The Predictive Marketing Analytics for B2B Marketers Forrester Wave

Allison Snow

When my colleague Laura Ramos and I set out to “Wave” the B2B predictive marketing analytics space, we knew that while there are impressive results across the board here, marketers struggle to identify differentiation among its vendors. We’re thrilled to have published Forrester’s first Wave that provides clarification to B2B marketers who seek to connect their business requirements to a predictive marketing analytics solution.

The Wave process begins by screening dozens of interested vendors and each participating vendor – household name or not – brings exceptional capabilities to the market.

We included 11 vendors in the assessment: 6sense, BrightTarget, EverString, Infer, Lattice, Leadspace, Mintigo, MRP, Radius, The Big Willow, and Versium.

Forrester Waves have a track record of delivering objective guidance to technology buyers of all stripes, supported with an interactive tool that marketers can use to zoom in on the capabilities that are important to them. We chose to focus on how well these offerings give marketers the ability to deploy predictive marketing analytics across the customer life cycle, to integrate with other popular martech solutions, and leverage a variety of data sources. Laura and I prioritized the following core principles as we built and assigned weights to each of the 28 criteria:

  • The extent to which solutions can reliably predict an outcome in a specific time frame.
  • The ease with which marketing and sales can execute campaigns using model output.
  • The degree to which model output supports engagement across the customer life cycle.
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Forrester Methodology To Select Business Intelligence Implementation Service Providers

Boris Evelson

Business Intelligence (BI) pros continue to look for outside professional services. Forty-nine percent of decision makers say their firms are already engaging and/or expanding their engagements with outside data and analytic service providers, and another 22% plan to do so in the next 12 months. There are two main reasons for this sustained trend:

  • The breadth and depth of BI deployments cannot be internally replicated at scale. Delivering widely adopted and effective BI solutions is not easy. It requires rigor in methodology, discipline in execution, the right resources, and the application of numerous best practices. No internal enterprise tech organization can claim this wealth of expertise and experience; this only comes after delivering thousands of successful and unsuccessful BI projects — which we believe is solely the realm of management consultants and systems integrators. These partners have collectively accumulated such experience over many years and thousands of clients and projects.
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Is Business Intelligence (BI) Market Finally Maturing? Forrester Three Big BI Market Predictions

Boris Evelson

No. The buy side market is nowhere near maturity and will continue to be a greenfield opportunity to many BI vendors. Our research still shows that homegrown shadow IT BI applications based on spreadsheets and desktop databases dominate the enterprises. And only somewhere between 20% and 50% of enterprise structured data is being curated and available to enterprise BI tools and applications.

The sell side of the market is a different story. Forrester’s three recent research reports are pointing to a highly mature, commoditized and crowded market. That crowded landscape has to change. Forrester is making three predictions which should guide BI vendor and BI buyer strategies in the next three to five years.

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Introducing The Forrester Wave™: Digital Intelligence Platforms, Q2 2017

James McCormick

Early Digital Intelligence Platform Players Deliver Great Value - Yet Have Many Opportunities For Improvement

As digital disruption continues its unstoppable march, digital engagement is rapidly evolving and customers’ expectations that they will get what they want during moments of digital interaction continue to grow. Now more than ever, firms need to understand their customers during and across these moments — and use this understanding to surprise, delight, and personalize. To do this, firms and their insights pros need to cultivate digital intelligence (DI), which Forrester defines as:

The practice of developing a holistic understanding of customers across digital touchpoints for the purpose of optimizing and perfecting the experiences delivered and decisions made by brands during moments of engagement.

To build a holistic understanding — and synchronize engagement optimization — across a growing digital customer engagement edge, firms have procured a plethora of DI tech to deliver capabilities such as web analytics, mobile analytics, behavioral targeting (personalization) capabilities, and more. Initially, the tech was procured in isolation by various relevant teams, including those for web, digital, marketing, mobile, and products. But leading practices have reached a tipping point; they are starting to mature their DI strategies to the point of coordinating the adoption and integration of this tech. The result is that the last 18 months have shown a growth in interest and adoption of platformsthat deliver multiple DI capabilities.

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PLM TechRadar Report: Democratized PLM Offerings Expand Functionality and User Base

Nate Fleming

As the product development process and product usage creates higher volumes of data, PLM is a necessary tool to consolidate disparate sources of product information. From this repository, engineering can use product usage data to inform next generation products, operations can improve product development processes, and business stakeholders can focus on linking products to holistic customer experiences. These opportunities reveal the benefit of opening PLM up to stakeholders beyond the product development organization, thus bringing the customer closer to product ideation and development. 

A catalyzing functionality in this democratization of PLM are role-based applications which open once-complicated PLM software solutions to new users across the organization.  These applications improve usability, solution adoption, time-to-market, and collaboration by incorporating more cross-functional input to the product development process.  PLM vendors, large and small, are rolling out role-based application modules for customers, and end user buyers say they are beginning to get requests from their internal constituents for this type of functionality. 

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What Exactly The Heck Are Prescriptive Analytics?

Mike Gualtieri

Prescriptive analyticsPrescriptive analytics is about using data and analytics to improve decisions and therefore the effectiveness of actions. Isn’t that what all analytics should be about? A hearty “yes” to that because, if analytics does not lead to more informed decisions and more effective actions, then why do it at all? Many wrongly and incompletely define prescriptive analytics as the what comes after predictive analytics. Our research indicates that prescriptive analytics is not a specific type of analytics, but rather an umbrella term for many types of analytics that can improve decisions. Think of the term “prescriptive” as the goal of all these analytics — to make more effective decisions — rather than a specific analytical technique. Forrester formally defines prescriptive analytics as:

"Any combination of analytics, math, experiments, simulation, and/or artificial intelligence used to improve the effectiveness of decisions made by humans or by decision logic embedded in applications."

Prescriptive Analytics Inform And Evolve Decision Logic Whether To Act (not not act) And What Action To Take

Prescriptive analytics can be used in two ways:

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Get ready for Business Intelligence market next wave of M&A

Boris Evelson

Business intelligence (BI) is a runaway locomotive that keeps picking up speed in terms of enterprise interest, adoption, and spending levels. The result: Forrester now tracks 73(!) vendors in the segment. Their architectures and user interfaces vary, but they support similar use cases. Forrester started the original research with fewer than 30 vendors in 2014 and ended up with 73 in the current 2017 update. Expect this dynamic to continue for the foreseeable future. Even though the BI market is quite mature from the point of view of the number of players and breadth and depth of their functionality, it is still quite immature regarding business and technology maturity, adoption, and penetration levels in user organizations. Vendors will continue to seize this opportunity — new players will keep springing up, and large vendors will continue to acquire them.No market, even a

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Analyze This! Cisco Spends $3.7B To Buy AppDynamics

Milan Hanson

Cisco’s intent to acquire AppDynamics – officially announced on Wednesday Jan 25 2017 – is quite a surprise. Then again, it isn’t. 

It’s a surprise because AppDynamics was one day away from its IPO, giving nary a hint of courting a suitor.  That would be an awfully expensive and troublesome camouflage.  And if it was camo, it was amazingly airtight in this notoriously leaky information age.  (As I write this, several press outlets report the deal went from idea to agreement in three days.)  

It’s not a surprise because: 

·        AppDynamics’ APM competitors have been rapidly broadening their monitoring to yield better analytics with fewer blind spots.  Cisco gives AppDynamics an exceptionally clear view of network performance and AppDynamics gives Cisco a clear view of application performance.  APM solutions must continue to expand their data ingestion to provide optimum value.   

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Divide (BI Governance From Data Governance) And Conquer

Boris Evelson

Stop! Before you invest even 10 minutes of your precious time reading this blog, please make sure it's really business intelligence (BI) governance, and not data governance best practices, that you are looking for. BI governance is a key component of data governance, but they're not the same. Data governance deals with the entire spectrum (creation, transformation, ownership, etc.) of people, processes, policies, and technologies that manage and govern an enterprise's use of its data assets (such as data governance stewardship applications, master data management, metadata management, and data quality).  On the other hand, BI governance only deals with who uses the data, when, and how.

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Business Intelligence Skills

Boris Evelson

So you have gone through the Discover and Plan of your Business Intelligence (BI) strategy and are ready to staff your BI support organization. What skills, experience, expertise and qualifications should you be looking for?

  • Since the term BI is often used to also include data management processes and technologies, let's assume that in your case you are only looking for expertise required to build reports and dashboards and it does not include
    • Data integration (ETL, etc) expertise
    • Data governance (master data management, data quality, etc) expertise
    • Data modelling (relational and multidimensional) expertise
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