March 27th 2017

Wise Practitioner – Predictive Analytics Interview Series: David Talby at Atigeo

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Semantic Natural Language Understanding with Spark, David Talby PAW BLOG ImageMachine-Learned Annotators & Deep-Learned Ontologies at Predictive Analytics World San Francisco, May 14-18, 2017, we asked David Talby, Chief Technology Officer at Atigeo, a few questions about his work in predictive analytics.

Q: In your work with predictive analytics, what behavior or outcome do your models predict?

A: Over the past five years I've worked on a wide range of predictive analytics projects in the healthcare space. Clients were mostly healthcare providers – where models were built for patient risk prediction, population health management, forecasting clinical & financial metrics, automated clinical coding and other specialty-specific challenges. For payers, the main application of machine learning was around fraud, waste & abuse – both to augment human experts investigating claims, and to automate the review of free-text clinical notes.

Q: How does predictive analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: In healthcare, the two most common goals are to save lives and save money. Quite a few projects do both. Uncovering fraudulent clinicians & pharmacists, for example, is often justified due to its high financial ROI, but also provides major benefits by finding cases where patients are harmed, mistreated or subjected to wasteful procedures.

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: Most of the work we've done is confidential, but one published example from 2013 was regarding a readmissions prediction model we've built at the time. We were able to build a completely automated model, that did not apply any curated medical domain expertise and was solely based on our automated feature engineering algorithms, that beat that best performing academically published model at the time by 20% (in terms of AUC improvement). We were then able to beat that model by an additional 45% by building an ensemble between that model and others models that our data science team built. We've seen then further improved both the core algorithms and scalable training pipelines around them, and it is often surprising how much of a lift can be achieved at a fairly short amount of time over commonly accepted benchmarks.  

Q: What surprising discovery or insight have you unearthed in your data?

A: One surprising discovery, for me at least, was the huge variety of clinical language, guidelines and practices across different doctors and hospitals. We have found while human biology is the same across the US, and doctors supposedly follow similar best practices – the effects of healthcare being 'hyper local' are far greater. This has direct implications when applying machine learning – models transfer very poorly across hospitals, provider groups and geographic locations, whether they are on structured or unstructured data. This happens in healthcare to an extent that's far greater than what I've seen in e-commerce, web search and financial systems, which are other verticals in which I've worked before. 

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: The talk describes the three key tasks that you must perform to build a natural language understanding pipeline: Building an annotations pipeline, training machine learned annotators, and expanding your ontology via deep learning. The talk comes with full source code, available as free Jupyter notebooks that rely only on open source libraries, so that anyone can download and hack away after the talk. The example we walk through is from the healthcare space, but the design and tasks are general and apply to natural language in any domain-specific setting – understanding patents, SEC filings, academic papers, tweets, emails or transcribed phone calls. It's a technical talk and should be fun and useful for people looking to learn how to get this done for their own projects.

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Don't miss David’s conference presentation, Semantic Natural Language Understanding with Spark, Machine-Learned Annotators & Deep-Learned Ontologies, on Tuesday, May 16, 2017 from 3:55 to 4:40 pm at Predictive Analytics World San Francisco. Click here to register to attend

By: Eric Siegel, Founder, Predictive Analytics World

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March 24th 2017

Wise Practitioner – Predictive Workforce Analytics Interview Series: Haig Nalbantian at Mercer

By: Greta Roberts, Conference Chair, Predictive Analytics World for Workforce 2017 


In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, The Pay Haig_Nalbantian 150Equity Revolution:  How Advanced Analytics are Helping to Close the Gender Pay Gap in Organizations, we interviewed Haig Nalbantian, Senior Partner, Co-leader Mercer Workforce Sciences Institute at Mercer. View the Q-and-A below to see how Haig Nalbantian has incorporated predictive analytics into the workforce of Mercer. Also, glimpse what’s in store for the new PAW Workforce conference, May 14-18, 2017.

Q: How is a specific line of business / business unit using your predictive analytics method to inform decisions? 

A: We’ve been conducting pay equity modeling and assessments either alone or as part of a broader workforce analysis since the early 90s. In the past five years or so, this area of work has grown enormously. More and more of our clients – in the US and, increasingly in Europe as well – are conducting annual pay audits to proactively address pay equity issues for women and minorities. In working with us, they choose to rely on comprehensive predictive models of base pay and total compensation that account for the multiple individual, group and market factors that drive pay in organizations. In this way, they not only isolate the effects of specific demographics themselves, thereby assessing if and to what extent there are unexplained pay disparities associated with gender or race, but also get a deeper insight about explained differences – that is, of the root causes of persistent differences that show up in raw (unadjusted) comparisons of pay levels.

While those concerned with legal challenges regarding pay equity commonly use statistical controls to explain pay differences and reduce estimates of the size of pay disparities, the more strategically-minded leaders in this domain use these same controls to better understand why pay disparities exist and what can be done systematically to reduce or eliminate them in a sustainable way. I am pleased to see more organizations moving away from a predominately legal or compliance view of pay equity to a more expansive strategic view that seeks to address systemic sources of gender and racial disparities in pay. Mercer’s When Women Thrive research has shown that aggressive evaluation and management of pay equity is a leading indicator of greater success in other aspects of employment equity. Specifically, those organizations which have specialized, independent teams using statistical methods to assess and ensure pay equity as part of the annual compensation process are significantly more likely to do better in securing a more diverse workforce and leadership team. Focus on pay equity and you are likely to end up with better diversity outcomes overall.

Many of our clients do, in fact, rely on our predictive modeling approach to pay equity, commissioning us, on an annual basis, to estimate statistical models of pay determination to assess if and to what extent pay disparities exist and make adjustments where bona fide pay gaps are found. They typically do this work as part of the annual compensation review.  

Q: If HR were 100% ready and the data were available, what would your boldest approach to pay equity deliver?

A:  In the best of all worlds, organizations will evaluate and address pay equity in the broader context of what the organization actually rewards. Our team has undertaken analyses of the drivers of pay across literally hundreds of organizations in the US and abroad for almost twenty five years now. We find the drivers of pay vary significantly across and even within organizations. They also vary over time as changing business models and objectives and changing labor market dynamics force organizations to adapt their rewards to help drive corresponding changes in their workforce. Effective pay equity practices must account for such changes and help ensure that pay equity actions align with evolving reward strategies. So, for example, if a new business strategy places a premium on certain new roles, it is important, from a gender pay equity perspective, not only to know that women in those roles are paid on par with comparable men, but that women are getting the opportunity to access these new and valued roles.

If these new roles command higher pay, disproportionate representation of men would end up increasing the raw pay gap and likely diminishing the prospects of women to be successful in the organization. A successful pay equity process will keep tab of underlying changes in what is being valued by the organization to ensure women, minorities and other groups of interest are not systematically disadvantaged by market- or internally-driven shifts in the valuation of skills, knowledge, capabilities, experience, behaviors and roles.

Properly designed, a pay equity assessment is folded into the annual compensation review; it becomes an opportunity to assess the strategic alignment of rewards with business goals. Most our clients pursue this approach. A pay equity review is not a one-time study; it is an ongoing process of rewards review, one that is of significant strategic importance to the organization.    

Q: Do you think "black box" workforce predictive methods will become widely embraced in the pay equity domain?

A: “Black box solutions” are for functional tacticians at best, not practitioners of strategic workforce management. Strategic workforce management requires understanding and effectively communicating the story within the data. By design, black box solutions bypass the story, substituting claims of “predictive validity” instead. Time may prove me wrong, but I have yet to see a compelling human capital storyline emerge from statistical relationships or algorithmically-generated predictions alone. Explanatory analytics – understanding what’s behind relationships detected in the data – is, in my view, central to building and articulating a story that can engage leaders and compel action. Since I view pay equity as fundamental to reward strategy, I am reluctant to embrace the use of automated data analytics as the basis of pay equity assessments. If pay equity is part and parcel of rewards alignment, there is no substitute for careful modeling and interpretation of the drivers of rewards.  

Q: Is there a risk of making the pay equity process too complex?

A: Our domain of workforce analytics always carries the risk of being overwhelmed by complexity of approach or analytical techniques. This has never deterred our team, however, from pursuing a more sophisticated technical solution if we are sure that solution will lead to more accurate conclusions and better results. The proof ultimately is in the results achieved. As I mentioned in my interview last year, sports analytics has definitely added complexity to the statistics tracked and followed by front office professionals, field managers and coaches, players, player representatives and sports journalists, but they have gained speedy adoption in the industry. Few of these stakeholders really grasp the technical dimensions of sports analytics. Nonetheless, they are pervasively used – because they work, because they lead to better decisions and more targeted investments. Staying away from sophisticated analytics on grounds of complexity is a cop out, one that is becoming increasingly untenable in the HR field.

The analytics used for pay equity are not all that complex. Most HR leaders have a basic understanding of multivariate regression analysis. Even if they don’t, they can readily understand that measuring pay disparities and determining their sources requires accounting for other non-demographic factors that also influence pay levels. That’s what good modeling will accomplish. More complex is the way in which the methodology is practically applied and how the results are translated into action.

So, for example; if pay strategies and pay determination are different across business units, functions, geographies, occupations and job families, do you need to model each of these separately? What determines the degree of segmentation used? Technical requirements, such as minimum required population sizes for statistical modeling, may trade off against practical business considerations. There is no pure science to inform such decisions. Similarly, once you identify pay disparities or, for instance, employees who are “under-paid” relative to peers – i.e. “outliers” – how do you close the gaps? Do you address outliers only in groups where demographic disparities have been detected? Should you make adjustments for women and non-whites only? Implementation questions such as these are generally more “complex” and challenging to navigate than are issues around methodology. Seldom do we get drawn into detailed conversations about statistical techniques. On the other hand, we do have extensive discussions about implementation issues and the “philosophy” behind pay actions.

In sum, complexity is not a major barrier for workforce analysts in the pay equity area. A richer explanation of such issues is found in Stefan Gaertner, Greenfield, G and Levine, B. “Pay Equity: New Pressures, New Challenges,” Human Resource Executive Online. April 12, 2016.

Q:  What is one specific way in which predictive analytics is driving workforce decisions?

A: Pay equity is perhaps the area where we see the most tangible results from our predictive modeling work. First of all, clients don’t ask us to do this work if they are not prepared to act on the results. Organizations understand that you don’t sit on pay disparities if you find them. You have to take reasonable action to remedy bona fide pay inequities once found.

Due diligence is always required in implementing pay actions. No statistical model can alone determine if there are pay disparities, certainly not at an individual level. First of all, there is always the potential for error in the raw data on which such models are estimated. Further, there is statistical error in the estimation of the models themselves. Not all relevant factors influencing pay may be captured in the organizations archival workforce (HRIS) data. And some jobs or career levels may be so thinly populated that it is impossible to make accurate statistical comparisons that account for differences in job or role. At a certain point, judgement comes into play.

Once individual outliers are identified, you need to carefully review them to sort out those cases where there are good technical or business explanations for the pay differences observed and those differences related to gender or race that remain unexplained. The modeling helps narrow the field for such hands-on review, but it does not bypass this need entirely. As in most areas of workforce analytics, science and art come together to render the best solution.

Still, there is no question that the analytics delivered here are hugely impactful. When you do this work, you know you are going to have an immediate effect on the client organization and the employees whose pay is at issue. Doing such consequential work is very satisfying. But it carries a huge responsibility. Because you will deliver point estimates of pay differences that may translate into actual payouts to individuals, you cannot rely on large sample sizes to overcome any data error. Precision in working the data you have is critical. Those who do this work have to be on their toes. Always!

Q: How does business culture need to evolve to realize the full promise of predictive workforce analytics such a pay equity modeling?

A: I think I largely answered this question in my response to the first question above where I reference Mercer’s When Women Thrive study. That study showed that pay equity is basically the tip of the spear in organizations’ efforts to secure gender diversity in their leadership and workforce generally. If you don’t get the pay side right, it is unlikely you’ll be doing well on the representation, promotion, retention, hiring or performance sides either. Rewards are consequential. They signal what is valued in an organization. If you don’t signal you value women, minorities or other groups of interest, you are unlikely to secure them as a vital, engaged, representative and effective part of your workforce. So start with pay equity.

But don’t stop there. If I am clear about anything in our field, it is that effective human capital management requires a systems view. The dynamics process that produces your workforce – we call it your “internal labor market”- consists of multiple moving parts that interact with each other continuously to affect the mix of talent embodied in your workforce. What happens on the reward side influences what happens on the retention side, the development side, the performance side; and vice versa. The best analytics will de-mystify this process, help you understand what drives it and, thereby, help you shape your internal labor market to meet the needs of your business. Workforce diversity and pay equity should be seen in this light. In the end, they are all about the business.

Organizations that do in fact recognize their workforce as an asset need to know what’s happening to that asset and the return they’re getting on that asset. Taking a systems view helps deliver and better process this information. Workforce analytics teams can help foster this view in the way they analyze data and communicate results. This approach enhances the power of their work. It also helps engage leadership in a way traditional HR often failed to do. Such engagement makes all the difference in making the resulting strategies successful.

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Don't miss Haig’s conference presentation, The Pay Equity Revolution:  How Advanced Analytics are Helping to Close the Gender Pay Gap in Organizations, at PAW Workforce, on Wednesday, May 17, 2017, from 2:15 to 3:00 pm. Click here to register for attendance

By: Greta Roberts, CEO, Talent Analytics, Corp. @gretaroberts and Conference Chair of Predictive Analytics World for Workforce

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March 22nd 2017

Book Review: Weapons of Math Destruction by Cathy O’Neil

Cathy ONeal 3 17 2017

 

By: Eric Siegel, Founder, Predictive Analytics World

Originally published in Analytics Magazine

Book: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil (Crown, September 2016)

Book review bottom line: Definitely go read this book, despite the fact that it does convey a certain oversimplifying, "black-and-white" position.

Related reading: I devoted all of Chapter two of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (2013; 2016) to ethical issues that arise in predictive analytics’ deployment. Parts of that chapter, along with my article “The Risk of Prejudice in Computerized Prediction” in Profiles in Diversity Journal, cover some of the same topics of concern in Weapons of Math Destruction.

My Review of This Book:

Cathy O'Neil's New York Times Bestseller Weapons of Math Destruction belongs squarely in the "must read" category. In this first-of-its-kind book, the author, an industry insider and experienced expert, thoroughly covers the sociological downside of data science.

In the world of big data, there's a lot of music to be faced. With all its upside, data science's deployment risks being prejudicial, predatory, exploitative, buggy, blindly trusted, and secretive. And it has the potential to magnify the consumer’s personal economic struggle rather than remedy it.

These risks permeate across the field. The book's broad coverage includes examples from all the main business application areas to which predictive models commonly apply: marketing, online ads, credit scoring, insurance, workforce analytics, law enforcement, and political campaigns.

By providing such a uniquely comprehensive treatment of data's downside, this book addresses two dire needs: increasing awareness and opening the door to prolific discussion.

Click here to access Eric Siegel’s full book review in Analytics Magazine (above are only the first four of the review’s 21 paragraphs).

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March 20th 2017

Wise Practitioner – Predictive Analytics Interview Series: Angel Evan at Angel Evan, Inc.

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference co-presentation, Identifying Unique Gamer Angel Evan PAW BLOGTypes Using Predictive Analytics at Predictive Analytics World San Francisco, May 14-18, 2017, we asked Angel Evan, Founder of Angel Evan, Inc., a few questions about his work in predictive analytics.

Q: In your work with predictive analytics, what behavior or outcome do your models predict?

A: I work in marketing, so typically we are trying to predict a customer behavior or outcome. For example, whether or not a customer is likely to cancel a subscription or the probability they will respond to an ad. 

Q: How does predictive analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: If I were to cite just one thing, I would say that it helps us (and our clients) figure out where to focus, i.e., which customers are most at risk of leaving, or which customers represent the most potential revenue. 

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: While I cannot share actual figures due to NDA, I can say that we recently finished creating a predictive model for a large wine brand to determine which customers were most likely to cancel their wine club memberships. Wine clubs are a vital revenue stream for most wineries, as they represent the single highest percentage of profitability. As a result of our analysis and predictive model, the company saw a decrease in customer churn in just 60 days. 

Q: What surprising discovery or insight have you unearthed in your data?

A: Which predictive variables cause individual customer segments to act the way that they do.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: The old data attributes of media planning, especially household income, don’t necessarily influence customer spending the way people think. Traditionally media and strategy planners would target people with high household incomes based on the perception that they have more disposable income. What we’re seeing is that user behavior is a better predictor of outcomes. 

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Don't miss Angel’s conference co-presentation, Identifying Unique Gamer Types Using Predictive Analytics on Tuesday, May 16, 2017 at 11:45 am to 12:05 pm at Predictive Analytics World San Francisco. Click here to register to attend

By: Eric Siegel, Founder, Predictive Analytics World

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March 17th 2017

Wise Practitioner – Predictive Analytics Interview Series: Paul Speaker at The Dow Chemical Company

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Creating an industrial Revolution for Analytics at Predictive Analytics World San Francisco, May 14-18, 2017, we asked Paul Speaker, Senior Data Scientist at The Dow Chemical Company, a few questions about his work in predictive analytics.

Q: In your work with predictive analytics, what behavior or outcome do your models predict?

A: Our group’s work is involved in developing predictive and prescriptive analytics in the sales, commercial, and supply chain spaces. 

Q: How does predictive analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: We have a set of predictive models, which, when used together, can do anything from drive demand planning to prioritize the targeting of customers to prevent churn or other leakage.

Q: What surprising discovery or insight have you unearthed in your data?

A:  Looking at data at the transactional level always reveals behavior not just of customers but of everyone who is involved in “creating” the data.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: While the primary focus of innovation in analytics has been from increasingly complex predictive algorithms, the creative combination of predictive results of relatively simple models can result in even more value.

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Don't miss Paul’s conference presentation, Creating an industrial Revolution for Analytics on Tuesday, May 16, 2017 from 3:55 to 4:15 pm at Predictive Analytics World San Francisco. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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March 10th 2017

Wise Practitioner – Predictive Analytics Interview Series: George Iordanescu at Microsoft

By: Bala Deshpande, Co-Chair, Predictive Analytics World Manufacturing

In anticipation of his upcoming conference presentation, Predictive Analytics Solution George Iordanescu Blog PageTemplate for Early Prediction of Assembly Line Failures, at Predictive Analytics World Manufacturing Chicago, June 19-22, 2017, we interviewed George Iordanescu, Data Scientist at Microsoft.  View the Q-and-A below for a glimpse of what’s in store at the PAW Manufacturing conference.

Q: What are the challenges in translating the lessons of predictive analytics from other verticals into manufacturing?

A: Rather than challenges, from the Data Science perspective I would say there are actually exciting opportunities. The Cortana Intelligence solution How-to Guide I am presenting showcases an important aspect that is specific to assembly line failures data: We provide a generic Advanced Analytics solution that uses Machine Learning to predict failures before they happen. Early prediction of future failures allows for less expensive repairs or even discarding, which are usually more cost efficient than going through recall and warranty cost.

Q: In your work with predictive analytics, what behavior do your models predict?

A: A key point is that customers’ pain points – the failures- and their subject matter expertise are actually the real goldmine. By specifically looking at returns and functional failures at the end of assembly line and combining these with domain knowledge and root cause analysis we provide a generic advanced analytics solution with a modular design that encapsulates main processing steps and leverages test systems already in place. So no new equipment is necessary, since we use machine learning to detect subtle patterns in test systems measurements that pass the regular quality checks but are still indicative of a future failure.

Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?

A: A good scenario is the OEMs who use contract manufacturers (Apple-Foxconn, MSFT-Jabil), and want to minimize post sale service and warranty costs: Use ML to build models that use test/shop floor data (that belongs to OEM and or CMs) to predict before shipping, field return and repairs that may happen months or years after the device is shipped. This enables predicting future failures while the device is still in the early manufacturing line stages or is already assembled but is not yet shipped, so that fixing or even discarding it may be cheaper than going through recall and warranty costs.

Q: Can you describe a successful result, such as the predictive lift (or accuracy) of your model or the ROI of an analytics initiative?

A: A good example is Jabil where they are not only predicting that a potential issue or failure could occur, but by having visual insight into all levels of production and operations, the company also now knows why the failure was predicted. That it is actionable information Jabil can use to avoid a costly loss, while shortening product lead times and delivering superior quality.

Q: What surprising discovery have you unearthed in your data?

A: I think it is surprising and very empowering to see that fundamental information comes from the client: Failure data, domain knowledge, and test systems already in place. This setup happens to be a natural example of a Machine Learning concept called Gradient Boosting, where an imperfect (weak) model (i.e., the existing quality check system) is extended and improved by focusing on its errors, i.e., the hard cases given by the assembly line failures.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Manufacturing.

A: We show how a solution can be implemented and deployed fast in the cloud, using the flexible on-line Microsoft Azure platform that decouples infrastructure components (data ingestion, storage, data movement, visualization) from analytics engine that supports modern data science languages like R and Python. The solution modeling component can thus be retrained as needed and be implemented using high performance Azure Machine Learning algorithms, or open source (R/Python) libraries, or from a third-party solution vendor.

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Don't miss George’s conference presentation, Predictive Analytics Solution Template for Early Prediction of Assembly Line Failures on Tuesday, June 2017 at 10:30 to 11:15 am at Predictive Analytics World Manufacturing Chicago. Click here to register to attend.

By: Bala Deshpande, Founder, Simafore and Conference Co-Chair of Predictive Analytics World Manufacturing

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March 3rd 2017

Wise Practitioner – Predictive Analytics Interview Series: Afsheen Alam at Allstate Insurance

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of her upcoming conference presentation, Our Success with Agile Afsheen Alam IMAGE 3Analytics at Predictive Analytics World for Business Chicago, June 19-22, 2017, we asked Afsheen Alam, Program Manager Marketing Analytics and Big Data at Allstate Insurance, a few questions about her work in predictive analytics.

Q: In your work with predictive analytics, what behavior or outcome do your models predict?

A: Insurance quoting and binding behavior.

Q: How does predictive analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: Lead management, Direct Mail marketing, Digital Marketing channels.

Q: What surprising discovery or insight have you unearthed in your data?

A: We have several models with various lines of business and some variables keep showing repeatedly across all lines of business and various models.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: My talk is about Agile Applications to the Modeling process. It will explain how a streamlined process can deliver a pipeline of models in a relatively short period of time by having structured sprints with defined deliverables. A model is only as good as its speed to market and execution for business results. The Agile process helps deliver those results in a creative environment.

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Don't miss Afsheen’s conference presentation, Our Success with Agile Analytics on Tuesday, June 20, 2017 from 3:55 to 4:15 pm at Predictive Analytics World Chicago. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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February 27th 2017

Wise Practitioner – Predictive Analytics Interview Series: Jennifer Bertero at CA Technologies

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of her upcoming conference presentation, Redefining Analytics for Jennifer Bertero IMAGE 2Marketing, at Predictive Analytics World San Francisco, May 14-18, 2017, we asked Jennifer Bertero, VP, Business Analytics at CA Technologies, a few questions about her work in predictive analytics.

Q: In your work with predictive analytics, what behavior or outcome do your models predict?

A: Our analytics work has been focused mostly on the magic triangle of sales, marketing and customer success. We’ve been working on helping to improve customer experience with the customer team. We’ve helped to prioritize sales efforts on opportunities and accounts that are most likely to buy. We’re also helping marketing to identify what prospects are ready for the sales process and how they can more effectively nurture leads.

Q: How does predictive analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: Analytics is pervasive in decision making. We’re influencing strategic, as well as operational decisions. For example, analytics to predict which accounts have high propensity to buy our products have been used by sales to devise a customer segmentation strategy. Analytics that predict whether a sales opportunity will be won or lost is used by sales to prioritize which opportunities they should target. Customer experience analytics is helping our product teams design their products with customer experience in mind. In addition, it’s helping identify those customers early who are not likely to renew their contracts.

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: This is one of the most difficult but important parts of the analytics process. We’ve learned to identify up front what metrics we’re going to track and what success looks like – where we can. There are also discovery projects that yield insights that then lead to business process change. For example, the insights we provided to the customer engagement team allowed us to track intent, activity, and questions answered in our online communities. We correlated these to the expensive calls coming into the support line. We helped influence the business to increase support engineer engagement in communities to solve problems before they ever reach for the phone. We increased questions answered by 28%, helped decrease support call costs and increase by double digits our overall company NPS score.

Q: What surprising discovery or insight have you unearthed in your data?

A: We discovered that if a customer's question about our products is not answered in the digital communities within 2 days, our support desk received a call with the same question. This led to the business process change and improvements I just described in customer support. In marketing, we discovered that most of our leads were coming in from small to medium-sized businesses but our marketing efforts and sales process was built more for large customers. We used those insights to tailor our campaigns more effectively and funnel leads to the right sales teams by the right products.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: We’ve built our analytics shop from ground up and now can hardly keep up with the demand from the business. After consistently delivering value for two years, we’re now being asked to help shape the strategy of the company, using insights and analytics to help the C level make better and data driven business decisions. Coming from the business side, I understand the business of trying to compete and win in an extremely competitive, fast-paced industry. I’ll share the key business metrics to focus on when working on analytics projects and how they can directly help the business strategy and performance. Analytics can be used as a secret weapon. Today, it’s not a nice-to-have; it’s a must-have. It’s nice to be needed but it’s more important to be relevant, timely and directly tied to success.

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Don't miss Jennifer’s conference presentation, Redefining Analytics for Marketing on Wednesday, May 17, 2017 from 11:15 am to 12:00 pm at Predictive Analytics World San Francisco. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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February 23rd 2017

Wise Practitioner – Predictive Analytics Interview Series: Michael Dessauer at The Dow Chemical Company

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Listening Down the Value Chain: Michael Dessauer IMAGE 2Using Text-based Predictive Models to Find New Opportunities for B-to-B Business, at Predictive Analytics World San Francisco, May 14-18, 2017, we asked Michael Dessauer, Data Scientist at The Dow Chemical Company, a few questions about his work in predictive analytics.

Q: In your work with predictive analytics, what behavior or outcome do your models predict?

A: Our market listening modeling efforts focus on two types of predictions: consumer sentiment relative to our current value propositions and consumer needs to validate / identify future technology focus areas.

Q: How does predictive analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: One specific example is identifying cross-selling opportunities using our custom-developed recommender engine.

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: We have assisted businesses on price optimization which significantly lifted their margins while reducing business resources needs.

Q: What surprising discovery or insight have you unearthed in your data?

A: We have been able to better quantify a market disruption by eCommerce-exclusive brands which was surprising in its significance.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: The audience will hopefully leave with a clear understanding of market listening’s importance and value for B-to-B businesses through real-life examples.

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Don't miss Michael’s conference presentation, Listening Down the Value Chain: Using Text-based Predictive Models to Find New Opportunities for B-to-B Business on Tuesday, May 16, 2017 4:20 to 4:40 pm, at Predictive Analytics World San Francisco. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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February 17th 2017

Wise Practitioner – Predictive Analytics Interview Series: Steven Ulinski at Health Care Service Corporation

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Challenges of Information and Steve Ulinski IMAGE 2Cyber Security Using Predictive Analytics at Predictive Analytics World for Business Chicago, June 19-22, 2017, we asked Steven Ulinski, Security Data Scientist at Health Care Service Corporation, a few questions about his work in predictive analytics.

Q: In your work with predictive analytics, what behavior or outcome do your models predict?

A: Honestly, we don’t know yet. We are just beginning to think data driven and move past rule based systems. Ultimately we are looking to predictive analytics to help us identify an attack on our systems before it happens so we can implement changes in our security posture to prevent any data loss. At this time, it’s more about finding breaches faster, and the ability to respond faster.

Q: How does predictive analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: In the cyber security industry, predicative analysis has provided a new method to identify a security incident, for instance using random forests against historical data. This is still a descriptive analysis, and we are looking for systems and models that will move us past this, and actually identify threats before they become threats. We want to actually move past predictive and get to prescriptive.

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: We’re interested in preventing data breaches. We know that predictive analytics will increase our false positives rates. Ultimately, we will rate the value and quality of the models if they can discover data breaches in a timely manner.

Q: What surprising discovery or insight have you unearthed in your data?

A: In the past year we have researched how predictive analysis, other data analysis, and AI systems can help us. We haven’t truly begun to analyze our data with predictive analytics yet. However, I think the biggest challenge that we have with the data is the volume, variety, and velocity. Consider an enterprise firewall infrastructure. They generate a massive amount of information per hour. We want to analyze the firewall information with operating system information, user web behavior, and other factors to have a holistic view for threat detection. We have a lot of information, from various different logging mechanisms. Our analysts just cannot keep up with the data. We are looking to predictive analytics and other systems to help wrangle in our data. This is one reason Information Security systems are leveraging Big Data and ML.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: The Rexer Data Science Survey showed that Information Security analytic goals were raised from 3% in 2013 to 5% in 2015. Additionally, only 1% of the time is being dedicated to Information Security. There are significant challenges in analyzing Information Security data. I’ll be discussing more about these challenges.

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Don't miss Steven’s conference presentation, Challenges of Information and Cyber Security Using Predictive Analytics on Wednesday, June 21, 2017 from 3:30 to 4:14 pm at Predictive Analytics World Chicago. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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