Bombora Intent Data Added to InsideView Apex

InsideView Apex now supports intent segmentation across 3000+ B2B Bombora topics.
InsideView Apex now supports intent segmentation across 3000+ B2B Bombora topics.

InsideView added Bombora intent intelligence to its Apex “go-to-market” ICP/TAM service.  InsideView highlighted two use cases: Refining a list of ideal prospects to focus on those showing intent and expanding a list of buyers to find additional prospects with similar intent and other characteristics.

Refining an ICP list helps marketers target their best prospects based upon open web research currently being performed at their ICP accounts.  Thus, custom campaigns can be targeted to accounts likely in market for specific solutions.  Instead of blanketing ICP accounts with general messages, a more refined approach can be taken with a higher likelihood of the message resonating.

Conversely, Intent data can be used to expand an ABM list.

“InsideView’s Targeting Intelligence platform provides a single point of access for all B2B data and targeting signals, from firmographics, contact details, news events, personal connections, technographics, and now intent data. Adding Bombora intent data makes go-to-market planning in Apex that much more powerful. Now you can discover even more ideal prospects, and home in on the specific accounts in your target market that are not only an ideal fit for what you sell but are also currently in-market.”

  • InsideView VP of Products Marc Perramond

I find the first use case, refining a list for targeted messaging, to be more compelling.  Intent data is ephemeral.  A firm that is researching a topic this month will have moved onto other topics a month or two later.  Using intent data to message to these companies on intent-based topics today is powerful.  It allows vendors to reach out to prospects before they have begun talking to prospective vendors.  It is a powerful method to answer the questions whom to call (account-wise), when to call, and what to say.

InsideView Apex intent filtering allows marketers to target their ABM list with messages that are likely to resonate.
InsideView Apex intent filtering allows marketers to target their ABM list with messages that are likely to resonate.

However, vendors should be careful about using intent to expand their targets.  Identifying the key intent topics is crucial, but building an ABM list based upon ephemeral intent signals risks adding firms that had surging interest over the past few weeks, but have already made purchasing decisions, chosen not to pursue a technology, or were simply performing due diligence.  Surge scores simply mean that there was a recent peak in topical interest above the mean.

Defining who are your best candidates amongst your pool of ABM accounts for specific messages is a clear winner.  Adding accounts to an ABM list based upon a short-term surge in interest is likely to result in wasted marketing dollars.

Until recently, Bombora has had more success selling their intent file to predictive analytics companies than sales intelligence firms.  However, sales intelligence companies are now figuring out how to present intent data to sales reps and marketing professionals without having to provide sales training sessions on the nuances of intent data.  For example, DiscoverOrg recently redesigned its OppAlerts to focus on the few key topics relevant to the client and then limited the intent signals to the top few percent of surging accounts.  This level of refinement gives sales reps confidence that when they see a surging topic at an account, it is truly surging and not simply an anomaly.  And because numeric surge scores have been removed, the rep merely needs to know that there is a high certainty that individuals at the account are actively researching the topic in question.

B2B tech media company TechTarget delivers intent data to sales reps from its Priority Engine service.  While other intent services are at the company level, TechTarget has opted-in readers performing current research on a topic.  Thus, TechTarget can identify company, executive, topic, and buying stage for its intent file.

D&B WorldBase Reaches 300M Companies

 

Promotional image posted on LinkedIn.
Promotional image posted on LinkedIn.

It was only a few years ago that Dun & Bradstreet’s WorldBase file reached 200 million records, but this week the file hit 300 million active and inactive company profiles.  The dataset is used for sales, marketing, research, master data management, credit risk, and supplier risk products.  It is also licensed to many other vendors (the majority of which are not allowed to publish the provenance of their data). While sales reps do not use inactive companies, they are important for risk products, master data management, compliance, and database cleansing.

Two key features of the WorldBase data set are the D-U-N-S Number, their de facto global numbering system, and global linkages which tie together global company family trees.

The WorldBase file is a key asset for Dun & Bradstreet products such as DNBi, D&B Hoovers, D&B DataVision, and D&B Optimizer.

Congratulations on reaching this milestone, Dun & Bradstreet.

DemandBase Revenue Growth

One of Demandbase's core technologies is real-time visitor intelligence for ABM.
One of Demandbase’s core technologies is real-time account-level visitor intelligence for ABM.

Nathan Latka interviewed Demandbase CEO Chris Golec back in Q4. Demandbase is growing rapidly and now employs 300. In November, Golec said the firm was likely to achieve 50% or greater growth in 2017. 2016 revenue was around $75 million and the firm was above a $100 million run rate in November. Average revenue per customer is around $20,000 per month. Small customers may select a single module for $2K to $3K per month but then add multiple solutions as they grow. Net revenue retention is around 110%.

The firm has between 50 and 60 quota carrying reps, 20 to 25 marketers, and 10 to 15 administrative staff, with two thirds of the company focused on data, R&D, engineering, and other functions

The firm has 400 to 600 customers with top customers spending a couple million dollars per annum.

Golec expects the firm to be cash flow break-even during the first half of this year.

Demandbase, founded in 2007, was an early and forceful proponent of Account Based Marketing. For several years, they had a monopoly on the positioning, but ABM caught fire as a B2B sales and marketing process with several enterprise software firms including Marketo and Salesforce now offering ABM solutions.

“ABM as a category – the interest level has reached the investment community and so as investors do their research they discovered that Demandbase is the largest and pioneered the category itself.  So we had a lot of inbound interest.  At the same time, we started developing some new innovations using AI and massive data that we’re sitting on. So it really unfolded into a whole new level of innovation.”

  • DemandBase CEO Chris Golec

DemandBase has already received $156 million in funding, including a $65 million round last May. Both Salesforce and Adobe have taken investment stakes in Demandbase.

While some MarTech firms are struggling with revenue growth and churn, that has not been an issue at Demandbase. “ABM is more of a business process and our position is much more of a platform where we’re helping customers throughout the whole lifecycle of attracting, updating, engaging, converting, and upselling them.”

The firm has ten staff in London helping grow European sales. “ABM adoption in the UK and Western Europe is really starting to pick up.”

Source: Nathan Latka SoundCloud Interview of Chris Golec

Gartner Predicts Increasing Sales & Marketing Tension Due to ABM

Todd Berkowitz, Research Vice President at Gartner, sees Account Based Marketing (ABM) as increasing tensions between sales and marketing in the short-term.  While ABM has long been advocated as a facilitator of departmental alignment, he sees ABM as disrupting sales processes and generating friction:

“Between ABM and adoption of various new technologies and data types, there is a lot of disruption that is happening with regards to sales teams. Even if these changes are going to be beneficial to tech companies in the medium-term, and some of the “A sellers” get on board quickly with the changes, there are many sales reps that will have to be dragged kicking and screaming into the new world. (This is why I always advise trying an ABM pilot with a select set of reps). So even if there is pretty good alignment and agreement between CMOs and sales leaders, don’t expect all reps to magically do what they are being asked to do. There needs to be an adjustment period, along with good sales enablement, before everyone plays nicely.”

So, while ABM will facilitate agreements in process, messaging, and metrics in the medium-term, it will generate resistance amongst sales reps unwilling to adopt new processes and tools or unconvinced of its value.  This friction is probably exacerbated by predictions of sales force reductions due to the implementation of AI and other information and workflow technologies.

WWII Era Poster (U.S. National Archives and Records Administration)
WWII Era Poster (U.S. National Archives and Records Administration)

Resistance to technological change has long been an issue.  Early in the Industrial Revolution, The Luddites sabotaged British plants, particularly cotton and wool mills.  While sales reps are unlikely to sabotage initiatives (or their careers), they may hesitate to learn new platforms or adopt new processes.  As such, the problem may be more akin to soldiering, the assembly line equivalent of reducing individual productivity to the level of the laggards on the line.  Frederick Taylor, the father of time and motion studies, was very concerned about soldiering and recommended piece work rates to create productivity incentives.  But sales reps are very attuned to incentives.  While they may be hesitant to adopt new technologies, they will do so if they help make them more efficient and effective at selling.  So long as sales reps are paid on a commission basis and long-term employment is tied to making quota, the level of soldiering should be minimal.

This isn’t to say that sales reps won’t resist learning new tools.  If they believe the time invested in such training is less than the incremental revenue for the lost selling time spent in training, then they will avoid training and learning new tools.  However, if they see others on their team benefiting from the new tools, they will not hold out long term.  Thus, tool training needs to be visibly supported by management with an emphasis upon the benefits to sales reps (e.g. less time spent on non-sales tasks and more time interacting with customers and prospects, improved account intelligence, improved account targeting and message timing).  With the proper incentives and information, resistance should be minimal.

To help ensure adoption, vendors should be looking to integrate solutions into CRMs, email, and mobile devices so that new tools are integrated into current workflows.  They should also be providing inline tool tips, initial training focused on their capabilities which provide high levels of efficiency and efficacy improvements, tool-based win stories, and usage tools for tracking training, usage, and ROI.  A few gamification elements may also be in order, but they should be organic to the product and not hokey.

Oceanos for Salesforce (Beta)

Oceanos ListOptimizer supports quarterly batch updates to account, contact, and lead records.
Oceanos ListOptimizer supports quarterly batch updates to account, contact, and lead records.

Contact data management vendor Oceanos is working with Datarista to bring an SFDC-based contact service to the market.  The Oceanos ListOptimizer service, currently in in beta, will be generally available in Q1.  Sales Operations can run counts, perform company and contact searches, and ensure ongoing data integrity.

The service supports standard company and contact list building with running counts as variables are selected.  New execs may be added as lead records or accounts and contacts.  Duplicate checking is performed.

Batch Salesforce updates are performed quarterly.  In 2018, the updates will run every other month with contact changes updated weekly.

Oceanos offers best-in-class contact records from over a dozen vendors.  When records are deployed to customers, they are subject to real-time reverification against FreshAddress, FullContact, and Pipl.

Contact management services are purchased on a credit basis with custom pricing plans based upon volume and intended usage.

In other news, Oceanos recently inked a deal to deliver its ContactAPI to The Big Willow intent data platform. “Targeting prospects before the market even knows they exist provides our customers a first mover advantage,” said Big Willow CEO Charlie Tarzian.  “With the Oceanos ContactAPI, we provide our users targeted contacts for intent-qualified opportunities that accelerates engagement.  With 15 years in the space, they’ve earned a stellar reputation and we’re thrilled to take this next step in our partnership.”

Along with connectors and APIs, Oceanos offers free data health checks and a team of data consultants to assist with data hygiene and analytics initiatives.

Lattice Engines: Predictive Model Building

The Lattice Data Cloud Explorer highlights the top fields by category and helps admins determine which fields should be exported to other platforms.
The Lattice Data Cloud Explorer highlights the top fields by category and helps admins determine which fields should be exported to other platforms.

Lattice Engines has taken the pole position in the emerging Predictive Analytics space.  In yesterday’s blog, I covered its pricing, value proposition, content, and integrations.  Part two covers model building.

When first launched, Lattice Engines and its peers had long deployments and black-boxed models that required data science expertise.  The firm now offers 24-hour deployments, simplified model building, and greater transparency around models and recommendations.  Furthermore, the system allows marketers to either build their own models or import industry standard PMML files constructed by their data science teams.

Predictive models are built by importing training files which are matched against the Lattice Data Cloud using D&B DUNSMatch logic and Lattice proprietary techniques.  Training models contain examples of both positive and negative outcomes (e.g. win / lose, renew / drop).  A model is typically available within thirty minutes of the training file upload.

Ideal Buyer Profile scores (Lattice’s term which is similar to Ideal Customer Profile scores) are available to sales and marketing and include both scores and recommendations.  Marketing can view the model via a graphical Data Cloud Explorer which highlights the key signals and variables in the model and makes the data available for export to other platforms.

To make the data more actionable for sales reps, Lattice provides Salesforce Talking Points which display recommendations and explanations that include Lattice data, transactional history, and buyer behavior.  A Lattice Buyer Insights CRM I-frame contains Lattice recommendations, talking points, company profiles, company fit, engaged contacts, engagement activity, intent analysis (surging topics), web activity, and purchase history tabs.

Lattice Recommendations with related scores. Sales reps can explore any of the recommendations by clicking on them.
Lattice Recommendations with related scores. Sales reps can explore any of the recommendations by clicking on them.

Future plans include a user interface for segmentation analysis and simplifying intent scoring to high/medium/low.


Part 1: Lattice Engines Overview.

Sparklane €4m Funding Round

Sparklane Lead Scoring
Sparklane Lead Scoring

Sparklane, which describes itself as “a publisher of sales intelligence SAAS solutions,” announced that it received a €4m funding round from XAnge and Entrepreneur Venture Investment Fund.  The round raised its total funding to €7m.  XAnge also participated in Sparklane’s previous funding round.

“We were won over by Sparklane’s disruptive positioning and the impressive performance of its management team, prompting us to offer them our renewed support as we participate in this fundraising initiative alongside Entrepreneur Venture,” stated Guilhem de Vregille, Deputy Director of XAnge.

The round allows Sparklane to continue its European expansion.  The French company established itself in the UK in 2016 and is currently eyeing the German market.  The funding will also be directed towards expanding its artificial intelligence capabilities, and growth in their sales and R&D teams.

According to Chairman Frédéric Pichard, the funding round is a “real vote of confidence,” in the company.  “Our goal remains the same: to help marketing and sales people identify their future customers more quickly using Artificial Intelligence.”

Sparklane offers predictive lead scoring and prospecting tools for sales and marketing teams in the UK and France.  Their Predict platform processes client CRM data to define an Ideal Customer Profile (ICP), apply predictive lead scores, and identify look-a-like prospects.

Sparklane supports nearly 350 clients across banking, insurance, technology and business services.  The firm was listed in Deloitte’s 2016 EMEA Fast 500 list of technology companies with 265% revenue growth between 2012 and 2015 (three-year CAGR of 54%).