predictive 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.

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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!

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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.

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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.

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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.

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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.

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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.

7月 142015
 

Dust off that old aphorism about an ounce of prevention. Oil companies applying analytics for predictive maintenance can see a substantial downtick in the unanticipated equipment repairs that quickly eat into an oil well’s profitability. Maintenance is far from a trivial concern in the oilfield. A pumping oil well is […]

From downtime to upside with oilfield predictive maintenance was published on SAS Voices.

5月 082015
 

Not so long ago, I started my retail/merchandising career in the juniors division at the corporate office of a retailer. It was so exciting to be in a place where I could wear the clothes that I worked with, and I was sure that picking out cute clothes all day was what I was meant to do! SAS Analytics helps retailers get merchandising right.

Then about a year later, I moved to the "missy" sportswear division, which I thought would be great, too, because cute clothes are easy to pick out in any area, right? Well - I thought wrong. You see, this company was based in Florida, where the missy area for retailers is long on conservative and short on trendy.

My first red flag was raised during my first “Hit or Miss” meeting, where the merchants display their top-selling items (hits) and their slow movers (misses). As you might have guessed, the very first hit was very far from a hit in my book. I just could not imagine 3,200 or so ladies actually choosing to wear a bedazzled hot pink shirt with flamingoes and seagulls swallowing up the fabric!

SAS analytics helps retailers with merchandising.Sadly, one of the "miss" items was a very cute peasant top that I could see wearing myself. Suddenly I got a sickening metallic taste in my mouth as I realized how bad a fit I was for missy sportswear in Florida.

Merchandising is not as easy as one initially thinks because it involves selecting merchandise for different arrays of individuals who may not all have the same taste as you. So how do we go about figuring out what to buy? This is an age old question…

There’s competitive shopping, but relying on that always puts you behind the trend. The largest tactic for conquering this question has been analyzing what sold in the past. Many merchants try to bring items back in the next season that had mediocre performance the previous season but only to then find that they are this seasons dogs.

The customer is constantly changing and evolving. Do you buy items that you already have? No, of course not. So then how do you determine what the customer will want? That is the magical question to merchandising.

Unless you've been living under a rock, I’m sure you’ve heard the term “Big Data”. Through all of the customer transactions in store and online, there becomes a wealth of data regarding sales. Then you also have the wealth of information on social media. I think we all are still dying to know what color that dress was on Facebook. For those who might not remember, a photo portraying a dress in two different colors went viral on social media. But what if you could instead ask the audience which color they would prefer? You’d know what color of the dress to buy before you buy it! Jackpot!

Answering the magical question to merchandising is now possible through analytics. We are able to understand which attributes such as colors, patterns, fabric, or silhouettes drove your business. But most importantly, we are able to predict which combinations of these attributes will drive your business in the future, including combinations that you didn’t even have in your assortment last season! We are able to integrate social media data as well.

We are able to do this through the use of predictive analytics. Utilizing predictive analytics gives merchants the ability to predict the evolution of the trends. This truly takes the guess work out of buying! If I had these analytical insights at the start of my career, I would have been able to leverage the insights and know that rhinestones and hot pink were the next evolution of the trend!

If you want to understand how analytics can help your business and why SAS can help you, check out the 2015 Forrester Wave for Predictive Analytics where SAS is named a leader. For a broader sense of our industry-specific expertise, I encourage you to visit our Retail Industry Solutions page. Let me know what you think!

 

tags: analytics, customer preferences, merchandising, predictive analytics, retail

The post Predicting customer preference evolution in retail appeared first on Customer Analytics.

3月 302015
 

Bookies have long turned a trade in predicting the fate of our politicians in the general election. According to Ladbrokes, gamblers are set to spend a staggering £100m betting on this year’s result. The outcome of the May 7 vote is anticipated to be the hardest election to predict in […]

The post Get the inside track on the UK's General Election result appeared first on SAS Voices.

11月 212014
 
Every day there are news stories of fraud perpetrated against federal government programs. Topping the list are Medicaid and Medicare schemes which costs taxpayers an estimated $100 billion a year. Fraud also is rampant in other important federal programs, including unemployment and disability benefits,  health care, food stamps, tax collection, […]