Archive for February, 2017

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.

———————

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

No Comments yet »

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.

———————

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

No Comments yet »

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.

———————

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

No Comments yet »

February 13th 2017

Wise Practitioner – Predictive Analytics Interview Series: Lauren Haynes at The University of Chicago

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of her upcoming conference presentation, Data Science for Social Good: Lauren Haynes IMAGE 2How Predictive Analytics Can Help Governments and Non-Profits, at Predictive Analytics World for Business Chicago, June 19-22, 2017, we asked Lauren Haynes, Senior Project Manager at Center for Data Science and Public Policy at The University of Chicago, 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: We work at the intersection of public policy and predictive analytics – at DSaPP we work in healthcare, social services, governments, non-profits, education, transparency, economic development, public safety, and criminal justice. To that end we help organizations identify the inspections organizations should do to find the highest volume of violations for housing and environmental enforcement, students at risk of dropping out of school, individuals at risk of re-entering the criminal justice system, etc.

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 think predictive analytics can help organizations drive decisions of where to use limited resources – if you are responsible for 100,000 buildings and can only inspect 1,000 of them a year, knowing which 1,000 are highest risk for having a violation helps use those inspections effectively. Similarly, if you can only enroll a small population in an intervention or program, being able to identify those most at risk maximizes the value of the intervention. 

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

A: Syracuse deals with hundreds of water main breaks, leaks, and other issues that require attention each year, distributed without a clear pattern across the entire city – in partnership with the city, DSaPP built a model to predict water main breaks. Based on the model’s accuracy at predicting past years’ incidents, the team predicted that 32 of the top 50 highest-risk water mains would break in the next 3 years. If you simply used the age of pipes as a way to prioritize which city blocks should be replaced first, only 5 percent of the top 50 water mains on your list would go on to break in the next 3 years. If you used the history of breaks at different locations, looking at the number of occurrences of breaks per city block, only half of your top 50 riskiest mains would break. But most water main breaks are “first-time offenders,” without prior breaks at that location. Going by past breaks alone, you would never predict any breaks that have previously had less than three water mains breaks, and replacement efforts would only focus on a handful of neighborhoods. In the two weeks after the DSaPP team delivered the risk scores to their Syracuse partners, two of the water mains listed in the top 50 ruptured. The Syracuse Office of Innovation has quickly integrated the model into their work, using it to guide an infrastructure planning process and decide where to do “dig once” combinations of water main replacement and road resurfacing.

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

A: Applying data science and predictive analytics to the social sector is as much about change management and organizational readiness as it is about technology.

———————

Don't miss Lauren’s conference presentation, Data Science for Social Good: How Predictive Analytics Can Help Governments and Non-Profits, on Tuesday, June 20, 2017 from 4:45 to 5:30 pm at Predictive Analytics World Chicago. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

No Comments yet »

February 10th 2017

Wise Practitioner – Predictive Analytics Interview Series: Daqing Zhao at Macy’s

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Macy’s Advanced Analytics in Daqing Zhao IMAGE 2Customer Centric Strategies at Predictive Analytics World San Francisco, May 14-18, 2017, we asked Daqing Zhao, Director, Advanced Analytics at Macy’s, 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: Macy’s has 150 years of history and is an iconic brand in retail, providing superior omni channel shopping experience for our customers through stores and online channels.  Macy’s makes extensive efforts to protect customer privacy and identity information.  In order to serve our customers better, we need to understand our customer preferences in order to recommend the right product, and send the relevant information, to give our customers the frictionless shopping experience. In particular, we predict what categories, and brand a customer will likely to make a purchase, how many they would spend, as well as how they interact with our marketing channels.  We also predict customer retention, customer life time value and other customer metrics to help our marketing activities.

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

A: Using Predictive Analytics, we are able to best organize our data to predict customer preferences and behaviors, in order to optimize our marketing activities, such as emails, direct mails and product recommendation.

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

A: We do not disclose quantitative numbers.  Predictive analytics, however, is very useful to drive results.  For example, if we predict a customer having high propensities to make a purchase in some categories, and very low propensities to convert on some other categories, we often see differences of an order of magnitude in conversion rates or average spend in these categories.

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

A: One such surprise is that for some segments of customers, having low scores in all categories may be because we do not have sufficient data about these customers.  It is not necessary that they have no interest in any our categories.  With higher uncertainties in the lower scores, it may not be optimal to make decisions based on the small differences in the scores.

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

A: Our customers are diverse and our products and business goals are complex.   We don’t rely on one methodology, one solution, or one platform for our data driven, omni channel, personalized marketing efforts.  We take a portfolio approach in our predictive methodologies, data sources and hypotheses, perspectives and strategies.

———————

Don't miss Daqing’s conference presentation, Macy’s Advanced Analytics in Customer Centric Strategies, on Wednesday, May 17, 2017 from 10:25 to 10:45 am at Predictive Analytics World San Francisco. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

No Comments yet »

February 6th 2017

Wise Practitioner – Predictive Analytics Interview Series: Thomas Schleicher at National Consumer Panel

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Combining Inferential Statistics with Predictive Modeling to Evaluate Changes in Your Business, at Predictive Analytics World for Business Chicago, June 19-22, 2017, we asked Thomas Schleicher, Sr. Director, Measurement Science at National Consumer Panel, 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 models predict a number of behaviors and outcomes, including attrition, compliance and the number of recruits needed to replenish households lost through churn. We also used predictive analytics in combination with test-control comparisons to further optimize business decisions.

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

A: At NCP, the ideal set of households participating on our market research panel would be fully representative of the nation's households while also fully participating in the panel by scanning and submitting all of their purchases. Using predictive analytics can help to ensure that the most optimal set of households is selected and maintained with respect to these and other metrics.

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative? Do you see a role for integrating more “qualitative” impacts into a quantitative model?

A: We regularly evaluate ROI when we test new ways of engaging with our panelists in efforts to encourage them to remain as contributing panelists for years. However, we also take into consideration the value improved participation has to our clients. Although we are making progress on putting numbers on this sometimes qualitative impact, it is possible to undervalue an initiative by only referencing items that are easily quantifiable.

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

A: As we continue to develop our valuation of panelist compliance, we are learning additional ways to benchmark difficult to measure variables. More to come…

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

A: Although achieving “perfect” scores on your organization’s key performance indicators (KPIs) can be elusive under rapidly changing business conditions,  the combined use of inferential statistics with a business-smart, predictive model process is one demonstrated way to achieve continuous improvement with respect to your KPIs.

———————

Don't miss Thomas’ conference presentation, Combining Inferential Statistics with Predictive Modeling to Evaluate Changes in Your Business on Wednesday, June 21, 2017 from 4:15 to 5:00 pm at Predictive Analytics World Chicago. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

No Comments yet »

February 3rd 2017

Wise Practitioner – Predictive Workforce Analytics Interview Series: Kevin Zhan at The Advisory Board

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

Kevin Zahn IMAGE 2
In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, Changing Talent Management through Predictive Analytics, we interviewed Kevin Zhan, Workforce Analytics Practice Leader at The Advisory Board. View the Q-and-A below to see how Kevin Zhan has incorporated predictive analytics into the workforce of The Advisory Board. 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 decisions?  How is your product deployed into operations?

A: I can give two quick examples:

1) Helping business lines prioritize their employee investments – Every division in our company is using our predictive attrition models to better understand and anticipate which employees may be retention risks. In a world with limited resources and time, it is critically important that we develop a systematic way to help HR Business Partners (HRBPs) and line managers better prioritize which employees they should have retention conversations with. HRBPs have been using our models in conjunction with their own invaluable human intuition to prioritize conversations with staff.

2) Building a more effective benefits package through Predictive Benefit Elections (this is in the last stage of being operationalized) – We’ve developed a methodology to predict what future benefit selections will look like for the next 3 years. This will be used by our benefits team to increase their budgeting accuracy as well as evaluate the firm’s suite of benefit packages and usefulness for staff.

Q: If HR were 100% ready and the data were available, what would your boldest data science creations do?

A: I’d say if HR were 99% ready and available. Since after all, we know HR data will never be 100% accurate ;-). Simply having HR data 99% available and accurate would be a dream come true. One of my boldest ideas would be to integrate people performance with financial performance. I would develop a way to predict what our end of year revenue and margins would look like based on staff engagement, staff behavior / performance, and compensation. Another grand idea would be to understand and capture natural human tendencies and behavior to determine which roles in our company would be the best fit based on their innate persona.

Q: When do you think businesses will be ready for "black box" workforce predictive methods, such as Random Forests or Neural Networks?

A: I think businesses are already hammering for this type of information and insights, and are less concerned with what specific method we deployed to arrive at these conclusions.  Black box methods will give you a prediction that a certain event will happen. However, if you only provide businesses with this percentage, chances are your ability to effect change is equal to 1 minus the percentage corresponding to your highest probability scenario (which will most likely not be very high). People data is unique in that there is a level of human intuition involved in any analysis that comes out of people data. Thus, your ability to gain buy in and drive change will primarily depend on how successful you are in telling the story from the models through incorporating that “human element” and translating them into actionable insights.

Q: Do you have suggestions for data scientists trying to explain the complexity of their work, to those solving workforce challenges?

A: I would say all workforce-related data science projects should always be done in partnership with a HR Business Partner, or an expert that understands the business. This will help the data scientist focus on what is the true workforce question they are trying to answer and what potential business impact this may have.

If they would like to explain the intricacies of their actual roles and how they perform the analysis, I would recommend for them to use the analogy of creating a brand new flavor of cupcake. If we imagine solving a workforce challenge as creating a new flavor of cupcake, and the ingredient testers are the HRBPs and the final assemblers are data scientists, then in order to bake a delicious cupcake, the HRBPs / ingredient testers will need to set up groups of willing taste testers to try each combination of ingredients. At the same time, the data scientist / final assemblers will carefully document their behaviors and preferences. After all testing is done, the data scientist / final assembler will look at all of the data and will be able to discern which combination of flavors will result in the “next best thing” of cupcakes.

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

A: It helps leaders and HRBPs prioritize workload and engagement with individual staff.

Q: How does business culture, including HR, need to evolve to accept the full promise of predictive workforce?

A: HR needs to be educated on the benefits of predictive work and how it can help them be more strategic in their investments and save the business and themselves money, time, and resources. This in turn will accelerate the willingness and quick embrace of predictive work.

Q: Do you have specific business results you can report?

A: Ask me again this time next year.

——————

Don't miss Kevin’s conference presentation, Changing Talent Management through Predictive Analytics, at PAW Workforce, on Wednesday, May 17, 2017, from 11:30 am to 12:15 pm.  Click here to register for attendance

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

No Comments yet »