Founder and CEO, DealSignal
Marketing and sales machines run on data. Data is the oil that keeps the operations smooth and effective. To better understand the power of data and its role in analytics and marketing performance, we spoke to Rob Weedn, Founder and CEO at DealSignal.
Tell us about your journey into the marketing technology industry.
My background is a mix of executive roles in product management, marketing, and sales at SaaS software companies in CRM and Big Data/analytics and I’ve served as an advisor for various start-ups on go-to-market strategy, demand generation, and sales execution, so I’ve seen first-hand how the right martech and salestech solutions can help teams align and drive more revenue. At DealSignal, our customers are demand generation, marketing and sales professionals—the people on the front-lines responsible for driving growth and creating tomorrow’s strongest B2B companies. It’s a very exciting space.
What inspired you to start DealSignal?
With the rise of martech, Big Data, and AI, especially the maturation of machine learning, it became clear that the industry was moving towards the ability to deliver truly predictive marketing and sales—isn’t that what we all want? Being able to know exactly which levers to pull when and how to reliably produce revenue?
When DealSignal started, we were initially focused on predictive revenue scoring. And because predictive is only as accurate as the data you’ve got—as they say, garbage in, garbage out—it required us to develop a sophisticated, AI-enabled process for enriching and validating customers’ CRM data. Time and time again, we had customers marvel at the speed, scale, and quality of our data coverage and accuracy, to the point where we realized that our unique process for multi-sourcing, enriching and validating contact and account data, on-demand, was the more tangible business opportunity. And as we’ve seen over time, marketing and sales must have high-quality systems for data and process, while predictive has been nice-to-have and more ephemeral.
What is the ‘State of Marketing Transformation’ in 2018? How do you and your company enable customers to adapt to this state quickly?
Today’s marketing machine runs on data. Whether you’re doing more inbound, more outbound or moving to an account-based marketing (ABM) approach, complete and accurate data is key for knowing your audience, targeting effectively, and being able to personalize your messaging and outreach, which has been shown to increase conversions. Bottom-line: you have to know who you’re marketing/selling to and you need to be able to reach them in a truly personalized way.
Given these imperatives, marketers need to raise their standards when it comes to data coverage and quality. Most marketers have very limited coverage of their target audience of ideal buyers, usually only 10-20%, in their marketing automation and/or CRM system. For the coverage they do have, many marketers currently rely on solutions that provide only 50-80% accuracy and contactability. That level of data quality, if scored on a regular basis with a CRM health analysis, would result in a B- to F grade. The problem is, bad data corrodes your marketing and sales efficiency and performance.
In contrast, DealSignal provides perfect contact and account data, or near-enough to perfect that we can guarantee it 100% with very little risk on the margin. We consistently deliver impeccable quality and contactability in the 94-100% range (A to A+) across all personas, industries, and geographies, which puts our customers on the honor roll when it comes to data coverage and quality. Moreover, it provides marketers and sales teams with the most relevant data to cover their target market, personalize campaigns, and drive higher performance in terms of engagement, conversions, and revenue.
What are the top issues teams must address in order be successful with account-based marketing, or ABM?
I recently wrote about the five building blocks for establishing a solid ABM foundation, where I talk about preparing and potentially revamping your data, systems, processes, content, and analytics to plan, execute, iterate, and scale ABM to a high-performance machine. However, most of the market is still facing issues in the planning and initial execution phases, an area where data and analytics can help greatly improve demand planning, segmentation, and targeting/personalization.
We solve the first major issue by helping marketers and sales teams know their Total Audience in terms of contacts and accounts. SiriusDecisions’ Demand Waterfall starts with identifying Target Demand and the buying teams, or Demand Units, involved in B2B purchase decision making. That’s your total audience: all the contacts that fit your target personas and ideal customer profile. So to effectively scope an ABM program, you need to know your total audience, both in terms of the accounts and, because people still buy from people, the specific contacts at each account. As we mentioned, most marketing teams have not measured their total audience, nor used that as a foundation for their budgeting and planning process. We help by providing Total Audience Metrics (TAM) for both contacts and accounts with near perfect accuracy.
Once demand planning is based on accurate data and analytics, marketers and sales teams invariably take next steps to enrich their CRM data to improve data quality and fill any major coverage gaps in high priority segments and personas by sourcing net-new contacts and accounts. We help solve the two-part problem of enriching and acquiring your total audience with perfect quality data so that teams are set up to execute ABM programs more effectively.
What’s the difference between the traditional definition of TAM: total available market and total audience metrics?
Total addressable market is a classic, static, top-down analysis, based on partial, sample market data – the type of market sizing typically performed by market research and analyst firms. “Classic TAM” is a ballpark sizing of the market. It’s not frequently updated and there’s no real way for marketing and sales teams to plan and execute programs against the accounts and contacts in that TAM.
The Total Audience Metrics (TAM) that DealSignal provides is an accurate, bottoms-up, dynamic analysis of the actual counts of a company’s total audience—the accounts that meet their ICP criteria: based on industry, location, employees, revenue, technologies used, etc.—and the contacts that fit their ideal buyer persona: based on titles, title classifications, locations, keywords, exclusions, and suppression. Plus, TAM includes a process to dynamically discover, enrich, and verify the underlying accounts and contacts for that audience, so with one click, marketers can have complete and accurate data for any or all segments of their total audience to use in their marketing and sales programs.
Where do you see TAM Analysis heading to with the increasing maturity of data science and contact data platforms?
On the contact side, we’ve been able to build a multi-sourcing strategy that gives our customers comprehensive coverage across all accounts and contacts globally, across industries and across all company sizes. On the account data side, we continue to add more firmographic and technographic data points. On the contact data side, we are looking at buyer intent based on what people like and share on social media.
Today, data science helps us build advanced classification algorithms to provide fine-grained filters for both accounts and contacts, and machine learning helps us predict when employees are likely to change jobs and how to generate highly-accurate work email addresses. As we start to scale the feedback loop on the conversions, we’ll leverage more advanced predictive algorithms and AI to suggest look-alikes to recently won deals, and updates to personas and account filters based on all the leads that didn’t convert to SQLs last quarter.
How should B2B marketing organizations strategize and deliver on their goals with marketing or contact data platforms?
B2B marketers should be aware that static data, whether in their own CRM or marketing automation system, or in a sales intelligence or contact data vendor’s static database, it decays at an alarming rate – about 15-20% each month. People change jobs or get promoted, companies move, phone numbers change, etc. So if you’re powering your martech and salestech systems with stale data, it will negatively affect your performance, much like a high-performance sportscar running on regular unleaded – short term you will hear knocking and pinging, longer term it can ruin the engine.
Since data at rest quickly becomes dated, we don’t trust it, you shouldn’t trust it, and you certainly shouldn’t rely on it to define or optimize your marketing and sales strategies. We believe in dynamically refreshing and re-verifying data on-demand, when it needs to become active in a marketing or sales process – and we’ve uniquely designed the DealSignal platform to do just that in order to help demand gen and sales teams achieve their goals.
Like Amazon, Uber, and Instacart, we are working to change the industry from static to dynamic – B2B marketers should demand a fast, reliable, dynamic data process with a 100% quality guarantee.
Where do you see the state of adoption of ABM and where is it going?
Currently, we see ABM moving from early adopter to mainstream for what we can call ABM 1.0 – a more mainstream group of marketers are planning or starting their ABM journey. We believe it’s still early days for what we might call ABM 2.0 – where a majority of marketers are managing ABM optimally across all plays, at scale, with high performance and conversions. According to research from the ABM Consortium, 81% of marketing leaders aren’t confident in their teams’ ability to execute ABM and only 18% of existing ABM programs produce positive, measurable revenue impact.
The typical plays in ABM we see people doing today are account selection, account intent, and advertising using platforms like 6Sense, Bombora, TechTarget, and The Big Willow to determine which accounts are in market, then they’re retargeting/advertising to those accounts using a solution like Terminus or AdRoll.
So, what we’re seeing is a huge wave coming. As part of ABM, marketing needs to do a lot more to help sales build pipeline, engage the full Demand Unit, and produce personalized content for all personas and all stages of the customer journey. While not a small ask, this will yield a huge leap in customer experience, engagement, and conversion. Advertising, content marketing and social media are driving some top of the funnel increases, especially when combined with intent, but to drive real pipeline and revenue growth, you must address your total audience, and engage the most relevant buyers first. That means marketers need to understand the accounts that sales is focused on, you need clear definitions and agreement on your ideal customer personas, and then, with either marketing automation or sales automation, you must jointly deliver a series of highly targeted and personalized messages in a way that truly drives SQL, MQL, and pipeline opportunity growth.
And that’s where we’re getting a tremendous influx of interest around TAM: contacts in high-priority accounts, many times those accounts that are in-market with intent. And, as companies move to a named account ABM model, sales gets on board and wants to go engage their full account list, all the key personas, and they want marketing’s help with the data on the accounts and contacts, the operations and systems to store it in an organized way, the marketing and sales automation processes to engage, and compelling, personalized content, likely powered by data insights.
…It’s a lot to pull together, and that’s why we believe there will be consolidation in the martech industry at the data, process, and analytics layers – a subject for another article!
Thanks for chatting with us, Rob.
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