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Beyond Predictive: 4 Reasons for B2B Marketers to Consider Prescriptive Analytics in 2018 - MarTech Advisor

Beyond Predictive: 4 Reasons for B2B Marketers to Consider Prescriptive Analytics in 2018 - MarTech Advisor | The MarTech Digest | Scoop.it
In the first stage – descriptive analytics, past data is studied to learn from past behavior and to assess how it can affect future outcomes. The second stage – Predictive Analytics uses data to predict what could happen next. The third stage which is also often referred to as the final frontier in the Big Data analytics game is Prescriptive Analytics - where data helps prescribe different actions.

Here are 4 reasons for B2B marketers to consider Prescriptive Analytics in 2018 

1. Combines the best of descriptive and predictive analytics

2. Drives informed (digital) marketing decisions

3. Improves customer-focused strategies to enhance CX

4. Helps mitigate business (even Sales and Marketing) risks
CYDigital/marteq.io's insight:

Beyond Predictive: 4 Reasons for B2B Marketers to Consider Prescriptive Analytics in 2018 - MarTech Advisor

 

I'll wait for AI to come first...or when pigs fly.

 

This news comes to you compliments of marketingIO.com. #MarTech #DigitalMarketing

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A Big Data Cheat Sheet: From Narrow AI to General AI - Stats and Bots

A Big Data Cheat Sheet: From Narrow AI to General AI - Stats and Bots | The MarTech Digest | Scoop.it
  • Narrow AI and Automation
    The majority of currently active Artificial Intelligence is actually Narrow AI. Narrow AI is usually software that is automating a traditionally human activity, and in most cases it outperforms humans in efficiency and endurance.
  • Artificial General Intelligence (AGI) and Machine Learning
    For Artificial Intelligence to have complex, nuanced conversations that can pass for human, it needs to be able to solve new problems on the spot. It needs to interpret accents it has never heard before, vocabulary through context, and create sentences it has never had to express before.
  • Conscious AI, AKA True AI
    To many people anything less than conscious AI is just very complex automation. Because of this controversy, those people might consider Conscious AI to be the only AI that should be called AI.
CYDigital/marteq.io's insight:

RYZZ: It’s a new approach to MarTech for B2B Marketers.

 

#MarTech #DigitalMarketing

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Don’t let big data make your head spin: Building a user-friendly marketing ecosystem - Marketing Tech News

Don’t let big data make your head spin: Building a user-friendly marketing ecosystem - Marketing Tech News | The MarTech Digest | Scoop.it
These analytics tools are broken into three primary categories:

Analytics platforms: Integrate and analyse data to uncover new insights.  This is where the data science work is done and the tools vary from simple analysis to complex predictive modeling.  These typically powerful, but generic technologies, deliver descriptive, diagnostic and predictive analytics telling us what happened, why it happened and what will happen next.

Analytics applications: Can go past generic analytics tools by being laser focused in a specific category or vertical.  For example, a digital media optimization application will ingest and analyse the data. It will also provide descriptive, and diagnostic insights that go further by providing application-specific predictions of what will happen next and recommendations of how to respond – for instance where you should put your media funding). 

Visualisation platforms: This is where the rubber hits the road in analytics.  Specifically designed - as the name might suggest - for visualising data; taking complex data and presenting it in intuitive, simple-to-read visual formats that illuminate the information.  The goal is to simplify the process and let an impactful dashboard - or visual - tell the story.  Similar to analytics platforms, there are generic and application-specific visualisation platforms.  Application-specific visualisation platforms go well beyond generic tools by providing ready-to-go visualisation packages specific to the application; saving months of development time.
CYDigital/marteq.io's insight:

I wish...

 

Visualize your Marketing Stack. marketingIO will analyze your marketing technology and deliver a visual of your MarTech Stack. Free. Go here: http://go.marketingio.com/stack_analysis 

 

marketingIO: MarTech for B2B Marketers. Strategy, Software, Services, Support, Staffing.  Go here to submit RFQs: http://www.marketingio.com #MarTech #DigitalMarketing

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Big data has no role in digital marketing - Mumbrella

Big data has no role in digital marketing - Mumbrella | The MarTech Digest | Scoop.it
What big data doesn’t allow for is agility and true behavioral insights in relation to marketing efforts. We live in a fast-paced world, and by the time big data projects are completed, the landscape has often already shifted, making many insights obsolete.

When it comes to marketing, big data just doesn’t cut it. It’s a whale trying to maneuver in a lap pool; it’s too big, too slow and likely to create a lot of frustration. That’s why I’m a huge proponent of small data – or agile data.

Various agile data collection methods allow companies to keep their finger on the pulse of their consumer base. In turn, companies investing in agile data are able to be far more responsive to the needs of their consumers by making faster, data-driven decisions. Agile data allows you to meet consumers on their playing field.
CYDigital/marteq.io's insight:

CT for the author's definition of agile data.

 

Visualize your Marketing Stack. marketingIO will analyze your marketing technology and deliver a visual of your MarTech Stack. Free. Go here.

 

marketingIO: MarTech for B2B Marketers. Strategy, Software, Services, Support, Staffing.  Go here to submit RFQs. #MarTech #DigitalMarketing

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Are your marketing pros ready to handle big data? - CIO

Are your marketing pros ready to handle big data? - CIO | The MarTech Digest | Scoop.it
A marketer with strong data skills can offer deeper insights into how marketing campaigns pan out. Modern marketers know how to interpret data and -- most importantly -- analyze it to find meaning that will shape and drive future programs, leading to greater overall success.

The recommendation is that all marketers educate themselves on the most popular channels people are using on a daily basis to access content from. It's also vital to have a finger on the pulse of what the next best app will be -- like when Twitter came on the scene and completely changed the way people share and interact. And with that insight that comes an even greater need to understand analytic platforms -- to take a spreadsheet or chart of information and find consistencies, inconsistencies and outliers that will inform future marketing campaigns.

Just because a marketing campaign gets a big response, that doesn't mean it's necessarily positive. Maybe your latest Tweet went viral for negative reasons, which resulted in an increase of traffic to your site or more chatter about your product on the internet, but not in a positive light. A marketer needs to be able to weed through these types of interactions to figure out what type of engagement the brand is receiving.

Developing strong relationships between IT and marketing might be a new frontier for some executives, but it's an increasingly important shift that is happening in the business world.
CYDigital/marteq.io's insight:

Email your comments to joe_rizzo@marketingIO.com. I’ll publish it here. marketingIO: One Source for All Marketing Technology Challenges. See our solutions.  

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In time for summer, Informatica opens its Marketing Data Lake - Marketing Land

In time for summer, Informatica opens its Marketing Data Lake - Marketing Land | The MarTech Digest | Scoop.it
The data management provider has announced its new Marketing Data Lake, which it describes as the first such repository specifically for the needs of marketers.

As Informatica explains in a recent e-book on the subject, a data lake is “a data repository that lets you store and process all your data, from multiple sources, in its native format without having to pre-structure it.”

It complements data warehouses, the company notes, in that generates data schemas “on read,” or when the data is exported and used, instead of “schema-on-write” when it goes into the repository, as with warehouses. A schema is a blueprint for data organization.
CYDigital/marteq.io's insight:

Bringing Big Data closer to the masses?

 

NEW: Experience Remarkable Planning Accuracy With New, FREE Growth Hacking Tool. Go here: http://goo.gl/UjcA8x 

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The Realities of Big Data for B2B: Very high to very negative returns - B2B News Network

The Realities of Big Data for B2B: Very high to very negative returns - B2B News Network | The MarTech Digest | Scoop.it
Big Data is a tool most people immediately associate with B-to-C channel, rather than B-to-B. That might be because, in the former, there are tens or hundreds of millions of customers and an exponentially larger set of data points. And that would be wrong, according to a sampling of observers and practitioners.

A range of observations from industry participants combine to provide a kind of best practices for how B-to-B firms can take advantage of Big Data. The focus has to be on how data is gathered and processed, with emphasis on elements such as using a single authorized source of data, using scorecards managed by well-trained employees and managing business rules to make sure data is accurate and believable. Any company thinking of using Big Data sit down with a firm and be “interviewed” by that firm to break down goals and then have software tailored to that because one size does not fit all.

Of course, this costs money and the upfront expenditure—for potentially mixed results—may be daunting to many businesses, especially the smaller ones.
CYDigital/marteq.io's insight:

Negative inasmuch as the cost exceeds the benefits. No doubt. Spend the budget on predictive.

 

marketingIO: One Source for All Marketing Technology Challenges. See our solutions.  

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Google Analytics: Unlocking Big Data In 10 Steps | Growth Hacking Marketing Blog

Google Analytics: Unlocking Big Data In 10 Steps | Growth Hacking Marketing Blog | The MarTech Digest | Scoop.it
Learn how to automatically extract your big data sets from your Google Analytics account to a dedicated google sheet, nice and clean.


marketingIO: One Source for All Marketing Technology Challenges. See our solutions.

CYDigital/marteq.io's insight:

Brilliantly constructed! A must!

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EntreCulturas's curator insight, November 23, 2017 11:56 AM
El big data es la herramienta que almacena todos los datos de la red. A la hora de crear un blog es muy importante poner el plugin Google Analytics para que recuente las visitas que se reciben, por ello el big data debe estar desbloqueado. 

Los pasos son los siguientes: 

1 - Analizar el modelo transaccional adecuado. 
2 - Seleccionar el modelo de geolocalización. 
3 - Codificar el modelo Big Data.   
4 - Comprobar que los datos son los correctos. 
5 - Crear un escritorio de Google Analytics. 
6 - Desarrollar el escritorio.
7 -  Añadir un geolocalizador a través de Google Apps
8 - Crear hojas de cálculo dinámicas. 
9 - Estadísticas
10 - Visualizaciones   
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Big data: Salesforce Wave partners with Google, Cloudera - FierceCMO

Big data: Salesforce Wave partners with Google, Cloudera - FierceCMO | The MarTech Digest | Scoop.it
Salesforce strengthened its big data offering last week, announcing partnerships with powerhouses including Google, Cloudera, Hortonworks, New Relic, Informatica and Trifacta. Data preparation providers Informatica and Trifacta also joined the fold, charged with helping translate big data into a form digestible by business executives.

The move makes Salesforce Wave a front-end solution to help businesses slice and dice the extensive data reserves held both by its partners and its clients.

Salesforce launched the Wave data service late last year to help businesses extract actionable data from growing pools of recorded clicks and digital interactions. The new partnerships increase the amount of data its clients can access, and also give them access to the analytical capabilities of those partners.


Dark marketing clouds ahead? Let us help you see clearly. Contact us.

CYDigital/marteq.io's insight:

FYI, as SFDC moves further into the Enterprise.

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Hey marketers: Don't you dare underestimate data and market research - Mashable

Hey marketers: Don't you dare underestimate data and market research - Mashable | The MarTech Digest | Scoop.it

► About Us: iNeoMarketing provides Marketing Technology services, applications and support to B2B companies who do not have the required resources, knowledge or expertise. Visit us at ineomarketing.com. ◄

CYDigital/marteq.io's insight:

Reinforcing the obvious, which is necessary. The image is a 2 minute interview snippet about Delta and data with Ms. Pattek.

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7 Limitations Of Big Data In Marketing Analytics - Marketing Land

7 Limitations Of Big Data In Marketing Analytics - Marketing Land | The MarTech Digest | Scoop.it

► About Us: iNeoMarketing provides Marketing Technology services, applications and support to B2B companies who may not have the required resources, knowledge or expertise. Visit us at ineomarketing.com. ◄

CYDigital/marteq.io's insight:

With the exception of the monoliths, B2B analytics is sufficient for a data-driven strategy without the need for Big Data.

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When Big Data Becomes Smart Data - Digital Marketing Blog by Adobe

When Big Data Becomes Smart Data - Digital Marketing Blog by Adobe | The MarTech Digest | Scoop.it

► About Us: iNeoMarketing provides Marketing Technology services, applications and support to B2B companies who may not have the required resources, knowledge or expertise. Visit us at ineomarketing.com.

CYDigital/marteq.io's insight:

Yes, it is B2C focused, but there are definite lessons for the B2B marketer.

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PHILIP B's curator insight, February 11, 2015 10:39 AM

Smart data comes with real time intelligence and self-learning governance / solutions to separate the buzz from reality.

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Dos and Don’ts of Becoming a Successful Data-Driven Marketer - ClickZ

Dos and Don’ts of Becoming a Successful Data-Driven Marketer - ClickZ | The MarTech Digest | Scoop.it

Digest...


DON'T let fear or uncertainty stop you from starting today.

DO learn the languages of analytics.

DO combine your data with other data sets. DON'T think you can do it alone.

DON'T expect the data to tell you everything you need to know about your customers. DO augment your data-driven insights via other types of intelligence such as qualitative and/or contextual research.

DO tear down the walls to collaborate.

DON'T forget it's a work in progress.

DO be flexible.

DON'T use the past to hold back future decisions.

DO make data your friend not your enemy.

 

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CYDigital/marteq.io's insight:

DO deploy the assistance of IT. DON'T think you can do this on your own.

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IAB: Marketers Eager to Optimize Data Tools [Study] - ClickZ

IAB: Marketers Eager to Optimize Data Tools [Study] - ClickZ | The MarTech Digest | Scoop.it

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CYDigital/marteq.io's insight:

Primarily B2C oriented.

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Big Data and Analytics Trends 2015 [Infographic] - B2B Infographics

Big Data and Analytics Trends 2015 [Infographic] - B2B Infographics | The MarTech Digest | Scoop.it

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The Next Step for Big Data - ClickZ

The Next Step for Big Data - ClickZ | The MarTech Digest | Scoop.it

Digest...


There are a number of factors coming together that are changing the way we should think about big data.

  1. Big data is no longer about how you collect and scale the system to handle large amounts of data cost-effectively.
  2. The number of data collection nodes are growing. Everything a consumer uses in their daily lives is becoming digital, connected, and able to phone home with interesting real-world information about location, behavior, speed, temperature, interest, etc., which exponentially increases the number of variables marketers can use to triangulate on and build profiles against.
  3. Big data and the robust audience profiles created can now be used for more than a standard broadcast (or push) advertisement. This data can be used smartly to create immersive, content – rich messages while still being relevant, meaningful, and pleasant to the consumer. This reduces the annoyance and waste associated with traditional messaging.

 

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CYDigital/marteq.io's insight:

I know: its B2C. Eventually, the data collected on the B2B side will be substantial, enough so that you'll want to consider B2C strategies.

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Big data investment up, but marketers still struggle with analysis - Trends & Ideas - BizReport

Big data investment up, but marketers still struggle with analysis - Trends & Ideas - BizReport | The MarTech Digest | Scoop.it
At this year's conference, DMA2014, a survey of 584 attendees by Infogroup found the number reaping rewards has risen significantly, now standing at almost half (49%).
However, the survey also highlighted that marketers continue to struggle with analysis of the huge amounts of data now available to them. For 21%, analysis is their biggest concern moving forward into 2015, followed by data implementation (16%) and collecting data (15%). More than half of marketers surveyed said they do not collect enough data while 10% said they collect too much. Of those who collect too much data 25% believe collecting data remains their biggest challenge.


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CYDigital/marteq.io's insight:

If there's one area that is so amorphous, it's Big Data for the B2B marketer. We need a few beacons of light to provide guidance as to how to manage this asset.

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Marketing Automation 2.0: Marketing Intelligence - Forbes

Marketing Automation 2.0: Marketing Intelligence - Forbes | The MarTech Digest | Scoop.it

Digest...


To prioritize pipeline to find great customers, marketers need a deep understanding of how to market to the right customers. Marketers have access to new data sources, superior analytics, and better integrations that allow them to generate high value pipelines.

 

The transition starts with understanding two key changes in the marketing ecosystem: (1) The explosion of data and (2) The inadequacy of disparate tools.

 

1. The Explosion of Data

The value of big data doesn’t stem from the volume of data you aggregate, but rather, from the relationships between the data. To extract insights from sets of data, data scientists have to take the volume, velocity, variety, and veracity of data into account; they don’t just experiment on data, they merge, organize, clean, and test data as well. One tool might include Predictive Lead Scoring and another might be hiring a team of analysts that require extensive training.

 

2. Ineffective, Disparate Tools

According to Bizo ’s survey of over 800 B2B marketing leaders, less than 20 percent believe they are using data well. And for marketers targeting SMBs, the data problem is even worse. Only 8 percent of marketers have a 360-degree view of their SMB customers. Given the universal challenges facing marketers today, specific to big data and customer insight, this is not necessarily surprising.

 

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CYDigital/marteq.io's insight:

And this is why I believe the forthcoming SFDC Analytics Cloud and IBM's free use of Watson will be gamechangers for B2B marketers.

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12 Big Data Facts for Marketers in 2014 - DM News

12 Big Data Facts for Marketers in 2014 - DM News | The MarTech Digest | Scoop.it
1. One third of all data will be stored in or will pass through the cloud by 2020. (Chassis Plans)

2. Dirty data, or poor data quality, costs U.S. businesses $600 billion annually. (Fathom)

3. Eighty-three percent of companies have started some sort of Big Data program, although many remain in fledging stages—especially small businesses that have fewer resources to invest. (Experian Data Quality)

4. San Francisco is ranked the number one U.S. city in open data innovation, followed by New York and Boston. (U.S. City Open Data Census)

5. On average, companies collect customer and prospect data from 3.4 channels. The most common channel for interacting with customers is a company's website, followed by the sales team and then the call center. (Experian Data Quality)

6. There are nearly as many pieces of digital information as there are stars in the universe. (Realcomm)

7. Seventy-nine percent of companies have an analytics team. The median number of employees on an analytics team is 22. (Experian Data Quality)

8. By 2018 the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills to fill data-centric jobs. (McKinsey Global Institute)

9. In 2000, just one quarter of the world's stored information was digital. Today, more than 98% of all stored information is digital. (Foreign Affairs)

10. Brands and organizations receive 34,722 Facebook Likes every minute. (Big Data Insight Group)

11. An estimated 80 to 90% of the data in any organization is unstructured—or doesn't fit neatly in a traditional row-column database. Some examples include email messages, word processing documents, videos, photos, audio files, presentations, and Web pages. (Webopedia)

12. The Big Data market will reach $16.9 billion by 2015, up from $3.2 billion in 2010. (International Data Corporation)
CYDigital/marteq.io's insight:

 Yes, I know it's B2C oriented, but the sheer size is mind boggling.

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Stop Searching for That Elusive Data Scientist - HBR

Stop Searching for That Elusive Data Scientist - HBR | The MarTech Digest | Scoop.it
Instead, develop the skills on your team.


Digest...


Based purely on the data, a really good data scientist will probably tell you the odds are poor that you’ll be able to find and hire really good data scientists. Surveys say there simply aren’t enough people with the unusual blend of software skills and statistical savvy to go around. Arguably even more important, high-impact data scientists bring collaborative temperaments and business acumen to data-driven initiatives. Unfortunately, there’s no shortage of individuals with just enough statistical and software knowledge to be data-dangerous. For many organizations, a mediocre data scientist may be worse than none at all.

 

The smartest thing I’ve seen organizations start doing is seed-fund and empower small cross-functional data-oriented teams explicitly charged with delivering tangible and measurable data-driven benefits in relatively short periods of time. The accent is on the word team; the emphasis is on building greater data capability than better digital infrastructures. The goal is to make all of the organization — not just the geeks and quants — more conversant in how to align probability, statistics, technology and business value creation. No black boxes or centers of analytic excellence here; they want data science to be a cultural value, not just a functional expertise.

 

Without exception, every team I ran across or worked with hired outside expertise. They knew when a technical challenge and/or statistical technique was beyond the capability. But, unsurprisingly, the outside advisors — in one case, an academic, in others, quants from digital consultancies — were better able to collaborate with teams that had really tried to get their minds around a design challenge. The relationship was less of an RFP box-ticking exercise than a shared space for experimental design.

 

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CYDigital/marteq.io's insight:

Smart advice: build the cross functional team instead.


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B2B data - Get smart not big - Biznology

B2B data - Get smart not big - Biznology | The MarTech Digest | Scoop.it

Digest...


This seven step process is effective in developing a data strategy and plan that gets both approved and funded.

  1. Elect a Captain

Gather a multi-functional team and elect a “data captain.”  

  1. Segment and sub-segment the market

The goal is to arrive at a clear view of the market segments, and then define each using data descriptors.  

  1. Determine needs vs. wants

Prioritize what data is really needed to execute vs. what everybody wants.

  1. Identify data sources

Some data will only be available from internal sources, and some will be needed from outside vendors.  Carefully research the most accurate and reliable outside data vendors and establish a relationship and costs with them.  Be sure to also audit their data for accuracy and completeness.

  1. Agree on data quality and accuracy standards

For each data element, agree on an acceptable level of accuracy and its value. Then establish the updating and cleaning processes in accordance with the value and accuracy standard. 

  1. Decide on internal vs. external database development

One good approach is to select a qualified B2B database service provider to develop the database with the understanding that eventually it will be transferred in-house. 

  1. Find quick, easy and/or important wins

Don’t go for a budget approval without first identifying projects and/or results that are quick, easy and/or important. 

 

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CYDigital/marteq.io's insight:

I like this: not Big Data, but Smart Data. B2B is not steeped in Big Data...there just isn't enough data. But whatever data we have we need to be smart about how to leverage it. And don't forget supplementing your data with 3rd party sources so that one step closer to predictive.

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Big Data, Bigger Opportunities [Infographic] - B2B Infographics

Big Data, Bigger Opportunities [Infographic] - B2B Infographics | The MarTech Digest | Scoop.it

 

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The Big 9 Big Data Sources [Infographic] - Get Elastic

The Big 9 Big Data Sources [Infographic] - Get Elastic | The MarTech Digest | Scoop.it

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CYDigital/marteq.io's insight:

A great infographic that simplifies Big Data.

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Big data landscape v 3.0 - FirstMark

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B2B Marketing’s Big Data Myth: “It Only Applies to B2C” - Forrester | #TheMarketingTechAlert

B2B Marketing’s Big Data Myth: “It Only Applies to B2C” - Forrester | #TheMarketingTechAlert | The MarTech Digest | Scoop.it

Advanced/ Condensed...


To exploit the business opportunity hiding in big piles of data - marketers must understand that data is increasingly:

  1. Diverse, making integration costly and complex. Look at the data pouring out of smart devices, wearable computers, sensors, social media, video, etc. and it’s easy to believe that 90% of all data has been created during the past 2 years (according to the US Chamber of Commerce and Norway’s SINTEF ITC.)
  2. Large, challenging your technology and analytics ability.  
  3. Messy, challenging your firm’s obsession with using only high quality, curated data for analysis.
  4. Not owned or controlled by you (at least a good chunk of it is this way), challenging the natural tendency to hoard data in silos and shy away from data sharing.

 

For B2B marketing executives, the best place to start their Big Data journey is by focusing on their buyers' journeys and by doing something that may seem a little counterintuitive: Start by understanding your best customers and work backwards through their journey to identify the patterns that led them to be successful and to stand out from the rest. 

 

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CYDigital/marteq.io's insight:

Big Data, as it applies to B2B, is a misnomer compared to B2C. Not even close. The real question is: how do I collect this information, append it with 3rd party data, then make sense of it to find pockets of opportunity and competitive advantage.

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