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.

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.

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.

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.

"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"

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

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.

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.

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

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.

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%.

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.

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.