Digital Intelligence

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.

8月 112017
 

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:

  • Uncomplex.
  • Relevant.
  • Timely.
  • 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.

{\mathrm {Conversion\ rate}}={\frac {{\mathrm {Number\ of\ Goal\ Achievements}}}{{\mathrm {Visitors}}}}

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:

Macro conversion – Someone completes an action that is important to your business (like making you some money).

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.

Hyperparameters, digital analytics, and key performance indicators 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.

5月 252016
 

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:

Data Aggregation for Web Analytics

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:

Data Aggregation for Advanced Analytics

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?

20-80 Rule

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.

SAS-Tag

 

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.

Data Model

The output of this data model schema summarizes all digital visitor behavior at this level of detail:

  • Customers.
  • Anonymous visitors.
  • Sessions (or visits).
  • Interactions (or clicks/hits).

Complete View

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 analyticsmachine 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.

SAS CDH

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:

  1. Customer-centric rather than channel-centric
  2. Focused on enterprise goals as opposed to departmental
  3. Enabled for audience activation and optimization
  4. 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:

  1. Decision Trees
  2. Clustering
  3. Forecasting
  4. Logistic Regression

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.

tags: 360 Discover, Data Driven Marketing, data science, Digital Analytics, Digital Intelligence, digital marketing, Integrated Marketing, marketing analytics, predictive analytics, Predictive Marketing, SAS Customer Intelligence 360

SAS 360 Discover: Predictive marketing's new secret weapon was published on Customer Intelligence.

4月 202016
 

Just last weekend, I was considering buying a new camera lens. I already had a few brands in mind, so I looked online at their websites to learn more about their product information. I was able to conduct a comparison on different brands and lenses to narrow down to a specific 50mm lens provided by a major brand. I added the lens to my cart online, but wanted to get a closer look of it, so I chatted online with a representative to see if there were any lenses available at stores near me. This digital channel was my first point of interaction with the brand, but what impact did that have on my buying experience? Would responsive design come479424735 into play? Would the brand proactively contact me about similar products? Or would they simply react to inquiries that I had as a consumer? But today’s consumers expect immediate, individualized messages – would this brand deliver?

The fact of the matter is that a lot of brands don’t have the capabilities to modify messages, offers and interactions across channels, devices and points in time so that they are more relevant to the end consumer.

 Enter SAS

SAS Customer Intelligence 360, launching this month to the marketplace, offers an all-encompassing view of customers no matter how they choose to engage with you across digital properties.

A complete customer view

SAS Customer Intelligence 360 can give you detailed insights from digital channels customers interact with to create the most effective and relevant actions. The solution rapidly transforms digital data into a complete 360-degree view of the customer, meeting each customer’s needs at the right time, place and in proper context. Multiple decision-making methods, such as predictive models and multivariate tests, help ensure that customers gets the most relevant and personalized offers.

Data integration

Data is also easy to integrate with many offline customer channels though SAS Customer Intelligence 360 and its customer decision hub. Customer interactions are based on previous engagements on all other platforms. The data hub is able to convert all of this into customer-focused actions. With this data integration, the Customer_decision_hubbrand is able to gather my interactions and information from all available sources; not just the website, but the call center, mobile apps, social media and point of sale.

Offline customer data can be appended to digital data to further augment the view of me as a customer. These data sources, typically demographic or transactional in nature, gives marketers valuable insight into a customer’s true needs in order to create more relevant offers, better targeted activities and more efficient use of marketing resources. This capability allows the brand to see me more than just page clicks. They’ll see me as a father with young children, interested in photography and seeking to buy a 50mm lens to capture fleeting family moments.

Insights into future actions

You don’t need to be a data scientist to harness the power of predictive marketing; SAS Customer Intelligence 360 includes guided analytics to provide marketers a forward-looking view of customer journeys. This enables them to better understand business drivers and incorporate them into segmentation, optimization and other analytic techniques. Marketers can better forecast how customers will perform in the future. The solution acts as the data scientist – enabling marketers to become more efficient and effective in the analytical techniques they embed into marketing initiatives.

Web data collection

Each web page is embedded with a single line of HTML that automatically collects page information without expensive tagging. With this feature, the webpage configuration might change simultaneously with what I click on, the order and timing of my clicks, each keystroke, etc. Dynamic data collection offers me more relevant content as I navigate through the brand’s site. Any customer activities are recorded privately and securely over time so that once a customer is identified, the information is connected automatically.

Simply put, SAS Customer Intelligence 360 offers marketers the confidence to manage their digital customer journeys in a more personalized and profitable way. Marketers gain a complete view of their customers and transform this data using analytical insight into customer-centric knowledge and future actions. With this solution, brands can interact with customers on a personalized level and customers will be more satisfied with their entire relationship with a brand, not just a single transaction. Customer loyalty goes up and attrition goes down.

And as for me, I got the lens I was looking for, and was satisfied with the customer experience. Of course I have ideas on how to improve it on behalf of this brand, and SAS Customer Intelligence 360 fits into that picture.

tags: customer decision hub, customer journey, data hub, data scientist, Digital Intelligence, Predictive Marketing, Predictive Personalization, SAS Customer Intelligence 360

SAS Customer Intelligence 360: Digital discovery and engagement brought into focus was published on Customer Intelligence.

4月 152016
 

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. Predictive MarketingDespite 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:

  1. It's confusing to distinguish among the measurement approaches available.
  2. Marketers bombard customers with extraneous content.
  3. 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 . . . 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.

If you enjoyed this article, be sure to check out my other work here. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

tags: Advanced Analytics, customer intelligence, Data Mining, data science, Digital Analytics, Digital Attribution, Digital Intelligence, digital marketing, Digital Personalization, marketing analytics, Predictive Marketing, Predictive Personalization, segmentation

Introduction to SAS for digital personalization and attribution was published on Customer Intelligence.

3月 312016
 

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?Modern Marketing

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]

If you enjoyed this article, be sure to check out my other work here. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

tags: Advanced Analytics, customer analytics, customer intelligence, data integration, data management, Data Mining, data science, Digital Analytics, Digital Intelligence, digital marketing, Integrated Marketing, marketing analytics, predictive analytics, Predictive Marketing, segmentation, web analytics, webcast

Introduction to SAS for digital analytics and segmentation was published on Customer Intelligence.

3月 232016
 

The business opportunity to intelligently manage customer journeys across their lifecycle with your brand has never been greater, but so is the danger of not meeting their expectations and losing out to savvier competitors. In my opinion, the current state of most digital analytic practices continue to be siloed, tactical, and narrowly fixated on channel-obsessed dashboard reporting. That might come across as presumptuous, but keep this in mind - customer-centricity is a hot topic at the C-Suite level, and your CMO has  stated (or will very soon) that your organization is transforming into a personalization super force that will be marketing to the segment of one. If that is the case, the category of digital analytics has got to step up its game!

The antidote is digital intelligence which represents a strategic shift in approach to marketing analysis that uses insights from traditional and modern channels (we're talking online AND offline) to enable actionable, customer-obsessed analytical brilliance.

The era of the empowered customer is unraveling itself — trends in which consumers, not brands, own influence, backed by the rapid rise of digital. I strongly believe that no matter how important a company's products or services are with my life, the majority of brands I do business with continue to perform channel-centric analysis, and remain unaware of the different interactions I have with them across ALL channels. I don't care about your email or search marketing KPIs. What I care about is how you treat Suneel, no matter what device, channel, or platform I select to interact with you on.

Meanwhile, digital marketing spend continues to grow at a tenacious pace, cementing the importance of digital channels in managing the customer journey. Digital marketing is effective in all phases of the customer life cycle, ranging from acquisition, upsell/cross-sell, retention, and winback, proven by the ongoing shift of wallet share to online channels. While these are exciting times for omnichannel marketers, these more holistic approaches bring challenges. In today's fragmented digital landscape, long-established methods focused on web analytics and aggregated customer views are ill-equipped to keep pace with:

Digital interaction bread crumb trails

Customers (and prospects) interact with brands across an array of online channels and devices, creating new paths to generate incremental value associated with marketing-centric KPIs. However, customers expect personalized relevance in moments of truth, raising the bar for analytics and marketing execution. A brand's digital presence is much more than a website, such as social media, mobile applications, and wearable technologies. Conventional web analytics only track onsite behavior and lack the ability to comprehend tech-savvy customers in 2016.

The collapse of the digital silo

Brands typically construct offline and online interaction channels confined from one another, so let's reflect on that for a moment. Isn't it time we recognize that customer data is customer data, regardless of where the ingredients are collected? To deliver comprehensive customer insights, brands seek to merge digital and offline data sources together. Digital & customer analytics teams are attempting to work together, but their projects struggle due to a clash of approaches & culture. Some of the main drivers are:

  1. Data — Customers leave trails of information for marketers to chew on, and are available in structured, semistructured, and unstructured formats. There's no excuse anymore for brands to not be able to work with all three. Approachable technology exists to integrate multiple sources of online and offline customer data in meaningful ways to analyze and take action on.
  2. Skills — Have you ever sat in a meeting with data scientists and web analytic ninjas? It's like they speak two different languages, and communication between these two segments is critical for an organization to innovate in its commitment to customer analytics.
  3. Analysis — There is a reason why there is so much discussion around the application of advanced analytics. In many ways, digital marketing is ripe for analytical maturity, ranging across segmentation, attribution, and personalization. The discipline has proven its value to help differentiate a brand from its competition. When are the days of Data-Scientist“good enough” analytics going to end? Let's keep the science in data science, and stop succumbing to the false hype that sophisticated predictive marketing can be accomplished through black box, easy-button solutions.

Dynamic interaction management

Brands seek to react intelligently to shifts in consumer behavior in milliseconds, which makes the intersection of predictive analytics and data-driven marketing vital for orchestrating the customer journey. To reach your target audience in opportunistic micro-moments, the requirement of real-time actionable analytics with direct connections to personalization and marketing automation systems is the queen bee. The sole dependence on isolated, retrospective reports and dashboards of aging web analytic solutions has serious limitations in modern marketing.

Given the investment and revenue at stake for most brands, it is increasingly important to champion support of the development and continuous optimization of digital channels. Simply put, analytical sophistication lives at the center of that process. Yet most organizations continue to approach digital analytics focused on discerning traffic sources and aggregated website user behaviors. Given the intricate complications and aspirational promise of digital marketing, brands should consider modernizing and maturing their approaches to customer analytics because:

  • CX matters: Customers don't care about the challenges related to identity management across multiple visits (or sessions), browsers, channels, and devices. Does your web analytic platform support your team's abilities to recognize and track customers, not clicks or hits, across the fragmentation of touch points? With careful consideration towards the areas of data management, data integration, and data quality, analyzing customer-centric (or visitor-centric) digital activity on their journeys to making (or not making) a purchase with your brand is absolutely feasible.
  • "Good enough" analytics must end: Digital analytic teams must graduate from machine gunning their organizations with traffic-based reports that summarize the past to producing predictive insights that marketers can interpret, and take action with. I'm always impressed by web analytic teams that produce an array of historical reports with beautiful visualizations, segmenting and slicing away at their tsunami of clickstream data. However, how much impact and relevance to the business can this approach have? Customer-centricity demands that we re-engineer our thinking, and make the shift from reactive to predictive marketing analytics.
  • There's nothing exciting about siloed channel analysis: To deliver the elusive and mythical 360 degree view of customer insights, it turns out you don't need magical wizards like Gandalf or Albus Dumbledore by your side. Have you ever wondered why web analytic software doesn't allow you to perform data stitching with offline data sources? How about data mining and predictive analytic capabilities? Well, it boils down to how digital data is collected, aggregated, and prepared for downstream use cases.

Web analytics has always had a BIG data challenge to cope with since it's inception in the mid 1990's, and when the use case for analysts is to run historical summary reports and visual dashboards, clickstream data is collected and normalized in a structured format as shown in this schematic:

Data Aggregation for Web Analytics

This format does a very nice job of organizing clickstream data in such a way that we go from big data to small, more relevant data for reporting. However, this approach presents challenges when performing customer-centric analysis which requires data stitching across online and offline data sources. Why you ask? Because you cannot de-aggregate data that was designed for channel and campaign performance summarizations. Holistic customer analysis, from a digital viewpoint, requires the collection and normalization of granular, detailed data at an individual level. Can it be done? Of course it can.

Multi-source data stitching, data mining and predictive analytics require a specific digital data collection methodology that summarizes clickstream data to look like this:

Data Aggregation for Advanced Analytics

Ultimately, the data is collected and prepared to contextually summarize all click activity across a customer's digital journey in one table row, including a primary customer key to map to all visits across channels and devices. The data table view shifts from being tall and thin, to short and wide. The more attributes or predictors an analyst adds, the wider the table gets. The beauty of this approach is it allows marketers and analysts to be curious, add more data sources, and allow algorithmic analysis to prioritize what is important, and what isn't. This concept is considered a best practice for advanced customer analytics.

  • Beware of blind spots: As time passes, customers in every industry are progressively sharing more data about themselves through existing and emerging digital outlets, such as mobile applications, wearables, and other connected technology. The opportunity to ingest and analyze these new sources should excite any marketer who claims to be data-driven. However, does your web analytics platform allow you to analyze these new digital touchpoints? A brand's ability to absorb, integrate, analyze, and derive marketable insights from emerging data sources is key in this new paradigm to avoid being blindsided by customers and the competition.

The path to digital intelligence from traditional web analytics needs to cover the diversity of data, advanced analytic techniques, and injection of prescriptive insights to support decision-making and marketing orchestration. Digital intelligence is a transformation for web analytic teams — making it a competitive differentiator if executed well. It aims to transform brands to become:

  1. Customer-centric rather than channel-centric: As customers and prospects weave across an ocean of marketing channels and connected devices, digital intelligence supports the integrated analysis of interactions in concert, rather than with disconnected channel views. In addition to visibility across all channels, analysis is highly granular to identify, track, and prioritize next-best-actions for individuals. In other words, hyper-personalization to the segment of one!
  2. Focused on enterprise goals as opposed to departmental: To enable omnichannel analytics, digital intelligence is highly dependent on customer data management capabilities across all data types – structured, semistructured, and unstructured. This includes fusing interaction and behavioral data across all digital channels with first-party offline customer data, as well as second- and third-party data (if available). This enriched potpourri of data must be prepared to feed the analytical ninjas that sit within the marketing organization, line of business or centralized customer intelligence team, because it is their job to exploit this stream of information and generate insights for the organization as a whole.
  3. Enabled for audience activation and optimization. The mission of digital intelligence is the direct application of analytics to generate data-driven evidence that helps business stakeholders make clearer decisions. The potential of data mining exponentially increases with richer customer data to support segmentation, personalization, optimization, and targeting - in other words, connecting data and analytics to the delivery of relevant content, offers, and awesome experiences.
  4. Analytical workhorses: The incredibly fast-moving world of digital interactions and campaigns mean that marketers desperately need quicker analysis. Waiting days or weeks for reports and research equates to failure. Digital intelligence delivers efficiency at a pace that more nearly matches users' decision-making schedules.

SAS Customer Intelligence offers a one-stop modern marketing platform to comprehensively support the mission of digital intelligence - from digital data collection, management, predictive analytics, and marketing delivery across online and offline channels. On April 19 at SAS Global Forum 2016, SAS Customer Intelligence 360 will make its debut, and digital intelligence will be a primary topic. This new offering will drive unprecedented innovation in customer analytics, putting predictive analytical intelligence directly in the hands of digital marketers, business analysts, and data scientists. In the last few months, industry analysts have previewed and validated our abilities in advanced and customer analytics.

We are very excited for the future and potential of digital intelligence. The question is...

Are you excited?

 

If you enjoyed this article, be sure to check out my other work here. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

tags: customer intelligence, Data Mining, data science, Digital Analytics, Digital Intelligence, marketing analytics, personalization, predictive analytics, Predictive Marketing, segment of one, web analytics

Web analytics vs. digital intelligence - what's the difference? was published on Customer Intelligence.

1月 262016
 

I begin this blog post with one goal in mind. I want to raise awareness on the subject of customer and marketing analytics, and why this field is exploding in interest and popularity. Let's begin with a primer for the uninitiated, and lay down some definitions:

Customer Analytics: The processes, technologies, and enablement that give brands the customer insight necessary to provide offers that are anticipated, relevant and timely.

Marketing Analytics: The processes and technologies that enable brands to assess the success of their marketing initiatives by evaluating performance using important business metrics, such as ROI, channel attribution, and overall marketing effectiveness.

If you aren't a fan of textbook definitions, here is a creative alternative:

Still not on board? Here's my perspective on the subject:

Customers are more empowered and connected than ever before, with access to information anywhere, any time – where to shop, what to buy and how much to pay. Brands realize it is increasingly important to predict how customers will behave to respond accordingly. Simply put, the deeper your understanding of customer buying habits and lifestyle preferences, the more accurate your predictions of future buying behaviors will be.

Marketers need to be enabled to benefit from approachable and actionable advanced analytics to make more powerful decisions within today’s complex and interconnected business environments.  In my mind, the big picture boils down to one, two or three core enablers, based on your organization's goals and preferences:

Image 2

 

 

 

 

Marketing analysts tasked with making sense of customer data, big or small, have to migrate through a complex maze of myths and realities about technology platforms, advanced analytics solutions and, most importantly, the magnitude of customer analytics efforts. On the surface, it appears that customer analytics is a well-entrenched discipline in many organizations, but under the hood, old problems persist around data integration and data quality while new ones emerge around the real-time application of insights and the ability to rein in digital data for customer-based analysis.When I speak with clients, there are two key themes that I continually hear:

  1. Data is a big challenge. As customer interactions with brands increase and diversify, brands need to integrate data effectively in order to provide the contextual and real-time insights their customers are growing to expect. Haven't you grown tired of saying we spend 80 percent of our time on data management related tasks, and 20 percent on analysis?
  2. Analytic talent is hard to find. Brands struggle to find individuals with the right analytic skills to meet the challenges they are facing today. Without the talent to unlock actionable insights, modern customer analytics cannot meet its potential. (Given my public affiliation with The George Washington University's M.S. in Business Analytics program, I'd recommend checking it out if you are hunting for quality talent.)

To me, these themes point to a workflow entitled the marketing analytics lifecycle:

Image 3

 

 

 

 

 

 

With the growing importance of customer analytics in organizations, the ability to extract insight and embed it back into organizational processes is at the forefront of business transformation. However, this requires considerations for where relevant data resides, the ability to reshape it for downstream analytic tasks (predictive modeling vs. reporting), and how to take action on the derived insights. Furthermore, there are the roles of different people within the organization that need to be considered:

  • Marketing Analyst/Technologist
  • Data Scientist/Statistician
  • Marketing Manager
  • Supporting IT Team

Customer analysis touches all of these roles, and to enable this audience comprehensively, all aspects of the marketing analytics lifecycle must be supported. To directly address this, I want to to highlight what SAS is doing to help our clients meet these challenges.

Marketing Analytics Lifecycle Stage #1: Integrate and Prepare Data

Customer analytics is highly dependent on the quality of the ingredients we feed into analysis. Now, the digital marketing industry has been taken by storm by the emergence of Digital DMPs, like Oracle BlueKai, Neustar, and Krux, who aim to provide marketers support in programmatic ad buying and selling. Marketers and publishers are learning that harnessing their first-party data; developing single and consistent identities for their consumers across devices and systems, like email and site optimization; and gaining access to second-party data are mission critical. However, the subject of data mining and predictive analytics has largely been ignored by the Digital DMP space. Brands who want to exploit the benefits of advanced analytics have additional considerations to support their data management challenges. The following video highlights how SAS helps manage and prepare data of all sizes, from 1st party customer data to clickstream and IoT, specifically for analytics:

 

Some of you might be questioning the value of this, so let me offer a different perspective. Over the past few years, I have developed a personal frustration of attending various marketing conferences and repeatedly observing high-level presentations about the potential of analytics. Even more challenging has been the recent trend of companies presenting magical (i.e., "easy-button") black-box marketing cloud solutions that address every imaginable analytical problem; in my opinion, high-quality advanced analytics has not reached a point of commoditization, and remains a point of competitive differentiation. Do not be mislead by sleight-of-hand magic!

Marketing Analytics Lifecycle Stage #2 & #4: Explore Data, Develop Models, and Deploy

What types of marketing challenges are you attempting to solve with customer analytics? Srividya Sridharan and Brandon Purcell are two leading researchers in the space of customer insights, and recently released a report entitled How Analytics Drives Customer Life-Cycle Management recommending the deployment of various analytical techniques across the customer life cycle to grow existing customer relationships and provide insight into future behavior. Highly recommended reading! Let's review some of the most common problems (or opportunities) we view at SAS with our clients.

Image 4

Within each of the categories, a myriad of analytic techniques can be executed to assist and improve your brand's abilities to address them. The following video is a demonstration of how I used SAS Visual Statistics and Logistic Regression analysis to understand drivers by marketing channel of business conversions on a website or mobile app. The benefit of understanding these data-driven drivers is to influence downstream marketing personalization and acquisition campaigns. In addition, capabilities related to group-by modeling, deployment scoring and model comparison with other algorithmic approaches are highlighted.

 

 

Big digital data, scalable predictive analytics, visualization, approachability, and actionability. Stay thirsty my friends, because it is our clients who are expressing their needs, and SAS is stepping up to meet their challenges!

If you would like to learn more on how we address other marketing and customer analytic problems, please click on any of the following topics:

  1. Personalization
  2. Attribution
  3. Segmentation
  4. Acquisition
  5. Optimization

With that said, we have one final stage of the lifecycle to review.

Marketing Analytics Lifecycle Stage #3: Explain Results and Share Insights

An individual's ability to communicate clearly, succinctly and in the appropriate vernacular when presenting analytical recommendations to a marketing organization is extremely important when focused on driving change with data-driven methods. I recently wrote a blog post on this topic entitled Translating Predictive Marketing Analytics, and if you're tired of reading, here's another video - this time focused on explaining the results of analytical exercises in easy-to-consume business language.

 

As I close this blog post, I want to leave you with a few thoughts. For your brand's customers, technology is transparent, user-enabling, and disintermediating. The journey they embark with you on is fractured and takes place across channels, devices, and points in time. The question becomes – are you prepared for moments of truth as they occur across these channels over time? Customer analytics represents the opportunity to optimize every consumer experience, and revisiting a point I made earlier, the deeper your understanding of customer buying habits and lifestyle preferences, the more accurate your predictions of future buying behaviors will be.

If you enjoyed this article, be sure to check out my other work here. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

 

tags: Business Analytics, business intelligence, customer analytics, customer intelligence, Customer Lifecycle Management, Data Mining, Digital Intelligence, marketing analytics, Marketing Attribution, personalization, Predictive Marketing, segmentation

The analytics of customer intelligence and why it matters was published on Customer Analytics.

12月 222015
 

Although the title of this blog posting has all the ingredients to attract the eyes of an analyst, the content is targeted for all personalities of a digital marketing organization. Before we jump into the marketing analytic use case regarding forecasting, scenario analysis, and goal-seeking  for digital analytics, let's spend some time on the magic of stories. As Tom Davenport stated in his fantastic article titled, Telling a Story with Data:

"The essence of analytical communication is describing the problem and the story behind it, the model, the data employed, and the relationships among the variables in the analysis. When the relationships among variables are identified, the meaning of the relationships should be interpreted, stated, and presented relevant to the problem. The clearer the results presentation, the more likely that the quantitative analysis will lead to decisions and actions—which are, after all, usually the point of doing the analysis in the first place."

While creative visionaries and data scientists are both tremendous organizational assets within a team, it is the alliance between these two segments that will push marketing forward. Although aspirational, this is a difficult challenge to overcome. Let me begin by sharing a bit of my story - one that began with a four year career start in graphic design and creative marketing communications, and then taking making a leap to the quantitative side of marketing. I've seen and listened to how DIFFERENT these two segments of the marketing world are, and now as a preacher for the potential of marketing analytics, one's ability to make analysis interpretable and approachable is critical.

Google recently published a nice article titled, Staffing Your Marketing Measurement Team: Why You Need Data Storytellers, and one takeaway that I love from this piece is:

"The true value of data emerges when marketers are able to use it to tell a meaningful story. Enter the data storyteller, or marketing measurement analyst. This is the person who can push the tools, translate insights across the business, and motivate stakeholders to participate."

This quote nails the crux of the issue - if we don't take ACTION on the insights of analytics, it was nothing but a school project. Influencing decision-makers within an organization isn't easy, and if they do not understand the analysis, nothing will ever change. There are people who are good at creative marketing strategy, and there are people who are good at marketing analytics. However, there aren't many people who can toggle between the two, and serve as the translator who inspires both sides.

In my personal opinion, the recent surge in analytic technologies becoming more approachable is key. The special ingredient in that trend is visualization and analytics joining forces in ways we have never seen before. Why is this happening? Seeing and understanding data is richer than creating a collection of queries, dashboards, and workbooks. According to the infamous American mathematician John W. Tukey:

"The greatest value of a picture is when it forces us to notice what we never expected to see.”

The "ah-ha" moment. The best part of my work day!

In addition, when analytics becomes approachable, interpretable, and transparent to the entire marketing organization, the behavioral change of how we work together highlighted in this video becomes a reality:

Visual Analytics represents a new category of interactive and collaborative technology to provide a path to be curious and innovative. Marketers are imaginative, and are constantly pushing to analyze new and exciting data sources (i.e. clickstream, social, IoT wearables, etc.), which require the ability to scale to very large amounts of information. However, what is different here is the ability to perform sophisticated analysis, and produce visualizations to support data-driven storytelling.

Finally, we arrive at the digital analytic use case. The intention is to highlight my personal approach to tip-toeing that fine line of producing meaningful analysis, while narrating the marketing storyline. Here is the description of the business case, and my demonstration video.

Business Challenge:

How do I allocate digital media spend to drive more traffic to my website in a future time period?

Marketing Applications:

  1. Identify the most important acquisition channels (i.e. attribution)
  2. Simulate & optimize ad spend to acquire incremental traffic and meet business objective

Let me know what you think in the comments section below. If you enjoyed this article, be sure to check out my other work here. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

tags: data visualization, Digital Analytics, Digital Intelligence, digital marketing, Forecasting, Goal-seeking, marketing analytics, predictive analytics, Predictive Marketing, Scenario Analysis, visual analytics, visual statistics, web analytics

Forecasting, goal-seeking, and magical stories for digital analytics was published on Customer Analytics.