the opposite, and I love learning how machine learning can be elegant. This is why I want to talk about hyperparameters!
Anyone who has ever built a predictive model understands the iterative nature of adjusting various property settings of an algorithm in an effort to optimize the analysis results. As we endlessly try to improve the predictive accuracy, the process becomes painfully repetitive and manual. Due to the typical length of time an analyst can spend on this task alone - from hours, days, or longer - the approach defies our ability as humans to practically arrive at an optimized final solution. Sometimes referred to as auto tuning, hyperparameters address this issue by exploring different combinations of algorithm options, training a model for each option in an effort to find the best model. Imagine running 1000s of iterations of a website conversion propensity model across different property threshold ranges in a single execution. As a result, these models can improve significantly across important fit statistics that relate directly to your KPIs.
At the end of running an analysis with hyperparameters, the best recipe will be identified. Just like any other modeling project, the ability to action off of the insight is no different, from traditional model score code to next-best-action recommendations infused into your mobile app's personalization technology. That's genuinely exciting, courtesy of recent innovations in distributed analytical engines with feature-rich building blocks for machine-learning activities.
If the subject of hyperparameters is new to you, I encourage you to watch this short video.
This will be one of the main themes of my presentations at Analytics Experience 2017 in Washington DC. Using digital data collected by SAS Customer Intelligence 360 and analyzing it with SAS Visual Data Mining & Machine Learning on VIYA, I want to share the excitement I am feeling about digital intelligence and predictive personalization. I hope you'll consider joining the SAS family for an awesome agenda between September 18th-20th in our nation's capital.
SAS Customer Intelligence 360 is a new digital marketing hub offering that enables users to plan, analyze, manage, and track customer journeys. It includes SAS 360 Discover for digital intelligence and SAS 360 Engage for execution capabilities that enable marketers to dynamically create, manage, and place digital content across a variety of channels. These new enhancements to the our customer decision hub extends the capabilities of an organization to orchestrate omnichannel customer activity. Our intent behind this new offering? To enable our clients to take predictive action through their customer-preferred channels, and deliver a desirable, personalized experience.
Now that I have delivered the trendy marketing description, let's get down to business on the intersection of digital marketing, analytics, optimization, and personalization. No matter who you chat with (insert any marketing cloud vendor who recently delivered you a sales pitch), or what 2016 marketing conference you attended, it's safe to say this is a popular topic. Let's review the popular buzzwords at the moment:
- Predictive personalization
- Data science
- Machine learning
- Self-learning algorithms
- Segment of one
- Real time
It's quite possible you heard these words at such a high frequency, you could have made a drinking game out of it.
What concerns me as I scan the marketing landscape of offerings is the amount of overlap in product descriptions. Does every company do data science equally well? Will the wizardry of automation solve all personalization challenges? While we can all agree that personalization is important, it’s apparent that different organizations can have distinct views of what it actually means, and what it takes to be successful. At the center of the debate is the role of data and sophisticated analytics in personalization best practices. For the past few years, I'm repeatedly asked by clients one specific question:
Do you NEED a data scientist to be successful with data-driven personalization?
At first, I attributed this concern to the perceived lack of analytical talent available for hire. However, there are more than 150 universities offering analytic and data science programs, and talent is flooding into industry. So I placed that stereotype aside and began digging deeper, only to observe that numerous marketing cloud brands were positioning their technology to assume data scientists are NOT needed. This supports the trend of vendors highlighting "easy-button" solutions that address every imaginable obstacle related to scalable personalization. I came across lots of cute descriptions like the "just for them" algorithm, or the "magical machine learning" algorithm. Even imagery of hip marketers wearing augmented reality eyewear while deciphering targeting strategies. Are you kidding me?
When it comes to personalization, I'm uncomfortable with black-box automation, lack of statistical best practices, and the removal of the human analyst. Although
improvements can be achieved in using automated optimization algorithms, they can’t glean the why behind the what. Machine learning doesn't fully replace real learning. Real learning happens when analysts and marketers partner to dig into data, connect the dots and unearth insights that help brands interact with customers. Embedding those insights into hypotheses, then testing and validating completes the process.
In some ways, automating personalization ignores issues that statisticians and data scientists traditionally think about: sampling populations, confounders, model stability, bias and overfitting. In the rush to take advantage of the hype around big data, these ideas seem to be ignored or not given sufficient attention. Marketers need to pay careful consideration to the nuances of consumer behavior, brand management, and the impact of the facilitated experiences they are delivering.
Remember, SAS has passionately loved everything about analytics for 40 years. We do not care for black-box solutions, so we embedded transparent analytical features into SAS 360 Engage. It was developed with this in mind. We are the leader in advanced customer analytics, and deployed our prowess in data science for digital personalization through "blue-box" analytical decision helpers. These decision helpers are specifically built to be integrated and easy to turn on for marketers that prefer automation and efficiency. However, supporting analysts will have the option to dig deeper into the why behind the decisions. Analysts can exploit their own targeting model creations in conjunction with SAS 360 Engage's automated helpers by leveraging our deep library of algorithms and approachable tools. SAS will not limit you to a single algorithm to solve for complex consumer behavior. Compare, contrast, and most importantly, truly optimize!
This concept of "blue-box" addresses an opportunity for SAS to solve for customer journey challenges, while meeting the needs of your team's preferences. We recognize that some departments don't have data scientists available, and have real needs for automation. SAS 360 Engage offers analytical automation without challenging your team to hire new analytic resources immediately. However, as your organization begins to experience success through data-driven methods, your needs will mature. SAS 360 Engage will support that growth trajectory, and offer attractive features for analysts, statisticians, and data scientists to insert their value-add into your personalization mission.
Do I believe a data scientist is a requirement for data-driven personalization? I believe in a future where approachable technology and analytically-curious people come together to deliver intelligent customer interactions. Analytically-curious people can be data scientists, citizen data scientists, statisticians, marketing analysts, digital marketers, creative superforces, and more. Building teams of these individuals will help you differentiate and compete in today's marketing ecosystem, and SAS 360 Engage was built for the data-driven.
In the coming weeks, I will be releasing follow-up posts to drill into demonstrations and use cases of SAS 360 Engage. Keep an eye on this space!
In April, SAS 360 Discover was introduced at SAS Global Forum 2016. Since my career started at SAS over five years ago, I have been anticipating this important announcement. In my opinion, this is a major breakthrough for the space of digital intelligence.
In my first year working at SAS, I learned of research and development to address industry needs for digital marketers. Although technologies from Google, Adobe and others address web analytics with measurement reporting, there was a shortcoming.
Historically, web analytics has always had a huge data challenge to cope with since its inception. And when the use case for analysts is to run summary reports, clickstream data is normalized:
It nicely organizes raw clickstream into small, relevant data for reporting. However, this approach presents challenges when performing customer-centric analysis. Why? Holistic customer analysis requires the collection and normalization of digital data at an individual level. This is one of the most important value props of SAS 360 Discover.
Multi-source data stitching and predictive analytics require a data collection methodology that summarizes clickstream:
The data is prepared to contextualize all click activity across a customer's digital journey in one table row, including a primary key to map to all visits across browsers and devices. The data table view shifts from being tall and thin to short and wide. The beauty of this is it enables sophisticated analysis to prioritize what is important, and what isn't. This concept of data collection and management is considered a best practice for advanced customer analytics.
How many marketers do you know who wake up in the morning and claim they can't wait to hear about how analysts are spending 80 percent of their time preparing raw web behavior data, rather than focusing on analysis and actionable insights? None, you say? Exactly! Wouldn't you rather hear your marketing analysts spend their time doing this?
I have always appreciated SAS for what it can do with structured, semi-structured, and unstructured information, but there has always been one dependency – where do I point SAS to obtain the originating data? SAS 360 Discover eliminates this requirement, and provides data collection mechanisms for your brand's website(s) and mobile apps.
In addition, the raw semi-structured data streams SAS natively collects are run through a pre-built relational data model using SAS Data Management for various forms of contextualization that stretch far beyond traditional web analytic use cases.
The output of this data model schema summarizes all digital visitor behavior at this level of detail:
- Anonymous visitors.
- Sessions (or visits).
- Interactions (or clicks/hits).
The data model schema will allow for additional configurations and introduction of other digital data sources to accommodate your organization's evolving needs. More importantly, the benefits of the output are profound, and listed below is a summary of SAS 360 Discover benefits:
- Digital data normalization to support online and offline data stitching of customers.
- When offline data is residing in your organization's data warehouse, information is available at the customer level (not a click or hit level). That's a problem when you want to link it with web or app data. The amount of time analysts spend reshaping raw HIT extracts from their web analytics solution is astonishing, and quite difficult. Customer analysis requires online/offline data stitching, and overcoming this obstacle was a problem SAS set out to solve.
- Measurement reporting and visualization of customers and segments.
- The reporting remains critical as an entry stage for analytics. SAS believes there should be no limit to how many reports and dashboards can be produced to meet business objectives. In other words, unlimited ad hoc reports using SAS Visual Analytics, which is the analysis tool that is packaged with SAS 360 Discover.
- Predictive analytics, machine learning, and data science of customers and anonymous traffic.
- Fueling the SAS customer decision hub
- Brands gain a competitive edge if they stop perceiving customer engagement as a series of discrete interactions and instead see it as customers do: a set of interrelated interactions that, when combined, make up the customer experience. By folding in all known customer level information into a common hub, SAS can analyze, score and take intelligent, contextual actions across channels.
The path to digital intelligence from traditional web analytics covers the diversity of data, advanced analytic techniques, and injection of prescriptive insights to support decision-making and marketing orchestration. Digital intelligence is a transformation — making it a competitive differentiator. It aims to convert brands to become:
- Customer-centric rather than channel-centric
- Focused on enterprise goals as opposed to departmental
- Enabled for audience activation and optimization
- Analytical workhorses
I suspect you would love to see demonstrations of the data that SAS 360 Discover collects from websites and mobile apps in action:
In addition, here is the on-demand video of the SAS Global Forum 2016 keynote presentation of SAS Customer Intelligence 360.
As a marketing analyst at heart, it is extremely gratifying to share my excitement for SAS 360 Discover. The time for predictive customer marketing in the digital ecosystem is here, and the 800-pound gorilla in advanced analytics has just unleashed your new secret weapon.
As promised a couple of weeks ago, I am very happy to share Part 2 of a webcast series highlighting how SAS participates in the space of digital analytics for data-driven marketing with applications for personalization and attribution. Before launching the video, let me set some context for what you are about to see.
Why do we care about the intersection of digital analytics and personalization? Honestly, it is increasingly important to predict how customers will behave so you can personalize experiences with relevance. The deeper your understanding of customer behavior and lifestyle preferences, the more impactful personalization can be. However, digital personalization at the individual level remains elusive for most enterprises who face challenges in data management, analytics, measurement, and execution. 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 marketing easily overwhelms traditional planning and design approaches that rely on historical conventions, myopic single-channel perspectives and sequential act-and-learn iteration.
The majority of technologies in use today for digital personalization have generally failed to effectively use predictive analytics to offer customers a contextualized digital experience. Most 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 predictive analytics to contextualize experiences using 1st-, 2nd- and 3rd-party customer data. In my opinion, I believe the usage of digital data mining and predictive analytics to prioritize and inform the marketing teams on what to test, and to analytically define segment audiences prior to assigning test cells, is a massive opportunity. Marketers are very creative, and can imagine hundreds of different testing ideas – which tests do we prioritize if we cannot run them all? This is where advanced analytics can help inform our strategies in support of content optimization, as it allows the data to prioritize our strategy, and help us focus on what is important.
Moving on to our second subject of interest, we transition to the wonderful world of marketing attribution. At the very core of this topic, modern marketers recognize that customers expect brands to deliver relevant conversations across all channels at any given moment. The challenge is to uncover the interactions that drive conversions through integrated measurement and insights. However, organizations struggle to employ a holistic measurement approach because:
- It's confusing to distinguish among the measurement approaches available.
- Marketers bombard customers with extraneous content.
- Today's misaligned data makes customer level measurement a very difficult task.
It seems like attribution has been a problem for marketers for a very long time. According to a popular quote by Avinash Kaushik of Google:
“There are few things more complicated in analytics (all analytics, big data and huge data!) than multichannel attribution modeling."
The question is: Why is it challenging? SAS strongly believes three years later that we are living in a game-changing moment within digital analytics. Marketers are being enabled with approachable and self-service analytic capabilities, and this trend directly impacts our ability to improve our approaches to problems like attribution analysis. However, rules-based methods of attribution channel weighting continue to be far more popular in the industry to date, which contradicts the recent analytic approachability trend. The time has arrived for algorithmic attribution . . .
Did I whet your appetite? I hope so...please enjoy episode two of our two-part webcast series, now available for on demand viewing:
SAS for Digital Analytics: Personalization and Attribution [Part 2]
SAS Customer Intelligence offers a one-stop modern marketing platform to comprehensively support the objectives of predictive personalization and algorithmic attribution - from digital data collection, management, predictive analytics, omnichannel journey orchestration, delivery across online and offline channels, and measurement. On April 19 at SAS Global Forum 2016, SAS Customer Intelligence 360 will make its debut, and subjects like digital intelligence and predictive personalization will be primary topics. This new offering will drive unprecedented innovation in customer analytics and data-driven marketing, putting predictive analytical intelligence directly in the hands of digital and integrated marketers responsible for the customer experience.
Digital analytics primarily supports functions of customer and prospect marketing. When it comes to the goals of digital analysis, it literally mirrors the mission of modern marketing. But what exactly is today's version of marketing all about?
Honestly, we've been talking about this for years. And years. We ALL know it's what we should be doing and conceptually it's very simple, but practically, it has been very hard to achieve. Why?
Even with great web analytics, there have always been critical missing insights, which meant we didn't know for certain what the next-best-interaction for each customer was at any point in time. In addition, the development of insights and the use of analytics to define high-propensity audience segments has been distinctly slow and batch-driven in nature, delaying relevant delivery of targeted interactions. So we may get the message right, but we probably don't deliver it in a timely, consistent way, which has a dramatic impact on customer responsiveness and marketing effectiveness.
So in today's connected, always-on, highly opinionated world, we need to be a little sharper in meeting our customer's basic expectations, never mind surprising, delighting, and impressing them. While the concept of customer-centricity continues to increase in importance, improving our analytical approach to support this premise is vital.
SAS recognizes today's modern marketing challenges with digital and customer analytics. It is our mission to enable marketers to benefit from approachable and actionable advanced analytics to make more powerful decisions within today’s complex and interconnected business environments. That sounds great, right? I sense some of you reading this are raising an eyebrow of suspicion at this very moment.
Practically speaking, we want to show you exactly what that means. On March 29th, 2016, we aired episode one of a two-part webcast series, and it is now available for on demand viewing:
SAS for Digital Analytics: Introduction & Advancing Segmentation [Part 1]
We genuinely hope the webcast provided a proper introduction to how SAS participates in the space of digital analytics for data-driven marketing, and please come back in a couple of weeks when we will post Part 2 in this series entitled: SAS for Digital Analytics: Personalization & Attribution [Part 2]