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.

The IAB Data Center of Excellence and the Data & Marketing Association (DMA) have released “The State of Data 2017,” a study showing that U.S. companies will have spent $10.05 billion on third-party audience data in 2017 for advertising and marketing efforts. However, slightly more – $10.13 billion – will have been spent on third-party solutions to support its activation.

The research, compiled by Winterberry Group and based on an analysis of financial information provided by a range of the nation’s leading commercial data providers, estimates that of the money spent on third-party solutions, $4.3 billion is expected to be spent in 2017 on supporting data integration, processing, and hygiene. These types of services help maximize the accuracy and actionability of audience data, while providing contextual brand safety. Spending on hosting and management solutions is anticipated to hit $4.2 billion by end of the year. Analytics, modeling, and segmentation solutions are expected to take in $1.63 billion.

The study revealed that of the $10.05 billion expected in third-party audience data more was spent on Omnichannel than others, reflecting the value marketers are placing in reaching consumers on multiple devices: 
    Omnichannel ($3.53 billion)
    Transactional ($2.99 billion)
    Digital ($2.07 billion)
    Specialty ($0.88 billion)
    Identity ($0.56 billion)

60 per cent of enterprise B2B marketers are using a DAM but only 23 per cent are actively using a content marketing platform to support their editorial processes. Net, just over half even have a tool to managed their valuable content assets and a very small number have codified processes that happen in something more formal than an excel spreadsheet and email. 

Atomic content and personalisation is only possible if we, marketers, shift our mindsets, build some new skills and use the tools available to us to attend to the care and feeding of our content library.  A few months ago I wrote a very nerdy post about metadata and attributes. It sparked a lot of conversations about how hard it is to do this today. But trust me, it’s getting easier.  More tools – some CMPs and some re-imagined DAMs – are making it simpler to store and retrieve assets. The infusion of artificial intelligence (AI) into some of these systems will augment work done by marketers and creatives – taking the drudgery out of tagging and unlocking the value in our content – even making recommendations about which assets may work best for a given situation.

"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"

Are you using your marketing automation platform to its fullest capabilities and potential? If you're not including data from social media channels, you're missing out on leads - and sales.

Up to 6.5 percent of your marketing/sales data loses validity every month. Data enrichment tools help solve this problem without manual efforts.

• Collectively exhaustive: Your connectivity layer should be “fit for purpose” for powering your entire stack, not just a component of it. 
• Low maintenance: Your connectivity layer should eliminate, or at least minimize significantly, the overhead associated with maintaining third-party APIs. 
• Open: Your connectivity layer should be partner and channel agnostic.  
• Flexible: Your connectivity layer should be able to “translate” in order to support all of the various schemas utilized by each of the endpoints across your marketing stack. 
• Secure: Your connectivity layers needs to be secure and reliable from end to end. 

An in-depth study on how poor marketing data can affect everything from your digital marketing strategies to your conversion rates.

• A new report from eMarketer on marketing technology found that companies are shifting their focus away from tech and toward data when looking at marketing platforms. 
• The reason for this shift is marketing tech is now an established part of managing the business and there is more value in utilizing data for personalization and other customer-centric marketing activities, according to report author and eMarketer analyst Nicole Perrin.
• The report points out that marketers are realizing that data is central to the marketing technology ecosystem.

As marketing technology becomes a standard part of managing a business, many companies have become comfortable enough with the concept to shift away from their initial focus on platforms, and to focus instead on data.

“The spotlight is on data because of customer expectations,” said eMarketer analyst Nicole Perrin, author of a new report on marketing technology. “Customers expect marketers to provide a personalized brand experience across channels.”

When marketers are surveyed about what they hope to gain from adopting marketing tech, “data” is one common response. This might seem like circular logic, but data—specifically, customer data—is the undisputed center of the marketing tech ecosystem.

Siloed and fragmented data across operational systems causes a multitude of problems, including, but not limited to:

• Inefficient operations due to manual processes and manual data reconciliation
• Incorrect orders due to poor customer or product data, leading to costly rework
• Revenue loss due to wrong discounting or a poor understanding of customer entitlements
• Compliance risks by not enforcing regulatory requirements for handling certain customers or products
• Poor sales effectiveness, as professionals must hunt for customer information, what product(s) the customer currently has and what they need

A modern data management approach helps:

1. Establish data integrity and accuracy

2. Provision data in context

3. Be flexible and agile

"Abundant data is better data. Everything about it is more delicious. Here are the trends in the kinds of data we are using to drive our businesses.

• From smaller samples sizes to larger sample sizes
• From self-reported input to behavioral input
• From less-recent to more-recent data
• From data collected at one point in time to data spanning months and years of data
• From periodic to real-time collection
• From qualitative to quantitative data
• From strangers to prospects and customers"

Not every company can afford to have full-time people dedicated to analysis. Hence let me make optimal recommendations for three types of companies.

If you are a handful of employees small business, here's your balance between data capture, data reporting, data analysis: 05 | 20 | 25.

You are a small business, I get it, you only have a part of one resource dedicated to data. No problem. Ensure that part-time resource is spending their time with a 05 | 20 | 25 balance. At your size, you'll still win with data.

If you are a numerous employees medium sized business that is growing at a ferocious pace, here's your DC | DR | DA balance: 10 | 25 | 65.

Lastly, if you are a massive employees corporation here's your DC | DR | DA balance: 15 | 35 | 50.

Salesforce is one of the top platforms for businesses to gain and measure marketing tech data. However, many are still not consolidating that data, and this can make it difficult to truly understand how campaigns are faring. 

• First, consolidation creates a single source of data truth
• Second, Salesforce allows marketers to drill down into vital information
• Third, working within the same platform creates a better alignment with sales
• Fourth, a central data repository is more secure

Sales - Some 7.5% of essential contact information in B2B sales databases become outdated within three months, according to recent research from Radius.