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AI, Predictive Content, and the Customer Experience - Kapost

What does “predictive content” mean? Simply enough, it predicts what type of content is shown to each individual to make the most of every interaction. Predictive content relies on the uses of machine learning (ML) and predictive analytics to automatically put the most relevant content in front of each person across web, mobile, and email channels.

Based on market research, marketing and AI are still trying to find a balance. In a 2016 B2B Gallup report, 71% of consumers are uninterested or disengaged and 60% of B2B customers just don’t care about brand relationships. Instead, they prefer authentic human interaction. So AI may be filtering for relevant content, but is it really engaging the consumer?


As AI and consumer relationships try to weave themselves together seamlessly, one thing keeps getting in the way: the consumer’s desire for authenticity. It’s not about how often one sees content or how many followers people have on any given social network; it’s about the authenticity of the content and how brands engage with their followers in meaningful ways.
CYDigital's insight:

AI, Predictive Content, and the Customer Experience - Kapost

 

So long as the content meets the needs of the reader, all is well.

 

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

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Strengthening Predictive Analytics with Intent Data - MarTech Series

Strengthening Predictive Analytics with Intent Data - MarTech Series | The MarTech Digest | Scoop.it
A complete predictive analytics solution combines a sound understanding of your target market and multiple sources of intent data and real-time engagement data to accurately predict and target new accounts. Target market data includes current customer intelligence and lookalike modeling, plus firmographic data derived from organization characteristics and technographic data that looks at organizations’ current solutions to glean information about purchase behavior. Real-time engagement data comes from responses to various sales and marketing tactics, including direct mail, display advertisements, inside and field sales outreach, and email campaigns to help round the solution out.

Intent data can build on target market intelligence with first-party and third-party data by helping uncover the content research and engagement trends for solutions in your stack. This type of data includes first-party data such as website traffic monitoring that companies can already access internally, and can be an invaluable advantage for a predictive solution. True intent data incorporates third-party data such as intelligence from the B2B web, making it even more powerful as a contributor to a predictive strategy.

Combined, this internal and external intent data provides a framework from which sales and marketing teams can begin to characterize the accounts that make up their current and prospective customers. Intent data forms part of a solid groundwork from which a predictive customer acquisition strategy can build if it has broad coverage of the target market. Real-time engagement provides the final piece to a true predictive solution.
CYDigital's insight:

Strengthening Predictive Analytics with Intent Data - MarTech Series

 

And everything goes into a CDP.

 

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

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Why CFOs should care about predictive marketing analytics - Marketing Tech News

Marketing feels they’re not getting recognition for their achievements. Finance sees marketing as an endless cost centre. This is not an easy fix. Forrester research shows 78% of respondents agree that marketing-finance alignment is vitally important, but only 15% feel that the two departments currently work together towards shared goals. At the very core of this rift is an inability to measure the impact of marketing in wider financial terms. That’s where predictive marketing analytics steps in.

Advanced predictive creates a centralised metric that brings marketing and finance onto the same page: Customer lifetime value (CLV) which represents predicted lifetime revenue in quantitative financial terms.

CLV allows marketing to see which accounts are most worth targeting so it can better focus its efforts, and in turn build a better business case for reinvestment; and finance can see exactly where revenue is coming from, through a clear, centralised dashboard.

Finance executives get complete future-based oversight of marketing performance towards tangible financial goals – so they can make better decisions about the wider business.
CYDigital's insight:

"Why CFOs should care about predictive marketing analytics - Marketing Tech News"

 

This may be wishful thinking: a rock-solid system for attribution is a prerequisite.

 

Click/tap to view the original article.

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

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kunal Gaur's curator insight, March 7, 1:55 AM

If CFO wants to assume a Strategic role then partnership with Marketing team is a must !!!  


Find out how Finance and Marketing can work together to achieve shared goal of higher growth and better margins.  

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KyoceraVoice: Five Common Workflow Problems -- And How To Solve Them - Forbes

KyoceraVoice: Five Common Workflow Problems   --   And How To Solve Them - Forbes | The MarTech Digest | Scoop.it

"Predictive analytics can be applied in the following ways:

  • GTM Strategy: Predictive marketing platforms provide CMOs with greater visibility into what sets their best customers apart as well as a clear picture of their full market potential.  
  • Targeting & Messaging: CMOs can use predictive to determine their ideal customer profile(s) and prioritize resources on the best customer segments for their business.  
  • Outbound Marketing: With predictive, CMOs can drive customer acquisition and revenue growth by filling the funnel not only with more leads but with leads that are most likely to turn into high value customers. 
  • Inbound Marketing: For CMOs who have an inflow of leads, predictive analytics is used to prioritize the best inbound and existing prospects. 
  • Account Based Marketing: CMOs can use predictive to enhance sales and marketing alignment with data-driven account selection and insights, and then use multi-channel integrations to put your ABM plan into action."
CYDigital's insight:

Yes, of course, this is all great. But it needs to be baked into the application(s).

 

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Predictive Analytics Market Worth 12.41 Billion USD by 2022 - MarketsandMarkets

According to a new research report "Predictive Analytics Market by Type (Services, Solutions (Financial Analytics, Risk Analytics, Customer Analytics, Marketing Analytics, Sales Analytics, Web & Social Media Analytics)), Deployment, Organization and Industry Vertical - Global Forecast to 2022", published by MarketsandMarkets™, the Predictive Analytics Market size is expected to grow from USD 4.56 Billion in 2017 to USD 12.41 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 22.1% The key factors driving the Predictive Analytics Market include increasing business interests toward advanced analytics for future estimations.
CYDigital's insight:

FWIW...

 

Subscribe to The MarTech Digest: http://bit.ly/miocom #MarTech

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How to Use Predictive Analytics to Engage Customers Throughout Their Journey - MarketingProfs

How to Use Predictive Analytics to Engage Customers Throughout Their Journey - MarketingProfs | The MarTech Digest | Scoop.it
Stage 1: Targeting

To curate a highly targeted, qualified prospect list, marketers should build their lists from machine-learning-based predictive models, which deliver significantly more accurate data intelligence than traditional models that use a simplistic rules-based approach. New, innovative machine-learning models learn from and leverage the intelligence that resides in the CRM, such as historical information about who bought products or services in the past. 

Stage 2: Education

Marketers can apply predictive analytics to display personalized webpages based on a consumer's personal preferences. This is achieved by applying machine-learning algorithms that track online habits that help marketers create personal online experiences. Second, when marketers follow up via phone or email, they can personalize the interaction based on knowledge from previous interactions or insights derived from external data. 

Stages 3 and 4: Purchase and Cross-Sell/Upsell

Predictive analytics can be applied to match product offers to each customer based on demographic data, purchase history, and data from previous customer interactions—ensuring each product recommendation is valuable and relevant, to optimize sales and customer service.

Stage 5: Satisfaction

Using predictive analytics, marketers can forecast which customers are likely to churn; marketers can then apply retention campaign dollars more effectively. 
CYDigital's insight:

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The Next Frontier Of B2B Sales Is Predictive Analytics

The Next Frontier Of B2B Sales Is Predictive Analytics | The MarTech Digest | Scoop.it
How Businesses are Using Predictive Analytics in the Workplace

1. Superior Sales Forecasting

2. Eliminating Uncertainty

3. Assisting HR and Hiring

4. Preventing Workplace Injuries

5. Employee Training

 What Does the Future Hold?
CYDigital's insight:

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

 

#MarTech #DigitalMarketing

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The Essential Elements of Predictive Analytics and Data Mining - Lynda

The Essential Elements of Predictive Analytics and Data Mining - Lynda | The MarTech Digest | Scoop.it

"A proper predictive analytics and data-mining project can involve many people and many weeks. There are also many potential errors to avoid. A "big picture" perspective is necessary to keep the project on track. This course provides that perspective through the lens of a veteran practitioner who has completed dozens of real-world projects. Keith McCormick is an independent data miner and author who specializes in predictive models and segmentation analysis, including classification trees, cluster analysis, and association rules. Here he shares his knowledge with you. Walk through each step of a typical project, from defining the problem and gathering the data and resources, to putting the solution into practice. Keith also provides an overview of CRISP-DM (the de facto data-mining methodology) and the nine laws of data mining, which will keep you focused on strategy and business value.


Topics include:

  • What makes a successful predictive analytics project?
  • Defining the problem
  • Selecting the data
  • Acquiring resources: team, budget, and SMEs
  • Dealing with missing data
  • Finding the solution
  • Putting the solution to work
  • Overview of CRISP-DM"
CYDigital's insight:

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

 

#MarTech #DigitalMarketing

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Predictive analytics: Four prerequisites of an effective strategy

Predictive analytics: Four prerequisites of an effective strategy | The MarTech Digest | Scoop.it
1. Appropriate sources of data 

2. Data cleanliness and usefulness

3. Automation and machine learning

Machine learning may be something that more and more martech vendors are trumpeting as a part of their software, but making use of these algorithms more broadly in your company's analytics teams is not a plug-and-play scenario.

4. Meeting business objectives
CYDigital's insight:

Again about the importance of data as a prerequisite to a strong predictive engine.

 

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

 

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Prepare Your CRM Now for Predictive Analytics - DestinationCRM

Prepare Your CRM Now for Predictive Analytics - DestinationCRM | The MarTech Digest | Scoop.it
The vast amount of information and the speed at which it flows are two of the biggest challenges many companies face. According to Aberdeen’s research, 96 percent of organizations suffer from ineffectual use of data. One misgiving that potential users may have about predictive analytics is uncertainty over the accuracy of the data on which conclusions are based. In order to provide the best analysis, the data involved must be adequately prepared. This step is so important that some analysts spend more than three quarters of their time simply preparing the data for analysis. Automating data preparation allows users to maintain data governance while reducing stress on IT. Gartner analysts researching predictive analytics recommend that companies begin with clean, accurate, and complete data in their sales force automation solutions prior to implementing analytics. 

But inaccurate data is not the only factor that can sabotage a forecast; sometimes information is scattered in so many locations and in such a wide variety of formats that it cannot be consolidated. Companies must integrate data into a unified view of the customer across all systems to increase the accuracy and relevancy of the data to be analyzed. Companies that utilize analytics are 42 percent more likely to standardize data captured across multiple channels to ensure ease of software integration.

Besides “clean” data, predictive analytics must have access to a wide variety of data sources, as it “learns” with every new data point. At the same time, it is important to avoid incorporating too many sources too quickly. An agile approach that leverages smaller, already consolidated sets of data allows for a rapid return on investment and incremental expansion into more complex sources, and ensures continued support for a predictive analysis initiative. 
CYDigital's insight:

Any marketing system requires a robust DB.

 

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

 

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The Evolution of Marketing: From Manual Through Automation To Predictive - Oracle

The Evolution of Marketing: From Manual Through Automation To Predictive - Oracle | The MarTech Digest | Scoop.it
“Likely to overhaul the way we do business and even the way we live, big data and AI [artificial intelligence] are two of the most sweeping revolutions of the 21st century,” writes Dr. Jacob Shama, CEO, Co-Founder of Mintigo in Intelligent Customer Engagement. “It empowers us to sift through mind-boggling masses of raw data, process it, structure it, and apply it to insight development on a grand scale […] Now, big data and AI are also powering marketing.”

The number one challenge marketers face is creating and nurturing demand real time. For Shama, predictive marketing—the application of data science to traditional marketing—is the answer. “Exploiting new technologies such as AI to amass and process vast amounts of information on companies and decision makers, predictive analytics scientifically guides marketers to the campaigns that create the highest engagement and produce the highest revenue.”
CYDigital's insight:

Predictive + AI eliminates both customer journey analysis and persona creation. The former is science, and the latter is more art.

 

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

 

#MarTech #DigitalMarketing

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MarTech: Making Marketers More Independent - MarTech Advisor

MarTech: Making Marketers More Independent - MarTech Advisor | The MarTech Digest | Scoop.it
While marketing automation tools can help simplify the marketing process, artificial intelligence (AI) has proven to be incredibly helpful in improving the overall customer experience. Perhaps, this is the reason why major companies are “remaking themselves around the technology.” AI platforms help marketers create a structured messaging system that can actually “speak” to the consumer by parsing and sifting through heaps of consumer information.  Driving creative efficiencies through AI-powered automation augments content marketing, production, delivery and overall presentation of campaigns.
With predictive analytics being one of the key AI capabilities, marketers can now prioritize the use of their marketing resources. For startups and small business marketers, creating a lean and productive strategy is crucial to success, in these cases predictive analytics keeps the business growing without bloating the marketing budget. AI driven predictive analytics can help marketers become more agile with real-time feedback on campaigns.
CYDigital's insight:

Predictive is not AI. 

 

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

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The Analytics Understanding-Activation Gap: Why Marketers Struggle To Leverage Predictive Analytics - Forbes

The Analytics Understanding-Activation Gap: Why Marketers Struggle To Leverage Predictive Analytics - Forbes | The MarTech Digest | Scoop.it
There is a gap between marketers understanding predictive analytics well (44% of respondents) and them having actually implemented or used it (11% of respondents). There are really two key drivers of this gap. First, it is primarily because predictive analytics and predictive marketing are new and marketers have only recently started to learn about and realize the benefits of predictive. So that means that it’s primarily been early adopters who have added predictive analytics to their marketing tech stack—giving these firms a competitive advantage. The gap between understanding and usage will close as more marketers continue to learn about predictive’s value proposition and see case studies of its effectiveness.  Second, there aren’t many skilled individuals or predictive marketing vendors yet who can help firms develop a competency. Over time, individuals who are skilled in predictive analytics and firms available to consult marketers will expand predictive capabilities and use cases, and predictive will become an essential tool in the marketing tech stack.
CYDigital's insight:

It needs to be baked-in.

 

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

#MarTech #DigitalMarketing

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6sense Ranked a Leader Among Predictive Marketing Analytics Platforms for B2B Marketers by Forrester

6sense Ranked a Leader Among Predictive Marketing Analytics Platforms for B2B Marketers by Forrester | The MarTech Digest | Scoop.it
CYDigital's insight:

Go here for a complimentary copy from 6Sense: http://content.6sense.com/rs/958-TTM-744/images/ForresterWaveReport.pdf

 

Monetize your excess MarTech with RYZZ. It’s a new approach to MarTech for B2B Marketers.

 

#MarTech #DigitalMarketing

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Predictive Analytics for Right-Brained Marketers - MarTech Advisor

Predictive Analytics for Right-Brained Marketers - MarTech Advisor | The MarTech Digest | Scoop.it
Data, analytics, algorithms, statistics, probability and all such left brain terms make my head spin. If you are a right-brained marketer like me, then you prefer to go by gut and instinct, love conceptual explanations and enjoy creating solutions rather than having a machine give them to you. Unfortunately, anyone remotely connected with marketing today cannot escape data, numbers, fact and technology. Analytics is all of that and more. It’s time to face (and slay) our fears. This article will attempt to decode Predictive Marketing Analytics so it makes sense to a B2B marketer thinking about the what, why, when and how of it.
CYDigital's insight:

One impact to consider: Predictive applied to 3rd party databases will kill Personas. A superb overview of Predictive.

 

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

 

#MarTech #DigitalMarketing

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[FREE] Predictive Analytics Buyers Guide for SMBs - MarTech Advisor

[FREE] Predictive Analytics Buyers Guide for SMBs - MarTech Advisor | The MarTech Digest | Scoop.it
MTA has created this guide for marketers to:
  • Make sense of the predictive marketing analytics ecosystem, and place the major components of your strategy in perspective.
  • Understand the key success factors to making predictive marketing analytics work for you
  • Get an overview of the major considerations while evaluating predictive analytics vendors
  • MTA proprietary green zone for best fit Predictive marketing analytics vendors for SMBs
  • Vendor profiles
CYDigital's insight:

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

 

#MarTech #DigitalMarketing

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Is Predictive Analytics Your ABM Silver Bullet? - Chiefmarketer

Is Predictive Analytics Your ABM Silver Bullet? - Chiefmarketer | The MarTech Digest | Scoop.it

"Let’s look at some examples:

  •  The CEO of a company on your list just informs analysts during an earnings call that the company will spend 100% of its IT budget this year on personalization initiatives—which does not match up with your product portfolio.
  • A company on the list announces it is acquiring a competitor and its #1 priority for the year is the successful integration of the two companies.
  • •    A new CEO has just been named to a targeted company and in an interview she reveals a major strategy shift and a new set of priorities. Historical data—however recent it might be—will not reflect any of these critical nuggets.


None of this revelatory information would appear in the data. Given that the data can’t tell the full story, you can miss out on opportunities if you solely rely on the data.

So, while you may have a predictive analytics tool telling you that the company is well-positioned to buy your product, you would have a much stronger case for a sale if you told a customer, “Your CFO said on the last earnings call that almost all of your IT spend will be dedicated to driving personalization initiatives. I would like to speak with you about how our product has helped many companies like yours drive personalization…”"

CYDigital's insight:

Yet another example of how to creatively use technology...thinking outside the box.

 

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 

 

#MarTech #DigitalMarketing

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Predictive analytics or gut instinct: Which works better? - TechRepublic

Predictive analytics or gut instinct: Which works better? - TechRepublic | The MarTech Digest | Scoop.it
Yes—there is a middle ground between going 100% analytics, where you risk missing out on great ideas just because your analytics can't confirm them; and 100% gut feel, where you don't have any analytics to give you at least some confirmation for your idea before you try it.

Here are three recommendations for companies that want to incorporate both gut feel and analytics in new product development:

Create a product development intake process that can accommodate both gut feel ideas and the ideas that come out of your analytics.

Secure the support of your management

Whether you are using predictive analytics or gut feel, know when to pull the plug
CYDigital's insight:

Use predictive as a tool, nothing more. At the end of the day, it's your call.

 

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 

 

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The ROI of Predictive Analytics for Marketing - Smart Insights Digital Marketing Advice

The ROI of Predictive Analytics for Marketing - Smart Insights Digital Marketing Advice | The MarTech Digest | Scoop.it
Chart of the Day: Predictive Analytics Users Get Better Results Predictive Analytics isn't a new marketing technique, but interest in it has increased with. Marketing topic(s):Customer Experience Management CXM. Advice by Dave Chaffey.
CYDigital's insight:

Now this is a slam dunk.

 

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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What Is Predictive Analytics? A Definitive Guide For B2B Marketers - Radius

What Is Predictive Analytics? A Definitive Guide For B2B Marketers - Radius | The MarTech Digest | Scoop.it
Predictive analytics has been defined in many ways. The more technical definition reads, “predictive combines vast amounts of historical and contextual data to create a probabilistic model that predicts which actions or audiences have high likelihood to succeed and which have a high probability to fail.”

However, a description of the emerging technology category can be drastically simplified – a system that uses your past data to predict outcomes. Or, one of our favorite explanations which came from the Chief Analytics Officer at Swift Capital, predictive is simply the 80/20 rule.

We know 80% of our revenue will come from 20% of our prospects. And 80% of pipeline will come from 20% of our campaigns. Predictive identifies the 20%.
CYDigital's insight:

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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How machine learning makes predictive analytics better - IT Toolbox

How machine learning makes predictive analytics better - IT Toolbox | The MarTech Digest | Scoop.it

"Unlike traditional predictive analytics methods which begin with the data, machine learning starts with the outcome variables and teaches a computer to automatically look through data for predictor variables and their interactions. Driven by data, computers "learn through experience" by searching through data sets to detect patterns and trends—no programming (by humans) required. The computer uses the patterns it discovers to adjust its own program actions. Because machine learning-based predictive models can adjust and improve over time as they take in new data, they can provide more timely analysis for real-time decision-making.

 

For example, by combining machine learning techniques with predictive analytics retail companies can analyze unlimited amounts of causal factors simultaneously to help them enhance consumer interaction and create more accurate demand forecasts. Whereas traditional demand forecasting and planning systems have been restricted to demand patterns found only in historical data, machine learning-based forecasting can incorporate real-time streams of data that can be used to analyze demand for an unlimited number of products, providing immediate consumer insight to a "what-if" analysis that can be used to influence future demand. Machine-learning based statistical models are updated automatically; no human intervention is required to run the model multiple times to refine the results."

CYDigital's insight:

Fascinating article, and strongly recommend you CT for the full text. It demonstrates how predictive continues to expand.

 

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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The Ultimate Guide for Choosing Algorithms for Predictive Modeling - AnalyticBridge

The Ultimate Guide for Choosing Algorithms for Predictive Modeling - AnalyticBridge | The MarTech Digest | Scoop.it
The algorithms that are right for you depend on what you are trying to accomplish. For example:
 
  • Classification algorithms are great if customer retention is your focus or if you are trying to put together a recommendation system.
  • Clustering algorithms work well for segmentation or use with social data.
  • Regression algorithms are generally used as a way of predicting outcomes from events that are calendar driven.
CYDigital's insight:

Peek under the hood...

 

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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How to Choose the Right Predictive Analytics Tool for Account-Based Marketing - NuSpark Marketing

How to Choose the Right Predictive Analytics Tool for Account-Based Marketing - NuSpark Marketing | The MarTech Digest | Scoop.it
When you’re in the market for a predictive analytics tool, there are deliverables that are essential for success. Ideally, the product you use should offer:
  • Data from Multiple Sources: Most predictive analytics vendors make use of multiple data points to help “feed” the models and forecast potential high-value accounts. These include licensed data from aggregators, website traffic, web scraping and even job listings.
  • Development of Customer Profiles: B2B organizations are accustomed to the idea of defining their ideal client and creating personas to inform marketing and content. A powerful predictive analytics tool takes this a step further, by using profiles to discover prospects as well as reach them. 
  • Filling the Gaps: With lead scoring based on having as much information about prospects as possible, fleshing out gaps in a customer profile delivers a level of enrichment and accuracy that can be useful determining the prospects’ position in the sales funnel. 
  • Making it Personal: Predictive analytics tools that integrate with other marketing and communications options give clients a better chance of reaching target audiences.
  • Reporting Mechanisms: The vendor needs to be able to report on issues such as the ongoing impact of marketing interactions and their effect on customers’ lifetime value to the company.
CYDigital's insight:

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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Predictive Sales and Marketing Data Sources - David Cummings

Here are a few of the predictive sales and marketing data sources:

  • Demographic – Different personas with attributes like job title are valuable when building predictive models
  • Firmographic – Characteristics of the company like size, industry, and location are key data points
  • Social – Significant amounts of publicly available information on social media include location, frequency, interests, and more
  • Technographic – Technologies implemented in a company and on their website (e.g. web server, CMS, marketing automation vendor, etc.) provide a unique profile of the business
  • Digital Behavior – Marketing automation tracks page views, email opens, ebook downloads, and every other digital fingerprint — all useful for understanding the ideal buyer
CYDigital's insight:

marketingIO’s Outsourcing delivers the vetted talent you need under your complete management and control. Contact us to see how. #MarTech #DigitalMarketing

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Predictive Marketing: The Next Must-Have Technology for CMOs - CMSwire

Predictive Marketing: The Next Must-Have Technology for CMOs - CMSwire | The MarTech Digest | Scoop.it
Predictive analytics lets you take large sets of data and mine them for actionable insights using specific types of AI. These models are created by data scientists and software that use machine learning algorithms to produce the most accurate predictions as possible to aid businesses in their decision-making.

Top uses of predictive analytics for marketing include:

  • Defining your ideal customer profile and identifying prospects that match
  • Providing a common framework for decision-making between sales and marketing
  • Creating personalized content, offers, and campaigns for high-value customer segments
  • Increasing conversion rates, closed deals, and deal size
Deciding to use predictive analytics is the first step, but effectiveness varies from vendor to vendor. Be prepared to do some comparison shopping before you find the best fit.
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