Customer Experience Suffers As Organizations Make Inconsistent Use of Analytics, Accenture Research Finds

Intuition still reigns over fact-based decision making when it comes to customer engagement

When asked to evaluate how well they are using analytics, such as applying data, statistical and quantitative analysis, descriptive and predictive models to drive decisions and enhance customer experience, most organizations’ perceptions are very different from reality, according to new research from Accenture (NYSE: ACN).

Accenture’s Customer Analytics Survey of 800 directors and senior managers at blue chip organizations in Brazil, China, Germany, Italy, Japan, Spain, United Kingdom & Ireland and the US and Canada showed that more than half (55 percent) of respondents felt their methods for segmenting customers and providing relevant experiences are either “ideal” or “very good.” However, related consumer research tells a different story. Accenture’s Global Consumer Survey[i] found that only 21 percent of consumers believe the companies they do business with are good at providing a tailored, relevant experience. Further, it showed that two out of three consumers changed service providers in the past year. With an average cross-industry churn (or defection) rate of 64 percent, organizations that hope to grow must provide more relevant and satisfactory customer experiences.

Most Organizations Don’t Know What They Don’t Know

The Customer Analytics research revealed that more than half of the organizations surveyed do not take advantage of analytics to help them target, service or interact with customers. This suggests that many organizations may be unaware of what is really important to customers and lack the capability to measure their performance in engaging with them.

When making decisions about what customers want, many organizations are just as likely to rely on personal experience (which 23 percent of respondents described as “very important”) as analysis of data and facts (which 22 percent called “very important.”)

“Ten years ago, organizations could get away with relying on intuition to make decisions about how to engage with customers but today’s world is wired and interconnected in such a way that customer expectations and influence are greater than ever before,” said Julio Hernandez, global lead for customer analytics at Accenture. “Descriptive and predictive analytics enable organizations to draw fact based conclusions about what customers are actually doing and what they are most likely to do and need. Whether in product features, delivery or price, organizations are leaving money on the table by not applying the power of analytics to these decisions,” added Hernandez.

Even Analytically-Minded Organizations Are Missing the Big Opportunities

Even amongst organizations actively using analytics in marketing, sales and service, most are not applying it broadly across the full spectrum of marketing and customer activities. This includes critical areas such as pricing (86 percent of respondents not using), product/service delivery (77 percent not using) and product development (59 percent not using). Given the impact that new product development and delivery have on an organization’s future growth potential, applying analytics to make better informed decisions in these areas is an opportunity for companies to gain a significant competitive advantage and maximize their market potential.

What is Holding Organizations Back?

The study revealed that less analytically mature firms are much more likely to perceive their data sources as accurate and reliable than organizations with more developed analytic capabilities. This may be because larger firms are conducting more regular and complex data manipulation and so are more aware of the shortcomings of their data and analytical capabilities.

Corporate culture also poses challenges. While almost 70 percent of respondents said their senior management was totally or highly committed to analytics and fact based decision making, corporate culture still presents a major barrier. Less mature firms are more likely to cite overall corporate culture as a major barrier (42 percent of respondents versus 33 percent of more analytically mature firms). The more analytically mature firms cite budget limitations (47 percent versus 29 percent), departmental culture (40 percent versus 29 percent of less analytically mature firms) and senior management support (45 percent versus 31 percent) as obstacles to better segmentation.

Failure to Recognize the Customer’s Perspective

The findings suggest that most organizations analyze and segment customers according to their value to the organization rather than according to their customers’ unique needs and preferences. For example, across organizations at each end of the analytics maturity spectrum, the most commonly used metrics to segment customers tend to be company focused, such as: profit per customer (cited by 41 per cent), lifetime value (27 per cent) and share of wallet (how much of a customer’s total spending occurs with the company) (24 per cent). Whereas indicators of customer requirements such as customer needs and behaviours, service levels received and psychographics are often used least.

“Regardless of how analytically mature they are, most firms largely focus on using analytics to better understand the customer’s value to the organization and much less on the organization’s value to the customer,” said Hernandez. “This leads to a customer experience that is over-indexed on meeting the needs of the organization versus the customer. Organizations will benefit from a more balanced approach to using analytics, one that takes into account what’s sustainable for the business as well as what’s relevant to the customer.”

About the Research
This research is based on 800 telephone interviews with customer facing directors or senior managers in major blue chip organizations with responsibility for, or knowledge of, the use and application of customer analytics in the company. The research was conducted in March 2011 across eight countries (Brazil, China, Germany, Italy, Japan, Spain, United Kingdom & Ireland and the US & Canada) with 100 interviews in each country.

For a copy of the findings please click here.

About Accenture
Accenture is a global management consulting, technology services and outsourcing company, with more than 215,000 people serving clients in more than 120 countries. Combining unparalleled experience, comprehensive capabilities across all industries and business functions, and extensive research on the world’s most successful companies, Accenture collaborates with clients to help them become high-performance businesses and governments. The company generated net revenues of US$21.6 billion for the fiscal year ended Aug. 31, 2010. Its home page is www.accenture.com.

[i] Accenture 2010 Global Consumer Survey , published February 8, 2011

Navigating Predictive Analytics: Benefits and Challenges of Using Graph Theoretic Methods Leveraging Contextual Network Information for Better Accuracy

Nick Lim is the CEO and Founder of Sonamine, LLC. Over the past 15 years, Nick has worked in analytic environments with large amounts of data. Leveraging his training at Harvard, he has led product and strategy teams at MicroStrategy, Enpocket and Nokia to build systems that predicted ad clicks and consumer behavior using terabytes of information.

Predictive Analytics: Benefits and Challenges of Using Graph Theoretic Methods Leveraging Contextual Network Information for Better Accuracy

The practice of predictive analytics has come a long way since the advent of operations research. Current predictive models routinely use more than 100 variables, most of them are characteristics of the object being analyzed, including age and gender for customer analysis, or color and text for advertisements. In this article, we will explore how graph methods can be used in predictive analytics.

Graph methods refer to techniques that analyze a network of objects, rather than pie charts and trend lines. Networks are made up of nodes and connections among the nodes. Other synonyms include vertices, edges, and arcs. Graph methods analyze the link structure, rate of diffusion and the clustering of nodes in a network. Many concepts in the popular press such as Google’s page rank, social network analysis, influencer marketing and driving directions all utilize graph theoretic methods.
Real World Use Cases
Why are these graph methods useful for predictive analytics? One reason is that additional contextual predictors can be derived using graph methods. Let’s start with a well known example: Google uses page rank as one of many predictors for the relevance of a web page. The link structure in the world-wide-web network provides valuable contextual information about which pages are deemed most relevant by the web page creators—this contextual link structure is then used to predict relevance for a user’s query.

Another example: If you are trying to predict the revenue contribution of each employee, in addition to the educational level, job responsibility and experiences, you might consider how well the employee relates to the customers or other managers. The contextual information added here is not something inherent about the employee. IBM conducted such a study with 2600 of their consultants, and created a model with revenue as the outcome target variable and three sets of predictor variables.1 In addition to the standard employee and job-related characteristics, two sets of contextual variables were added. One was related to the structure of the web of relationships, and the other was related to how each employee was related to managers and customers. It turned out that graph-derived contextual predictors were highly correlated with revenue.
Enriching Context Where It Seemingly Did Not Exist
One common question raised is how much of the contextual information is already captured in the demographic data. After all, there is a common understanding that birds of the same feather flock together. To tackle this question, a group of researchers at MIT studied how the Yahoo instant messenger network affected the adoption of the Yahoo Go product.2 They discovered that only 50% of the contextual network effect could be explained by the common underlying demographic variables.

This finding has big implications for predictive analytics in data poor environments. A data poor environment would benefit greatly from using contextual predictors that you already have. For example, if you are trying to predict churn in a prepaid mobile customer base where there is little to no demographic information, using the social network contextual information can greatly improve the prediction accuracy and lower the false positive rate.

Another type of contextual network information that can be included into predictive analytics is the underlying form of the social community that one belongs to. In the study with Yahoo data above that was trying to predict product adoption, they found that the chances of you adopting a product rises if more people in your social community have already adopted the product.

Besides social contextual network data that can be added to predictive functions, you can also deduce contextual information. For example, in trying to predict fraud from a set of credit applications, we can construct a network of information attributes. Using the basic idea of that people with bad credit tended to relate to other similar people, some researchers generated the graph-based hub and authority variables from such a network and added them to a support vector machine prediction function.3 The graph-derived context variables improved the fraud prediction by approximately 20%.

Clearly, the benefits of adding graph-based predictors are better lifts and lower rates of false positives. The improvements range from 20% to 100% depending on the richness of data available today. These benefits translate directly into bottom line results to the extent that your company is already putting predictive analytics into daily operations. So what are the practical aspects of putting this into operation?
How Do You Get Started?
Let’s start with the data: Where do you obtain the data to construct such contextual networks? The IBM study looked at the anonymized email traffic, address books and buddy lists. If you were a retailer like Amazon, you could use wish lists, product gift transactions and share-the-love data to construct the social context for predictions. Financial services companies can use the trading data to construct the contextual network among the traders or credit application data to construct an information network. Telecommunications companies can leverage the call detail records. In fact, the explosion of data collection means that most organizations already have this contextual network information just waiting to be accessed and utilized.

One unique aspect of contextual network predictors is that they are usually specific to the target outcome you want to predict. This is the definition of contextual after all. So if you want to predict churn, then you need to understand how the contextual network predisposes a subscriber to churn. Such contextual churn insight will certainly be different if you were trying to predict employee revenue or product adoption.

Since the contextual environment is changing, the window that an enterprise has to leverage the additional predictive power of contextual networks is also dynamic. Most customers will switch vendors very soon after they learn about a bad experience from a friend, so vendors must respond in a timely manner. Practically, this means that the calculation of graph-based predictors must occur more frequently.

Some contextual network information is relatively stable over time. For example, if someone has a preferred takeout restaurant, she will likely continue to call that restaurant even after she gets a new phone number. These “creatures of habits” can be more easily identified, say in the case of fraud or terrorism prevention.
Challenges
One potential impediment to leveraging contextual network information involves the privacy and data security issues. Since contextual networks tend to involve personal transactions, companies would like to ensure the highest security to prevent breaches. Various regulatory bodies at different state, country or global levels might have conflicting guidelines on what information is considered private and cannot be used for data analytics.

Another obstacle today is the scalability challenge. Often these contextual networks are many times the size of the current data used for analysis. Contextual networks rely on relationship information, so sampling runs the risk of losing valuable information. James Kobelius of Forrester postulates that the advent of social network analysis will catalyze the need for petabyte-sized data warehouses.4

Another obstacle is the lack of software tools that will generate these graph-based predictors. Types of specialized graph based calculations include community detection, diffusion simulation, vertex similarity, topology and centrality analysis. Open source tools such as Jung and Pajek work well for R&D, while proven commercial tools such as Sonamine are geared toward large scale and production graph mining.

A last obstacle is education. Contextual network predictors are unlike other types of data being captured and analyzed. Enterprises must first capture this new data, and that will require some mind-set shift. Setting up an infrastructure to model the contextual network will require new tools and approaches. Finally, using these contextual networks in predictive analytics that improve the bottom line will require the cooperation of multiple disciplines—analytics, marketing and operations.

Despite obstacles, the time to investigate and leverage these graph-based methods is now. IDAnalytics, a company that provides identity theft protection, has been issued a patent for a system and method for identity-based fraud detection through graph anomaly detection. How will you start adding graph-derived predictors into your predictive analytics?

Predictive Analytics is playing a key role in helping conserve wildlife in northern Kenya according to press reports by IBM

Marwell Wildlife is conducting a survey on Grevy’s zebra where local nomadic herdsmen are interviewed about their attitudes towards the zebras, what they think the key threats facing them are and where they think they are within this large area. The herdsmen have very good knowledge about wildlife and interviewing them is a very efficient means of collecting information over this vast and inaccessible area.

Video Interview

Marwell Wildlife selected IBM predictive analytics software to help identify patterns and analyse data which will help inform decisions on conservation measures for Grevy’s zebra.

By improving Marwell’s insight into herders’ attitudes through the surveys, and combining this with information from aerial surveys, camera traps and radio collars on the zebras, Marwell now has a more detailed understanding of the issues surrounding the zebra and are therefore able to understand the main threats facing the species, which allows limited conservation resources to be focused towards these areas.

For example, one of the main reasons for hunting is to produce traditional medicines from the zebra’s body fat. If herding communities were able to access modern medicines then the need for hunting would be much reduced.

“The IBM predictive analytics software is critical in analysing the information we collect from the field. The data from the surveys is vast and complex and requires powerful software to analyse it. The software is ideal for identifying trends and patterns from this data,” said Dr. Guy Parker, Head of Biodiversity Management at Marwell Wildlife. “In the case of the recent interview survey, the software enabled us to determine peoples’ attitudes towards the Grevy’s zebra. Furthermore, we were able to determine what influence factors such as education level, age, location, and wildlife benefits had upon peoples’ attitudes. This is the kind of complex multi-variate analysis that the IBM predictive analytics software is designed to tackle.”

“The work at Marwell Wildlife takes the use of analytics to a whole new level,” said Colin Shearer, predictive analytics strategist at IBM. “It is great to see analytics play such a critical part towards the conservation of Wildlife.”

Dr. Guy Parker will explain the benefits of using predictive analytics to achieve sustainability of wildlife in his presentation ‘The Management of Information’, to take place on Wednesday September 15th from 14.45 – 15.30 during the START Summit. For more information, please visit: http://www.ibm.com/uk/start.

This analytics solution is powered by IBM SPSS predictive analytics software.

For more information about Marwell Wildlife please contact Helen Jeffreys, PR Officer, Marwell Wildlife helenj@marwell.org.uk.

Marwell and partners surveyed the far north of Kenya at the beginning of 2010. Because the area was so difficult to survey, Marwell decided to interview local people. Pastoral communities live across the whole of northern Kenya and move their livestock with the rains. They know a great deal about the landscape and its wildlife. Therefore, talking to them is the most efficient means of collecting information.

The far north of Kenya provides a link between northern Kenya and southern Ethiopia. This area is also vast and may contain large numbers of wildlife. In addition, there are currently only 2 conservation areas in the whole of the far north, so there is little protection for the wildlife there.

Marwell has been working with Kenyan conservation partners for 15 years to conserve the Grevy’s zebra. This has involved working with the Kenyan government to get the Grevy’s zebra protected by law, conducting surveys to work out where the Grevy’s zebra occur and in what numbers, and working with local communities in northern Kenya to protect the Grevy’s zebra and other wildlife.

Marwell Wildlife works with a number of conservation organisations including: Northern Rangelands Trust, Lewa Wildlife Conservancy, Grevy’s zebra Trust, Kenya Wildlife Service, Denver Zoo and St Louis Zoo, as well as a number of community conservancies across northern Kenya.

There are believed to be approximately 2,500 Grevy’s zebra left in the wild. However, there is some uncertainty about the numbers because they range over a vast area and are notoriously difficult to count.

While Grevy’s zebra used to live across the Horn of Africa, today they are only found in northern Kenya – their stronghold – and small areas of southern Ethiopia. The core of their range is in central northern Kenya where their numbers have stabilised and they appear to be recovering.

More information on IBM Analytics

Today, IBM is working with more than 250,000 clients worldwide on analytics projects, including 22 of the top 24 global commercial banks, 18 of the world’s top 22 telecommunication carriers and 11 of the top 12 U.S. specialty retailers

In just four years, IBM has invested more than $11 billion, dedicated 6,000 business consultants and opened seven Analytics Centers of Excellence around the world to help clients uncover hidden insights within their data.

To learn more about IBM business analytics please visit: www.ibm.com/gbs/bao.

More information on START

START is a national initiative by The Prince’s Charities to promote and celebrate sustainable living. It aims to demonstrate what a more energy efficient, cleaner and healthier future could look like. During 2010, START will grow into a vibrant and diverse programme, which will engage people right across the UK. The START founding partners include: IBM, Addison Lee, Asda, B&Q, BT Group plc, EDF Energy, M&S, Virgin Money, Waitrose and Your Water companies. For more information go to: www.startuk.org.

The IBM Summit is the business-to-business component of START and each day will engage 120 global leaders in business, the public sector and academia to discuss the varying economic, societal and environmental aspects of sustainability. Each of the Summit’s nine days has a unique theme including cities, energy, transportation, skills & people, youth, supply chain, finance and analytics. The final day of the event will combine the work of all days into a discussion on Smarter Business.

How companies use real-time Predictive Analytics data to plan for the future.

Fortune magazine reports that in a tough global economy, sloppy decision making and “going with your gut” can get you punished–swiftly. That’s why leading companies are increasingly turning to a new management discipline called predictive analytics to compete and thrive. Rather than relying on intuition when pricing products, maintaining inventory or hiring talent, managers are using data, analysis and systematic reasoning to improve efficiency, reduce risk and increase profits.

In simple terms analytics means using quantitative methods to derive insights from data, and then drawing on those insights to shape business decisions and, ultimately, improve business performance. Thus predictive analytics is emerging as a game-changer. Instead of looking backward to analyze “what happened?” predictive analytics help executives answer “What’s next?” and “What should we do about it?”

Accenture research shows that high-performance businesses have a much more developed analytical orientation than other organizations. They are five times more likely than their low-performing competitors to view analytical capabilities as core to the business. Our research shows that there are big rewards for organizations that embrace analytics decision making.

Some of the most famous examples of analytics in action come from the world of professional sports, where “quants” increasingly make the decisions about what players are really worth. Consider these examples from the business world:

Best Buy was able to determine through analysis of member data that 7% of its customers were responsible for 43% of its sales. The company then segmented its customers into several archetypes and redesigned stores and the in-store experience to reflect the buying habits of particular customer groups.

Olive Garden uses data to forecast staffing needs and food preparation requirements down to individual menu items and ingredients. The restaurant chain has been able to manage its staff much more efficiently and has cut food waste significantly.

How Intelligent is Your Business?

Joel G. Block

I have recently been exposed to a whole new side of business operations and management. This underutilized tool combines accounting, finance, technology and operations. It makes smart companies smarter, and executives across the country and around the world are standing up and taking notice. You need to consider it for your business because being smart means moving fast. And for entrepreneurs, and companies that want to compete like they are entrepreneurs, precisely extracted information means serious business horsepower. What is this new magic bullet? In a phrase,”Business Intelligence.”

It seems so obvious that a company would want as much business intelligence as it can get. But I have been inside hundreds, if not thousands, of companies — and most businesses are not organized intelligently. The proverbial right hand rarely knows what the left hand is doing. When Operations doesn’t communicate with Accounting, invoices don’t go out on time. When Sales & Marketing doesn’t work synchronously with the Inventory Management System, trouble is on the horizon.

According to Wikipedia, Business Intelligence (BI) refers to computer-based techniques used in spotting, digging out, and analyzing business data, such as sales revenue by products and/or departments or associated costs and incomes.

This is a very workable definition but it is a mouthful. To be more succinct, Business Intelligence means having the right information for your business at the moment you need it.

It isn’t sexy — but it’s critically important to businesses of all sizes and shapes. A recent IBM commercial asks if anyone would blindly cross the street knowing there were no cars driving by 5 minutes, 5 hours, or 5 days ago. Of course not, but isn’t this how most businesses make the majority of their decisions?

An engineer in Los Angeles has made Business Intelligence his life’s work. Tooraj Kazeminy has done dozens of implementations for a significant number of companies — many of which you have heard of — to help them get a handle on their data and turn it into useful information. Look him up and speak with him. It will be worth the call. He has shown me how valuable this technology can be. This isn’t just a good idea. It’s mandatory to stay ahead of the competition.

What if you could have access to technologies that provide historical, current, and predictive views of your business operations? There are software platforms that provide many of the common functions of Business Intelligence technologies such as reporting, online analytical processing, analytics, data mining, business performance management, benchmarking, text mining, and predictive analytics.

Because platforms already exist, your company won’t have to pay to reinvent the wheel. Order an assessment of your current business structures and data management issues, and you could be on your way to having better reporting, better information, less noise, and more focus on your industry or company’s Key Performance Indicators. It’s not terribly expensive to implement these platforms and the return on investment is fast and dramatic. The value is outstanding.

You might be wondering if your software already does this. It might do some of it. But if you have operations software to manage your plant, or Salesforce type software to handle lead flow, and some accounting package to control the company finances, you will end up with multiple systems all with different reports. The problem with this is different perspectives might not translate into accurate and reliable reporting.

Business Intelligence platforms take each of the data warehouses your company uses and takes the specific information each executive, or each department, needs to support better business decision making. This is why Business Intelligence systems are often referred to as decision support systems.

Don’t confuse Business Intelligence with competitive intelligence. Both support decision making. Business Intelligence mostly uses internal data, while competitive intelligence works with mostly external data, though some internal information will be sought to support decision making.

Use this as a starting point. Call on resources to learn more. Contact Tooraj Kazeminy to see if these concepts can help your company. As a fellow entrepreneur, I encourage you to use this information to stay ahead of the curve.

Often dubbed a “Growth Architect” by his clients, Joel Block advises companies on explosive growth strategies by driving revenue and sales. Well known in the capital markets, Joel is a successful entrepreneur, speaker, advisor and astute investor. Joel is President/CEO of Bullseye Capital (www.bullseyecap.com), a full-service real estate company that supports owners and buyers of real estate assets with brokerage, leasing, property management, and mortgage services.

The company also provides investment opportunities to accredited investors. A leader in real estate syndication, the company invests in properties and offers seminars to assist others in acquiring the skills needed to raise syndicate capital to acquire properties. Imagine knowing how to pool funds to purchase any real estate investment, whether it is for single family, multi-family, commercial, or another kind of investment property. For full information, go to http://www.syndicatefast.com.

Article Source: http://EzineArticles.com/?expert=Joel_G._Block

Memphis Cuts Crime With Predictive Analytics Reports Reflect

Recent press reports from Information Week reflects that Tennessee city attributes 31% drop in crime rate to knowing when and where to put cops on the street.

What started as an experimental predictive crime-prevention initiative in 2005 is now a proven program that’s about to get a big boost with new data and new forms of analysis.

IBM announced on Wednesday that the Memphis Police Department (MPD) is enhancing a predictive-analytics-based “Blue CRUSH” (Criminal Reduction Utilizing Statistical History) program by adding public call data to the information it analyzes to thwart crime before it occurs.

Mark Armstrong, president of WhamTech, discusses how the company helps customers such as the government integrate their disparate data sources. Microsoft Windows Server Group Product Manager, Manlio Vecchiet answers questions about Hyper V, network access protection and the adoption to date of Windows Server 2008. We caught up with CEI’s CEO D. Raja to talk about the state of the custom development market, what CEI’s role in it is and also about the entrepreunerial nature of the Pittsburgh PA market.
We caught up with CEI’s CEO D. Raja to talk about the state of the custom development market, what CEI’s role in it is and also about the entrepreunerial nature of the Pittsburgh PA market.
Launched five years ago by the University of Memphis Department of Criminology and Criminal Justice, the Blue CRUSH program started with analysis of historical data on gun crimes committed in North Memphis. Using predictive analytics software from SPSS (which was acquired last year by IBM), the University modeled patterns of crime and shared the results with area law enforcement agencies.

“Of course we knew the areas that had a lot of gun-related crime, but [Blue CRUSH] analyses helped us see patterns of exactly when and where the incidents were occurring,” said John Williams, Crime Analyst Unit Manager at MPD. With better insight, the department was able to send patrols to the right places at the right times, increasing arrest while also curbing incidents, Williams said.

Some consumers remain wary to conduct mobile transactions but perception, reality aren’t in sync
The State of Mobile Security

Early successes with Blue CRUSH led MPD to partner with the University as well as county and federal law enforcement agencies in 2006, pooling data and sharing insights on crime patterns. Analyses gradually expanded citywide, and the system now works in tandem with an MPD Real Time Crime Center (RTCC) monitoring and analysis hub opened in 2008.

Each week, the last four weeks’ worth of crime data is joined to spatial geographic information system data. Incidents are then plotted on digital map of the city within the RTCC that is updated and monitored around the clock. Blue CRUSH analyses of 28-day and seven-day statistics pinpoint crime hot spots, with details down to the day of the week and times of the day that are most active.

Blue CRUSH is credited as a primary driver of a 31% reduction in serious crime in Memphis since 2006, but Williams said MPD hopes to do better. Predictive analyses are currently based strictly on crime reports submitted by police officers. Not included are the more than two million emergency and non-emergency calls received from the public each year. MPD is now adding these call records to the Blue CRUSH analysis.

“There are some parts of town where we get reports of shots fired, gang members hanging out, or drug activity, but when we get there, the subjects are gone and there are no arrests,” Williams explained.

Call-volume and call-pattern analyses will not only uncover hot spots that don’t show up in arrest reports, it will help the department predict where it can put resources and change its approach to be more effective.

“If we can deploy some of our unmarked cars and undercover officers in these areas, we’re going to make arrests,” Williams said.

InformationWeek has published an in-depth report on energy-efficient government data centers. Download the report here (registration required).

Predictive Analytics Gains More Market Acceptance

In this three minute interview, Phil Ice, Director Research and Development Public talks about how Predictive Analytics is gaining more acceptance at the university level.

Dr. John F. Elder heads a data mining consulting team with offices in Charlottesville, Virginia and Washington DC. Founded in 1995, Elder Research, Inc. focuses on scientific and commercial applications of pattern discovery and optimization, including stock selection, image recognition, text mining, biometrics, drug efficacy, credit scoring, cross-selling, investment timing, and fraud detection.

Dr. Elder has authored innovative data mining tools, is active on Statistics, Engineering, and Finance conferences and boards, is a frequent keynote conference speaker, and is General Chair of the 2009 Knowledge Discovery and Data Mining conference in Paris. John’s courses on data analysis techniques – taught at dozens of universities, companies, and government labs – are noted for their clarity and effectiveness. Dr. Elder was honored to serve for 5 years on a panel appointed by the President to guide technology for National Security.

Check out the three minute interview on the Predictive Analyticswebsite.

Transcript:

Bill Cullifer: I am on the phone with Dr. John Elder, CEO and Founder of Elder Research out of Virginia. Good afternoon. Thanks for agreeing to this interview.

John Elder: Thanks for having me.

Bill Cullifer: You bet. You recently provided a presentation at the conference entitled the High ROI of Data Mining for Innovative Organizations. Can you summarize that session for the subscribers of this podcast?

John Elder: Sure Bill. I just gave a talk about nine of the kind of more interesting success stories we have had at Elder Research over the last decade or so and we will get them at a higher level. So, it handled the way data mining really helps and I have broken it into three parts, approving and emphasizing good, detecting and eliminating bad, streamlining and automating are… I gave examples of how each of those had worked [Voice Cross Over]

Bill Cullifer: Can you cite an example?

John Elder: Sure. One kind of interesting one was Anheuser-Busch[Phonetic] pictures of beer on the shelf, they like to correlate how the positioning of their product related to [Inaudible], they also want to know if they are missing any stock [Inaudible] so, that’s a relatively laborious process, to take a picture and turn it into some [Inaudible] interpretation of what exact product and what exact orientation is there. So, of course… so we did a pilot process, automated identification of what[Phonetic] product was there. Recognition is a very hard [Inaudible] is one that’s well lived, well under[Phonetic] good condition. Products are trying to make themselves be recognized a whole lot easier than say identifying somebody who just canceled on a [Inaudible] might be for instance but we were able to get 90% accuracy which was our goal instantaneously essentially meaning that they would spend one tenth as much time editing it as they would doing it by previous metric.

Bill Cullifer: Time is money. Right?

John Elder: Exactly. So, that would save them millions of dollars there. So, that was an example of automating and speeding up. There were a couple of examples of return by eliminating, but that there… the examples [Inaudible]. We had projects we did for the IRS and projects that we did for a large [Inaudible] electronics firm that saved tens of millions of dollars, actually the numbers were astoundingly large compared to the [Inaudible].

Bill Cullifer: So, if I… and the subsribers of this podcast were considering entering into the predictive analytics realm, any high level recommendations on first step?

John Elder: Yeah, the… one of the extreme[Phonetic] things that came out as I was putting this talk together were you know what were some characteristics that were common to some of these successful projects and all of them were technically successful, but not all of them were completed and that was interesting to see what were the differences there. For instance, on the Anheuser-Busch[Phonetic] one, they never put it in production because the day that we were to sign the development contract was 09/11; that was definitely a whole lot of a hell of a tragic event and there were some other times when a project didn’t see the way to appreciate it[Phonetic], but most of the nine examples I gave complete business successes saw their way into implementation and one of the key characteristics that we needed was a champion who would take the business risk inside the organization to see it all the way through and what you really want[Phonetic] there is nothing harder than starting something brand new because all those who talk about the old way of doing it will hold it and those who talk about the new way only lukewarmly support it and can still do [Inaudible]. So, we found that the innovative organization part of the title was really [Inaudible] organization that allowed it’s… that rewarded success for its people, not just punished because that is the environment in which a project will thrive and the return can be [Voice Cross Over]

Bill Cullifer: Sounds like excellent advice and we certainly appreciate your time.

John Elder: Thanks for having me.

Today’s topic is Predictive Analytics and this is the second in a series of audio podcast interviews on this topic. Dr. Eric Siegel is a former computer science professor at Columbia University and the President of Prediction Impact.

Q:  How do companies benefit?

A:  Well, the business case for a predictive analytics initiative is clear because having a predictive score for each customer is so clearly actionable.  For example, knowing which customers are worth targeting for an offer is about as actionable as business intelligence can get.

To be more specific, let’s say we generate scores to predict the risk each customer has of canceling or defecting from a subscription-base service. Targeted customer retention has impact on such businesses — such as ISPs, magazine subscriptions, online dating, you-name-it.

Retention offers – say a discount – are usually expensive.  You can’t extend the offer to all subscribers.  The only way to target a retention campaign precisely where it’s needed is with predictive scores that earmark which customers are most likely to leave.  When you follow these predictions, you don’t expend the cost of the offer on customers that don’t need it. So, the campaign ROI is much better, growth rates improve and bottom-line profit increases.

So, in general with predictive analytics, each customer’s predictive score informs actions to be taken with that customer.  You just can’t get more actionable than that.

To listen to the entire interview visit: http://predictiveanalytics.org/?p=7