predictive analytics

4月 292016
 

The Barnett Shale in North Texas hit a historic mark on April 25: Its rig count fell to zero. Two hundred rigs once harvested the 40 trillion cubic feet of natural gas in this massive basin, stretching beneath 17 Texas counties. Today, nothing. This dramatic silence in North America’s second-largest […]

All quiet on the Barnett Front was published on SAS Voices.

4月 192016
 

Of course everyone has heard all the hype on big data and how it can help business’ become more successful. But have you thought about the different types of big data? How the different types of data can support different initiatives within your business?

Structured versus unstructured data in retail is a key topic to first understand in order to create a successful plan. Structured data is data that sits in a database, a file, or a spreadsheet. It is generally organized and formatted. In retail, this data can be point-of-sale data, inventory, product hierarchies, ect. Unstructured data does not have a specific format. It can be customer reviews, tweets, pictures, and even hashtags.

So now that you know what structured versus unstructured data in retail is, let’s talk about how to use it. Customer reviews are a great way to understand why a certain product is or isn’t working. Word clouds are a tool to visualize large amounts of customer reviews. Finding key words that are continuously being used canRetail-Transaction_50B9900 give insight in to product defects. For example, if ‘fits small’ is frequently used then you can be proactive by adding this to the product description or above the size selection. This will reduce customer returns and money lost on shipping fees.

Unstructured data can also be analyzed for sentiment analysis. This gives insight in to whether the customer’s response is positive, negative, or neutral. A great example of this is being able to analyze your customer’s twitter responses. Let’s say you post a tweet with products you are thinking about buying for your spring line and your brands hashtag. This enables retailers to understand your customers’ response before you even buy the product. This technique can also be used in-season and give insight to merchants on areas of opportunity or risk so that open to buy can be managed. Break down the silos between merchandising and marketing and enhance collaboration.

It doesn’t take a data scientist to use unstructured data analytical techniques either. If you’re looking to use unstructured data in your business process, check out more information on SAS Visual Analytics. Also, take a look at the 2015 Forrester Wave report where SAS was named a leader in Big Data Predictive Analytics Solutions.

tags: big data, predictive analytics, retail, sentiment analysis, unstructured data

Structured Versus Unstructured Data in Retail was published on Customer Intelligence.

4月 082016
 

How do universities predict which students will enroll? And how do they determine what actions recruitment officers should take to entice students to pick their university? These were two of the key questions tackled by Lisa Moore, Institutional Research Analyst at University of Oklahoma, during her presentation at The Texas […]

Data-informed recruiting was published on SAS Voices.

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 062016
 

It’s hard to believe that another year is over. 2015 is behind us; 2016 is ahead. As I looked back over this year, I recalled starting last year at the National Retail Federation Big Show. I presented in the SAS booth on “Optimizing Pricing Decisions.” The presentation was simple and used the concept of […]

The price is right: Four steps to better pricing decisions was published on SAS Voices.

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.

11月 112015
 

Marketing analytics continues to explode with more data sources and fascinating predictive marketing approaches to solve important business problems, yet one challenge continues to bubble up. The ability to translate the technical math behind predictive analytics into easy-to-understand business language and visualization to help c-suite executives make data-driven decisions with confidence. Developing this business skill is highly valuable as leadership decisions will not be made with data-driven evidence without transparent understanding, and how one communicates to a senior executive within the C-Suite versus a departmental technical manager is very different.

This was the challenge I embarked to address at the 2015 &Then DMA conference in Boston, Massachusetts. 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. There is a reason that the data scientist is the sexiest job of the 21st century, there are over 120 universities offering business analytic graduate degree programs, and U.S. President Obama appointed the first ever chief data scientist earlier this year . It is my personal belief that data driven marketing is on the rise, and will continue to provide competitive differentiation for organizations that invest in best practices and talent, as compared to others that select the short-cut approach.

When it comes to championing analytics within a marketing organization, part of the solution is to enable and perform effective marketing analysis that incorporates analytics across the spectrum - descriptive, diagnostic, predictive, and prescriptive. However, I strongly believe there are other important, and often, overlooked components that complement an analytic team's ability in becoming successful.

  • The ability to communicate and frame an analytics problem as it relates to a marketing challenge
  • The ability to explain the findings of the analytics process in sufficient detail (i.e. telling a story with data visualization) to ensure clear understanding
  • The ability to connect the dots between analysis, and empowering a downstream marketing process

As a principal solutions architect by day for SAS, and a professorial lecturer by night at The George Washington University, I take aim to raise awareness of these subjects to my clients and students. An individual's ability to communicate clearly, succinctly, and in the appropriate language vernacular when presenting analytical recommendations to the marketing organization is extremely important when focused on driving change with data-driven methods and visualization. My main intent is to prove that the days of leaving a business meeting where the CMO states “that was interesting, but maybe next year” are over.

Suneel Image 2

Did I succeed? You be the judge:

 

Let me know what you think in the comments section below. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

 

tags: customer intelligence, Data Driven Marketing, data storytelling, data visualization, DMA, DMA &Then, marketing analytics, predictive analytics, Predictive Marketing

Translating predictive marketing analytics through visualization was published on Customer Analytics.

11月 032015
 

In anticipation of SAS Forum Portugal 2015, I wanted to kick off my first contribution to the SAS Customer Analytics Blogosphere sharing an interview I completed with Sofia Real on the topics of modern digital marketing, predictive analytics, optimization, and personalization. Does that sound like a nasty traffic jam you might want to avoid? Absolutely not, as the time has arrived for predictive marketing to have it's moment in the bright sun, and with Gartner recently naming SAS a Leader in digital marketing analytics, it's official - the 800 pound guerrilla in advanced analytics is locked in on solving complex issues facing the space of data driven marketing. Making digital personalization more relevant for target audiences is just like preparing a delicious meal; it all comes down to the ingredients and preparation process to rise to the occasion!

1. How can analytics help the everyday life of a marketer focused on website or mobile app content strategy and optimization? 

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.

Presently, marketers primarily use a variety of online testing approaches that include A/B testing and various methodologies within multivariate testing (MVT) for optimizing content. A/B testing is a method of website or mobile app optimization in which the conversion rates of two versions of a page (version A and version B) are compared using visitor traffic. Site or app visitors are presented either version A or B. By tracking the way visitors interact with the content they are shown – the videos they watch, the buttons they click, or whether they sign up for a newsletter – you can infer which version of the content is most effective. Multivariate testing uses the same core ingredients as A/B testing, but it can compare more than two variables. In addition, it reveals more information about how these variables interact with one another.

Lastly, for digital marketing practices with an advanced analytic strategy, the usage of 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 best practice, in my opinion. 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. To bring this to life, check out a video example I created of predictive marketing analysis using SAS Visual Analytics and Decision Trees to provide digital-centric insights!

Suneel.Figure11

2. What are the advantages of using these various optimization approaches? Are they restricted only to the marketing department?

Online testing is appealing not only because it is efficient and measurable, but also because it cuts through noise and assumptions to help marketers present the most effective content, promotions, and experiences to customers and prospects. The evolving digital marketing landscape drives a greater mandate for online testing: to operate in more channels, handle more data and support more users. Online testing must move beyond traditional on-site experimentation to fully optimize a multifaceted digital customer experience.

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 predictive analytics to contextualize digital customer experiences.

There are three areas where current trends in digital personalization are falling short:

  • Single-channel digital interactions: Most online experience delivery platforms offer predictive analytic capabilities for a single section of a website in order to support marketing acquisition (rather than the entire digital journey), but do not provide (or limit) functionality for integrating predictive insights across multiple data sources (online and offline), primarily because cloud-based solutions were not designed to incorporate on-premises first-party offline data. In other cases, uploading that data would violate internal IT policies regarding the sensitivity of sharing customer data and associated risks.
  • Black-box vs. white-box scoring: Many digital experience delivery technologies offer predictive capabilities, but do not offer transparency. That is, they aim to provide insights for a specific scenario (such as next best offer recommendations) with algorithms that are more or less opaque. Marketers or their supporting analysts can’t see into the process of the prediction, limiting their ability to improve the predictive model while minimizing false-positives and false-negatives.
  • Extreme dependency on business rules: Other platforms rely heavily on predefined (or subjective) customer profiles and interaction campaign design. As firms who have adopted this approach begin to mature, these rules expand exponentially, forcing marketers and campaign planners to manage hundreds of rules. Business rules have a place in predictive analytics, but they are the bread, and predictive models must be the filling in between the bread.

There is a broad selection of standalone predictive analytics solutions that can support the delivery of exquisite digital experiences. These solutions enable any department (not just marketing), data scientists and developers to design, develop and deploy predictive models to websites and mobile applications. Standalone predictive solutions surpass embedded predictive capabilities that are found in many digital experience platforms because they have the ability to:

  • Incorporate large and varied data sets from numerous sources, producing unanticipated insights. Unlike the digital experience platforms, which aim to own the data, predictive analytic capabilities can support either cloud-based or on-premises platforms, enabling marketers to find customer patterns across a variety of internal and external data silos. Often, the goal-oriented nature of predictive analytics leads to unexpected customer insights that firms might not have found by using traditional segmentation methodologies. The key is to ensure that the data sources are available for real-time personalization applications, meaning that clickstream data (historical and in-session), demographics and other valuable inputs can be processed, analyzed, scored and treated within milliseconds.
  • Allow for monitoring of predictive models and adaptation to new developments. Over the long term, data-driven marketers must evaluate predictions for effectiveness. If a model’s predictive confidence level drops below a certain threshold, its business value decreases, and it might become no more useful than rules-based personas. When a model becomes unacceptably inaccurate, users should be able to modify the algorithms and variables that are used to make the predictions in order to return to higher accuracy levels.
  • Provide both the predictive insights and the logical rules. Despite their power, predictive models must also be constrained with information about the real world in order to deliver the most value.

I am a strong believer in supporting my thoughts and opinions with real evidence. Check out another video example I created using SAS Visual Statistics to perform approachable, analytical segmentation (rather than subjective rules-based approaches) using both clickstream behavioral data and third-party append data (sourced from a partnered MSP or digital DMP) to provide insight into informing personalization strategies and increasing relevance.

Suneel.Figure12

3. How does this all fit in a modern marketing omni-channel strategy?

Most organizations have several customer-facing web and mobile applications with varying levels of visitor traffic. Before undertaking a digital-personalization initiative, the organization has to first identify the most suitable digital application for personalization and its related content management systems. Some of the factors that go into this decision include:

• Average number of daily visitors
• Geographical and time-of-day distribution of visitors
• Purpose of the web application
• Existing hosting platform (cloud or on-premise)
• Ease of website modifications for personalization

After the most suitable web application and its related content management system have been identified, the following components (implemented by what I will refer to as engines) are recommended for a robust digital-personalization solution:

• Collection Engine: Collects digital behavioral data, for every session and every user accessing any of the digital properties of the organization
• Normalization Engine: Transforms raw digital behavioral data into a normalized data model, suitable for data-stitching with offline data, as well as for feeding business intelligence reporting, and predictive analytics
• Analytical Engine: Consists of all tools and processes used by organization to analyze the normalized data and build predictive marketing models
• Decision Engine: Uses the output of the predictive marketing analytical models and processes to perform decision orchestration in staged or real-time consumer interactions (both outbound and inbound processes)
• Personalization Engine: Presents optimized and contextually aware content across marketing channels (online or offline) using treatments received from Decision Engine

4. What are main steps a company must take to adopt this kind of procedures? Does it Imply changes in the traditional processes?

I would like to highlight three phased approaches, based on varying levels of digital marketing and analytic maturity of an organization:

Startup Phase

In this phase, the enterprise installs and configures the required tools and software to work in conjunction with its digital application to personalize content (by using a rules-based randomization model) and collect required data that will be used in upcoming phases.

Suneel.Figure10

 

Analytics Phase

During this phase, the organization assembles the data captured by the collection engine and merges it with internal customer data into a common analytical data mart for building models to support staged personalization.

Suneel.Figure25

Operational Execution Phase

During this phase, the enterprise monitors analytical performance and continues to improve its predictive models by periodically downloading data that was captured by the collection engine and deploying model scoring to the real-time decision engine.

Suneel.Figure27

For readers who made it this far, I thank you for your attention and commitment to this blog posting. If you enjoyed the content, and would like to dive deeper into my thoughts about making digital personalization delicious by leveraging predictive analytics, please consider downloading a technical white paper I authored earlier this year here, or viewing an on-demand webinar available here.

Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

 

tags: customer intelligence, Data Driven Marketing, Digital Analytics, Digital Data Mining, Digital Intelligence, digital marketing, personalization, predictive analytics, Predictive Marketing

Digital marketing, predictive analytics, and making personalization delicious was published on Customer Analytics.

9月 012015
 

“When building a predictive model, we find the JMP Pro interfaces to be very intuitive, allowing us to work closely with other JMP Pro users to build the model together.” -- Amy Clayman, Data-Driven Decisions Circle, VCE Beyond Spreadsheets is a blog series that highlights how JMP customers are augmenting […]

The post Beyond Spreadsheets: Amy Clayman, Voice Systems Engineering appeared first on JMP Blog.