Martech enablement series: Part 7 — Insights, intelligence and integration

Welcome to Part 7 of: “A Nine Part Practical Guide to Martech Enablement.” This is a progressive guide, with each part building on the previous sections and focused on outlining a process to build a data-driven, technology-driven marketing organization within your company. Below is a list of the previous articles for your reference:

  • Part 1: What is Martech Enablement?
  • Part 2: The Race Team Analogy
  • Part 3: The Team Members
  • Part 4: Building the Team
  • Part 5: The Team Strategy
  • Part 6: Building the Car

In these previous parts, we looked at how your martech team is parallel to an automobile race team. We spent time investigating how a race team constructs their team and then builds a strategy for winning their individual races and the overall race series. We then looked at how this is also a successful approach to constructing and strategizing for a martech team, identifying this process as “martech enablement.”

As we discussed in Part 1 of this guide, martech enablement is ultimately about obtaining insights and providing tools and processes to take action to affect your marketing efforts in your marketing organization. In Part 6, we discussed “building the car” with a focus on breaking down the systems in your martech stack that allow you to take action.

In this article, we will explore the systems that provide insights and enable team collaboration. We’ll also look at tying them all together with integration approaches, tools and strategies. Once again, a shout-out to Scott Brinker for producing the “Marketing Technology Landscape” to help make sense of all the martech products available.

Insights and intelligence

When you’re driving your car, a number of tools inform you how to take action. Looking out your windshield, windows and mirrors gives you immediate data that you respond to. Additionally, you have tools like your instrument dashboard, GPS, traffic data, your radio, and even your passengers.

Race drivers and the team as a whole have sophisticated systems in and around the car that are collecting information, as well as experts to analyze the information in real time, providing actionable insights that the team can use before, during and after the race. This is a huge part of the team’s competitive advantage that they use to win races.

Part of the martech enablement process is to leverage the data within your martech stack so that experts within your team can analyze that information to provide actionable insights, so your marketing organization can win your race.

To reiterate a point made in Part 6 of this guide, a solid data strategy is one of the most important components of martech enablement. This provides the foundation for extracting and “mashing” this data in a way that you can measure. A sound approach is to understand your organization’s KPIs (key performance indicators) and craft a data strategy that supports collecting data to enable measurement of those KPIs.

Many systems and categories of tools assist in the area of gaining insights. Below is a list of some of the systems used to provide visibility and understanding:

  • Web analytics platforms
  • AI/predictive analytics
  • MPM — Marketing performance management
  • Marketing attribution systems
  • Business intelligence (BI) systems
  • Dashboards
  • Data visualization tools
  • Social media monitoring
  • Sales intelligence
  • Audience and market research data

As you progress through the martech enablement process, your “insights” toolset will grow in both size and maturity. I want to remind you to stay focused on letting this part of your stack evolve from the incremental team objectives and series and race goals. Don’t lead with a goal of creating a cool BI environment or dashboard. Let these grow out of the goals driving the martech enablement process.

Strategic vs. tactical insights

I want to spend a minute discussing the difference between strategic and tactical insights and their alignment with your team, series and race objectives. For a refresher on these, see Part 5 of this guide.

When measuring and analyzing performance against your team and series goals, you’re looking at strategic insights where understanding the current level and performance trend is desirable. Think in terms of tools that show you the results of your marketing efforts across time. A tactical insight will generally be more closely aligned with your race goals and will be a singular value or KPI.

Relating this to our race team analogy, a strategic goal could be wanting to improve the team’s average finish position from the current state to some future targeted goal. Over time, you could measure and graph the improvement and trend toward that goal.

A tactical goal might be the desire to come in third place or better in a particular race. Your insight tool could represent that number as a single KPI. That isn’t to say that you may never analyze performance trends during a race, such as average lap speed. But there are values that benefit from analyzing as a trend and others that are just fine to analyze as a current and ending value.

Team management and collaboration

When it comes to management and collaboration in the race team, both pre-race and race-day systems are needed to support the team’s operations. These tools are necessary to get things done right in your marketing organization. Good management and collaboration tools help great people be a great team. Here are some of those systems:

  • Project management
  • Workflow
  • Collaboration tools
  • Business Process Management (BPM)/Agile & Lean
  • Talent management
  • Vendor management
  • Budget and finance

The nuts, bolts, welds, hoses and wires

It’s important to have a strategy and tools to hold all of this together. There are a few strategies to contemplate with systems integration and martech. Your marketing organization will likely take several different approaches to integration. These are generally broken down into three categories: native integration, IPaaS (integration platform as a service) and custom integration.

As technology matures, and the interoperability of products grows, companies are building “connectors” that allow for the exchange of data between their products and other widely used ones. These native integrations generally require some technical implementation or configuration, but the product manufacturers have done much of the heavy lifting to allow for the exchange of data between systems they have connectors for.

IPaaS is a “suite of cloud services enabling development, execution and governance of integration flows connecting any combination of on-premises and cloud-based processes, services, applications and data within individual or across multiple organizations,” according to Gartner. These platforms enable a more systematic way of creating and controlling data exchanges between products in your martech stack.

Custom development is as it sounds: a process in which software engineers develop custom applications to create and manage data exchanges between products and systems in your martech stack. Regardless of whether you take advantage of the aforementioned native integrations or IPaaS, you will likely at some level need to leverage good technologists to do some custom integration work along your path to martech enablement.

Stack it up!

To review, all the categories of the stack between Part 6, “Building the car,” and this part, “Supporting technologies,” your cohesive martech stack is composed of the following types of systems:

Intro to Part 8: Running the series and the races

Now that we’ve gone through the people, the strategy and the stack, we can move on to the execution part of martech enablement. In Part 8 of the guide, we’ll get into how your team iteratively and incrementally moves your marketing organization toward digital transformation and maturity.

I look forward to continuing to share with you about martech enablement in Part 8 of this guide.


Some opinions expressed in this article may be those of a guest author and not necessarily MarTech Today. Staff authors are listed here.


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Facebook Developing Artificial Intelligence to Flag Offensive Live Videos

Facebook Developing Artificial Intelligence to Flag Offensive Live Videos

Facebook Developing Artificial Intelligence to Flag Offensive Live Videos
Facebook Inc is working on automatically flagging offensive material in live video streams, building on a growing effort to use artificial intelligence to monitor content, said Joaquin Candela, the company’s director of applied machine learning.

The social media company has been embroiled in a number of content moderation controversies this year, from facing international outcry after removing an iconic Vietnam War photo due to nudity, to allowing the spread of fake news on its site.

(Also see: Facebook CEO Mark Zuckerberg Details Steps to Fight Fake News)

Facebook has historically relied mostly on users to report offensive posts, which are then checked by Facebook employees against company “community standards.” Decisions on especially thorny content issues that might require policy changes are made by top executives at the company.
Candela told reporters that Facebook increasingly was using artificial intelligence to find offensive material. It is “an algorithm that detects nudity, violence, or any of the things that are not according to our policies,” he said.

The company already had been working on using automation to flag extremist video content, as Reuters reported in June.

Now the automated system also is being tested on Facebook Live, the streaming video service for users to broadcast live video.

Using artificial intelligence to flag live video is still at the research stage, and has two challenges, Candela said. “One, your computer vision algorithm has to be fast, and I think we can push there, and the other one is you need to prioritize things in the right way so that a human looks at it, an expert who understands our policies, and takes it down.”
Facebook said it also uses automation to process the tens of millions of reports it gets each week, to recognize duplicate reports and route the flagged content to reviewers with the appropriate subject matter expertise.

Chief Executive Officer Mark Zuckerberg in November said Facebook would turn to automation as part of a plan to identify fake news. Ahead of the November 8 US election, Facebook users saw fake news reports erroneously alleging that Pope Francis endorsed Donald Trump and that a federal agent who had been investigating Democratic candidate Hillary Clinton was found dead.

However, determining whether a particular comment is hateful or bullying, for example, requires context, the company said.

(Also see: Facebook in Crosshairs as Fake News Battle Heats Up)

Yann LeCun, Facebook’s director of AI research, declined to comment on using AI to detect fake news, but said in general news feed improvements provoked questions of tradeoffs between filtering and censorship, freedom of expressions and decency and truthfulness.

“These are questions that go way beyond whether we can develop AI,” said LeCun. “Tradeoffs that I’m not well placed to determine.”

© Thomson Reuters 2016

Tags: Facebook, Artificial Intelligence, AI, Fake News, Facebook Fake News, Facebook Live, Mark Zuckerberg, Social, Apps

[“Source-Gadgets”]

Google Announces Artificial Intelligence Group for Google Cloud

Google Announces Artificial Intelligence Group for Google Cloud
Alphabet’s Google announced the formation of an artificial intelligence group for Google Cloud, the tech company’s latest gambit to increase its market share in the lucrative cloud computing business.

Diane Greene, who leads Google’s cloud business, announced the team at an event at the company’s facilities in San Francisco. The group will be led by Fei-Fei Li, an artificial intelligence professor at Stanford University, and researcher Jia Li.

“What really attracted these two people to come and be in Google Cloud is a chance to democratize machine learning and artificial intelligence,” Greene said.

In the meanwhile, Google delivered a vote of confidence in London’s future as a technological hub after the Brexit vote on Tuesday by announcing plans for a new building in the King’s Cross area of the city that will house thousands of extra engineers.

Google’s Chief Executive Sundar Pichai said computer science had a great future in Britain, citing the talent pool, educational institutions, and passion for innovation present in the country.
“That’s why we are investing in London in both engineering talent and infrastructure,” he said.
The 10-storey building, Google’s first wholly owned and designed outside the United States, will increase its presence in King’s Cross to more than 1 million square feet, enough for more than 7,000 employees in total, the company said.

Google has 5,700 employees and contractors in the UK, including about 2,000 engineers housed in the recently opened building in King’s Cross where Pichai announced the expansion.

© Thomson Reuters 2016

Tags: Google, Internet, Apps, Google Cloud, Artificial Intelligence

[“Source-Gadgets”]

Twitter Buys Artificial Intelligence Firm Magic Pony

Twitter Buys Artificial Intelligence Firm Magic Pony

HIGHLIGHTS

  • Magic Pony’s team will be joining Twitter Cortex.
  • Magic Pony includes scientists with expertise in machine learning.
  • Terms of the deal were not disclosed.

Twitter said Monday it was acquiring British-based artificial intelligence startup Magic Pony to bolster its capacity for analysis of visual content.

Magic Pony’s technology, based on research to create algorithms that can understand the features of imagery “will be used to enhance our strength in live and video and opens up a whole lot of exciting creative possibilities forTwitter,” said Twitter co-founder and chief executive Jack Dorsey in a blog post.

“Machine learning is increasingly at the core of everything we build at Twitter. It’s powering much of the work we’re doing to make it easier to create, share, and discover the very best content so that every time you open Twitter you’re immersed in the most relevant news, stories, and events for you.”

Terms of the deal were not disclosed.

The move comes with Twitter struggling to grow its user base and achieve profitability amid disappointment in the platform after a long-awaited 2013 initial public offering.

Rob Bishop, Magic Pony’s CEO and co-founder, said joining forces with Twitter “gives us the opportunity to bring the benefits of that research to hundreds of millions of people around the world, and allows Magic Pony to contribute to better quality viewing experiences on Twitter.”

Magic Pony team includes scientists with expertise across computer vision, machine learning, high-performance computing, and computational neuroscience.

Bishop will be based out of Twitter’s headquarters in San Francisco, and co-founder Zehan Wang and the other members will join Twitter’s London office, according to a statement.

Magic Pony’s team will be joining Twitter Cortex, a team of engineers, data scientists, and machine learning researchers. Twitter’s other machine learning acquisitions include Madbits in July 2014 and Whetlab in June 2015.

Tags: AI, Artificial Intelligence, Magic Pony, Social, Twitter
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