Archive for the ‘Placement’ category

Do You Fear Artificial Intelligence Will Take Your Job?

July 10th, 2018

Artificial intelligence (AI) has been around longer than most people realize. The intent behind much of AI is to free us from mundane repetitive tasks, giving us more time to grow our intellects and businesses, with more interesting, evolving actions. We want what we want when we want it. AI offers us that access with speed and accuracy when we need it.

In London, self-driving robots deliver food. In Pasadena, California, a robot named Flippy can cook it. Last fall, an autonomous train made its way across the Australian outback for the first time, and Zhuzhou, China, began testing a trackless and driverless train that navigates city streets by means of lines painted on the road. From writing articles for The Washington Post to creating music, artificial intelligence is everywhere. And its adoption is rapidly becoming necessary for businesses to stay competitive.

How does this affect human employees? As co-founder of a company that utilizes artificial intelligence to provide customer support solutions, I believe that low-skilled jobs are most likely to be affected and most chances of being automated. White collar jobs are also at risk though with AI taking a bigger role in the financial industry.

But despite all this, the future for human employees may be much brighter than many recent predictions. While AI destroys jobs, it also creates them. And according to a report from the research firm Gartner, artificial intelligence is currently creating more jobs than it destroys, with a net increase of over two million jobs by 2025. This includes not only the obvious jobs such as software engineers but also low-level jobs such as training AI to recognize objects or human activity and many others.

AI may destroy jobs and it may create them, but it’s not always about man versus machine. AI can be at its best when it helps humans to perform jobs. For example, last year, Walmart announced it was beginning tests of shelf scanning robots at 50 locations. These robots are not intended to replace human workers but to make them more efficient. The robots free employees from the tedious task of walking the aisles looking for out of stock products and allow them to focus their time on filling the shelves, replacing items left in the wrong place and fixing problems that the robots notify them of. The goal here is to reduce the number of times a customer looks for an item only to discover an empty shelf.

In the pharmaceutical industry, artificial intelligence can take on tasks that human minds simply can’t do. According to a study from Tufts University, it can take over a decade and cost over $2.5 billion to develop a drug from start to approval and market. However, most drugs don’t make it to market, some failing early, but others failing close to the end when years and millions or billions of dollars have already been spent. AI can leverage the vast amounts of data regarding medicine and health, thus potentially lowering the rate of failed trials. It can also help find appropriate patients to participate in clinical trials, model the behaviour of molecules to help predict how they will behave in the human body, and find genetic biomarkers that allow medicine to be tailored to individuals.

Artificial Intelligence Needs Humans

In the above examples, artificial intelligence plays a part in preventing human errors. However, AI also still needs human oversight to prevent its own errors.

In July of last year, a bot designed to create phone cases based on popular image searches went terribly wrong and began creating cases with disturbing medical imagery and inappropriate images which were listed for sale on Amazon by the third party seller. In a far less amusing example of AI gone wrong, it took less than a day for Twitter to teach Microsoft’s AI account “Tay Tweets” to spout racism, sexism and love for Hitler. To prevent such malformed sentences, AI models need more training data and a proactive human oversight.

The stakes grow far more serious when AI operates heavy machinery or is involved in healthcare. Autonomous vehicles have been lauded for their potential to reduce collisions, 94 percent of which are caused by human error, according to the NHTSA. After all, autonomous vehicles won’t drive while drunk, tired or distracted. However, autonomous vehicles have already failed to prevent two deaths despite the presence of safety drivers. The safety driver of a 2016 Tesla S may have relied too heavily on the autopilot. Data showed that he ignored seven warnings to return his hands to the wheel before the vehicle failed confused the white of a tractor-trailer for open sky and drove right into it. Though we all believe, the autonomous vehicles would become the norm in the next decade, safety regulations and substantial human oversight are very much needed and will be needed for the foreseeable future.

From exploring places humans can’t go to finding meaning from sources of data too large for humans to analyze, to helping doctors make diagnoses to helping prevent accidents, the potential for artificial intelligence to benefit humans appears limitless. There is valid concern that even as AI saves lives and helps businesses thrive, it will destroy livelihoods. Without a doubt, AI is taking over jobs once done by humans. However, it also creates jobs, and AI needs people to train it and watch over it. At its best, AI works with people instead of in place of them — removing the tedious parts of jobs so employees can focus on better things, doing tasks that humans were unable to, and helping employees better do their jobs.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Priya Mohanty.

A Crossroads: Artificial Intelligence And Advertising

July 2nd, 2018

According to Elon Musk, artificial intelligence (AI) is “our biggest existential threat.” Whether that is fact-based or hyperbole is determined by the reader’s perception of Musk as either the real-life Iron Man or a classic hype marketer. Whether Elon is trying to wake humanity before it’s too late or simply sell tickets on a Mars-bound Tesla is beside the point. The focus here should be that Elon Musk has discovered and successfully exploited the intersection of AI and advertising and so can you.

We have reached an inflection point in the advancements in, and democratization of, artificial intelligence. The technology allows businesses to harness its value daily to realize new business development opportunities and capitalize on tangible results — chief among them is a more intelligent way to advertise products and services, verify campaign efficacy and measure return on investment. On top of this, and perhaps in direct contrast with Musk’s comments, legacy radio and television are poised to be the prime benefactors of this AI-driven advertising revolution.

For many years, digital advertising has been the safest and arguably most sound bet in advertising. Cookie tracking, intellectual property (IP) targeting and other technologies allow brands and agencies to gather user and audience data in order to target their advertising campaign placement, track overall efficacy and measure return on investment (ROI) down to the click. This level of ROI transparency is tough to argue with and ultimately led to digital ad spend reaching $209 billion worldwide, which accounted for 41% of the market in 2017. Digital finally eclipsed traditional TV spending, which brought in $178 billion, or 35% of the market. With no definitive way to target, track and measure advertising efficacy, radio and television have been struggling to compete with digital until now.

While digital advertising currently makes up the lion’s share of brand and agency advertising spending, traditional radio and television companies are fighting back in a big way thanks to artificial intelligence. Developments in natural language processing, logo recognition, object detection and other AI technologies have enabled radio and television broadcasters to bring structure to a medium that has been heretofore impossible. With every word, logo, object and face indexed in near real-time, radio and television content becomes just as searchable, trackable and actionable as digital content. This is critical because without true structure — a temporal record of exactly what aired — agencies and brands had been struggling to successfully target, engage and unlock the value hidden within radio and TV. Now, with AI, legacy challenges have fast blossomed into new opportunities and there is plenty of nectar to go around.

Just take it from top media agency Carat’s chief compliance officer (CCO) Shannon Pruitt, who, in speaking to CNBC regarding product placement within traditional distribution windows, said, “Advances in audience targeting, the understanding of the role of product integration, as well as the focus on measurement capabilities to prove ROI, while still not comprehensive, has elevated the acceptance and pursuit of the opportunity to be part of the story.”

In other words, the convergence of AI technology used for audience targeting and ROI evaluation with the shifting desires of TV and radio audiences away from traditional interruptive-based 30- and 60-second commercials, has agencies and advertisers alike rushing back to TV and radio to cash in on new in-content branding opportunities. According to PQ Media, the “value of U.S. product placements will reach $11.44 billion in 2019,” surging up from $6.01 billion in 2014. This type of growth for traditional mediums who had been left with mere scraps in recent years is one very concrete example of how AI, when leveraged appropriately, can revolutionize an industry overnight.

So whether you are looking to streamline operational efficiency, innovate products or unlock additional revenue streams, you might consider taking a page out of Elon’s book and look towards artificial intelligence.


Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Logan Ketchum.

How Tech Enterprises Handle Big Data on Open Source and Ensure User Privacy

June 26th, 2018

The term “big data” gets thrown around a lot, especially taking into account its importance for driving AI technology. Finding ways to build scalable systems that provide valuable insights into what you’re doing well and what you could be doing better is imperative to maintain a competitive edge. And, as big data, artificial intelligence and machine learning become more advanced and interconnected each year, these scalable systems become more and more valuable.

When PicsArt was founded in 2011, the online landscape and the world of data collection, management and analysis were much less sophisticated. Since then, many startups have risen while others have faltered, and those that have found success were largely companies that were able to adapt to an increasingly data-driven marketplace. Today, our users generate a staggering 10 terabytes of data every single day. On a global scale, PicsArt has a medium- to large-size big data cluster with most of the large-size cluster functionalities enabled.

It was evident that we were stepping into the big data arena when our data met all four characteristics of big data: volume, velocity, variability and complexity. Once the volume of data we were dealing with was too large to fit into a relational or other standard database, the die was cast and we jumped into the big data scene with optimism and gusto. Besides that, because AI and machine learning became a mainstream technology, we were able to fully use it to benefit our users.

Adapting To A Big Data Mindset

When most people think about big data, they often imagine that the technical side would be the most difficult, but we found out through trial and error that approaching problems from a technical side first isn’t always ideal. Big data offers nearly endless possibilities, but if you don’t have a clear understanding of specific use cases and goals, you can unnecessarily prolong the development process. Since our system was constructed without a clearly delineated list of use cases, our data architects had to design it to handle as many future use cases as possible. The end result was a working system, with extensive support and capabilities, but the rollout time was longer than it could have been had we defined things better from the start.

Getting used to the sheer scope of data was a learning curve as well, especially since there was a lack of a big data community at the time. Initially, we placed responsibility for cleaning data on a single centralized team, which we quickly discovered would never work due to the constant barrage of thousands of events happening across multiple apps. Getting the data clean, we discovered, requires simultaneous efforts from the tech and business teams — it only works if everyone is on the same page. Big data is considered the new oil nowadays, but it’s also a huge challenge in terms of how to prepare it, process it, store it and most importantly, turn it into applicable knowledge. To make that happen, it’s important to define the most common use cases within the product and align technical and business team efforts from the beginning. Overall, maintaining flexibility, learning from mistakes and adapting was essential to getting past the first step to becoming a big data company.

Finding The Right Tools For The Job

As the value of big data became more evident, conferences started popping up, giving innovators and companies a way to gather and share strategies. Open source solutions for data analysis and collections became more common and more robust, and it got easier to find the right technology. The lesson that start-ups can take away from all this is to take advantage of the big data community that exists now and do so with direct aims in mind.

In a wide range of tools, it’s really important to find those that fit your business needs. That can be done only empirically depending on the size of your company. It is important to discover tools for data processing, data analysis, crash monitoring and infrastructure monitoring.

Using Data To Fuel Innovation

Each piece of data my company collects falls into one of three categories: user device info, user behaviour and uploaded images — complete with editing logs and intermediate steps. The metadata we collect is used to directly improve the user experience by responding to the way people use our app and then creating the tools they want.

Privacy is definitely an important topic for every tech enterprise that deals with a large amount of data. As an organization operates globally with data on citizens in European Union countries, they must comply with strict new rules around protecting customer data: The General Data Protection Regulation sets a new standard for consumer rights regarding their data. All of our users have the opportunity to adjust their preferable privacy settings and make sure they are comfortable with the data they are sharing with us.  

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Hovhannes Avoyan.

Organizations Striving to Close the Data Science Skills Gap

June 18th, 2018

Big data is undoubtedly one of the hottest trends of our age, and the promise of enormous amounts of data to fundamentally transform how our organizations operate is considerable.  For many however, the promise remains just that, with numerous barriers holding them back, whether it’s a lack of board level buy-in or poor quality data.

Arguably the most substantial drag on our efforts however has been a lack of skills.  It’s a situation that is likely to see companies aim to triple the size of their data science teams in the next few years.  That’s the finding of a recent paper from ESADE researchers.

The researchers examined over 100 Spanish companies from across a range of sectors, most of which had over €200 million in turnover.  The results revealed the long way we still have to go before data is at the heart of organizational behaviour.

Slow progress

Despite big data being technologically feasible for several years, over half of the organizations revealed that they are yet to have a culture of data-based decision making, whilst 40% admitted that they don’t have a specific leadership role for data.

This reticence is important, as the study found that companies with a more analytical culture performed better than those without.  This was reflected in both their financial performance and the perception of staff at the companies.  Indeed, some 78% of companies who were regarded as very analytical thought that this culture had a significant impact upon their performance.

The study found that data professionals tended to fall into one of two categories:

  1. Data scientists, who tend to perform advanced analyses.
  2. Data managers, who provide the business vision to connect these analyses to the strategy of the business.

The typical data team would have between 5 and 20 members, but pretty much every organization reported finding it difficult to find the talent they needed.  Despite these recruitment challenges, the majority of organizations wanted to considerably increase the size of their data teams in the next three years, with three times as many data scientists and 2.5 times te number of data managers.

Train or recruit?

The desire for data science skills is clear, but this study suggests that most companies want to hire in external talent, or in other words the finished article.  This strategy would be fine except by all accounts, that talent isn’t currently existing in the marketplace, so there appears to be an inherent hope that external bodies will train people for them.

A post was written previously about a similar issue when it comes to artificial intelligence skills, and data science and AI are so intertwined that the same surely applies.

Rather than attempting to hire in the finished article in an increasingly barren marketplace, companies are surely better off investing in data-science training and therefore upgrading their existing talent pool.  This approach has numerous advantages, not least of which is raising data skills across the board at a time when a growing number of organizations are attempting to democratize data science capabilities across the workforce rather than concentrate it within a data science function.

Organizations can achieve quick initial results by identifying employees with existing programming, analytical and quantitative skills and augmenting them with both the latest data-science skills and access to powerful tools, such as Python and Hadoop.

Spreading the availability of data education across the business, into marketing, finance, engineering and various other functions provides data literacy to people from various backgrounds.  This in turn will help to spread the data-driven culture that data advocates so crave.

A good example of this in practice is the Data University that Airbnb have created to provide anyone who wants to learn about data an opportunity to do so.  Already the company has trained over 500 (or 1/8th of the workforce) employees, with dividends already being reaped in the shift towards data-based decision making.

There has never been a better time to invest in the skills and talents of your workforce, with data promising to transform functions and processes throughout organizations that are already experimenting with a range of data science and machine learning initiatives.  Expertise is the principle barrier holding these back, so now really is the time to invest in the training that will bridge that gap.


Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Adi Gaskell.


SAP Mounts Formalized CRM Drive

June 11th, 2018

SAP has formalized its approach to Customer Relationship Management (CRM) by consolidating upon recent acquisitions and integrating these functions into its existing stack of database and data analytics technologies.

In specific terms, SAP has now brought together acquisitions including Hybris (acquired in 2013 – CRM and commerce software), Gigya (acquired in 2017 – customer identity management technology used to manage customer profiles, preferences, opt-ins and consent settings) and CallidusCloud (acquired in 2018 – technology that links salespeople with information related to pricing, incentives & commission all linked to a firm’s Enterprise Resource Planning (ERP) systems) — the combined sum of these parts will now be known as SAP C/4HANA.

As Forbes writer Bob Evans notes here, the overall technology proposition here is a direct play (Evans calls it a ‘head on assault’) at Salesforce, but with what SAP claims to be a more holistic connection to an enterprise’s deeper software stack and ERP systems, SAP’s bread and butter. This makes it CRM engineered more directly into a business supply chain. If you believe the marketing, this is what all the vendors like to call a 360-degree view of the customer.

With SAP’s existing business suite being labelled SAP S/4HANA, the firm has obviously adopted the same naming convention replacing the S-for suite with C-for CRM. The company’s drive to build a more established CRM offering will see it go head to head against not just Salesforce, but a selection of established players in this space including Oracle, Dynamics 365, Verint, Pegasystems and others.

Customer Experience division (the new SAP grouping that includes Hybris, Gigya, Callidus and other elements) president at SAP is Alex Atzberger. Suggesting that there have been four eras of CRM through the ages, Atzberger details them as:

  1. Basic customer sales-based lead optimization systems.
  2. So-called ‘point’ solutions designed to address one specific CRM issue.
  3. Cloud-based systems.
  4. More intelligent holistic connected CRM systems that connect the customer experience to the actual supply chain that an enterprise operates on a day-to-day basis.

Lamenting what SAP CEO Bill McDermott has called the “sales-only focus of legacy CRM solutions”, SAP thinks it can offer a new notion of CRM that exists in the 4.0 age. This is CRM that is more intrinsically engineered into (and integrated with) a customer’s wider software stack of applications and database management systems – and indeed the enterprise demand and supply chain.

“SAP was the last to accept the status quo of CRM and is now the first to change it,” said McDermott. “The legacy CRM systems are all about sales; SAP C/4HANA is all about the consumer. We recognize that every part of a business needs to be focused on a single view of the consumer. When you connect all SAP applications together in an intelligent cloud suite, the demand chain directly fuels the behaviours of the supply chain.”

In line with its new CRM offering SAP has also announced the SAP HANA Data Management Suite. This is software designed to combat what has been called ‘data sprawl’ resulting from firms who operate with highly distributed data that exists in lots of different locations, on different devices, on different platforms, in different states (structured, semi-structured and unstructured) and in different business workflows and business processes.

The SAP C/4HANA suite will offer full integration with SAP’s business applications portfolio, led by the SAP S/4HANA ERP suite.

Crowd-service: more help, from ‘any’ employee

There’s one other add on here for customer service. SAP has also acquired Switzerland-based Coresystems AG to improve field-service customer experience, especially in the manufacturing, energy, high-tech and telecommunications industries. This software service provides scheduling for customer-service requests and uses artificial intelligence-powered crowd-service technology. SAP insists that this broadens the ‘service technician pool’ (those people able to fix any particular problem that occurs in a company during its working day) to include company employees, freelancers and industry partners. The ‘crowd service’ concept means that enterprises can assign the best-qualified technician (or person able to help) to each service call by taking into account expertise, location and availability.

“All systems rely on data, yet the challenge facing companies today is distributed data — data that is not just in transactional systems but scattered across products, machines and people. It is about data that must be ingested, prepared and made enterprise relevant. SAP HANA Data Management Suite enables enterprises to turn massive amounts of data — both structured and unstructured — into valuable, usable knowledge, no matter where it resides,” notes SAP, in a product launch statement.

The SAP C/4HANA portfolio includes SAP Marketing Cloud, SAP Commerce Cloud, SAP Service Cloud, and SAP Customer Data Cloud (including the acquired Gigya solutions) and SAP Sales Cloud (including the acquired CallidusCloud solutions). Additionally, SAP Sales Cloud unites the SAP Hybris Revenue Cloud solution and SAP Hybris Cloud for Customer (comprised of SAP Hybris Sales Cloud and SAP Hybris Service Cloud solutions).

The SAP Hybris name (along with other acquired firms noted in this story) will now be retired to consolidate under the SAP Customer Experience business unit.

The real challenge here is…

Whether the next generation of CRM actually results from one vendor firing pot-shots or thinly-veiled swipes at one another or not, the big question here will come down to implementation, integration and interconnectedness of the systems being built.

As already suggested here, success in the 360-degree connected CRM world is a question of real end-to-end real-time synchronization between the demand chain and supply chain. That means using ERP and CRM — and a list of other favourite tech industry acronyms including Field Service Management (FSM), Human Capital Management (HCM), IT Service Management (ITSM) and more – and being able to access the data that resides in the clouds serving those functions.

Unless we the humans can get access to the right data, in the right cloud services, serving the right business processes, in the right configuration patterns… then we won’t be able to physically get our developers to code the right functional ‘scripts’ into the codebases that run the so-called ‘smart’ (CRM or otherwise) applications of the future.

There’s a gap in between pure theory and applied empirical success here and SAP will obviously now be working hard to make sure it has customer reference points to convince us that its vision holds water. Claims that CRM is dead and that we can now shout long live 360-degree ERP CRM require deeper analysis and the journey is just starting. This revolution will be televised.


Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Adrian Bridgwater.

Artificial Intelligence: The Gatekeeper between Brands and Consumers

June 5th, 2018

Artificial intelligence’s role in marketing has grown immensely over the past year– and we have only started to scratch the surface of its mammoth impact. AI is quickly (and permanently) changing marketing as we know it, and creative marketers must adapt to deal with the ultimate consumer bodyguard.

As virtual assistants get to know their owners better than they know themselves, they will be able to not only make product recommendations but carry them out without asking their owners. For example, within the next few years, a virtual assistant will know its owner’s lifestyle, preferences, financial position, risk appetite, personality makeup and schedule so well that it will do the grocery shopping, trade stocks, purchase new products, buy books and schedule appointments — without ever asking for permission.

While it may be scary to think about, this new reality is just around the corner. Marketers need to start acting today for this mega-disruption because marketers will soon have to market more to virtual assistants and less directly to customers.

A Matter Of Trust

But how could anyone ever give AI that much leeway over their life and choices? One word: trust. We all know that trust is earned, not given. As Alexa and Bixby start to make their owners’ busy lives easier, happier and more efficient (not to mention saving them time and money), consumers will trust their virtual assistants with more and more decisions. Here’s why marketers must act creatively and decisively: People will start trusting their virtual assistant more than brands.

We’re all aware of the sharp decline of consumer trust in CEOs and brands over the past few years. According to the 2017 Edelman Trust Barometer, the credibility of CEOs fell by 12 points last year to 37% globally, and just 52% of respondents said they trust businesses. According to a 2017 McCann survey, 42% of Americans find brands and companies less truthful today than 20 years ago. Talk about perfect timing for AI to fill the trust void in business leaders and brands today.

Taming the Beast

How can marketers work with and circumnavigate the AI beast (there is no slaying it) that stands between them and their customers?

  • If you can’t beat ‘em: Marketers should never surrender to AI. Instead, we must work with this gatekeeper. Virtual assistants will accept money from companies that want to have a voice on their platforms. This represents the transition from marketing directly to your customer base to marketing to virtual assistants in the very near future.
  • Messaging and imagery: These two aspects, especially when shared through video, are the most powerful and convincing tools in the modern marketer’s toolbox. Do not underestimate the role that emotion plays in the decision making process, regardless of channel, format or audience.
  • Communicating trust: In the future, imaginative marketers and designers can put their companies, brands and messages above virtual assistants by communicating that, although there is nothing wrong with hearing out Alexa, it is not the be all and end all. Human interaction will always trump technology and should be more trusted.
  • Human interaction: Face-to-face interactions between marketers and their customers should be occurring frequently, and this will be even more critical in the future as AI digs in as the gatekeeper to consumers. The nature, tone and outcome of a conversation with a marketing professional is entirely different from that with a sales professional. Marketers need to step up their interactions to not only learn what makes customers tick but to be the human interaction in an increasingly non-human world.

Upcoming Trends

AI is going to torch just about every part of marketing, branding and the buyer journey as we know it, forcing marketers to completely rethink everything they do if they want to stay competitive in the coming years. Below are four trends we will likely see in the coming few years as a result of AI innovation.

  • Decline in SEM: Text ad spending will decline sharply with the rise of virtual assistants and voice-based searches. A January 2018 Forbesarticle referenced a report that found that voice searches increased 35-fold between 2008 and 2016. Marketers will spend much less on traditional SEM in the future and more on virtual assistant platforms to ensure their brands are represented.
  • Increase in direct mail: What better way to combat today’s technology than with yesterday’s workhorse? However, dated marketing methods (e.g., postcards and envelopes) need a refresher to remain relevant. To get noticed amidst the junk mail, direct mail needs to stand out. Think creatively designed boxes that beg to be opened filled with cool, personalized stuff (for example, an inexpensive audio gadget that plays a message from the company CEO), non-sales messaging and directions to download the company’s latest virtual reality experience.
  • Decline in traditional research: Marketers will collect most of their (non-face-to-face) customer research from virtual assistants in the near future, providing valuable demographic, psychographic and lifestyle information on their target markets. Web analytics, surveys and social media data will still be valuable but will not be able to provide the same rich data that virtual assistants offer. This type of research will be expensive but necessary to help paint the most accurate picture (or buyer persona) of your customers.

Marketers’ jobs will become even more challenging in the coming years, forcing them to engage their customers more frequently to build better human relationships in a less human world.


Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Christian DeGobbi.

Testing At the Speed of Now

May 28th, 2018

We read about artificial intelligence (AI) revolutionizing marketing, but most marketers are still asking “What is AI? And what exactly is it doing for marketing?”

One very practical answer is that AI has accelerated market testing. Obsolete are the elegant, three-month experimental designs — the pre-test, post-test, test-market versus control group. Today, AI enables marketers to test in real time and take advantage of the rapidly changing market/channel environment.

Control Groups in the Cloud

Much of the rigor of market testing comes from defining comparable test and control groups.

Back when TV advertising was the main marketing weapon, control and test markets were geographically defined — areas of dominant influence (ADIs), where TV advertising could be managed. Today, thanks to data mining and computer algorithms, test and control groups can be very accurately defined and maintained in cloud-based data sets.

Control and test groups can be created in which for every member of one group, a mirror image, twin customer is found for the other.

Comparability is also much more refined today. Computers armed with AI capture the hundreds of characteristics and behaviours of each customer as they occur moment by moment and segment customers by finding the patterns of demographic and behavioural characteristics. Computer learning algorithms, another modality of AI, continually sift and sort and analyze the roiling sea of digital transactions.

Short and Long-Term Control Groups

The experimental design process starts with identifying control groups for each in a series of marketing campaigns.

The next step is to define stable, long-term control groups. These control groups allow for multiple touches across multiple channels. They are larger in size than campaign-level control groups, which means they permit more robust and definitive conclusions than are possible at the campaign level. A stable, long-term control group provides the marketer with the ability to understand the impact of marketing across a series of campaigns over time.

Once the campaign-level and stable control groups are defined they need to be updated and maintained. Customers are constantly sending new signals. The AI algorithms detect and analyze them and enable the marketer to respond appropriately. Machine learning creates and constantly updates predictive models to gauge the likelihood of the customer’s next action. This modelling helps the marketer understand where customers are in their buyer’s journey and which offers are most likely to get them to purchase. The models enable the marketer to treat high near-term purchase probability customers very differently from those who are less likely to purchase in the near term.

With model-based testing, the marketer can set up and continually refine complex business rules where customer behaviour is interpreted as they respond to email, interact with digital properties or engage with social channels.

Once the models and rules are tested and established, the marketer can create data-driven conversations with customers. Their marketing strategies and campaigns can cater to individual customers in the moment in ways the marketer knows they will respond favourably to.

AI Is Just a Tool

While it is tempting to believe that AI enabled marketing can take the place of the marketing department, in reality, AI is just a tool. It needs to be used by experienced marketers with good judgment who:

  • Develop communications and campaigns that deliver real value. The marketer must understand who her customer is, so offerings can be informative, helpful, approachable, generous and, most of all, respectful of the customer’s precious time.
  • Have the confidence to move fast. Get ahead of the audience and competition with a strong bench of kits with trigger-based solutions — to deploy in real time based on test results.
  • Know when to update and evolve market segments based on AI and computer learning.

AI-enabled tools give the marketer the ability to:

  • Quantify the probability that specific customer behaviour actually precedes, leads to or signals a future behaviour.
  • Quantify uplift in desired customer behaviours and incremental impact on revenue generation.
  • Move fast to design, implement, assess and revise tests of her marketing strategy.

Rather than replacing the marketer, AI enabled testing at the speed of now helps marketers apply their expertise and experience to implement real-time marketing.


Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor KN Kasibhatla.



Why You Must Treat Artificial Intelligence (AI) As A Very Special Technology

May 21st, 2018

There are lots of technologies that attract our attention – and money – these days. We’re obsessed with blockchain, cryptocurrency, IOT, big data analytics, cybersecurity3-D printing and drones. We’re excited about virtual reality, augmented reality and mixed reality. We love talking about driverless cars, ships and planes. We can’t wait for 5G and Wi-Fi domes that solve all of our network access problems; and while we’re getting a little worried about social media and privacy, we’re still addicted to our ever-more-powerful smartphones. We buy everything online. We’re into wearables. But there’s one technology that we all need to embrace: artificial intelligence (AI). While there are other families in the disruptive digital technology world, this one is special and one you cannot afford to treat as just another emerging technology. AI powers, amplifies and therefore supersedes them all.
Why so special?
First, AI is special because it’s more than one technology. In fact, it’s a family of technologies. Secondly, AI is special because its application potential is so wide. Next, AI is special because it learns and sometimes even self-replicates. AI’s also special because it satisfies ROI models of all shapes and sizes. Finally, AI is everywhere: which companies – and countries – are not investing in AI? There’s a bona fide arms race underway among the players (which shows no signs of slowing anytime soon).
What is AI?
AI includes at least machine learning, deep learning, image recognition, robotic process automation, natural language processing, text mining, vision systems, speech systems, neural networks and pattern recognition, among other methods, tools and techniques that according to the father of AI, John McCarthy, represent “the science and engineering of making intelligent machines, especially intelligent computer programs.”
What Can AI Do?
There is very little AI cannot do. The range of applications is staggering, including all of the vertical industries and every business process and model that supports them. AI will profoundly impact healthcare, transportation, accounting, finance, manufacturing, customer service, aviation, education, sales, marketing, law, entertainment, media, security, negotiation, war and peace. No industry or process is safe from the impact that AI – across all of its components – will have in the short-run and especially over the next seven to ten years. Keep in mind also that AI will integrate across business and technology architectures, data bases and applications.
What will AI change?
Everything. The timing – as always with the adoption of emerging technologies – is debatable. But the changes will not all be good. AI empowers good and evil. Note the ease with which fake news can be created and disseminated by intelligent “news” creators, and how easy it is for smart bots to service personal and professional confirmation biases intended to manipulate thinking and behaviour. At the same time, good bots will make much of our personal and professional lives more efficient and productive, freeing us to pursue other activities. Will AI eliminate jobs? Of course, and this time the elimination of jobs will include so-called knowledge workers as well as the traditional manufacturing jobs we associate with automation and robotics which will increasingly behave in unsupervised contexts. Much of this capability will arrive simultaneously across whole industries, such as the automotive industry which will utilize robotic AI to manufacture driverless cars and then manage their movement across cities and towns across the world. Similarly, healthcare will be impacted from lifestyles, monitoring, diagnosis and treatment. No, AI will not kill us, but it will augment and replace many of us in the workplace. Again, it’s a question of when, not if, but impact will be sweeping and will likely happen much faster than many analysts predict. Regardless of how bullish or bearish you are about displacement, it’s safe to say that tens of millions of jobs – and knowledge-based careers– will be impacted — and in many cases eliminated — in the next five-to-seven years.
Who’s playing?
Who isn’t? Investments in all things intelligent are unprecedented. All of the major technology companies are heavily invested in the technology, but the most important investment portfolio belongs to whole countries which have declared AI as a strategic national objective. China, for example, has defined AI as one of its core industries.
What to do about AI?
If your company is not already investing in AI, it’s way past time. Step one is the modelling of your current and aspirational processes informed generously by the potential of AI and predictions about the evolution of your industry. Elaborate process models should be developed, tested, simulated and inventoried to inform your AI pilot agenda. The simplest way to build this agenda is to identify the processes most amenable to AI and simulate the impact intelligent systems might have on the costs and benefits of the target processes. The most robust simulations should rank-order the processes that should be piloted with new technologies. Corporate partnerships should also be aggressively pursued, especially since AI is so broad. Companies need enabling partners that, for example, provide AI development and application platforms (which will come from their cloud providers in most cases). University partnerships are also valuable. Companies should befriend AI start-ups. Many AI technology companies will scan the start-up terrain for acquisition targets. Just as many established companies will scan the same environment for the same reason.

National governments should strategically commit to AI. This means that the national research laboratories – like the National Science Foundation in the US – should receive additional, directed funds to pursue a broad program of research and development that assures a global presence in the development and application of AI, which should be declared a 21st century moonshot.

Do you have the right AI talent in your company? If you administered an AI IQ test back at the ranch, how well would the team do? If your company is like most, you will need to invest in AI education and training starting with executive education about the strategic role of AI in your industry.

Finally, brutal process assessments are necessary to optimize AI. No process should be exempt from what AI might offer. But make no mistake, much of this is political. There will be Luddites who challenge the applicability and power of AI. But AI is different from the other “stand-alone” emerging technologies. AI can disrupt your business in ways you need to identify – before you’re disrupted. So do you need an AI “czar”? You absolutely do.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Steve Andriole.

Embracing AI and Automation Can Make Your Job Better

May 14th, 2018

By now, many people have heard of the impending “fourth industrial revolution,” and there’s more than a little trepidation surrounding the subject. Just as mechanization and the steam engine changed the landscape of manufacturing, the arrival of interconnected machine learning systems will inevitably transform the way products are made and sold.

The fourth industrial revolution may spark the fear that jobs will disappear. Emerging technologies will have a far-reaching impact, affecting almost every industry and economy on our globalized planet. However, artificial intelligence will serve in large part to augment – not replace – the jobs humans perform in the workplace. The emphasis on AI is to eliminate mindless busywork, making people more efficient, productive, and valuable.

John Carney, senior vice president of industries, communications and media at Salesforce, acknowledges there will be significant changes in the job market but explains that this has been the case throughout history: “If you look back in history with these paradigm shifts, these transitions, the data says that we created way more jobs than were eliminated. So, I think that’s going to happen again. There is going to be a transition.”

Forward-thinking employees should embrace this transition. Instead of being fearful of change, look to the following scenarios to consider how AI can improve your role.

  1. Marketers can finally give customers the experience they crave.

Unless you’re running a 24/7 call centre, your employees have to go home at some point. Enter AI, and the chatbot, which can give any number of customers the information they’re looking for at any time – even when the customer isn’t sure how to contact you directly.

Naveen Rajdev, chief marketing officer for Wipro Limited, a leading global information technology company, describes AI as the future of digital marketing, allowing business leaders to predict customer needs and respond. He sets the scenario: “It’s the middle of the night, and a customer mentions your brand in a tweet to ask about your store’s holiday hours. All of your employees are home asleep, and the customer assumes he’ll have to wait until morning for an answer. But then, just seconds later, he receives a personalized response answering his question.”

While AI won’t be able to answer every question, it will drastically reduce the burden on your human customer service reps. As a result, you can cut those lengthy hold times and make a big improvement to the overall customer experience.

  1. Programmers can focus more on the big picture and less on small jobs.

Programming can be tedious work, but AI is liberating programmers and allowing them to focus on the work that they enjoy. Praful Krishna, CEO of Coseer, describes how AI has improved his team’s workflows: “Our team members actually look forward to running modules that involve such tedious programming because now they can play with the AI, train it, monitor its results, and give feedback. It’s almost as if they have a team working for each of them.”

Utilizing AI has been a distinct win, but Krishna points out that there is a “trust curve.” New employees will manually check AI performance, which largely defeats the purpose of having it. Still, they eventually learn to trust it, and he reports that the process pays off in a matter of a few months or even less.

  1. Salespeople can shorten the sales cycle and bring in more qualified leads.

Sales departments have come a long way from simply making as many cold calls as possible. Today, there are many tools to ensure that valuable time is spent only on the most promising leads. AI has the potential to make this screening process even more precise and to impact the customer experience, improving the rate at which conversions are made.

AI can also take on time-consuming manual tasks that are nonetheless necessary. Jen Tadin of Gallagher, an insurance brokerage, explains how AI frees up time and improves productivity: “AI is used to prefill cumbersome underwriting questions that are required to get a quote. Much of the underwriting information is public domain; therefore, AI is critical in improving ease of doing business for both the prospect and company and assists us in closing more deals in less time.”

Using AI not only improves the experience and increases productivity for Gallagher employees, but it makes things easier and more convenient for customers as well.

  1. HR can speed up recruitment and training

Especially in large organizations, hiring hundreds or thousands of employees can create a massive backlog for HR. Combing through cover letters and résumés takes a huge amount of time that could be better spent elsewhere. Fortunately, AI can take on some of the burden.

Chatbots are able to perform basic background checks to immediately weed out undesirable candidates, while other AI tools can look for specific qualifications. Argentinian credit firm uses deep learning to screen applicants, saving HR employees as much as two-thirds of their time and allowing them to focus on higher-level tasks.

Cristian Rennella, the company’s HR director and co-founder, says he’s seen productivity increase by 21.3 percent: “We are sincerely surprised with the results and very happy because boring, manual, and repetitive tasks today can be performed automatically thanks to AI. And we can invest more time to interview the best candidates.”

Looking for more reassurance? Despite the proliferation of AI, the U.S. is continuing to experience record-low unemployment rates. The high-touch jobs that can only be performed by humans aren’t going away; humans still have a corner on emotional intelligence.


Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor William Arruda.

Blockchain As An Application Platform

May 8th, 2018

Many business use cases can be improved and/or solved by using distributed ledger technology. It can be used in many cases where trust services are needed by business applications. This can be utilized by using blockchain technology as an application platform to build the underlying trust infrastructure of the system.

Although Bitcoin, the first real implementation of blockchain, is a decentralized currency and payment system, the underlying constructs that form the basis of the system do not have to be limited to payment transactions, accounts, balances or users. Instead, blockchain technology in Bitcoin is nothing more than transactions secured and executed by a scripting language using cryptographic methods. This means that blockchain is a platform with a scripting language that can solve many use cases other than just cryptocurrencies.

This property of blockchain led to smart contracts, an innovation presented by the cryptocurrency known as Ethereum. In the case of Ethereum, developers can create private cryptocurrencies and contract-based applications using a Turing-complete language, which allows businesses to use this language to set their own rules and policies in such applications.

The distributed ledger technology used in blockchain offers multiple benefits to businesses that make a difference when implementing a solution that requires a high degree of trust for business transactions. Using the technology offers the possibility to reduce costs and offers the opportunity for businesses to build and maintain an infrastructure that delivers capabilities at lower expenses than traditional centralized models.

Blockchain can process transactions faster because it doesn’t use a centralized infrastructure. Although there is no system totally secure from cyber attacks, the distributed nature of blockchain provides an unprecedented level of trust. The unchangeable property of blockchain and its public availability among its users, whether in a public ledger or a private one, provides transparency. Any user of the system can query transactions on a real-time basis.

Bitcoin was the first implementation of a cryptocurrency based on distributed ledger technology. It was invented in 2009. and since then, it has been gaining popularity and traction by business owners seeking a distributed trust model. The Bitcoin consensus algorithm is based on proof of work (PoW). In PoW, transactions are collected into blocks by miners and added to the blockchain only if the miner can solve a cryptographic challenge that requires much computational power to be solved. The cryptographic challenge can only be solved by guessing, ensuring neutrality.

Other forms of proofs have been invented and incorporated into other solutions, such as the proof of stake in Ethereum and proof of elapsed time introduced by Intel.

Bitcoin and blockchain solved a very old digital currency problem that many other digital currencies tried to solve in the past known as the double spending problem. Double spending means spending the same digital currency twice, and Bitcoin solved this by ensuring distributed consensus.

Another cryptocurrency benefit that blockchain technology provides is that transfers can cross national boundaries in seconds, with minimum fees, and without going through third-party entities such as banks.

The U.S. government and Venezuela are currently investing in resources dedicated to research and to create their own cryptocurrencies tailored to their specific needs. Despite the vast success of Bitcoin and other altcoins, the shortcomings in the design have limited the global adoption and expansion of cryptocurrencies. The expansion of cryptocurrency use will require overcoming governmental requirements and concerns, such as protecting against money laundering, illicit trades, volatile value and the lack of recognition by trusted parties.

Blockchain For Digital Identity

The need for a single centralized source of truth about identities is becoming a necessity in every community and corporation. Imagine a decentralized digital identity system, a source of truth where every single data element, such as user attributes and credentials, are included in the system only by distributed consensus.

This model is the focus of many enterprises, including Microsoft and IBM. Users get more control over their identity as they can share it only with trusted parties. No single centralized entity can tamper with user identities or data.

For users, this model improves accessibility, privacy of their data and control over their personal data. For enterprises, this model reduces identity management cost, eases the monitoring process, and improves customer service and efficiency.

Blockchain For Real Estate

Smart contracts in blockchain are little programs that execute if certain criteria are met. Smart contracts were invented in the 90s by Nick Szabo. They were integrated into blockchain technology and cryptocurrencies by Ethereum. In a smart contract, parties can agree on a sequence of conditional execution paths based on events. This idea led to the use of blockchain within industries such as real estate. Actually, smart contracts can work for any system that involves a contract between a seller and a buyer.

In the real estate industry, dealing with properties involves several parties and individuals, including owners, lenders, investors and service providers. The transactions between these entities can be problematic with the existing traditional centralized systems. This difficulty comes from many factors, including a lack of trust among peers, fraud and deficiency of a single source of truth about real estate and its history. Blockchain technology offers the possibility to have a real estate system with a very efficient search engine and lookup source for the current properties on sale.


As you can see, blockchain technology offers plenty of opportunities for various applications. And as the technology continues to progress, its applicability will only continue to broaden.


Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Forbes Technology Council.