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Insightin Health raises $12 million for AI that uses big data to guide patients’ decisions

February 10, 2021 Big Data

Insightin Health, a company delivering personalized health care guidance, today announced it has raised $ 12 million. A spokesperson told VentureBeat the round will support Insightin Health’s plan to bring on more plan providers that service primarily Medicare, Medicaid, and accountable care organization members.

The global big data analytics market for health care was valued at $ 16.87 billion in 2017 and is projected to reach $ 67.82 billion by 2025, according to a recent report from Allied Market Research. It’s believed that health care organizations’ implementation of big data analytics might lead to an over 25% reduction in annual costs in the coming years. Better diagnosis and disease predictions, enabled by AI and analytics, can lead to cost reduction by decreasing hospital readmission rates, among other factors.

Baltimore, Maryland-based Insightin Health, which was founded in 2016 by Enam Noor, is a cloud-based marketing platform that allows organizations to develop data hubs that promote health plan sign-ups and retention. Prior to founding Insightin Health, Noor cofounded Insightin Technology, a cloud computing and enterprise software development company, and Desme, an interactive marketing company.

Insightin Health’s campaign automation system can create personalized and rule-based content delivery in real time, with a machine learning- and AI-driven approach that provides recommendations for touchpoints of member communications and activities. The company claims to combine medical, clinical, cognitive, and social determinants of health to recommend the next best action for each health plan member. For example, the platform can encourage sick members to make outbound calls and schedule telehealth or in-person appointments when available.

Insightin Health can also identify social needs at both a member and population level and offer a dashboard visually detailing members’ responses, activities, and challenges. The company cites research from startup HealthMine that found 60% of surveyed Medicare Advantage members say their health plan doesn’t encourage actions to improve health.

“Our proprietary platform and world-class team have put us in a position to transform the way health care is experienced. The continued interest from our investors and customers is a great validation [of] our company’s momentum since our last raise in 2018,” Noor told VentureBeat via email. “With the continued investment we’re putting into our platform, we’re hoping that more and more health insurance providers will be able to communicate with consumers on a one-to-one level. This way, we can get closer to humanizing the health care experience to improve satisfaction, increase engagement, and ultimately improve health.”

Late last year, Insightin Health teamed up with students from the University of Pittsburgh’s Swanson School of Engineering to predict the start of flu season, with the goal of optimizing flu shot timing and reducing health care costs in the process. More recently, Insightin Health launched a solution to support COVID-19 preparedness efforts. It scrubs infection and mortality data, creating a risk scoring model to assess information at a state and county level across the U.S.

According to Noor, Insightin Health has been profitable since launch and experienced 170% year-over-year revenue growth from 2019 to 2020. With over 5 million people on its platform, the company anticipates similar growth from 2020 to 2021.

Blue Venture Fund and Blue Heron Capital co-led the series A round Insightin Health announced today. It brings the company’s total raised to over $ 13 million. Insightin has 45 employees.

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Big Data – VentureBeat

From Data to Decisions with Actionable Insights

January 30, 2021 Sisense

There’s no industry that couldn’t be improved by actionable insights, and over time, every industry will be. For example, one fintech problem requiring actionable insights today is credit risk assessment. A well-structured fintech algorithm could easily use machine learning (ML) to advise a human agent to approve or reject a business loan application.

That advice is a quintessential actionable insight: The algorithm models the applicant’s probability of success by training on a mountain of historical data and market factors. The resulting insight is the algorithm’s classification of the applicant as an acceptable or unacceptable risk. The action, in this case, is the human agent’s decision to grant or deny the loan. Businesses of all kinds today are dependent on such actionable insights, ideally infused into workflows, driving better business outcomes without users having to leave their primary tasks to look for answers in the data.

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Actionable insights: The core of business intelligence

In every industry, actionable insights arising from analytics and AI are no longer a luxury, but a necessity for achieving competitiveness. E-commerce platforms require high-level fraud detection systems to advise their human reps on courses of action. Actionable insights have a special connotation in the field of data analytics: An algorithm evaluates a vast data store and effectively advises a human agent on the best action to take. Implicit among assumptions about machine learning is that algorithms will identify a pattern or outcome in time-series data, for example, which human beings could not do in practical time. There are countless opportunities for turning data into actionable insights in industries like:

Finance: Optimal portfolio trading
Healthcare: Diagnosis, intervention, administration
Advertising: Market analytics and placement
Pharma: Protein folding and drug synthesis
Retail: Demographics and style trending
Education: Assessment and teaching methods

The race to put actionable insights into the hands of users is on. When one player in a field turns data into actionable insights, all competitors must follow suit to keep pace. Innovative use of AI and ML methods in all fields will only accelerate this arms race, putting more powerful actionable insights in the hands of workers of all kinds.

Actionable insights example: Broadridge

Undoubtedly money is the great motivator, and AI solutions must improve profitability. Therefore, let’s see how ML models in asset management and optimal portfolio trading produce actionable insights. Broadridge Broadridge Asset Management Solutions is a $ 4.5 billion revenue assets management company with clients generating unique data stores in diverse containers. An analytics challenge here is the integration of multiple data streams to feed an analytics platform and subsequently generate actionable insights that are valuable across a portfolio spectrum.

On the fundamental level, preparing data for analysis is often called wrangling. To business intelligence execs, the top-level view is the integration of everything from containers to ML technologies and visualizations. Analytics must cope with both structured and unstructured data to achieve optimal results. As we’ll see, very few ML and AI analytics platforms today embody the data science and engineering sophistication to accomplish such a feat with an outcome of actionable insights whose profit justify their cost.

A particularly profitable outcome achieved by Broadridge is the use by its portfolio managers of ML analytics to forecast the best portfolio positions. Broadridge accountants are also leveraging analytics to generate actionable insights that include forecasting accounts receivable and payable outcomes leading to sharper awareness of overall assets.

Infusing actionable insights into workflows — the future of business

Turning data into actionable insights is where BI and data analytics platforms like Sisense shine. Modeling complex and multidimensional data parameters in innovative ways and presenting those insights within workflows will change the future of every industry. For example, these systems will be able to evaluate millions of model configurations and dynamically make corrections to them based on live event streaming data in order to continuously update business-facing users with the best possible insights. The result will be more businesses where all or most decisions are made on underlying data, not gut or guessing, leading to increased conversions, lower churn, and high revenues for those who embrace actionable insights. Those who don’t won’t have much of a future to speak of.

white label reports dashboards blog cta banner 770x250 1 1 From Data to Decisions with Actionable Insights

Eitan Sofer is a seasoned Sisenser, having spent the last 13 years building and shaping our core analytics product, focusing on user experience and platform engineering. Today, he runs the Embedded Analytics product line which powers thousands of customers and businesses, making them insights-driven. Eitan is also an avid music fan and surfer.

Tags: actionable analytics | Machine Learning

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Blog – Sisense

Visualize Dynamics 365 CRM data in Kanban View or Mind Map View for quick informed decisions!!

September 22, 2020 Microsoft Dynamics CRM

600x343xblog kb and mmr.jpg.pagespeed.ic.Rhd8xdQUn0 Visualize Dynamics 365 CRM data in Kanban View or Mind Map View for quick informed decisions!!

The world of business is dynamic in nature. Everything keeps on changing. In this fast-paced world, where decisions need to be made quickly, it becomes necessary to minimize the time spent on reading reports or sitting in meetings for status updates. The human brain captures visual information faster than text. Visualization makes it quicker and easier to understand any given data. With visual representation, information can be quickly grasped and communicated.

And to help you achieve this feat, Inogic has brought to you two amazing visualization apps for Dynamics 365 CRM/PowerApps – Kanban Board & Map My Relationships.

A Preferred App on Microsoft AppSource, Kanban Board is a productivity app that enables organized card-based view of PowerApps/Dynamics 365 CRM Entity records in the home grid. It is a visual tool that will help you organize data as per your business requirement. It helps to organize and rearrange records in multiple lanes and rows thereby enabling easy identification of the current status of the records. This visual display enhances the searchability and filtering of records with less time and effort.

Some of the interesting features of Kanban Board are as follows:

Visualize Views: You can easily visualize any Dynamics 365 CRM View as lanes in a Kanban View with the ability to configure the fields for defining the categories.
Drag & Drop: You will be able to drag and drop the cards across lanes and rows to quickly update the values of the underlying category field.
Quick Actions: You can configure cards to support up to 3 quick activity actions, using which activity records like Email, Phone Call, Task, etc. can be created.
Context aware: It works in context of the native Dynamics 365 CRM environment & responds to all native ribbon actions as well as the quick search available for traditional views.
Search: You can easily search for records through native quick search option available for views in Dynamics 365 CRM
Sort & Filter Lane: You can sort the records in ascending or descending order and also filter the data in the view by ‘CreatedOn’ date.
Configuration: You can configure this control for Unified, Mobile and Tablet experience.

Another app to bag the Preferred App on Microsoft AppSource badge, Map My Relationships is a productivity control that offers an easy way to visualize key information related to the record much like a mind map view for Dynamics 365 CRM records. With a quick glance, you will be able to gain all the important information related to the respective record without having to navigate around to get the various pieces of information. This control will further enable you to easily navigate to these related records and create activity records like Email, Task or Appointment. In this way, you can make swift decisions, take quick actions and be on top of the game.

Some of the interesting features of Map My Relationships are as follows:

Relationships: With support for 1:N, N:1 as well as N:N information, you can easily view all relationships between records.
360 degrees view: You will be able to see a summary of all the necessary information related to the record in a single view.
Quick Actions: You will be easily able to quickly record any activity like phone calls or appointments for the related records.
Easy Navigation: You can quickly and easily navigate to any of the records in the relationship.
Grouping & Aggregation: You can easily view aggregate values like Sum, Average, etc. without the need for creating rollup fields.
Configuration: You can configure this control for Unified, Mobile and Tablet experience

Seems intriguing, doesn’t it?

So, go and explore these amazing visualization apps from our website or Microsoft AppSource for a trial period of 15 days.

And feel free to contact us at crm@inogic.com for a personal demo how these apps have been helping businesses with quick informed decisions.

Until then – Stay Safe, Stay Healthy!

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CRM Software Blog | Dynamics 365

Speed Up Decisions To Steer The Ship Through This Time Of Unprecedented Disruption

April 16, 2020 BI News and Info

The current business and social climate has created unforeseen challenges for enterprises of all shapes and sizes. The disruptions, both internally and externally, are unprecedented in modern times. Within the enterprise, the CFO and the finance office arguably face some of the greatest pressures to steer the enterprise in the right direction.

Uncertainties and choices to be made

At this time, there are many uncertainties that must be navigated by CFOs and specifically the financial planning & analysis (FP&A) professionals on their teams. The choices are many and include:

Impact on revenue with disruption within sales channels
Cash flow position and liquidity management
Expense management and cost containment
Best-case earnings projections and guidance based on what’s known of the unknown

These choices don’t just come to the FP&A domain in a singular form, but as a collection of issues and choices to be addressed holistically. Should we tap into a line of credit to keep our working capital fluid? Will our current cash flow and liquidity allow us to cover short-term expenses and current and future capital initiatives? Can we take advantage of government programs with employee salary assistance? What do we tell our shareholders for balance-of-year projections? Are there opportunities available to assist our customers to succeed?

These issues are faced not only by finance but rather across the enterprise as different groups examine their budgets and potential tactics. Does marketing continue with current programs? Does the supply chain face disruptions? What is the effect on demand plans? What does human resources propose for the current headcount levels, and how does that affect outputs? All these individual budgets and models need to be brought together in a single view to enable understanding of the interdependencies.

Collaborative enterprise planning to break down silos

These challenges can easily be addressed by a modern planning paradigm: collaborative enterprise planning. Collaborative enterprise planning involves breaking down silos, allowing plans of all sorts to be linked instantaneously. This way, finance can get the true picture of current fiscal health to advise the best-case scenarios and steer away from poor ones. For example, finance can evaluate a sales plan’s impact on marketing campaigns, or link a reconciled S&OP process to a financial plan. An analyst can determine what headcount plans might impact employee productivity and cash positions.

Finance also needs to be in a position to make decisions quickly. With collaborative enterprise planning, the finance function can move away from scheduled plans. Today, it is no longer tenable to work in an environment where worst-in-class finance departments delay decisions because they are beholden to spreadsheets or outmoded planning tools. Rather, with the right technology on hand, they can simulate the impact of multiple scenarios immediately. They can use modern analytics complemented with predictive analytics and machine learning to support the recommended and best-case outcomes. Dashboards and built-in visualization allow for real-time reporting and an aesthetically pleasing experience for the end users with minimum chance of errors – unlike spreadsheets.

Instant discussion for fast decision-making

Finally, most organizations, including finance, are no longer working within four walls, but rather in a virtual capacity. Collaborative enterprise planning promotes instant discussion during planning and forecasts. Geo-political issues have made an extraordinary impact on commodity prices; this, along with pandemic-related issues, requires collaboration on the fly and consensus on the go. Decisions must be made immediately, not dependent on meetings, voice mails, and email threads.

With collaborative enterprise planning, finance departments can lead their enterprise to the best possible outcome and better results to navigate properly through these trying times. Collaborative enterprise planning enables finance to learn from all aspects of the organization, recommend and evaluate multiple scenarios, make the right decisions – and truly step up to be leaders in the enterprise.

For more information read this report from Ventana Research, “Collaborative Enterprise Planning: Improving the Business Value of Planning and Budgeting across the Enterprise.”

Follow SAP Finance online: @SAPFinance (Twitter) | LinkedIn | Facebook | YouTube

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How Millennials Make Decisions with Data in Microsoft Dynamics

March 14, 2020 CRM News and Info

Millennials have a different view on how to make decisions than previous generations. And now that 50% of the workforce is made up of millennials, knowing why and how they make decisions is crucial to having an effective business in the future.

Previous generations made a lot of decisions based on “instinct.” And while the modern workforce still needs human interpretation, they more so focus on data to make solid, confident decisions.

The Common Data Model underlying Microsoft Dynamics provides the foundation to centralize data and deliver consistent information across the organization. Visualizations help provide the context and relevance that support informed decisions. Here are just some of the benefits of this:

Instant Answers

No matter where they are working, employees can have a unified view of the financial and customer information to deliver real-time responses to the people they work with—both internally and externally.

Personalized, interactive data visualization

Microsoft uses Power BI as its business analytics solution and data visualization tool. Power BI delivers real-time insights from Microsoft Dynamics to provide visibility across the company so employees can easily connect, share, and analyze data from Excel queries, data models, and reports.

Artificial intelligence

In addition to connecting data from all departments, Microsoft Dynamics provides the gateway for the next generation of data management through artificial intelligence (AI) and augmented reality. Machine learning models can reveal insights from all data, including text, and images. Out-of-the-box Ai applications in Microsoft Dynamics can help employees gain new perspectives into things like predicting customer behavior or interpreting social and web interactions.

Mixed reality

Mixed reality lets employees work with cutting edge technology to visualize, collaborate, and learn. With Microsoft Dynamics mixed reality tools, workers can blend real and virtual worlds to produce visualizations, to share, imagine, understand, and design in real-time.

Millennials have grown up with information at their fingertips. They expect answers and solutions instantly to any question. In order to make proper business decisions, they need to have data easily accessible. Microsoft Dynamics gives the solution, making collaborating easy by connecting people on any device, from anywhere.

With the wave of new generation employees, the way work is being done is rapidly changing. To see why modernizing your business will help keep up with this change, download the full eBook “21 Reasons Millennials Prefer Microsoft Dynamics” at www.crmsoftwareblog.com/millennials to read 17 more reasons why Millennials prefer Microsoft Dynamics in the workspace.

Find a Microsoft Dynamics 365 Partner

By CRM Software Blog Writer, www.crmsoftwareblog.com

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CRM Software Blog | Dynamics 365

Under Pressure: 78 Percent of People Feel More Pressure from their Employer than Family When Making Big Decisions

February 6, 2020 NetSuite

Business executives are more anxious about big decisions at work than critical decisions at home that impact their family, according to a new study conducted by Oracle NetSuite. The new study, Unlocking Growth, which provides insights from more than 1,000 business executives in the UK, France, Germany, UAE, Benelux and the Nordics, found that 94 percent are overwhelmed by data when making decisions. Over a quarter are putting risk mitigation ahead of potential success to avoid impacting their career, and 21 percent rely on gut feel and intuition to make critical decisions.

“There’s a lot of talk about a changing economic, technological and political backdrop, but when you step back, organizations across Europe have an increasing number of growth opportunities if they can focus their time and resources in the right places,” said Nicky Tozer, VP EMEA, Oracle NetSuite. “To achieve that focus, organizations need to address the decision-making and planning challenges identified in this study so they can use data to adapt to change faster than their competition and unlock new growth opportunities.”

A Culture of Decision-Making Pressure

Executives across countries and industries are under immense pressure when making critical business decisions and as a result many are putting risk mitigation ahead of potential success.

Most executives (78 percent) said they experience more pressure when making a big decision at work than in their personal life.
Fears about negatively impacting revenue (40 percent), damaging personal reputation (22 percent), losing their job (17 percent), and adversely impacting co-workers (13 percent) are the top four areas executives are concerned about.
Risk aversion is even higher amongst organizations that define themselves as high-performers – 62 percent admit they actively pursue risk-averse decisions, even in the knowledge their choice may not be as successful.

An Unhealthy Relationship with Data

Information overload, time pressure and a lack of trust in senior management is strangling the decision-making process and leading executives to default to ‘gut feel’ to inform their decision-making strategy.

Almost all (94 percent) executives are overwhelmed by data during the decision-making process. Executives in France (99 percent) reported the biggest issues with data, while executives in the UK (92 percent) reported the least.
Time pressure and more complex processes are also making decision-making harder. 27 percent of executives have had less time to focus on critical decisions in the last year and 28 percent note more people have become involved in the process, an issue that was particularly prevalent in the UAE (51 percent).
Only 19 percent – falling to 12 percent in the Nordics – of business executives noted they trust senior management when seeking decision-making guidance. Colleagues (39 percent) and industry peers (21 percent) were the most trusted.
41 percent of respondents expect to turn to a robot as a source of support when making critical decisions in the next year. Executives in France (51 percent) were the most likely, while executives in the UK (33 percent) were the least.
67 percent acknowledge they are not making highly data-driven decisions, with UK executives (73 percent) the most likely to only partially consider data or default to “gut feel”.

A Positive Outlook for Growth and Message to Senior Management

Executives across countries and industries expect their organizations to grow, but highlighted the need to rethink the planning process to ensure data can be used to adjust business plans and that everyone is working towards a clear plan for success.

56 percent of executives expect their business to grow in the next two years. Executives from the UK were the most positive (63 percent) followed by the United Arab Emirates (57 percent), Germany (56 percent), Nordics (54 percent), Benelux (50 percent) and France (49 percent).
Retail industry executives (33 percent) were the most confident that their organizations will exceed growth targets followed by manufacturing (27 percent), distribution (22 percent), and software and technology (29 percent). Executives in professional services (16 percent) and nonprofit organizations (11 percent) had the least confidence.
Almost three quarters (74 percent) of executives say their organization is good at capitalizing on new opportunities, but there are serious concerns about the planning process. Only 31 percent say they are proficient at adjusting business plans based on data analysis and almost one quarter (24 percent) do not think senior management provides a clear plan for success, dropping to just 16 percent in the Nordics.

Methodology
For this study, NetSuite partnered with Raconteur to survey 1,050 manager level and above employees. Respondents originated from the UK (300 respondents), France (150 respondents), Germany (150 respondents), United Arab Emirates (150 respondents), Benelux (150 respondents) and Nordics (150 respondents) and represented small and mid-sized organisations from across a range of industries. Participants took part in an online questionnaire and were surveyed in October and November 2019.

About Oracle NetSuite
For more than 20 years, Oracle NetSuite has helped organizations grow, scale and adapt to change. NetSuite provides a suite of cloud-based applications, which includes financials / Enterprise Resource Planning (ERP), HR, professional services automation and omnichannel commerce, used by more than 19,000 customers in 203 countries and dependent territories.

For more information, please visit https://www.netsuite.com.

Follow NetSuite’s Cloud blog, Facebook page and @NetSuite Twitter handle for real-time updates.

About Oracle
The Oracle Cloud offers complete SaaS application suites for ERP, HCM and CX, plus best-in-class database Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) from data centers throughout the Americas, Europe and Asia. For more information about Oracle (NYSE:ORCL), please visit us at oracle.com.

Trademarks
Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners.

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Google’s ML-fairness-gym lets researchers study the long-term effects of AI’s decisions

February 6, 2020 Big Data

Determining whether an AI system is maintaining fairness in its predictions requires an understanding of models’ short- and long-term effects, which might be informed by disparities in error metrics on a number of static data sets. In some cases, it’s necessary to consider the context in which the AI system operates in addition to the aforementioned error metrics, which is why Google researchers developed ML-fairness-gym, a set of components for evaluating algorithmic fairness in simulated social environments.

ML-fairness-gym — which was published in open source on Github this week –is designed to be used to research the long-term effects of automated systems by simulating decision-making using OpenAI’s Gym framework. AI-controlled agents interact with digital environments in a loop, and at each step, an agent chooses an action that affects the environment’s state. The environment then reveals an observation that the agent uses to inform its next actions, so that the environment models the system and dynamics of a problem and the observations serve as data.

For instance, given the classic lending problem, where the probability that groups of applicants pay back a bank loan is a function of their credit score, the bank acts as the agent and receives applicants, their scores, and their membership in the form of environmental observations. It makes a decision — accepting or rejecting a loan — and the environment models whether the applicant successfully repays or defaults and adjusts their credit score accordingly. Throughout, ML-fairness-gym simulates the outcomes so that the effects of the bank’s policies on fairness to the applicants can be assessed.

ML-fairness-gym in this way cleverly avoids the pitfalls of static data set analysis. If the test sets (i.e., corpora used to evaluate model performance) in classical fairness evaluations are generated from existing systems, they may be incomplete or reflect the biases inherent to those systems. Furthermore, the actions informed by the output of AI systems can have effects that might influence their future input.

Google’s ML fairness gym lets researchers study the long term effects of AI’s decisions

Above: In the lending problem scenario, this graph illustrates changing credit score distributions for two groups over 100 steps of simulation.

Image Credit: Google

“We created the ML-fairness-gym framework to help ML practitioners bring simulation-based analysis to their ML systems, an approach that has proven effective in many fields for analyzing dynamic systems where closed form analysis is difficult,” wrote Google Research software engineer Hansa Srinivasan in a blog post.

Several environments that simulate the repercussions of different automated decisions are available, including one for college admissions, lending, attention allocation, and infectious disease. (The ML-fairness-gym team cautions that the environments aren’t meant to be hyper-realistic and that best-performing policies won’t necessarily translate to the real world.) Each have a set of experiments corresponding to published papers, which are meant to provide examples of ways ML-fairness-gym can be used to investigate outcomes.

The researchers recommend using ML-fairness-gym to explore phenomena like censoring in the observation mechanism, errors from the learning algorithm, and interactions between the decision policy and the environment. The simulations allow for the auditing of agents to assess the fairness of decision policies based on observed data, which can motivate data collection policies. And they can be used in concert with reinforcement learning algorithms — algorithms that spur on agents with rewards — to derive new policies with potentially novel fairness properties.

In recent months, a number of corporations, government agencies, and independent researchers have made attempts at tackling the so-called “black box” problem in AI — the opaqueness of some AI systems — with varying degrees of success.

“Machine learning systems have been increasingly deployed to aid in high-impact decision-making, such as determining criminal sentencing, child welfare assessments, who receives medical attention and many other settings,” continued Srinivasan. “We’re excited about the potential of the ML-fairness-gym to help other researchers and machine learning developers better understand the effects that machine learning algorithms have on our society, and to inform the development of more responsible and fair machine learning systems.”

In 2017, the U.S. Defense Advanced Research Projects Agency launched DARPA XAI, a program that aims to produce “glass box” models that can be easily understood without sacrificing performance. In August, scientists from IBM proposed a “factsheet” for AI that would provide information about a model’s vulnerabilities, bias, susceptibility to adversarial attacks, and other characteristics. A recent Boston University study proposed a framework to improve AI fairness. And Microsoft, IBM, Accenture, and Facebook have developed automated tools to detect and mitigate bias in AI algorithms.

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Big Data – VentureBeat

Appier raises $80 million for AI that improves marketing decisions

November 26, 2019 Big Data

Getting a customer onto a company’s website to check out its products is challenging enough in itself, but getting them to complete a transaction is a whole different ball game. Last year, 75% of all ecommerce orders were abandoned before finalizing the purchase — reasons include an overly complicated checkout process, limited payment options, hidden costs, registration friction, security concerns, and more.

This is one of the problems that Taiwan-based Appier is looking to help online retailers solve. Its AI-powered platform tracks customers’ activities across a website to improve the chances of them completing a transaction.

Today, Appier announced it had raised $ 80 million in a series D round of funding from Insignia Venture Partners, HOPU-Arm Innovation Fund, TGVest Capital, Temasek’s Pavilion Capital, JAFCO Investment, and UMC Capital. This takes the company’s total funding to around $ 160 million, following its 2017 series C round of funding which saw big names including SoftBank join the fray.

Tracking

Founded in 2012, Appier uses machine learning to crunch myriad data points in real time, such as the cursor position of a mouse, how the customer taps or swipes a screen, the amount of scrolling, and more — this is then used to determine their purchase intent. In tandem, Appier can also A/B test different campaigns to determine which ones are more effective in converting a casual window-shopper into a customer, which may include customized promotional offers. This is part of a product offering it calls AiDeal, which recently launched as a result of Appier’s acquisition of Japan-based Emin.

Appier raises $80 million for AI that improves marketing decisions

Above: AiDeal allows companies to A/B test different promotions.

It’s worth noting here that the platform isn’t exclusively about getting people to buy physical goods once they’re on a website. It can also be used to proactively target people who already have an app installed on their phone. For example, a video-streaming company that offers some free shows could use Appier’s AiQua platform to test and issue push notifications or in-app messages to drive subscription signups. Similar to AiDeal, Appier’s AiQua product was the result of its acquisition of an Indian startup called QGraph last year.

Elsewhere, Appier has long offered a product it calls CrossX Advertising, which can be used by retailers to, say, deliver better-targeted ad exposures to those most likely to convert — Audi, for example, used the platform to target test-drive ads at people aged 30 and over who had previously searched online for luxury cars.

In-house

Appier said that it develops its machine learning algorithms entirely in-house rather than using an “off-the-shelf” solution, and its models are trained through ingesting data from websites, apps, customer relationship management (CRM) software, and so on, which helps improve the machine learning model over time.

“The real-world environment — unlike that of a lab — is dynamic and diverse and ‘off-the-shelf’ algorithms don’t always cope with it well,” Appier CEO and cofounder Chih-Han Yu told VentureBeat. “Our clients need to be able to use our solutions to manage many different and fast-moving scenarios — different KPIs, varying data sources, etc. This means that our scientists spend a lot of time making sure our deep learning solutions can deliver optimal performance in any situation that our clients might face.”

With another $ 80 million in the bank, Appier said that it will push ahead with global market expansion and target its technology at new industries “beyond digital marketing.”

“Our latest investment brings with it new shareholders whose growth-stage experience will help us to scale faster towards our ultimate goal of revolutionizing the way enterprises adopt and leverage AI to grow, remain competitive, and manage continuous business transformation,” Yu added.

One example Yu provided was products to help companies automate the process of building AI models, enabling them to bolster their data science capabilities without having to hire a “complete data science team,” he said.

Data decisions

Appier’s methods of tracking customers on digital properties isn’t entirely a unique approach, with the likes of Contentsquare adopting similar techniques to tell companies why their customers may be abandoning their carts before completing a purchase.

Moreover, another thing Appier and Contentsquare have in common is that they’re both tapping a growing demand for platforms that crunch large amounts of data to improve decision making — this spans far beyond retail and marketing, and into areas such as insurance and even cities’ infrastructure projects.

“Appier is riding a strong long-term trend for enterprises leveraging data to make smarter decisions,” added TGVest Capital chairman DC Cheng. “Thanks to its unique use of AI technology in the digital marketing space, Appier has been a category leader since its inception and has the opportunity to expand into new corporate functions where data-based decisions are made.”

Including its Taiwan headquarters, Appier claims 400 employees across 14 offices in 12 markets in Asia Pacific (APAC), and Yu said the company is currently looking to expand to new markets, though it wouldn’t confirm whether one of those would be the U.S.

“We are planning to look beyond our current markets and explore opportunities in other parts of the world,” Yu said. “We look forward to sharing more news on this in the coming months.”

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Big Data – VentureBeat

Google’s AI explains how image classifiers made their decisions

October 15, 2019 Big Data

It’s often assumed that as the complexity of an AI system increases, it becomes invariably less interpretable. But researchers have begun to challenge that notion with libraries like Facebook’s Captum, which explains decisions made by neural networks with the deep learning framework PyTorch, as well as IBM’s AI Explainability 360 toolkit and Microsoft’s InterpretML. In a bid to render AI’s decision-making even more transparent, a team hailing from Google and Stanford recently explored a machine learning model — Automated Concept-based Explanation (ACE) — that automatically extracts the “human-meaningful” visual concepts informing a model’s predictions.

As the researchers explain in a paper detailing their work, most machine learning explanation methods alter individual features (e.g., pixels, super-pixels, word-vectors) to approximate the importance of each to the target model. This is an imperfect approach, because it’s vulnerable to even the smallest shifts in the input.

By contrast, ACE identifies higher-level concepts by taking a trained classifier and a set of images within a class as input before extracting the concepts and sussing out each’s importance. Specifically, ACE segments images with multiple resolutions to capture several levels of texture, object parts, and objects before grouping similar segments as examples of the same concept and returning the most important concepts.

To test ACE’s robustness, the team tapped Google’s Inception-V3 image classifier model trained on the popular ImageNet data set and selected a subset of 100 classes out of the 1,000 classes in the data set to apply ACE. They note that the concepts flagged as important tended to followed human intuition — for instance, that a law enforcement logo was more important for detecting a police van than the asphalt on the ground. This wasn’t always so, however. In a less obvious example, the most important concept for predicting basketball images turned out to be players’ jerseys rather than the basketball. And when it came to the classification of carousels, the rides’ lights had greater sway than its seats and poles.

The researchers concede that ACE is by no means perfect — it struggles to meaningfully extract exceptionally complex or difficult concepts. But they believe the insights it provides into models’ learned correlations might promote safer use of machine learning.

“We verified the meaningfulness and coherency through human experiments and further validated that they indeed carry salient signals for prediction. [Our] method … automatically groups input features into high-level concepts; meaningful concepts that appear as coherent examples and are important for correct prediction of the images they are present in,” wrote the researchers. “The discovered concepts reveal insights into potentially surprising correlations that the model has learned.”

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TIBCO receives Sirius Decisions ROI Honours Award 2019

October 14, 2019 TIBCO Spotfire

We are thrilled to announce that TIBCO has been chosen as a winner of Sirius Decisions’ annual Return on Integration (ROI) Awards in Europe. SiriusDecisions, the leading global business-to-business research and advisory firm, hands out their ROI Honours Award for those organizations that demonstrate outstanding achievements in sales, marketing and/or product alignment, based on the successful implementation of SiriusDecisions’ research, frameworks, and best practices to improve company performance and growth.

The award recognizes TIBCO Software’s marketing evolution from a product-led marketing strategy to a use-case focused go-to-market approach over the past couple of years. This journey started in Europe in 2017 and has now been adopted globally, supported by tightly aligned sales and marketing goals and proven by year-over-year growth both on the demand funnel, and in sales pipeline and revenue.

This week, at the Sirius Decisions Summit Europe in London, I will be presenting about how applying the Sirius Decisions’ models and methods have been the catalyst to a fundamental shift in TIBCO’s marketing strategy. Together with Emma Acton, Senior Director of EMEA Marketing, we will talk about how we transitioned to a needs-based, industry-focused marketing campaign architecture. We’ll discuss the major milestones of this journey, the tools and processes that played a crucial part in the change, and the lessons learned along the way.

The Return on Integration Honours Award is predominantly a recognition of the strong interlock between sales, marketing and product teams at TIBCO – because we are all #BetterTogether!

To learn more about the award and our presentation at the Summit, please visit the Sirius Summit page.

TIBCO receives Sirius Decisions ROI Honours Award 2019

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