RevOps Is the Life Jacket If You’re Drowning in MarTech

Marketing and Sales have spent the past two decades investing in mountains of technology to drive more revenue, faster.  This has fueled the MarTech category explosion and financial bonanzas for vendor unicorns and those acquired at heady valuations. Yet Marketing and Sales remains grossly inefficient. There are several reasons for this including that technology has reinforced, not torn down, organizational and data silos.  MarTech categories overlap resulting in redundant functionality yet leave significant gaps. The result is a complex patchwork of capabilities leaving the core objective – faster, sticker, more predictable revenue – elusive. Add to that the pervasive mismatch between positioning and actual capabilities, especially in the name of ‘customer experience’. And then there is the persistent data integrity problem that plagues just about every company. Also Read: What’s Going to Keep CMOs Awake at Night in 2019 We’re in this situation because we’ve been approaching efficiency gains from the bottom-up. Things will not change until we start addressing the problem from the top-down. No amount of feature/process level fiddling and hyper-automation will yield what we seek. The missing link is the codification of context setting strategic plans that guide investment, business process design, automation, and metrics. The good news is a handful of vendors are introducing solutions that capture the bigger picture in a framework that provides context for the rest of the Martech stack. One of the most critical strategies in an organization is the annual go-to-market plan. It is the agreed upon growth plan of how, where, when and who is accountable for what.  Every organization develops one. Go-to-Market plans are defined at a high-level and supported by functional plans including sales compensation, marketing plans, etc.  The challenge is sticking to the plan and reporting against it.  Too often teams forget the details of the plan. Countless cycles are spent on figuring what was agreed to and who didn’t do what instead of evolving the plan based on insights gained from cross-functional metrics and analyses. Also Read: Make Your Marketing Great One company, LeanData, is actively addressing this need and created a new market category – RevOps.  SiriusDecisions defines Revenue Operations as an emerging go-to-market paradigm ‘bringing the operational work of sales, marketing and customer success together under one roof’. “Companies have all these tools to achieve revenue and coordinate activities but aren’t orchestrating all the touches,” said Karen Steele, CMO of LeanData. “RevOps unites finance, business operations, sales and marketing ops functions through one platform to plan, execute and measure revenue activities, specifically Go To Market.” As a serial CMO, it always made sense to combine Marketing and Sales Ops. The insights were more meaningful and I want happy to give that function to Finance. Having it report into Sales or Marketing tainted the analyses’ credibility; Finance’s neutrality and comprehensive view of the organization’s performance strengthened the Ops team’s impact.  It made my day to hear from Steele that customer RevOps teams are starting to report into COO or CROs and, in LeanData’s case, to the CFO. Revenue Ops solution sits within an organization’s CRM and fixes a key weakness preventing greater unity between sales and marketing ops – inaccurate and fragmented silos of data across the Martech stack enabling customer touch points to be rationalized, optimized and personalized. RevOps won’t magically fix poorly designed processes. It will, however, put sunshine on them so they are addressed. Efficiencies are gained from actionable insights into revenue cycles ‘line of sight’, customer journey alignment, organizational productivity, and ROI analysis based on organization-wide data. LeanData’s approach underpins and aligns the Go-To-Market strategy. The fact they already have mastered consolidating, enhancing and maintaining data from multiple disparate source makes RevOps a logical next step.  Welcome to the ‘Needle Move Club’, LeanData, and redefining Martech’s future. First published in MarTech Advisors

What’s Going to Keep CMOs Awake at Night in 2019

5 Influencers Predict AI’s Impact on Business in 2019

With Artificial Intelligence (AI) already proving its worth to adopters, it’s not surprising that an increasing number of companies will implement and leverage AI in 2019. Now, it’s no longer a question of whether AI will take off. Instead, it’s a question of which companies will keep up. Here are five predictions from five influencers on the impact AI will have on businesses in 2019. From retail giants to Silicon Valley startups, a rapidly growing number of companies are embedding AI into their businesses. Even the public has begun to welcome AI into their homes. With 2018 soon coming to a close, it’s time to start asking what the future holds for AI and how its reach will continue to expand in 2019. We surveyed a diverse group of industry leaders for answers. Here, we’ll take a look at their insights and learn what AI has to offer in the coming year.

# Prediction 1: Improved Cyber Threat Detection, Will Lassalle

“My prediction for artificial intelligence in 2019 is specific to the 3 million employee shortfall in cybersecurity, where the opportunity exists for artificial intelligence to help automate threat detection and response,” said Will Lassalle, CIO of Lynx Technology Partners and JLS Technology USA. “This can ease the burden on employees, and potentially help identify threats more efficiently than other software-driven approaches, shrinking the attack surface instead of constantly chasing after malicious activity.” The numbers support Lassalle’s sentiment — according to Cisco’s 2018 Annual Cybersecurity Report, 32 percent of organizations are “completely reliant” on AI in terms of their security infrastructure. As AI platforms and tools continue to learn and improve, it wouldn’t be unlikely that 2019 would see those numbers climb even higher.

# Prediction 2: Machine Learning at Scale, Sam Charrington

“Over the past few years, early-adopter enterprises have launched their initial machine learning and AI projects, with many of those efforts recently coming to fruition,” said Sam Charrington, founder of CloudPulse Strategies. “In 2019 and 2020, these companies start to understand that scaling machine learning in the enterprise requires a different set of skills and technology than successfully delivering individual machine learning models and projects. As a result, we’ll start to see enterprises establish specialist machine learning infrastructure teams and platforms to help reduce the barriers to delivering machine learning at scale.” Charrington’s predictions are in line with a recent report from Indeed, which discovered that employer demand for AI-related jobs more than doubled between 2016 and 2018. However, the same report revealed that job-seeker searches for AI-related jobs are beginning to plateau. For leading companies, this means that finding and retaining AI talent in 2019 will be crucial.

# Prediction 3: Next-Gen Virtual Assistants, Evan Kirstel

“An AI-based personal assistant — aka Siri on steroids — will hit the market in 2019,” said Evan Kirstel, chief digital evangelist at EviraHealth. “It will be the true human-like, natural language-based personal executive assistant that we have been dreaming of (and that Siri, Cortana, Alexa and others have yet to achieve). It may come from Google, Microsoft, IBM Watson or a dozen other startups that are working in this space.” Kirstel’s forecast isn’t hard to believe: According to Adobe’s 2018 State of Voice Assistants report, an astonishing 48 percent of consumers will own a smart speaker after the 2018 holiday season, illustrating the demand for this technology already. Knowing that the market for voice assistants is already so strong, the race is on for a new and even more advanced option than we have today.

# Prediction 4: Adoption Across the Board, Isaac Sacolick

“Artificial intelligence and machine learning will increase adoption in 2019 in several ways,” said Isaac Sacolick, president and CIO of StarCIO. “Companies leading the adoption curve will invest more in reinforcement learning, address bias gaps in their data sets, and look to make their AI algorithms more explainable.” He added, “Mainstream organizations will take advantage of AI embedded in their strategic platforms such as AI-driven sales forecasting, marketing automation and AIOps. Lastly, public cloud vendors will make their AI offerings easier to use and more scalable for IoT applications.” Sacolick’s forecast seems to agree with a survey from McKinsey & Company found that nearly half of respondents’ organizations have integrated at least one form of AI into their standard business processes. The same report also found that companies across a wide array of industries are seeing value in AI, from manufacturing to human resources.

# Prediction 5: AI Solutions Put to the Test, Marshall Choy

“With a multitude of new AI products and services becoming available in 2019, the complex and large requirements of early adopters will truly test and challenge these new technologies,” said Marshall Choy, vice president of product at SambaNova Systems. “AI is not a product to be bought and sold, but a core enabling technology for driving innovation. Therefore, an AI-driven approach to tackling data management requirements will necessitate a focus on not only individual layers of the technology stack but also in how those layers interact and integrate with each other.” This prediction’s veracity is reflected in data from McKinsey & Company. Findings revealed that of those companies that have adopted AI, only 15 percent believe they have the right technological infrastructure and architecture in place to support AI systems. It’s clear that industry leaders don’t expect to see AI slow down anytime soon. Rather, experts anticipate better threat detection, a slew of new jobs, cutting-edge products and widespread adoption. Along with that, AI solutions will no doubt be put to the test in 2019 by the underlying technology that supports them. Also Read: Make Your Marketing Great With AI already proving its worth to adopters, it’s not surprising that an increasing number of companies will implement and leverage AI in 2019. Now, it’s no longer a question of whether AI will take off. Instead, it’s a question of which companies will keep up.

Assess How Great Your Marketing is

It’s said that self-reflection is a good thing. It helps us to understand ourselves more clearly. How we go about that self-reflection, however, determines the value of the effort. Do we give it cursory attention afraid to know the real truth or do we look at all the evidence revealing a great many things we’ve forgotten we do very well and the areas that need some attention? In times of market velocity and economic volatility, it’s essential that every organization routinely evaluate its effectiveness. Is it doing the right things in the best possible way to deliver the results needed today and tomorrow? Nowhere is that more crucial than in marketing. Not only because the discipline is undergoing rapid transformation but also because what defines marketing differs dramatically based on the company’s and its industry growth stage. Unfortunately, for most marketing organizations what should be an annual exercise typically happens only when a new leader arrives. There are many approaches you can use to conduct a marketing assessment; the one we have used with clients for years is evidence-based. It works because over the years we’ve built a comprehensive master template that is easy to customize and quick to complete. The assessment process evaluates strategies, plans, activities, investments and their results as evidenced by artifacts. Documents, files, information, and/or tools are artifacts specifically developed to accomplish an activity; they either exist or they don’t. Each assessment area has between 20 and 100 specific evidence artifacts that we look for, evaluate and then score.  An artifact is objectively assessed based on completeness, adherence to modern marketing and industry best practices including:

  • Appropriateness for the company and market maturity.
  • Soundness and completeness.
  • Resource allocation rationale.
  • Use of metrics to manage and measure ROI.
  • Identification of data integrity, process integration, and skill/competency gaps.
A marketing assessment for an early stage company in an emerging market will be more heavily weighted on mindshare, reach, content and influencer activities than a mature company in a mature market where demand generation, customer loyalty/evangelism and market share are the number one priorities. Below is a summary assessment of a very mature company that engaged us to conduct a detailed assessment as part of preparing their digital transformation plan.  The blue lines are the total median scores from each assessment category; the green line is the target state. This large client had a lot of work to do to embrace digital marketing and modern marketing best practices. The drill down details under each category in the diagram was instrumental in developing a realistic and achievable prioritized plan.

(C) 2018 www.newbizs.com

Contrast that with the assessment results for a technology start-up in an emerging market.  The green line is the target ideal state while the orange is the actual score.  There is no one size fits all, every company and its situation are different. Assessment approaches must compensate for this otherwise the results run the risk of being unachievable and unrealistic, defeating the very purpose of the evaluation.

(c) 2018 www.newbizs.com

It’s key that each assessment category can be drilled down to a detailed list of all the assessment points, descriptions, scores and where the artifacts are stored.  Below is the next level down for the “Demand Creation” category from the graph above.  The orange is line represents the company’s actual scores while the green line is the target.

(C) 2018 www.newbizs.com

Where does the evidence come into play? The focus is on the information and analysis, not on the artifact’s format.  Let’s look at the Lead Scoring sub-category: Definition: Defined and used consistently across inbound, outbound and Sales.  Segmentation and scoring model is updated every 6 months to reflect changes in buyer behavior, buying team engagement, journey map, market dynamics, and CTA/tollgates synchronized with dynamic web forms to improve score accuracy.

A partial list of artifacts sought:

  • “Ideal” lead profiles
  • Documented and agreed upon definitions and criteria for each lead stage/type
  • Lead scoring, nurture and retargeting definitions and algorithms
  • Consistent use of lead definitions by sales, marketing, ops and in reports
  • Weighting of each tollgate CTA – in aggregate or by persona/role
  • Lead journey stage calculation based on % engagement alignment with journey maps
Care must be taken to make sure there is no double counting of artifacts by duplicating them in more than one subcategory. The focus is less on is making sure the artifact in the right category but a comprehensive list. For each category and list of attributes, we work with marketing team members to find and review the attributes. The review consists of completeness, alignment with best practices, currency and consistency of usage.  Using the lead scoring subcategory, we’d look for a document(s) that defines ideal lead profile(s) that is current, has been signed off and consistently used by marketing and sales, and used as the initial screen of inquiries.  In the case of lead journey stage calculation, we’d look for a series of documents starting with outside-in developed detail journey maps by markets and at the persona/role level.  Additionally, are key CTAs (gated assets or registration required events/engagements) tied to key micro-moments or tollgates and weighed (vs. weighting content interaction). Are the algorithms for calculating lead journey stage documented and tested quarterly? Are systems capturing data to support the algorithm(s) random tested for accuracy quarterly? Evidence is complemented with cross-functional interviews to understand the underlying processes and methods. We’ve learned over the years to only share anonymized interview highlights with leadership teams. Otherwise inevitably someone succumbs to the temptation to share or worse confront team members with comments made in confidence. The final report should include a review of the assessment process, strengths/successes, root causes of improvement areas, recommendations, alternative courses of action, and suggested timelines  We recommend a more detailed presentation be shared with the marketing team and afterward that the team presents a summary recap to the rest of the organization. The advantage of an evidence-based approach is its objectivity. The objective is to not find fault or blame but to help marketing stay aligned to customers, company strategy, and best practices.  As growth and maturity comes from self-reflection, so to for organizations – especially during times of uncertainty, volatility and velocity. First published in MartechAdvisors.

When Demand Generation is a Waste of Time

First published by Martech Advisors on August 20, 2018