#Marketers beware: Privacy Please: How the GDPR Can Elevate Marketing

The EU’s new General Data Protection Regulation (GDPR) can be daunting, but it can have a positive impact on marketing. Here’s why.
— Read on insights.newscred.com/gdpr/

Marketing Transformation and Artificial Intelligence

Artificial Intelligence and Marketing
Artificial Intelligence and Marketing

7 examples where artificial intelligence is transforming marketing:

1. Content curation

Predictive analytics allows Netflix to optimize its recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription.

Uniting information from diverse datasets is a common use of AI.

Under Armour is one of the many companies to have worked with IBM’s Watson. The sports apparel company combines user data from its Record app with third-party data and research on fitness, nutrition etc.

The result is the ability for the brand to offer up relevant (personalized) training and lifecycle advice based on aggregated wisdom.

2. Search

In 2015, Google admitted it was using RankBrain, an AI system, to interpret a ‘very large fraction’ of search queries. RankBrain utilizes natural language processing (NLP) to help find relevance in content and queries, as well as better interpretation of voice search and user context (e.g. Google Now).

3. Predictive customer service

Knowing how a customer might get in touch and for what reason is obviously valuable information.

Not only does it allow for planning of resource (do we have enough people on the phones?) but also allows personalization of communications.

Another project being tested at USAA uses this technique. It involves an AI technology built by Saffron, now a division of Intel.

Analyzing thousands of factors allows the matching of broad patterns of customer behavior to those of individual members.

4. Ad targeting

As Andrew Ng, Chief Scientist at Baidu Research, tells Wired, “Deep learning [is] able to handle more signal for better detection of trends in user behavior. Serving ads is basically running a recommendation engine, which deep learning does well.”

Optimizing bids for advertisers, algorithms will achieve the best cost per acquisition (CPA) from the available inventory.

When it comes to targeting of programmatic ads, machine learning helps to increase the likelihood a user will click. This might be optimizing what product mix to display when retargeting, or what ad copy to use for what demographics.

5. Customer segmentation

Plugging first- and third-party data into a clustering algorithm, then using the results in a CRM or customer experience system is a burgeoning use of machine learning.

Companies such as AgilOne are allowing marketers to optimize email and website communications, continually learning from user behavior.

6. Sales forecasting

Conversion management again, but this time using inbound communication.

Much like predictive customer service, inbound emails can be analyzed and appropriate action taken based on past behaviors and conversions.

Should a response be sent, a meeting invite, an alert created, or the lead disqualified altogether? Machine learning can help with this filtering process.

7. Image recognition

Google Photos allows you to search your photos for ‘cats’. Facebook recognizes faces, as does Snapchat Face Swap.

Perhaps the most exciting implementation of image recognition is DuLight from Baidu…Designed for the visually impaired, this early prototype recognizes what is in front of the wearer and then describes it back to them.

Orchestrating User Adoption from the Innovators to Laggards

Words like change, transformation, and automation will always generate a wide range of reactions throughout the rank and file of an organization, and our reaction to these words are good indicators as to which group we belong to on the adoption curve.

Innovators and early adopters tend to be motivated by change and late stage adopters and laggards tend to resist it.  Paradoxically, both groups have roles of equal importance in the user adoption process.

Knowing where users and people belong on the adoption curve and organizing them into user groups will enable a phased approach to managing the user adoption process. This approach to user adoption is preferred and will provide a more seamless diffusion of your innovation throughout each stage of the user adoption process. Furthermore, this will ensure that by the time it reaches the laggard group, the bugs will have already been worked out.

Successful innovations will reach a tipping point – which is the point that it is widely accepted and adopted by laggards. However, by the time that happens, count on the next innovation already being in play, and have a plan in place to repeat the cycle of innovation again.

imgres-1

Documenting business processes, conducting frequent business process reviews, building and running knowledge management and collaboration portals, establishing a talent management program, and investing in the professional development of your people will have a direct impact to continued success of any future business transformation initiatives in the future.

As companies transform, it is important to retain a high level of diversity across the organization. Take into account the tacit knowledge that could be lost when choosing to acquire new talent.

Make the language of change pervasive throughout your organization and create a business culture that is comfortable with change and ready to adapt to it.

On a personal level, be prepared for change, because change will happen. Invest time in your own professional development. Always be learning and be willing to step out of the comfort zone.

For more on this topic, I suggest reading the following two books:

“The Tipping Point – How Little Things Can Make a Big difference” by Malcolm Gladwell.

“Diffusion of Innovations” by Everett Rogers

Follow my blog at Social2Direct.com

Marketing Transformation and Artificial Intelligence

Artificial Intelligence and Marketing
Artificial Intelligence and Marketing

Here are seven examples where artificial intelligence is transforming marketing:

1. Content curation

Predictive analytics allows Netflix to optimize its recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription.

Uniting information from diverse datasets is a common use of AI.

Under Armour is one of the many companies to have worked with IBM’s Watson. The sports apparel company combines user data from its Record app with third-party data and research on fitness, nutrition etc.

The result is the ability for the brand to offer up relevant (personalized) training and lifecycle advice based on aggregated wisdom.

2. Search

In 2015, Google admitted it was using RankBrain, an AI system, to interpret a ‘very large fraction’ of search queries. RankBrain utilizes natural language processing (NLP) to help find relevance in content and queries, as well as better interpretation of voice search and user context (e.g. Google Now).

3. Predictive customer service

Knowing how a customer might get in touch and for what reason is obviously valuable information.

Not only does it allow for planning of resource (do we have enough people on the phones?) but also allows personalization of communications.

Another project being tested at USAA uses this technique. It involves an AI technology built by Saffron, now a division of Intel.

Analyzing thousands of factors allows the matching of broad patterns of customer behavior to those of individual members.

4. Ad targeting

As Andrew Ng, Chief Scientist at Baidu Research, tells Wired, “Deep learning [is] able to handle more signal for better detection of trends in user behavior. Serving ads is basically running a recommendation engine, which deep learning does well.”

Optimizing bids for advertisers, algorithms will achieve the best cost per acquisition (CPA) from the available inventory.

When it comes to targeting of programmatic ads, machine learning helps to increase the likelihood a user will click. This might be optimizing what product mix to display when retargeting, or what ad copy to use for what demographics.

5. Customer segmentation

Plugging first- and third-party data into a clustering algorithm, then using the results in a CRM or customer experience system is a burgeoning use of machine learning.

Companies such as AgilOne are allowing marketers to optimize email and website communications, continually learning from user behavior.

6. Sales forecasting

Conversion management again, but this time using inbound communication.

Much like predictive customer service, inbound emails can be analyzed and appropriate action taken based on past behaviors and conversions.

Should a response be sent, a meeting invite, an alert created, or the lead disqualified altogether? Machine learning can help with this filtering process.

7. Image recognition

Google Photos allows you to search your photos for ‘cats’. Facebook recognizes faces, as does Snapchat Face Swap.

Perhaps the most exciting implementation of image recognition is DuLight from Baidu…Designed for the visually impaired, this early prototype recognizes what is in front of the wearer and then describes it back to them.

How Big Data Will Shape Industry in 2016 | Datafloq

how-big-data-will-shape-the-it-industry-in-2016Accurately predicting how Internet trends and future technology will play out is no easy task. This year alone there has been an increase in cyber threats and hacks, new software from Windows and the introduction of smart watches, and of course, a spike in big data. As technology advances, so does its growth for possibilities.

Below are six sure-fire predictions made by analysts for 2016:

1. A Huge Decline in Legacy Vendors

As indicated by a report released by the IDC, almost a third (30 percent) of IT vendors will cease to exist by 2020. As it appears now, many legacy vendors, especially larger ones, will either have to shut down completely, downgrade or partner with another company due to sluggish growth and lost earnings.

Good examples of this trend can be seen with Dell which is rumored to buy out EMC, and HP splitting its operations in half. In a nutshell, legacy vendors as a whole are missing the mark when it comes to delivering practical solutions in the tech industry—a red flag that many vendors will eventually end up being archived in exchange for private equity.

2. Appearance of More Wearables

Within the next two years, wearable health and fitness tracking devices will take the critical role workforce by storm. By 2018, it is estimated two million people will be required to wear health and fitness tracking devices as a safety measure. This includes firefighters, law enforcement, paramedics, remote field workers, airline pilots, industrial workers, professional athletes, and even political leaders.

This expected boom in wearable tech calls for IT professionals that are adept in device discovery. Network and device discovery at this level usually calls for a more formal network device discovery platform, and IT professionals that understand how to implement it.

3. Big Data Gets Bigger

In 2016, big data will have an even greater impact on how many industries function. Diverse industries are seeing the benefit of analyzing large amounts of data from healthcare to language translation. Understandably, more and more companies are adopting big data services and customizations; they’re catching on that utilizing insights based on algorithms is a much more practical strategy to successful marketing and business expansion as opposed to trial and error. By the year 2018, analytics will be embedded in every application to enhance functionality or convenience.

4. Cloud Providers Will Diminish

In reaction to the big data explosion, many big cloud providers will be vying for the chance to host big data storage. Google, Microsoft, and AWS all provide machine learning services as well as access to a range of massive data groups that can be used for analytics. Major public cloud providers will gain momentum and strength, with Amazon, IBM SoftLayer, and Microsoft grabbing a large percentage of the business cloud services market.

Unfortunately, smaller cloud service providers just won’t be able to invest in hosting machine learning services; this will likely be the catalyst for such companies to bail out of the market altogether. The volume of options for cloud management software and general infrastructure-as-a-service (IaaS) cloud services will significantly decline at the end of 2016.

5. More Business Content Generated by Machines

Technologies possessing the ability to proactively assemble and send information through automated composition engines will take a more active role. Business content such as legal documents, market reports, shareholder reports, press releases, articles and white papers will be generated more frequently by machines.

This shift in operations will increase by 20 percent in 2018, this includes machine learning in the IT sector as a whole. The initial companies predicted to expedite and implement smart machine technologies effectively will be startups and other fresh-out-the box companies.

6. Increase of Artificial Intelligence Implementation

According to esteemed analyst Daryl Plummer, the artificial intelligence (AI) trend is the result of enterprises and consumers willingly embracing the advancement of machine-driven technologies. Since the capability of applying smart technology for specific tasks dramatically improves the time, cost and energy generally contributed to recruiting, hiring, training and expansion demands associated with human labor, it’s little wonder, then, that artificial intelligence will play a larger role in a company’s infrastructure in 2016.

Source: Datafloq – How Big Data Will Shape the IT Industry in 2016

Article Author:

Xander SchofieldFollow Xander SchofieldLinkedIn Xander Schofield

How Big Data Will Shape the IT Industry in 2016 | Datafloq

how-big-data-will-shape-the-it-industry-in-2016Accurately predicting how Internet trends and future technology will play out is no easy task. This year alone there has been an increase in cyber threats and hacks, new software from Windows and the introduction of smart watches, and of course, a spike in big data. As technology advances, so does its growth for possibilities.

Below are six sure-fire predictions made by analysts for 2016:

1. A Huge Decline in Legacy Vendors

As indicated by a report released by the IDC, almost a third (30 percent) of IT vendors will cease to exist by 2020. As it appears now, many legacy vendors, especially larger ones, will either have to shut down completely, downgrade or partner with another company due to sluggish growth and lost earnings.

Good examples of this trend can be seen with Dell which is rumored to buy out EMC, and HP splitting its operations in half. In a nutshell, legacy vendors as a whole are missing the mark when it comes to delivering practical solutions in the tech industry—a red flag that many vendors will eventually end up being archived in exchange for private equity.

2. Appearance of More Wearables

Within the next two years, wearable health and fitness tracking devices will take the critical role workforce by storm. By 2018, it is estimated two million people will be required to wear health and fitness tracking devices as a safety measure. This includes firefighters, law enforcement, paramedics, remote field workers, airline pilots, industrial workers, professional athletes, and even political leaders.

This expected boom in wearable tech calls for IT professionals that are adept in device discovery. Network and device discovery at this level usually calls for a more formal network device discovery platform, and IT professionals that understand how to implement it.

3. Big Data Gets Bigger

In 2016, big data will have an even greater impact on how many industries function. Diverse industries are seeing the benefit of analyzing large amounts of data from healthcare to language translation. Understandably, more and more companies are adopting big data services and customizations; they’re catching on that utilizing insights based on algorithms is a much more practical strategy to successful marketing and business expansion as opposed to trial and error. By the year 2018, analytics will be embedded in every application to enhance functionality or convenience.

4. Cloud Providers Will Diminish

In reaction to the big data explosion, many big cloud providers will be vying for the chance to host big data storage. Google, Microsoft, and AWS all provide machine learning services as well as access to a range of massive data groups that can be used for analytics. Major public cloud providers will gain momentum and strength, with Amazon, IBM SoftLayer, and Microsoft grabbing a large percentage of the business cloud services market.

Unfortunately, smaller cloud service providers just won’t be able to invest in hosting machine learning services; this will likely be the catalyst for such companies to bail out of the market altogether. The volume of options for cloud management software and general infrastructure-as-a-service (IaaS) cloud services will significantly decline at the end of 2016.

5. More Business Content Generated by Machines

Technologies possessing the ability to proactively assemble and send information through automated composition engines will take a more active role. Business content such as legal documents, market reports, shareholder reports, press releases, articles and white papers will be generated more frequently by machines.

This shift in operations will increase by 20 percent in 2018, this includes machine learning in the IT sector as a whole. The initial companies predicted to expedite and implement smart machine technologies effectively will be startups and other fresh-out-the box companies.

6. Increase of Artificial Intelligence Implementation

According to esteemed analyst Daryl Plummer, the artificial intelligence (AI) trend is the result of enterprises and consumers willingly embracing the advancement of machine-driven technologies. Since the capability of applying smart technology for specific tasks dramatically improves the time, cost and energy generally contributed to recruiting, hiring, training and expansion demands associated with human labor, it’s little wonder, then, that artificial intelligence will play a larger role in a company’s infrastructure in 2016.

Source: Datafloq – How Big Data Will Shape the IT Industry in 2016

Article Author:

Xander SchofieldFollow Xander SchofieldLinkedIn Xander Schofield

Big Data Trends | Strategies driving investments in data

imgresLast year, IDG published a study 2015 Big Data and Analytics, Insights into Initiatives and Strategies Driving Data Investments that was based on interviews with 1,139 IT leaders from nine industries with high tech (16%), government (12%), financial services (11%) and manufacturing (9%) being the top four industries surveyed.

 

Key findings Infographic:

Below are a few key take-aways, the report is embedded at the bottom of this post:

  • 80% of enterprises surveyed have data-driven and big data projects in implementing or planning stages today versus 63% of SMBs. 37% of enterprises have deployed data-driven projects in the last year, and 18% are in the process of implementing or piloting projects as of today.
  • 83% of organizations prioritized structured data initiatives as critical or high priority in 2015, and 36% increased their budgets for data-driven initiatives.
  • Improving the quality of decision making (61%), improving planning and forecasting (57%) and increasing the speed of decision making (51%) are the three most common business goals and objectives driving data-driven initiatives in organizations today. The following graphic compares which business initiatives are driving big data investment and the positive impact of big data on each.
  • 36% of enterprises expect their IT budget allocations for data-driven initiatives increased in 2015, 41% anticipated budget levels would remain at current levels and 21% aren’t sure. Only 3% say data-driven and big data-related project funding will decrease.
  • Data analytics continues to accelerate as the most preferred solution for gaining greater business insight and value from data, with this category increasing in importance 55% from 2014 survey results. In enterprises, data analytics (65%), visual dashboards (47%), data mining (43%), data warehousing (40%) and data quality (39%) are the five most preferred solutions. In my discussions with CIOs in financial services and manufacturing companies, the shift away from pre-built dashboards with common metrics and key performance indicators (KPIs) to the flexibility of defining their own data models in metrics is the future. Dashboards in financial institutions need to have the flexibility of quickly integrating entire new metrics and KPIs as their business models change. For manufacturers, the need for interpreting shop floor data to financial results is what’s driving data analysis and dashboards in the many manufacturing industries adopting analytics today.
  • The number of enterprises who have deployed/implemented data-driven projects increased 125% in the last year, with 42% still planning data implementations as of today. The following graphic from the study illustrates a comparison of 2014 and 2015 plans for considering, planning and implementing data-driven projects.

 

View the report here:

Download the IDG Report: 2015 Big Data and Analytics, Insights into Initiatives and Strategies Driving Data Investments

Google paid Apple $1bn to be default iOS search engine | Technology | The Guardian

 

google-apple-650Lawsuit proceedings reveal Apple was paid handsomely to make Google default search engine on mobile Safari, while company’s total revenue from Android just $31bn…

 

Source: Google paid Apple $1bn to be default iOS search engine | Technology | The Guardian

Big Data Trends | Strategies driving investments in data

IDG Enterprise logoLast year, IDG published a study 2015 Big Data and Analytics, Insights into Initiatives and Strategies Driving Data Investments that was based on interviews with 1,139 IT leaders from nine industries with high tech (16%), government (12%), financial services (11%) and manufacturing (9%) being the top four industries surveyed.

 

Key findings Infographic:

Below are a few key take-aways, the report is embedded at the bottom of this post:

  • 80% of enterprises surveyed have data-driven and big data projects in implementing or planning stages today versus 63% of SMBs. 37% of enterprises have deployed data-driven projects in the last year, and 18% are in the process of implementing or piloting projects as of today.
  • 83% of organizations prioritized structured data initiatives as critical or high priority in 2015, and 36% increased their budgets for data-driven initiatives.
  • Improving the quality of decision making (61%), improving planning and forecasting (57%) and increasing the speed of decision making (51%) are the three most common business goals and objectives driving data-driven initiatives in organizations today. The following graphic compares which business initiatives are driving big data investment and the positive impact of big data on each.
  • 36% of enterprises expect their IT budget allocations for data-driven initiatives increased in 2015, 41% anticipated budget levels would remain at current levels and 21% aren’t sure. Only 3% say data-driven and big data-related project funding will decrease.
  • Data analytics continues to accelerate as the most preferred solution for gaining greater business insight and value from data, with this category increasing in importance 55% from 2014 survey results. In enterprises, data analytics (65%), visual dashboards (47%), data mining (43%), data warehousing (40%) and data quality (39%) are the five most preferred solutions. In my discussions with CIOs in financial services and manufacturing companies, the shift away from pre-built dashboards with common metrics and key performance indicators (KPIs) to the flexibility of defining their own data models in metrics is the future. Dashboards in financial institutions need to have the flexibility of quickly integrating entire new metrics and KPIs as their business models change. For manufacturers, the need for interpreting shop floor data to financial results is what’s driving data analysis and dashboards in the many manufacturing industries adopting analytics today.
  • The number of enterprises who have deployed/implemented data-driven projects increased 125% in the last year, with 42% still planning data implementations as of today. The following graphic from the study illustrates a comparison of 2014 and 2015 plans for considering, planning and implementing data-driven projects.

 

View the report here:

Download the IDG Report: 2015 Big Data and Analytics, Insights into Initiatives and Strategies Driving Data Investments

Mobile online checkout and AI to be front of mind in 2016: Deloitte | ZDNet

mobileMobile online checkout and cognitive technologies are set to boom in 2016, according to the latest predictions made by Deloitte.In the Technology, Media & Telecommunications 2016 report, Deloitte believes the number of individuals who use a third-party touch-based payment service to make a purchase on their devices — which covers both smartphones and tablets — will increase by 150 percent to reach 50 million regular users.

Source: Mobile online checkout and AI to be front of mind in 2016: Deloitte | ZDNet