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9A0-381 - Analytics Business Practitioner - braindump

Vendor Adobe
Exam Number 9A0-381
Exam Name Analytics Business Practitioner
Questions 50 Q & A
Recent Update October 17, 2018
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9A0-381 exam Dumps Source : Analytics Business Practitioner

Test Code : 9A0-381
Test Name : Analytics Business Practitioner
Vendor Name : Adobe
Q&A : 50 Real Questions

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Adobe Adobe Analytics Business Practitioner

Adobe Integrates Magento Commerce Cloud into Adobe adventure Cloud | killexams.com Real Questions and Pass4sure dumps

Barcelona: Adobe (Nasdaq: ADBE) these days unveiled its imaginative and prescient and strategy to make every event shoppable through integrating Magento Commerce Cloud into Adobe journey Cloud, enabling commercial enterprise corporations to create enormously engaging, personalised shopping experiences. Integration with Adobe experience Cloud’s content administration, personalization, and analytics options offers an unequalled offering for enterprises seeking to carry world-category commerce experiences. The company also unveiled a tremendous update of the Magento platform to empower small-and-mid-market merchants to extend the lead in commerce innovation and agility.

With rising consumer expectations and increased competitors, brands now compete on the high-quality of the consumer journey they present over a lifetime of consumer engagement, both on and offline. Adobe’s imaginative and prescient for adventure-driven commerce is to empower companies to unify conclusion-to-conclusion consumer experiences from introduction to commerce, riding loyalty and company boom.

“buyers predict every interplay with a brand to be contextual, intuitive and significant, however organizations have struggled to invariably bring customized experiences across the myriad of touchpoints,” observed Mark Lavelle, senior vice president of commerce, Adobe and former CEO of Magento. “The improvements we are bringing to market permit groups of all sizes and across industries to make each journey shoppable.”

enterprise Capabilities

the primary integrations between Adobe’s content material administration, personalization and analytics solutions in Adobe adventure Cloud and Magento Commerce Cloud at the moment are accessible and will empower enterprises to:

  • Create incredibly engaging shopping experiences: the mixing with Adobe experience manager lets enterprise manufacturers create and control powerful shopping experiences throughout each contact element right through the client experience; 
  • personalize every experience: Integration with Adobe goal, powered with the aid of Adobe Sensei, Adobe’s AI and desktop discovering expertise, enables agencies to optimize and convey contextually important looking experiences, driving client loyalty and letting businesses compete greater quite simply, and 
  • expect client needs: Predictive analytics in Adobe Analytics, powered by way of Adobe Sensei, assist companies proactively computer screen and analyze consumer records to locate patterns and predict future client behaviors to realize competencies challenges like particular delivery necessities or stock shortages and more desirable convert alternatives.
  • SMB Capabilities

    The newest unencumber of the Magento platform, underpinning Magento Commerce Cloud, accommodates huge neighborhood contributions and brings to market new service provider and developer experience developments. merchants and builders could be in a position to: 

  • hastily create attractive website content: today’s online marketers want to play a extra direct position in the construction of the online web site journey, and demand access to effective content material introduction equipment that enable them to perpetually design, verify and launch new web page content material. New PageBuilder, a powerful drag-and-drop enhancing device for web site content material, enables retailers to create a surest-in-classification looking event without inventive limits or the want for developer help. manufacturers that need to take potential of full pass-channel content advent, distribution, and lifecycle management can take capabilities of the Adobe experience supervisor integration.  
  • Create high-conversion cell experiences: while cell commerce continues to develop, merchants battle with low conversion quotes and the complexity of building and managing dissimilar experiences throughout channels. the brand new Magento innovative internet functions (PWA) Studio allows for retailers and developers to create official, fast and interesting cellular experiences to enhance conversion rates and boost engagement 
  • Streamline payment and possibility administration: merchants fight to sustain with customer adoption of choice payment functions and digital wallets, resulting in frustration at the checkout. With the launch of Magento payments, retailers can now instantly accept payments from valued clientele. retailers additionally advantage from providing the appropriate tenders, managing operations with minimal effort and receiving coverage from fraud and chargebacks. 
  • reach new audiences: Magento now integrates with Amazon income Channel to allow retailers to seamlessly sync Magento outlets to extend the attain and successfully promote, sell and fulfill across channels. Integrations with Google service provider middle and advertising Channels for Google sensible browsing Campaigns enable retailers to create and promote branded campaigns. 
  • increase performance and usability: The introduction of Bulk APIs and Asynchronous APIs advance the usability and performance of integrations with Magento. The release additionally marks an important investment in Magento Open supply with key capabilities like multi-supply stock, PWA and GraphQL in addition to ElasticSearch and Rabbit Message Queues. 
  • Streamline Product counsel administration & experiences: With the addition of Akeneo PIM and Yotpo ratings and stories to the Magento Premier technology companion software, merchants can capitalize on the cost of first-rate product information and social validation to maximise conversions and cut product returns. 
  • Analyze and optimize searching experiences: Analytics and optimization capabilities for enterprises at the moment are extending to small businesses. the integration of Launch, by way of Adobe, with Magento helps retailers right now analyze commerce records and optimize shopping experiences

  • 10 techniques the Adobe, Marketo Acquisition Will have an impact on Marketo's users | killexams.com Real Questions and Pass4sure dumps

    Marketo CEO Steve Lucas on stage.Marketo CEO Steve Lucas, who will proceed to guide the Marketo crew as part of Adobe’s Digital experience business, reporting to executive vice chairman and established manager Brad Rencher.

    Marketo, a San Mateo, Calif.-based mostly marketing automation business, and a significant player in the martech area, was bought by a private equity enterprise for $1.seventy nine billion in 2016 and now has been acquired by using Adobe for $four.seventy five billion. So how will this all shake out for entrepreneurs the use of the Marketo platform? We requested practitioners and experts how they think present Marketo clients can be impacted by probably the most advertising and marketing technology house's biggest dealer acquisitions. 

    ‘game-altering’ MarTech opportunities

    “Adobe has some splendid alternatives for integrating Marketo into its product stack, and items that could significantly bolster a few of Marketo's current choices,” stated Courtney Grimes, martech options engineer for Demandlab, a Marketo customer and partner due to the fact that 2009. “Integrating check and goal with Marketo can be a game-changer for entrepreneurs doing A/B checking out and take information-driven experiments to the next level.”

    connected Article: Adobe-Marketo Acquisition: What We understand so far

    more desirable Server, Datacenter insurance?

    Marketo debuted in 2016 as a large statistics re-architecture known as task Orion that was designed to velocity things up. however over the course, Marketo has taken hits from customers for performance concerns, reminiscent of sensible Lists that “take continually to run," e mail template adjustments which take 10 to twenty minutes to approve and complex crusade flows requiring developed-in delays to enable pastime information the time to synchronize throughout the platform earlier than it may also be leveraged for extra personalization. 

    “it be nevertheless the early days of this acquisition, but i'm specifically fascinated to look how this may affect Marketo's structure,” Grimes said. “there may be been growing pains as Marketo scales up its server and datacenter capabilities, and Adobe has been investing in greater cloud offerings over the last few years. i'm hoping business valued clientele will get a finest-in-type experience from a technical structure perspective.”

    related Article: Adobe Acquires Marketo: decent issues Are Seldom low priced

    end of Google Partnership?

    The acquisition of Marketo remaining week generally is a video game-changer for Marketo clients nevertheless it may additionally imply the downfall of a major tech partnership with Google. Marketo in August of 2017 introduced a partnership with the Google Cloud Platform that covered a multi-year collaboration with Google and its Google Cloud Platform (GCP) where Marketo runs its advertising and marketing automation products completely on Google's Cloud Platform. The announcement with Google also supposed Marketo received integration between its engagement platform and Google's G Suite, facts analytics and machine studying capabilities and a more desirable means to scale. It also got to shed its data centers in the procedure.

    Will this partnership live to tell the tale given that Adobe is in competitors with Google in digital advertising? Grégoire Michel, managing partner at inficiences companions, calls this an “area of risk” for Marketo users not only as a result of the Google Cloud partnership but additionally with the AI-enabled Marketo audience solution. “Will this continue to exist the Adobe deal when one knows that one of the crucial G Suite tools are direct rivals to one of the vital Adobe advertising Cloud products?” Michel speculated. “If not, that would be a big loss to Marketo users.”

    related Article: the new Marketo Google Partnership: What You need to recognize

    Marketo users ‘Dodged a Bullet’

    Adobe, although, will prove to be an outstanding factor for Marketo users, in accordance with Justin grey, CEO and founder of LeadMD, a Marketo accomplice, who thinks users “dodged a major bullet” within the Adobe acquisition. “Up unless the Adobe acquisition,” he stated, “the front working suitor rumored to eyeing up the ultimate of the exceptional independent marketing automation providers turned into SAP. SAP stands out as the kiss of death for Marketo and any semblance of innovation on the platform. Adobe is an inventive company with their sights on deepening their abilities in B2C and entering B2B with a bang — they did that with the announce of their intent to purchase Marketo.”

    Adobe’s Personalization Engine

    the place Marketo users will see an enormous boost, is in performance for you to are available in leveraging Adobe’s online personalization equipment, in accordance with grey. through better content management, online personalization, viewers profiling and ecommerce (Adobe’s fresh $1.sixty eight billion acquisition of Magento) Marketo clients will have a optimal-in-class suite of tools clean in hand, gray mentioned. “here is a huge win, as many B2B shops, which include the lion’s share of the existing Marketo person base, actually have B2C features to their business through freemium models, trials and SMB concentrated choices,” he talked about. “Marketo has also been specializing in increasing their B2C client base with fervor over the last 24 months and those clients will see areas of the Marketo platform which have traditionally underperformed, now become most efficient in classification. It’s an excellent complementary acquisition.”

    connected Article: Adobe Makes a Play for Mid-Market purchasers With Magento buy

    advertising of their DNA

    Paul eco-friendly, director of advertising know-how at extreme Networks and a ten-12 months Marketo person, leads the local Boston Marketo consumer community of greater than 720 individuals. He mentioned he sees this acquisition as “very really helpful” to existing Marketo purchasers and the encompassing advertising Nation group.  “Adobe,” eco-friendly said, “has marketing in its DNA. With Adobe, you've got a advertising and marketing powerhouse buying albeit a smaller however in spite of this, a advertising automation powerhouse. The synergies seem clear. Innovation, I consider, should be a herbal effect.”

    Rising expenses for Marketo clients?

    however does Adobe have rising fees in its DNA submit-acquisition? past adventure suggests that when a big suite seller acquires a smaller player, they are inclined to lift subscription rates, in line with Tony Byrne, CEO of precise Story community. “This basically happened when Adobe acquired Omniture, and it got here as some thing of a shock to some of these licensees,” observed Byrne, who blogged in regards to the Adobe-Marketo acquisition. “it's more prone to turn up for renewals than new clients.”  

    connected Article: Marketo's New CMO Completes put up-Vista govt Shakeup

    Separate items, Ecommerce Integration

    Adobe’s acquisition will gasoline assist Marketo's product vision helping up to date firms and extend and integrate Adobe’s portfolio serving creative thinkers globally, mentioned Inga Romanoff, CEO of Romanoff Consultants, a Marketo premier associate. 

    Romanoff mentioned issues if you want to and received’t change because of the acquisition:

    What received’t exchange

  • Marketo’s product vision
  • continued investment into Marketo product suite
  • Adobe’s basic money driver for the digital adventure section (Adobe advertising Cloud)
  • Adobe advertising Cloud product performance and focal point on B2C
  • Marketo and Adobe advertising Cloud will continue to be separate products inside the portfolio
  • what is going to change

  • Marketo gaining a market leader place integrating with ecommerce (Magento)
  • Marketo increasing into B2C phase with a product vigor play
  • twin Marketo/Adobe energy play in synthetic Intelligence (AI) for entrepreneurs
  • New nifty facets (for both Marketo and Adobe items):
  • potent traction in featuring deep insights and constructing greater customized campaigns
  • increase to video capabilities in advertising
  • more advantageous adventure builder offering massive volume of content material
  • linked Article: Adobe, Salesforce, Oracle, Marketo Have most effective advertising Hubs, Gartner Says

    retain Your own CRM Integration

    fear no longer, Marketo users. Grimes sees Marketo being got by way of Adobe as the optimal-case situation for current Marketo valued clientele compared to different shoppers that might have obtained the product because of the range Marketo offers."which you can connect any principal CRM to it or even build a setup to your own homegrown equipment,” Grimes mentioned. “as a result of Adobe doesn't provide an answer within the CRM market, Marketo will stay platform agnostic and proceed to guide and develop the entire current consumer integrations with Salesforce, Microsoft Dynamics, SAP and others.”

    Michel observed lead administration options for complex earnings are not making sense without a fantastic connection to the CRM. “The very good news for Marketo clients is that Adobe isn't a CRM seller," he spoke of. "... The Adobe deal might even have a positive have an effect on on the relationships between Marketo and Microsoft, and Microsoft is generic to be near Adobe but less close to Marketo on the grounds that the Marketo-Google partnership announcement. So for businesses the usage of Microsoft Dynamics in combination with Marketo, the Adobe deal is capabilities respectable news.”

    strong Integration knowledge

    What current Marketo tech will get more advantageous? Grimes stated “first rate opportunities for integrating Marketo into its product stack” similar to:

  • Integrating examine and goal with Marketo
  • Integrating Marketo's ContentAI platform with Adobe experience supervisor would allow organizations to present customized content at scale
  • Native integration with Adobe Analytics would be a true winner as neatly. “As much as i love Google Analytics, one of the vital things I've viewed entrepreneurs battle essentially the most with when the usage of Marketo is being capable of do actual evaluation of the site visitors and lift they may be featuring to their company web properties,” Grimes stated. “Having native support for Marketo internal Adobe Analytics and vice versa might streamline a technique that's intimidating for less technical entrepreneurs.”
  • Integrating Magento Commerce Cloud with Marketo; up beforehand, any ecommerce work completed in conjunction with Marketo has required lots of workarounds, she pointed out. "Having native integration between ecommerce, advertising and marketing automation and CRM would be a slam dunk for B2C companies," Grimes introduced.
  • linked Article: Microsoft, Adobe accomplice to enhance Analytics, Cloud internet hosting

    Conclusion: Wait and notice

    Naturally, it’s early days for this acquisition. Michel warned Marketo clients that ninety percent of the cost creation occurs all through the combination phase, now not before the announcement. “As somebody else observed,” Michel delivered, “strategy 10 percent, execution ninety p.c.”


    Adobe Debuts digital Analyst For Analytics | killexams.com Real Questions and Pass4sure dumps

    Adobe on Monday unveiled technology in Adobe Analytics that unearths insights devoid of the consumer asking. John Bates, director of product management at Adobe Analytics, says the product specializes in uncovering “unknown unknowns” -- the ability to bring together, analyze and take into account the data in accordance with triggers clients may also no longer ask for.

    Three years in the making, the virtual analyst -- a computing device-learning device developed on a variety of Adobe products like Anomaly Detection and Contribution evaluation -- integrates into Adobe Sensei, the company’s AI and laptop-discovering framework. It surfaces signals that would have in any other case long gone neglected.

    In a first for Adobe, the platform collects the statistics on behalf of brands and applies laptop getting to know to stronger have in mind preferences and tastes of people who log into Adobe Analytics, before combining it with insights that the equipment recommends. It normally analyzes the trending facts in keeping with similarities, identifies meta or macro activities and at last adjusts in response to computing device discovering.

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    commercial

    one of the crucial points that users can are expecting to look in the future will focal point on the platform suggesting next actions, actions to take because of the insights equivalent to electronic mail remarketing or focused on campaigns. “here's a stepping stone for a an awful lot broader vision,” he says.

    The records accrued by using Adobe on behalf of its purchasers raise at a double digit expense, 12 months over year, he says. They handiest use a sliver. “We assemble tons of of billions of records points, but the element being accessed through entrepreneurs and analysts across the commercial enterprise is between 1% and 3%,” he says.

    in addition to choosing unknown knowns, the expertise prioritizes  facts  primarily based  on  company  and  user  context  with out  the  person  having  to  on the spot  the  device.  it'll identify nuances in historical statistics for analysts. The platform also makes use of AI to establish and understand the context.

    Adaptive gaining knowledge of helps the platform consider the statistics crucial to the consumer and supply alerts. It offers  a  potential  to  “like”  or  “not  like” concepts,  which  reinforces  the  desktop  studying  model  and  make s the digital  analyst  more  intelligent.




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    Analytics Business Practitioner

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    Analytics and AI: digital transformation tips for the algorithm age | killexams.com real questions and Pass4sure dumps

    This new book brings managers up to speed on the business potential of analytics and artificial intelligence (AI), and how to gear their organisations up for change and innovation.

    Startups and enterprises looking to harness the power of exponential technologies can find useful checklists, frameworks, and case studies in the new book, AI and Analytics: Accelerating Business Decisions by Sameer Dhanrajani.

    “AI is the natural evolution to changing genres of sophisticated analytics, further strengthened by an algorithm economy,” Sameer begins. “AI is rapidly becoming the vehicle to address and solve the most pressing societal problems related to healthcare, security, education, and allied areas. This makes it akin to the next Industrial Revolution itself,” he adds.

    Sameer is the Chief Strategy Officer at Fractal Analytics, and was earlier at Cognizant Technology Solutions and Genpact. The book is drawn from his 150 blogposts over five years, but the book could do with a lot more editing to remove repetitive material. The painfully small font makes it difficult to read across the 372 pages, and many figures and charts are illegible.

    Here are some of my key takeaways from the book in terms of trends, impacts, organisational change, startup players, and future challenges. See also my reviews of the related books Big Data Revolution, Big Data @ Work, and Life 3.0.

    Trends

    The book begins by identifying key trends in this space: industrialisation of analytics; data collection and monetisation; analytics-led business transformation; and rise of the algorithm economy in areas like behavioural prediction. Market forces drivers for analytics are technological advancement, rise of consumerism, data explosion, and competitive pressure.

    Depending on the level of maturity with respect to reactive, tactical, and strategic use of analytics at the local or enterprise-wide level, companies can be classified as laggards, amateurs, practitioners or masters. Analytics in mature organisations is business-user friendly, and grows beyond the purview of CTOs and CIOs. In a data-driven economy, data sits at the core of every business model, leading to real-time insights and agile decision-making.

    AI is powering new models of assisted intelligence (in clearly-defined, rules-based, repetitive tasks, for example in Gmail), augmented intelligence (for example, clinical decision support; Netflix recommendations based on individual and group patterns), and autonomous intelligence (for example, facial recognition, driverless cars, automated trading).

    Transformation 1: Analytics

    The journey to analytics transformation is not a sprint but a marathon, Sameer cautions; it involves technology, process, culture and leadership change. Just gathering lots of data is not enough; many companies have large volumes of “dark data” in silos.

    The transformation calls for unification in approaches across divisions and functions, internally and externally. Companies should think big, frame the right questions, craft a data agenda with effective quality and governance, and embed insights at the right touchpoints to deliver immediate value, Sameer advises.

    Effective “data horsepower” depends on data quality, storage, assimilation, sanitisation, harmonisation, and visualisation. Data readiness should be followed by technology and business readiness. Security and privacy of consumer data should also be respected.

    Close cooperation is needed between the data management team, data modelers, data scientists, visualisation experts, domain experts, and business unit heads. Early initiatives can focus on projects and tactics, then scale up to strategy and partnerships.

    In the age of the predictive enterprise, the “data deluge” should be converted to a data monetisation engine. Data is worth monetising if it can predict behaviours, lead to actionable insights, refine understanding of existing and potential customers, and make a decision with precision.

    Sameer offers a 2X2 matrix comparing data maturity and analytical maturity to decide whether corporate decision-making is gut-based, reactive, tactical or forward-looking. Speed and quality of response are important if they can prevent fraud or failure, reduce customer waiting time, and improve resource utilisation.

    Decisions to build, buy or outsource analytics solutions and services should be taken based on criticality, differentiation, availability of in-house talent, and vendor considerations of ease of use, cost-effectiveness, and granularity of control.

    In additional to transactional Big Data about consumer activities, companies need “thick data” about consumer behaviours and emotions via qualitative insights. This helped Lego understand, for example, that children wanted not just quick gratification but the longer experience of imagining and creating.

    “The problem with Big Data is that organisations can get too obsessed with numbers and charts, and ignore the humanistic reality of their customers’ lives,” Sameer rightly cautions (see my review of the related book Sensemaking).

    Such insights help understand the “choice architecture” of consumers, and use methods like “social proof” to induce change via “behavioural nudges.” Such techniques are effective during election campaigns, for example.

    Transformation 2: AI

    Innovative giants such as the FANG companies are rapidly disrupting a number of sectors with AI. Robotic Process Automation (RPA) is disrupting IT services and BPO companies. A company should make itself AI-ready by embedding intelligence in processes and platforms, fostering a learning culture, encouraging innovation, deepening internal relations, re-skilling teams, and preparing for tough ethical challenges.

    Engaging with tech experts, consultants and startups also helps. The focus should be on processes and not just departmental functions or silos. “Algorithms can work at a speed and scale that cannot be easily matched by scaling the human workforce,” Sameer explains. Robots and algorithms can also build other robots and algorithms.

    Business leaders must figure out how to design and leverage algorithmic business models (“the economics of connections,” as described by Gartner). “Mr Algorithm” may well be the new member in the boardroom, Sameer jokes. “To some degree, every company will become a math house,” he adds.

    Inputs from fields like design thinking will become increasingly valuable, by providing human-centred creative angles. Companies should set up a design research capability, to combine context with analytics. This helps with problem reframing, structured ideation, and responsible and inclusive design; it applies to the consumer as well as internal enterprise contexts.

    Careers and skills

    Analytics practitioners need domain knowledge in statistics and machine learning, skills in storytelling, experience in data engineering, and a sense of academic curiosity. “The data must tell a story that can be understood by all stakeholders,” Sameer explains.

    Visual dashboards play an important role here as well. The data scientist’s role goes beyond collecting data and developing products; it is also about engagement and quality. Companies will need a culture of fact-based decision-making, powered by data science expertise and AI strategy. Success in this era even calls for being comfortable with uncertainty and acting with agility.

    The rise of AI will make it all the more important for humans to reposition themselves towards tasks requiring judgement, creativity, empathy, ethics and regulation. This could also be the time for a career pivot for traditional IT professionals, who can branch out into new areas like Blockchain and cybersecurity. Adaptability is key to unlearn and relearn, Sameer advises.

    Business impacts

    AI is well-suited to tackle complicated and complex problems, as well as tasks with drudgery, risk and repetition. Analytics and AI are transforming operations, HR, finance, marketing, and strategy. They reduce decision latency, strengthen fire-fighting, improve risk modelling, refine product categorisation, and speed up responsiveness in real-time scenarios involving airplanes, power plants, and self-driving cars.

    Impact areas are across sectors: banking (fraud mitigation, customer retention, risk management), automotive (connected cars, drones), insurance (digital actuarian models), healthcare (at the level of patients, drugs, treatments), retail (personalisation, supply chain optimisation, omni-channel commerce), and cybersecurity (monitoring and predicting attack patterns).

    “AI has been critical in research and development to eliminate human error, which is expected to have a major impact in fintech,” Sameer explains. Services can also be personalised to a “customer segment of one.” Robo advisors can help users with goal-based planning and wealth management.

    Other “man-machine learning” (MML) scenarios arise in automated and augmented underwriting in the insurance sector, such as claims management and fraud prevention. Smart wallets will guide users to more responsible spending habits. Better models can be used for collection services and punitive actions.

    Analytics in healthcare can be used at the level of medical records, diagnosis, treatment, and follow-up care. Real-world evidence (RWE) helps track differences and similarities across comparator groups, which can be used in designing disease treatment. Detailed datasets and analytics for health technology assessment can improve clinical trial design, for example the use of bio-markers.

    Analytics can also yield rich insights from patient portals such as PatientsLikeMe. AI is a “boon to the life science industry,” Sameer explains. Sectors like genomics show how coordinated approaches towards data processing are the future of the industry. Genomics can help identify high-risk patients in diseases like diabetes.

    AI can also be used to manage regulatory compliance, for example misinterpretation of rules. Open source tools are emerging to explore large genomic data sets, for example Integrative Genomics Weaver (by MIT and Harvard). The key is to manage large volumes of data in real time and find pertinent correlations quickly.

    “Predictive analytics has been used in retail for decades; however, in the last few years, AI with other advances in technology have super-charged the speed, scale and cost at which it is used,” Sameer explains; the accuracy has also increased.

    Diversity in retail options and the rise of mobile and social media have given consumers more choice and voice than before. “A data-rich industry, e-commerce benefits widely with AI adoption,” observes Sameer, pointing to intelligent product discovery via visual and voice search as an example.

    Customer expectations have risen, and they expect to be rewarded for their loyalty; they want exclusive, personalised, and relevant offers. Businesses will in turn need to get smarter to gather valuable data, assess deep context instantaneously, harness collaborative filtering, reduce customer churn, and detect and reduce lost sales.

    “The application of algorithms is witnessing a shift from productivity based in the back end to a core selling model in the front end,” says Sameer. “To be effective, customer intelligence needs to include raw transactional as well as behavioural data,” he adds.

    Attribute analysis and event sequence analysis are useful computational techniques in this regard to get a more complete view of the customer. In omni-channel retail, customers expect consistency, agility and ease at all times.

    Analytics and AI are creating fluid supply chains; smart logistics enables better demand planning and forecasting. Inventory tracking, route optimisation, product placement, and promotional displays are other areas where analytics makes an impact.

    CXO impacts

    Impact areas of analytics and AI also span different functions and managerial roles, such as marketing, advertising, HR, finance, and enterprise information management. For example, the CMO will be able to improve engagement marketing at scale in real time, harness contextual insight-driven automation, and break out of silos to enable effective cross-selling and upselling.

    Customer churn can be reduced, and loyalty and win-backs tracked in real-time. Social media listening and rich data flows within the organisation can improve brand perception analysis, and predictive powers will be enhanced on a scale not previously possible, says Sameer. Decisions based on such actionable insights will be smarter, faster, repeatable and replicable.

    “HR is shifting from being an art to a science,” explains Sameer, pointing to the use of chat bots, employee sentiment analysis, and fraud detection. The rise of talent sciences represents a new era in HR, by enhancing human capital management via better psychometric and behavioural data analysis.

    This includes pre-hire selection methods beyond gut instinct, training based on learning analytics, job rotation, attrition prediction, and succession planning. Gartner research classifies HR analytics into four levels: workforce summary, HR processes, employee performance, and workforce planning.

    CFOs will be able to dig below the numbers to their source along with better business correlations; this can improve invoice clearing and expense claim auditing. The rise of IoT and Industry 4.0 frameworks will create new types of CIO leadership persona: explorer, ambassador, clarifier, educator, attractor and cartographer, according to Gartner.

    Challenges

    A range of challenges is holding back the rapid rise of AI. These include lack of understanding about AI, absence of standardised and structured frameworks, inadequate data awareness or strategy, inability to vision or morph to new business models, and lack of re-skilling to make workforces AI-ready.

    “Enterprises must figure out how humans and machines can collaborate and complement each other to create competitive advantage and synergies,” Sameer urges. This also applies to risk management, where AI can help companies monitor and respond to cyber-attacks and fraudulent behaviour based on pattern analysis at the level of devices, processes and networks.

    The author rightfully identifies a range of other business, ethical and legal challenges that companies need to factor in as they increase their capabilities in analytics and AI. These include customer and employee privacy, enterprise change management, and upskilling to deal with work transformation.

    Case studies

    The book is packed with case studies and profiles of sectoral business impacts. A data focus can create “new business moments,” such as Google leveraging previously untapped hyperlinks as a data resource; AI now drives everything from its search platforms to driverless cars. A significant chunk of business at Amazon comes from its recommendation engine; the company’s other initiatives also represent the state of the art in deploying an “integrated strategy machine.”

    Nike has integrated data to avoid potential stock-outs and maintain optimal inventory levels. Data monetisation at Vodafone Netherlands led to stronger marketing success by improving relevance of targeting and market positioning.

    Tesco Bank uses ClubCard customer data to identify customer needs and create new personalised products and services. Freight company Pitt Ohio uses predictive analytics to increase repeat orders and reduce the risk of lost customers. Analytics helped PPL Electric improve its service reliability metrics.

    Data collection and open reporting have been used for public works monitoring in South Africa (WaterWatchers), health services tracking in Uganda (Cipesa), spotting illegal fishing structures (Iran), comparing educational policies (World Bank), and modelling agricultural risk in Kenya (GeoVentures),

    Emcien delivers its pattern-detection capability via cloud-based analytics-as-a-service to retailers and telcos. Host Analytics, 8thBridge, and Dachis Group offer insights-as-a-service, drawing on customer data and social media. JBara offers business best practices for improving customer profitability, while 9Lenses helps companies benchmark their performance.

    The Talla chat bot assists in interview question management. TextIO uses AI to detect biases in communication during interviews. HireVue helps companies analyse interview videos for cues from voice inflections, micro-expressions and verb choices. BluVision radio badges track employee movements and flag intrusions or time-wasting behaviours.

    Minhondo improves employee networking by profiling their workspace and online browsing activities. Bain has researched how companies track and improve efficiency of meetings to reduce wastage of time.

    JP Morgan uses algorithms to track and reduce rogue trading among employees. JP Morgan Chase has a contract intelligence platform (CoIn) to analyse legal documents. American Express uses predictive analytics to identify at-risk customers. Lemonade uses AI to speed up its home insurance signup process. Progressive uses telematics to track driver performance and customise a range of car insurance plans.

    Philips IntelliSpace uses AI to improve performance in ICUs. Moorfields Eye Hospital is using Google Deep Mind Health to track eye disease complications. Texas Health has partnered with Healthways to identify high-risk patients and offer customised intervention. Pfizer uses Watson for Drug Discovery to mine research articles for insights on better drug usage in cancer treatment.

    Healthcare company Merck created a data-driven solution called MANTIS (Manufacturing and Analytics Intelligence) which helped reduce inventory carrying costs; rollout began with a “lighthouse” project in the Asia-Pacific, followed by a broader rollout.

    Flipkart’s Project Mira uses AI to query users about what they want. At SnapDeal, 40 percent of total sales are driven by predictive algorithms. Pandora and NetFlix leverage hyper-granular behavioural profiles of customer shopping habits at scale.

    Alibaba’s TMall uses robots in its warehouses, and DHL uses autonomous forklifts. UPS improved its route schedules and customer service through its On-Road Integrated Optimisation and Navigation (ORION) system.

    WalMart blends offline and online shopping via in-store Pick-up Towers for online shoppers; facial recognition is also used to identify unhappy or frustrated customers at checkout lines. Macy’s has leveraged Big Data for real-time visibility and pricing, by drawing on location and demand insights. Toy retailer Entertainer used micro-segmentation analytics to increase digital channel sales. LinkedIn uses the “also viewed” feature to show profiles and jobs to those already viewed.

    Disney’s Magic Band bracelets yield valuable insights in consumer persona while helping visitors in park navigation, ticketing, and room bookings. Knorr, through a social media campaign called #WhatsForDinner, uses AI to recommend recipes based on what consumers have in fridges.

    GoFind uses a deep learning-based image search engine to help people find products in online stores. L’Oreal and CoverGirl use ModiFace’s facial modelling techniques to help with product discovery. TheTake uses AI to help people find products similar to what they see on TV. Google Assistant helps voice-based search of images on smartphones.

    Siemens is using the Industry 4.0 framework for automating supply chains and making factories self-organising. BMW ID profiles help leverage data in the connected car ecosystem. EverScreen uses AI with security cameras to track errors in checkout scanning.

    The Associated Press is generating reports with AI-powered robots, and focusing journalists on more investigative reporting. Pinterest uses a combination of AI and crowdsourcing to help users find what they want through better curation.

    Airbus used fuzzy matching and self-learning algorithms to reduce production disruptions. BP augments human skills with AI to improve field operations. Insurance company Ping uses AI to speed up its loan offering capabilities with better customer scoring capabilities.

    The US Postal Service uses deep-learning neural networks to automatically read handwritten zipcode digits. Other high-profile AI stories are exemplified by AlphaGo, Watson, and Big Blue.

    Startups

    Large companies such as Google, Microsoft, Amazon, Facebook, NetFlix, LinkedIn and GE are leading the AI wave. A number of startups have also entered the fray, such as SearchLight Health (healthcare sector analytics), Netradyne (determining causes of automobile accidents), Node (natural language processing), Dumbstruck (emotion analysis), Shift Technology (claims management), ControlExpert (auto insurance), Libra (genomics), Layar (AR), and Premise (consumer price index). New devices have also emerged in healthcare, such as Asthmapolis (GPS-enabled tracker to record inhaler usage).

    CB Insights classifies AI startups in fintech into segments such as credit scoring, direct lending, personal assistants, asset management, fraud detection, insurance, sentiment analysis, debt collection, reporting, and predictive analytics. “For traditional banking institutions, the focus and energy for innovation are simply not there, nor are the necessary IT budgets,” Sameer observes; this has driven many corporates to engage with startups.

    Many large firms have also acquired analytics and AI startups, or made strategic investments in them, for example Ford (Argo), Citibank (FeedzAI), RBS (Lavo), Roche (Bina Technologies), and Google (MoodStocks). Wells Fargo has a startup accelerator programme for the fintech sector. 

    The road ahead

    Emerging frontiers to watch are IoT (streaming analytics, edge responses, embedded intelligence), Blockchain (usage of new asset classes), and chatbots. “Algorithmic marketplaces will disrupt the analytics ecosystem and likely the whole software ecosystem,” Sameer predicts.

    Embedded and connected intelligence will transform the automotive industry, improving in-vehicle content and services while also spawning new business partnerships for road safety and fuel efficiency. A new range of creative possibilities will arise with self-aware automotive vehicles and networks, as AI transforms self-driven cars and drones.

    IoT represents new challenges via the need for edge analytics and not just centralised analytics; issues to tackle are performance, latency and security. New frameworks, partnerships and monetisation models will be needed for smart cities, homes, and seamlessly interconnected media and manufacturing sectors. Analytics will also help policymakers understand new frontiers like cryptocurrency, for example spending patterns, transaction traces, and international transfers.

    In the long run, though analytics and AI will advance, there will still be decisions that require insights beyond what AI can interpret. “We need to consider AI not as machines, but as colleagues,” Sameer advises. AI algorithms can augment human capabilities, and new business processes can be created based on augmented working strategy.

    More exploration and experimentation are called for in this brave new world. Ultimately, the evolution of technology is creating a “new normal,” and will drive humans to find a new purpose in life.


    Analytics as a Source of Business Innovation | killexams.com real questions and Pass4sure dumps

    About this paper

    Competitive advantage from analytics is changing, and for the better. For the first time in four years, MIT Sloan Management Review found an increasing ability to strategically innovate with analytics based on interviews with more than 2,600 practitioners and scholars globally.

    Learn more about key findings, including:

  • Wider use of analytics, better knowledge of its benefits and greater focus on applications have reversed a trend on the benefits of analytics.
  • Return on investment for analytics stems from the governing and sharing of data throughout the organization.
  • Machine learning enables organizations to discover more insight from their data, allowing employees to focus on other critical responsibilities.
  • About SAS

    SAS is the leader in analytics. Through innovative software and services, SAS empowers and inspires customers around the world to transform data into intelligence. SAS gives you THE POWER TO KNOW®.


    Predictive Analytics in 2018: What’s Possible, Who’s Doing It, and How | killexams.com real questions and Pass4sure dumps

    In 2009, Netflix offered $1 million to anyone who could improve the quality of its recommendation engine by 10%. It took two years, but a team finally won. Netflix paid the bounty—then ignored the code.

    As it turned out, the enhanced algorithms “did not seem to justify the engineering effort needed to bring them into a production environment.”

    Not only did the winning prediction engine fail to scale economically, it also addressed an outdated problem: The shift from mail to streaming during that same two-year window gave Netflix all the data it needed to develop newer, better algorithms.

    Predictive analytics, in other words, wasn’t a panacea. Nor, in the decade since, has it become one. But, in 2018, incremental gains no longer cost $1 million either:

  • You have more data;
  • Storage is cheap; and
  • Cloud computing is almost infinitely scalable.
  • This post details those changes and shows how several businesses—and not just behemoths—have cultivated the predictive analytics landscape.

    What’s changed in the last decade? 1. More data, more storage, more computing power

    Massive, cloud-based repositories of customer interactions, often called data lakes, are the raw source material for predictive analytics applications.

    Many companies have taken advantage of cheap cloud storage to stow away data for years—without even considering its potential use. (How many neglected data points do you have in Google Analytics, Google Ads, MailChimp, Marchex, Stripe, and similar services?)

    That dual growth in scale—of data collected and accessibility to it—has solved two primary challenges of predictive analytics implementation.

    Historically, raw computing power has been the other. As Andrew Pearson of Intelligencia notes, “Without significant hardware investments, predictive analytics programs either weren’t possible or too slow to be useful.”

    That, Pearson continued, has also changed: “Cloud-based analytics systems have added massive computer power into the mix.” Increasingly powerful systems cracked open the door for real-time predictive analytics.

    2. A world of real-time predictions

    For some, the age of “real-time” predictive analytics is here. Judah Phillips, the co-founder and CTO of Vizadata and founder of SmartCurrent, explained:

    We already live in a world of “real-time” predictive analytics. A simple predictive analysis is your arrival time in Waze. A more complex real-time prediction occurs billions of times worldwide every millisecond in matching certain types of digital advertising.

    Further, companies like Mintigo and Versium now offer real-time solutions for lead scoring, showing that the transition is technically possible. Possible, however, doesn’t mean perfect. Sam Underwood, a vice president at Futurety, acknowledged the complexity of necessary integrations:

    Especially in the mid-market world, the tools that gather data to turn into predictive modeling—CRM systems, social media aggregators, logistics, and purchasing systems—often do not have friendly APIs or other easy mechanisms with which to quickly gather and interpret data.

    That disconnect still thwarts even the most fundamental business cases for real-time predictive analytics. David Longstreet, the chief data scientist at FanThreeSixty, offered an example:

    In our world of sports and entertainment, for example, most sports teams do not know how many people are in a stadium for a game. Teams know how many tickets were distributed; however, they do not know in “real time” how many people are in the venue or stadium during the event.

    That knowledge gap hampers efforts to staff and stock the stadium appropriately. It’s also why interest in predictive analytics is almost universal, even if it vastly outpaces adoption.

    3. Slow adoption but soaring interest

    So how many businesses are actively using predictive analytics? According to research from Dresner Advisory Services, about 23%, a figure essentially unchanged from the prior year.

    predictive analytics adoption

    Less than a quarter of businesses are using predictive analytics—though almost all aspire to do so. (Image source)

    Interest, however, exceeds implementation. The same research suggests that 90% of businesses “attach, at minimum, some importance to advanced and predictive analytics.”

    So which questions are those 23% answering with predictive analytics? Let’s take a look.

    Which questions can marketers answer with predictive analytics?

    “They want to predict everything,” according to Underwood. And who wouldn’t want to know the exact foot (or web) traffic by month, day, and hour to streamline staffing (or allocate server resources)?

    But, Underwood continued, he tries to focus clients on “the one thing that, if we could predict it for you, would revolutionize your business.”

    In digital marketing, Phillips outlined myriad use cases for predictive analytics, including the capability to predict:

  • which advertising will be most effective—however you define effective.
  • which marketing campaigns, channels, touches, behaviors, and demographics are contributing to a business outcome, a form of “machine learning–based attribution.”
  • which segment, test, or personalization a user is most likely to respond to.
  • the probability of users to click on an ad, to download a whitepaper, to respond to an email, to respond to an offer, and other customer response you define.
  • which leads will convert—however you define conversion.
  • which customers will buy one or more products for a cross-sell or upsell.
  • the number of purchases or revenue that will occur in the future.
  • which customers will have high/medium/low lifetime value.
  • customer churn.
  • The novel opportunity of predictive analytics, then, is not what you can predict but the fact that you can predict. The historical data you currently analyze can probably become a prediction.

    Just make sure you have the data.

    What do you need to get started with predictive analytics?

    Data, data, and data. “Priority 1A and 1B are data sources,” stated Underwood. That’s true whether you plan to license software or hire an outside organization. (Both options are detailed later.)

    All uses require training data. That training data, in turn, is used to build a predictive model to apply to current data. “The only limitation we’ve run into,” Phillips noted, “is a company’s available data for training.”

    How much data is enough? According to Phillips:

    A few thousand records with a sufficient amount of positive and negative outcomes can be sufficient for marketing, sales, and product prediction.

    Not all data is created (or stored) equally

    “You have to understand—I grew up tearing tickets.”

    FanThreeSixty’s Longstreet has heard that same explanation from venue managers who have spent countless hours counting stacks of stubs after games. It’s a reason why vital data sources may not be easily accessible, or accessible at all.

    In stadiums, Longstreet explained, point-of-sale machines and ticket scanners exist for a single purpose—to complete transactions quickly and keep lines moving. Those systems do not store data efficiently for extraction, nor can they handle incessant server requests (unless hungry fans don’t mind waiting).

    For Underwood, clients tend to fall into one of two buckets, with half in each:

  • “The ideal client has an internal database set up and ready to go. We pull in the data, build the model, and are off and running.”
  • The other half have a mix of data sources, which inevitably include an offshore SQL database (or ten) managed by an external vendor whom no one can track down.
  • Stitching data sources together is a major development project that may require creating custom connectors, setting up third-party FTP drops, and other complex but thankless tasks. That work, however, is necessary: Models and their predictions are only as accurate as the data they’re built upon.

    Don’t forget external data sources

    Not all data comes from internal sources, either. External data sources, like weather reports, are often a critical addition to data lakes, especially for small businesses. As Underwood explained:

    Restaurants may use analytics to trigger email sends; for example, we can set up the email platform to sync with National Weather Service data to send an email about iced tea when the temperature in a given metro area is above 90 degrees.

    Likewise, we can trigger an email to send to customers in a given city if the system detects wind gusts of 40+ MPH. Both of these use cases reach consumers in a key moment of need, negating downstream ad spend and beating competitors to the punch.

    So you have a large, well-organized dataset. What do you do with it?

    How do you turn data into predictions?

    While the limitation of insufficient data has faded, another remains:

    Companies require either a dedicated team of data scientists to parse through these sets, or a software suite powerful enough to do so rapidly. For most small and medium-sized businesses, this usually means settling for subpar software, or forgoing it entirely.

    For businesses of all sizes, solutions branch into two options:

  • Purchase software and create predictions in-house.
  • Pay an outside vendor to develop models and visualizations for you.
  • 1. Predictive analytics software

    The marketplace for predictive analytics software has ballooned: G2Crowd records 92 results in the category. Pricing varies substantially based on the number of users and, in some cases, amount of data, but generally starts around $1,000 per year, though it can easily scale into six figures.

    G2Crowd lists both IBM’s SPSS Statistics and SAS’s Advanced Analytics as market leaders at the enterprise level. Along with RStudio, the pair are also tagged as leaders for mid-market companies; only IBM retains a place in the “Leaders” quadrant for small businesses.

    Historically, however, even industry-leading predictive analytics software hasn’t been a simple, jump-right-in experience. Take these two examples from IBM’s SPSS Statistics and RapidMiner:

    ibm spss statistics screenshot

    rapid miner screenshot

    While these platforms are powerful, users must format data files, link nodes, and develop visualizations. Learning how to do this—and having the time to do it—is a specialized, full-time job. (To believe otherwise is to expect a Microsoft Word license to write your Great American Novel.)

    Not surprisingly, the market is shifting. RapidMiner has rolled out a SaaS beta that, with a bit of manual adjustment, translates an Excel sheet full of, say, employment data to a prediction of employee retention:

    rapid miner saas excel sheet

    A dataset in Excel—a starting point in which most marketers are already comfortable. rapid miner intake

    RapidMiner parses the Excel file prior to crunching the numbers. rapidminer predictive visualization

    The resulting visualization shows the tool’s prediction performance and correlations between datasets and retention.

    Some companies, like Vizadata’s Phillips, see the user-friendly SaaS model as the future:

    We are democratizing data science, so that people with limited or no data science or engineering skills can predict. You simply upload your data and click next. We do all the heavy lifting.

    Our intelligence determines your dependent and independent variables and the type of analysis to run. You can go with our selections or override them—from regression, where we can do forecasting and optimization, to both binary and multiclass classification, where we can predict the probability of outcomes.

    vizadata screenshot

    User-friendly SaaS models make predictive analytics more accessible to marketing teams without data scientists.

    Like Vizadata, MIT’s Endor pursues this path. The platform uses a query-builder to allow anyone to ask questions like “Where should we open our next store?” or “Who is likely to try product X?” It then mines targeted datasets to provide answers, often in a matter of minutes.

    The inclusion of tangential datasets that fall outside consideration—or feasibility—for human observers is a recurring advantage of predictive analytics. Endor’s creators offer an example:

    A marketing department for a bank asks, “Who is going to get a mortgage in the next six months?” Machine-learning engines may detect a pool of, say, 5,000 customers who have a bank credit card and a high credit score, and are married—many of which may be false positives.

    Endor detects more specific clusters of, say, couples about to get married or going through a divorce, founders who recently sold their startups to Facebook, or customers who recently graduated from a local real-estate course.

    Of course, if you want to outsource the process entirely, outside vendors can organize your data, build models, and visualize predictions for you.

    2. Outside vendors Agencies offering bespoke solutions

    For most clients, Futurety starts by identifying the key business question—not a specific metric or visualization. Clients may come in for one-off projects, annual re-runs of their data, or ongoing work.

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    “The end result is not always clear at the beginning,” Underwood explained. “When we’re delivering to someone close to the outcome, like a marketing manager, they’re typically happy with the model, the finding, and the math behind it.”

    The “end result” could be several things:

  • Integration with a third-party platform, like an email client, to automate predictive messaging.
  • Plain-text predictive answers to guide practitioners.
  • Robust visualizations to demonstrate the process and value to the C-Suite.
  • At the end of each engagement, Futurety delivers the model back to the client for management and maintenance.

    Predictive analytics at work
  • Futurety has a small business client that helps aspiring performing arts majors gain admission to their dream college. But few high schoolers have broad knowledge of good programs. More often than not, they know only one name: Julliard.
  • Futurety trained its model on three years of placement data. Then, using new student data entered into a common portal, predicted where students would get accepted and succeed academically.
  • The predictive analytics model, which Futurety updates annually, delivers a simple list of recommended schools for students based on factors like grades and exposure to different musical or artistic styles.
  • The model takes into account whether past placements graduated or won awards.
  • All-in-one niche providers

    FanThreeSixty serves a narrow market: sporting venues. Because they work with a comparatively consistent dataset—season ticket, concession, and souvenir sales—they know the range of business questions, data outcomes, and relevant visualizations.

    This consistency incentivizes niche vendors like FanThreeSixty to develop proprietary dashboards to roll out to all clients.

    The interface allows Longstreet’s team to keep data science in the background: “The secret of machine learning is when you’re being prompted behind the scenes.”

    Distilled fully, FanThreeSixty’s goal (and Longstreet’s explanation of his role at dinner parties) is to “help teams sell more tickets and hot dogs.”

    Predictive analytics at work
  • FanThreeSixty mines historical data to see which concessions are most commonly purchased with a hot dog at a Major League Soccer venue.
  • If a customer purchases a hot dog, concession staff are prompted to ask whether a customer would like to add the most popular accompaniment. That recommendation—a prediction of fan desire—changes based on other variables.
  • Predictions consider more than 20 datasets—everything from the home location of season ticket holders to the weather—to tailor messaging before, during, and after matches.
  • During cold-weather games, for example, FanThreeSixty can automate push notifications with tailored coupons, like buy-three-get-one-free hot chocolate for a family of four.
  • Whether solutions are internally or externally managed, they‘ve long been common in enterprise businesses.

    Predictive analytics use cases at the enterprise level

    Marketing departments in large organizations have used predictive analytics for years:

  • AutoTrader. AutoTrader uses data from its 40 million monthly visitors to better understand the sometimes lengthy customer journey. They built propensity models based on search behavior and created high-value lookalike audiences.
  • Editialis. The French publisher uses predictive analytics in its email campaigns to “anticipate engagement at an individual level.” As a result, they’ve seen click-through-rates increase “dramatically.”
  • Predictive analytics can also coordinate offline and online interactions, with two clear use cases for marketers whose companies have physical products or storefronts:

  • Improved pricing. Smartphone data registers in-store browsing habits to improve online or offline marketing targeting, approximating the advantages enjoyed by ecommerce companies.
  • Inventory management. Full warehouses cost money; empty shelves cost money. Folding online data, such as search patterns, into sales data can better manage inventory, especially at a regional and local level.
  • In addition to external marketing campaigns, predictive analytics also supports internal project management. Large marketing campaigns have many moving parts—a new ad campaign needs new creative, new copywriting, new landing pages, etc.

    Coordinating the involvement of those teams and accurately estimating the time-to-launch is complex. Many fail to get it right, sometimes at great expense.

    whiteboard planning

    Marketers and software businesses may use predictive analytics for internal project management in addition to external campaigns.

    Predictive algorithms, as McKinsey notes, use a wider lens that captures historical patterns and unique project elements in a single frame:

    While every development project is unique, the underlying complexity drivers across projects are similar and can be quantified. If companies understand the complexity involved in a new project, they can estimate the effort and resources required to complete it.

    Predictive analytics models “take into account not only the complexity of the project (both the functional and implementation aspects) but also the complexity of the team environment.”

    Predictive analytics at work:

    More accurate internal project management, in an example McKinsey offers, can have a major impact:

  • A company initially planned a product update to take roughly 300 person-weeks of effort, an estimate based on the limited number of changes between the current product and a new design.
  • However, that estimate failed to take into account the fact that planned updates would affect many different teams. Predictive analytics models did take it into account and estimated that the project would take three to four times as long.
  • As a result, the company limited the work to the original product team, enabling them to deliver the update on time.
  • In addition to helping companies solve internal and external challenges, predictive analytics is also the foundation for some businesses.

    Building a business on predictive analytics

    Ken Lazarus, CEO of the recruiting platform Scout Exchange, has an advantage—the company has been around for only five years.

    That means that the company’s data sources are already primed for extraction into its predictive models that pair companies with the right recruiter.

    scout exchange processScout Exchange’s predictions pair companies with the right recruiter.

    The single best predictor of job placement, Lazarus and his team have found, is the track record of job recruiters. In contrast, pairing the right job description with the right resume remains exceedingly difficult.

    “Job specs are horrible,” he lamented. “The data isn’t on the paper. CVs are pretty horrible, too.” (Data augmentation, such as skills testing and video interview decoding, Lazarus noted, offer potential improvements.)

    Nonetheless, holes remain. Candidates will never disclose negatives on their resume, and important information might forever remain “non-data,” such as whether a candidate is a good “culture fit.”

    Scaling data gathering

    Scout Exchange has honed its predictions by focusing on enterprise customers—its algorithms feast on hundreds or thousands of openings from Fortune 500 clients.

    As a result, the platform takes in roughly 1 million data points monthly, with each new job posting yielding an additional 50 data points.

    scout exchange platform

    Higher employer ratings indicate an employer is more responsive than his or her peers. Likewise, higher recruiter ratings suggest a recruiter is more likely to succeed in submitting acceptable candidates than his or her peers.

    Still, human assessment by a recruiter—and their client—is necessary. Lazarus drew a parallel: “Would you let machine learning pick your wife? No. But would you let it pick the right matchmaker to help you find a spouse? Yes.”

    Those who are trying to solve the most complex human issues aren’t even in the business world.

    Predictive analytics with life or death consequences

    The greatest challenges for predictive analytics are those that deal with complex, individualized human behavior, such as the likelihood that a patient or crisis-line texter will commit suicide.

    Because success or failure is measured in human lives, these challenges are also the most urgent. And while these projects operate beyond the scope of marketing and business, they suggest the potential for predictive analytics as it evolves.

    “REACH VET is not about trying to find the veteran who’s sitting in the car in a parking lot with a gun in his lap,” Aaron Eagan, Veteran Affairs deputy director for innovation told a Washington conference.

    “What we found,” Eagan continued, “is that veterans at highest risk of suicide [also have] significantly increased rates of all-cause mortality, accident morality, overdoses, violence, [and] opioids.” Proactive alerts that trigger physician check-ins have improved primary-care appointment attendance and reduced hospital admissions for mental health issues.

    crisis text line

    The project is similar to a collaboration between Periscope Data and Crisis Text Line, a text-based suicide hotline.

    Leaning on natural language processing and predictive analytics, the program analyzed conversations, forecasted trends, and trained more than 13,000 volunteers. The results?

  • Wait times decreased to less than 5 minutes, an operational goal.
  • Capacity increased by 10% during peak periods.
  • Responses were prioritized based on machine-identified urgency.
  • Endor’s technology has taken on similarly serious challenges. Using 15 million data points from 50 known ISIS supporters, Endor identified 80 lookalike accounts in less than half an hour, with only 35 false positives—expert investigation was still necessary yet feasible.

    In a collaborative project with the U.S. Defense Advanced Research Project Agency, the platform also analyzed mobile data to identify patterns to predict future riots.

    Conclusion

    Predictive analytics is not immune to criticism: GDPR rebuffs some of the same collection methods that swell data lakes. And not all predictions, even the most accurate, are well-received. (Famously, Target unwittingly informed a father of his teenage daughter’s pregnancy based on seemingly benign shopping habits.)

    Predictive analytics experts point out that their algorithms search for patterns among values, not the values themselves. Regardless, insufficient data is unlikely to hold back the expansion of the industry—the IoT, wearables, and other data collectors already supplement traditional web and app analytics.

    User-friendly SaaS platforms are still an emerging opportunity. For most businesses, creating models and predictions from historical data still requires a dedicated employee to navigate complex software solutions or the outsourcing of that work to a vendor.

    For those postponing predictive analytics projects until the SaaS options are more mature, you would be wise to keep filling your data lake.



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