D&B Rev.Up ABX supports four channels supported by Dun & Bradstreet’s Lattice CDP and Data Cloud. Users may license one or multiple packages or an advanced package that supports all of the channels.
With D&B Lattice at its core, Rev.Up ABX supports AI-driven models for defining ideal buyers and their individual and account-level buying journeys. Marketers define the goals, segmentation, criteria, and historical period for the model (e.g., four quarters of historical records), and Lattice builds and scores across the segment. These models assist with building targeted audiences of buyers ready to engage across various stages of their journey.
Once a model is built, marketers can create and activate campaigns across channels. For example, an awareness campaign can be activated across paid social, Google ads, and Google search for either accounts or contacts. Lattice manages scheduled audience updates so that marketers do not need to manually update the activated segment.
Likewise, lower scoring leads triggered by website visits can be nurtured in Marketo, while higher scoring visitors are routed to Outreach as sequences or tasks.
Company-level analytics show the engagement journey across stages, contacts engaged, engagement activity, pages visited, firmographics, intent signals, and SDR alerts.
The D&B Lattice Model Builder score and rates contacts by estimated lift
The suite is GDPR and CCPA compliant with opt-in/opt-out flags shared across activation channels.
D&B Rev.Up is supported by the D&B Data Cloud, which includes
420 million global companies and 37 million corporate linkages
220 million global contacts, of which 54 million are C-level.
Digital identities spanning over 4 billion IP addresses, 22 million URLs, and over 500M devices ids.
17 million technology products used by 13 million companies.
3,500 intent topics spanning 14 billion digital signals.
Third-party alternative data sets with pre-assigned D-U-N-S numbers to ensure proper matching.
Firmographics and Technographics are available for all offerings, with intent data available for all but the Web edition in the default package (it is available in the advanced).
Rev.Up ABX is data agnostic, allowing revenue operations teams to deploy third-party licensed data sets such as G2.
Rev.Up ABX supports lookalike modeling, while the other products support three lead scoring and three account scoring models. Lookalike contacts may be sent to D&B Lattice, D&B Hoovers, or MAPs.
The D&B Rev.Up ABX Dashboard
Continue to Part III for a discussion of the Folloze partnership and product pricing.
The is the fourth, and final, blog on Dun & Bradstreet’s upcoming IPO. Dun & Bradstreet (NYSE Ticker: DNB) will be offering 65.75 million shares at an IPO price between $19 and $21. The offering would raise just over $1.3 billion and value the firm at $8 billion. [Top of Coverage]
North American revenue increased by $12.1 million or 4% (both after and before the effect of foreign exchange) in Q1 2020 vs. Q1 2019. North American Finance and Risk rose $10.7 million (6%) year-over-year. Finance Solutions were up $13 roughly million, while Compliance fell approximately $2 million.
North American Sales & Marketing grew revenue by $1.4 million (up 1%) in Q1. However, $4.9 million of S&MS revenue was attributed to Lattice, which was acquired by Dun & Bradstreet in July 2019. North American Advanced Marketing Solutions revenue rose $4 million due to increased demand, but D&B Hoovers and the Data.com legacy partnership with Salesforce posted declining revenue. The Data.com service is being phased out, so the $4 million in quarterly revenue drop was anticipated. However, the drop of $3 million in quarterly revenue at D&B Hoovers, attributed to lower sales, was surprising.
International revenue fell by $0.2 million in Q1. International Finance & Risk revenue increased $2.3 million, or 4% (both after and before the effect of foreign exchange) for the three months ended March 31, 2020. International Sales & Marketing revenue declined $2.3 million, primarily driven by lower product royalties from their WWN alliance.
Annual revenue dropped $139.8 million (8%), but the drop was due to purchase accounting deferred revenue adjustments (9%) due to the take-private transaction and Lattice acquisition. There also was a one month lag in international revenue reporting due to the take-private transaction resulting in an additional 1.5% drop in revenue.
2019 North American revenue rose by $44.1 million (3%) with increases in both product lines. The Finance & Risk division increased revenue by $16 million, or 2%. The Risk & Compliance products grew revenue by $11 million, and the D&B Credibility products contributed an additional $4 million.
2019 North American Sales & Marketing revenue grew $28.1 million (4%), with $17 million in increased revenue from Master Data solutions and $12 million from Lattice, which was acquired at the beginning of Q3.
2019 International revenue fell $3.1 million after the impact of foreign currency but was up 2% before foreign currency impacts of $9.5 million. “Excluding the impact of foreign exchange, growth of $6.4 million was primarily due to increased revenue in our U.K. market driven by higher demand and usage related to our Finance & Risk solutions, including Risk & Compliance products.”
2019 International revenue was negatively impacted by $1.8 million, mostly in the UK, “as a result of transferring legacy Avention contracts to our WWN alliances pursuant to preexisting agreements governing partner exclusivity in certain territories.”
The filing also provided some color into their 2018 performance vs. 2017 as a private company:
“The increase in Sales & Marketing Solutions reflects increased revenue from new business in our Master Data offerings of approximately $7 million as well as our Audience Solutions products (Visitor Intelligence and Programmatic) of approximately $5 million and Analytics products of approximately $5 million. The aforementioned increases were partially offset by lower royalty revenue from our Data.com legacy partnership of approximately $7 million and decreased revenue in D&B Hoovers of approximately $5 million.”
Dun & Bradstreet filed an S-1 to return to the public markets after being taken private by Black Knight (BKI), Thomas H. Lee Partners, Cannae, and CC Capital eighteen months ago. Dun & Bradstreet was reorganized and recapitalized with additional debt ($2.5 billion in increased liabilities). The bookrunners include Goldman Sachs, BofA Securities, J.P. Morgan, and Barclays.
Dun & Bradstreet (NYSE: DNB) will be offering 65.75 million shares at a price between $19 and $21.
The firm will once again be listed under the DNB ticker and will net at least $1.3 billion from the IPO. The IPO proceeds will be used to “redeem all or a portion of our Series A Preferred Stock that we issued in connection with the Take-Private Transaction.”
Dun & Bradstreet has 135,000 global customers, including 90% of the Fortune 500 and 60% of the Global 500. Its primary services support risk analysis (credit and supplier risk), marketing, and sales. Over the past five years, the firm has focused on analytics, Data-as-a-Service (DaaS), Master Data Management, and Audience Solutions (e.g. programmatic, visitor intelligence). The product line has been built both organically and via acquisition. Earlier this year, they acquired Orb Intelligence and its AI/ML tools for collecting firmographics and digital business identities. Last year, they acquired Lattice Engines, a leading Customer Data Platform.
This year, Dun & Bradstreet launched two new services: an ABM platform and an Analytics Studio that combines Dun & Bradstreet company intelligence with customer-owned and alternative data sources.
Dun & Bradstreet offers global services for risk analysis (credit, supplier), Master Data Management, Compliance, B2B DaaS, Prospecting, and Sales Intelligence. Key products include DNBi, D&B Direct, D&B Credibility, D&B Hoovers, D&B Optimizer, D&B Master Data, D&B Lattice, D&B Audience Targeting, D&B Visitor Intelligence, and First Research.
The firm now focuses on “business decisioning data and analytics,” which “enables companies around the world to improve their business performance.” Dun & Bradstreet’s Data Cloud “fuels solutions and delivers insights that empower customers to accelerate revenue, lower cost, mitigate risk, and transform their businesses.” Key data assets include the D&B WorldBase file with global company linkage; various analytical risk scores; credit and supplier risk reports; the global D-U-N-S numbering system for companies; country risk reports; industry overviews; and Hoovers company profiles.
The firm continues to invest in its global data. Dun & Bradstreet listed the following data initiatives:
“- We have significantly increased our investment in the breadth and depth of our data. We have specifically focused on better utilization of available data, automation of business data research, improvement of identity resolution, expansion of our individual contact database and implementation of tools to monitor and streamline our data supply chain so that we can generate better, more actionable business insights and outcomes for our clients. We are also proactively addressing data quality issues.
– Although we draw from approximately 16,000 proprietary and publicly curated sources, Dun & Bradstreet had historically focused on identifying and collecting a narrow subset of data that was appropriate for specific solutions. We have since reoriented our approach towards better ingesting all available data to effectively leverage previously disregarded sources of data and thereby improve the consistency, accuracy and predictive power of our solutions.
– We are also expanding the volume of the data we are able to offer. For example, we have increased D&B Hoover’s premium contact data from approximately five million e-mail contacts to approximately 16 million contacts in our Data Cloud from January 2019 through March 31, 2020, while simultaneously improving the accuracy of those contacts by 250% since the beginning of 2018. We specifically focused on individuals we consider having significant influence over the buying process at companies that are most important to our clients based on our verified usage analysis.
– We are also expanding our coverage of SMBs and incorporating new, alternative data sets to expand the breadth of companies covered and depth of information we are able to provide clients. As part of this initiative we acquired Orb in January 2020, which allows us to better capture the digital footprint of businesses as well as the digital exhaust that businesses generate. By incorporating additional data sets into our solutions, we can continue to expand and refine the insights we offer to our clients, which we believe will enhance our competitive advantage.
– We have implemented a data watch program (the “Data Watch Program”) to proactively monitor and repair issues before clients experience them. Since May 2019, both client issues as well as Data Watch Program issues are now being logged in our data quality repository. We have identified, logged and resolved a number of issues as a direct result of this initiative and are continuously working to address additional issues.”
Dun & Bradstreet S-1
Dun & Bradstreet has a set of content differentiators. These include the global D-U-N-S Numbering system; global linkage; financial and risk data for credit, procurement, and compliance functions; First Research industry profiles; and Audience Solutions for programmatic and visitor intelligence.
“Data is only valuable when it drives action that moves an organization towards its goals,” stated the S-1. “Underpinned by an integrated technology platform, our solutions derive data-driven insights that help clients target, grow, collect, procure, and comply. We provide clients with both curated bulk data to incorporate into their internal workflows and end-to-end solutions that generate insights from this data through configurable analytics.”
Dun & Bradstreet is opening up the year with a bang. First, they announced a partnership with Amazon Web Services (AWS), and then they acquired Orb Intelligence, business identity and firmographics data provider. The acquisition follows acquisitions of Customer Data Platform Lattice Engines in July, Sales Intelligence vendor Avention (now D&B Hoovers) in 2017, and B2B DaaS vendor NetProspex (now D&B Optimizer) in 2015. The acquisitions have helped transition Dun & Bradstreet from an old-line sales and marketing information vendor to a digital analytics and activation provider.
“The
acquisition of Orb Intelligence cements our strategy to link the digital and
physical worlds in the largest global repository of B2B data and to provide
enriched firmographic data to customer profiles to help our clients more
effectively execute campaigns to improve customer interactions and revenue
returns,” said Michael Bird, President of Dun & Bradstreet’s Sales &
Marketing Solutions division. “Clients can rely on Dun & Bradstreet
as the one-stop-shop for all of their data-driven, decision-making and customer
engagement needs.”
Orb
Intelligence employs machine language and natural language processing tools for
deriving firmographic and technographic intelligence from the open web and
government documents. Their global database spans 57 million companies.
Content includes web domains, URLs, IP addresses, social networks,
government ids, corporate linkage, funding, trademarks, and technographics.
Orb Intelligence has served as the “data backbone to many
of today’s most well-known B2B sales, marketing and analytics organizations focused
on digital marketing or sales initiatives.”
“This
will be something of a shockwave for many in the ABM tech industry as Orb is an
unknown ingredient in so many (in fact I would guess most) ABM MarTech
platforms,” wrote B2B IQ President Liam Blackwell (Note: Blackwell is also an
Orb Intelligence advisor). “It is often used as the backbone, with the
Orb number as the key for connection. It is going to be interesting to
see how D&B controls / monetizes future usage of the Orb data – this will
be a major worry for some of those platforms and obviously an opportunity for
other data providers.”
Orb
is an original data provider and does not compile or resell data from other
vendors. Along with company profiles, the firm maintains databases on US
educational facilities, government agencies and offices, and healthcare
providers.
If you already use other data providers such as Dun & Bradstreet, you can increase your match rate by 10-25% by matching unmatched records onto the Orb Database. We collect data from different sources than Dun & Bradstreet, which is why the Orb Database is often used to complement D&B data.
Dun & Bradstreet
listed several benefits for their customers, beginning with the ability to
cross-validate data across online and offline sources. Upgraded customer
profiles will improve the depth and accuracy of business attributes for digital
ABM programs and audience targeting. Enhanced content will flow through
to D&B Audience Targeting, D&B Visitor
Intelligence, D&B Hoovers, and D&B Lattice for anonymous visitor match,
programmatic targeting and sales outreach.
Dun & Bradstreet also sees a “measurable
impact” for the combined data cloud which “will simplify the connection
and segmentation of audiences, the creation of artificial intelligence (AI)
models, and activation of channels through the D&B Lattice Customer Data
Platform (CDP), to deliver the best sales and marketing campaigns.”
The transaction closed on January 8th.
The parties did not disclose deal terms.
LinkedIn lists 17 Orb Intelligence employees,
including CEO Maria Grineva, who is joining Dun & Bradstreet as a Vice
President.
The Dun & Bradstreet Sales & Marketing Product Stack
Dun & Bradstreet, which acquired Lattice Engines at the beginning of Q3, launched a Lattice Campaigns App for LinkedIn. The new app “improves campaign performance by creating and activating always-on AI-based audiences for LinkedIn Ads.”
“When Dun & Bradstreet acquired Lattice Engines, we were building on an existing partnership of the world’s most comprehensive B2B data and the world’s leading B2B Customer Data Platform,” said Dun & Bradstreet’s President of Sales & Marketing Solutions Michael Bird. “This is the first in a series of innovations we’re quickly bringing to market to power more effective and efficient digital marketing, demand generation and sales acceleration programs for our customers through the intelligent use of data.”
The Lattice Customer Data Platform supports “hyper-targeted”
LinkedIn advertising as part of a broader omnichannel engagement strategy. Lattice
combines first and third-party customer data, displays account and contact
insights, and uses “AI to segment their buyers and deliver hyper-targeted
engagement in an automated fashion across display, web, email, CRM and now
social channels.”
“Let’s say a buyer at a cold account starts visiting certain product pages on your website anonymously. Rather than just showing this buyer a generic ad about your brand, you could show them an ad with more specific copy and CTA [call to action] related to the product pages that they visited on your website.”
Dun & Bradstreet President of Sales & Marketing Solutions Michael Bird
The app creates matched audiences for LinkedIn ads and
then lets marketers adjust media spend to target high-performing audiences. The
app then updates LinkedIn audiences based on “changes in buyer engagement,
interest and company data. As a result, marketers can ensure that buyers
are engaged with the most relevant campaigns based on not only persona but on
profile, propensity, interest and buyer stage.”
Dun & Bradstreet is claiming a 42% increase in
click-through rates and triple the post-click conversion rates resulting in a
54% reduction in qualified lead expenditures.
According to Gartner, CMOs are spending 23% of their
marketing budget on paid media.
“In an environment where B2B marketers are
overwhelmed with data and technology options, our goal is to make their jobs
easier by connecting interactions across customer journeys,” says Bird. “This
allows marketers to target the right audiences with the right message and make
the best use of their ad dollars.”
Connectors for Facebook, Twitter, and other social
media platforms are in the works.
Customer Data Platform vendor Leadspace acquired B2B Hygiene vendor ReachForce. The two firms offer complementary functionality with ReachForce adding webforms (SmartForms) and a continuous data quality platform (SmartSuite) to Leadspace’s CDP.
Leadspace plans to merge SmartSuite into their CDP over the next six months. SmartForms will become an “activation product” for Leadspace.
“ReachForce
is a well-respected brand with an experienced team in the B2B marketing tech
space,” stated Leadspace CEO Doug Bewsher. “We’ve known them, and
competed against them, over the years, so we’re excited to be joining forces
now to move the B2B CDP space even further.”
ReachForce
will maintain its Austin office and staff while LeadSpace will continue to
operate in Hod Hasharon, Israel, and San Francisco.
Reachforce SmartSuite Customer Data Management processes.
The Reachforce SmartSuite provides real-time and continuous data quality management. Features include B2B data match and enrich; data standardization; de-duplication; email, phone, and address verification; data health reports; CRM and MAP connectors; and contact prospecting at target accounts.
ReachForce has its best-in-class SmartForms product, which is a key way that customers build an understanding of their customers, as well as SmartSuite, which provides a real-time data cleansing and management service. Combined with Leadspace’s best-in-class B2B customer data platform, there is a definite complementary and additive effect. SmartForms will become one of the activation products for Leadspace, and we will work over the next [several] months to combine the best of both data management platforms to provide a single end-to-end solution for B2B CDP.
Leadspace CEO Doug Bewsher
The Reachforce acquisition follows shortly after Dun & Bradstreet acquired Lattice Engines. Both Dun & Bradstreet and Leadspace now offer a CDP alongside a data quality hub, digital advertising, visitor intelligence, and CRM/MAP connectors:
Forrester’s Q2 2019 Wave report on B2B Customer Data Platforms placed Lattice Engines and Leadspace in the leader category with both holding the highest scores in strategy and Lattice Engines being ranked slightly higher for their current offering.
Prior to the acquisition, the Dun & Bradstreet CDP (D&B DataVision) was ranked a strong performer. The dual acquisitions help the vendors extend their leadership in the CDP space and increase the likelihood of additional consolidation within the B2B Customer Data Platform segment.
Leadspace did not disclose the acquisition price. Acquisition discussions began earlier this year.
Lattice Atlas provides a unified customer view for omnichannel audience activation and personalization.
Lattice Enginesannounced commencement of a private beta for its Atlas Customer Data Platform (CDP). Lattice Atlas matches internal and Lattice Engines data sources, provides a single view of the customer, and supports a centralized audience platform for cross-channel creation and measurement. The formal launch is planned for the end of the year.
According to Lattice Engines, “Marketing organizations struggle to scale their Account-Based Marketing (ABM) programs because each application they deploy has its own data, segmentation, activation and measurement modules. This has led to a fractured buyer journey because banner ads, social ads, emails and sales calls communicate different messages, which creates confusion. Lattice Atlas solves this problem directly by integrating all the application data into a single place and providing the ability to manage this data, segment on it, and activate it through open APIs.”
“A CDP connects existing systems to create a unified customer view that makes ABM possible. In a world that never stops changing, the power and flexibility of a CDP will help marketers deliver on the promise of ABM. The features you need in a Customer Data Platform (CDP) will depend on your business, existing systems, and intended use. There are a few key considerations when evaluating CDP solutions for executing ABM programs, including a unification of all data sources, segment creation, campaign execution and predictions.”
David Raab, founder of the CDP Institute
Lattice contends that ABM at scale requires a CDP supporting four key attributes:
Unified Customer Data: After aggregating and consolidating customer data, a CDP must link identity, behavior, purchase history, and firmographics.
AI-driven Audiences: The CDP must not only score accounts and contacts, but identify buying committees, assess buying stage, and recommend the next-best offer.
Omnichannel Activation and Personalization: The CDP suggests highly personalized campaigns across relevant channels. The messaging must remain consistent across all of the channels.
Enterprise Grade Governance: The CDP maintains data security and privacy while complying with relevant laws such as GDPR.
Lattice Atlas aggregates client data across platforms and appends it with data from the Lattice Data Cloud. First-party content is gathered from CRM, marketing automation, web visitor logs, transaction histories, product usage details, etc. The Lattice Data Cloud enriches the customer view with firmographics, intent data, and technographics. Lattice also maintains an ABM Identity Graph which organizes customer data by account, buying center, and contact.
“Lattice Atlas was a natural evolution of our platform,” blogged VP of Products Chitrang Shah. “Since day 1, our approach has focused on being deeply integrated with each execution application and managing all data under one platform. Because of this we not only capture the largest amount of data, but also all that relevant metadata that describes it. Lattice Atlas is built on our understanding of these applications and their data to create the first CDP for enabling ABM at scale.”
Audience creation tools predict conversion likelihood, purchase window, and likely spend. Atlas also supports next-best targets and next-best actions.
Lattice Atlas connectors support Marketo, Eloqua, Salesforce, and a set of REST APIs.
Other features include GDPR opt out for campaigns and all marketing communications, engagement thresholds to prevent marketing fatigue, and lead-to-account mapping.
The initial Atlas application will be Playmaker which offers prescriptive recommendations to sales teams. “Playmaker lets them quickly identify top products to sell across all audiences and programmatically deliver those recommendations to the sales teams,” said Shah. “It also has built-in interactive dashboard to track the engagements (or lack of it) and its impact on the pipeline, enabling out-of-the-box visibility into play ROI measurements and the ways to improve it.”
“The holy grail of B2B marketing is creating 1-to-1 experiences across the entire buyer’s journey. This is why the B2B world is so interested in ABM these days. In order to craft personalized experiences at scale, our customers need a data foundation to better understand their target audiences, and an execution platform to engage those audiences in meaningful ways. With Lattice Atlas, we now enable companies to engage their buyers with 1-to-1 omnichannel experiences, making B2B marketing as personalized as B2C marketing,” said Lattice Engines CEO Shashi Upadhyay.
Lattice has over 200 customers including PayPal, Adobe, Dell, and SunTrust Bank.
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.
Future plans include a user interface for segmentation analysis and simplifying intent scoring to high/medium/low.
Lattice Scores and Enriched Data are available within the Eloqua Canvas campaign builder.
In a 2016 survey of predictive analytics companies, Gartner sized the global market at between $100 and $150 million. Although Gartner remains bullish on the sector, the size must be disappointing to both the firms in the space and their investors. One of the early companies in the space, Lattice Engines, continues as a market leader with over 200 global deployments.
Lattice Engines supports both enterprise clients and high-growth companies with deployments beginning around $75,000. Pricing is based upon the number of managed leads or contacts in the instance along with the number of users. With revenue between $25 and $50 million (GZ Consulting estimate), the firm has a strong position in the nascent market.
Lattice Engines combines first and third-party data to build predictive models. External content includes firmographics, intent data, technographics, social data, and web crawled business signals. Content is licensed from leading vendors such as Dun & Bradstreet (WorldBase global company file), Bombora (intent captured from over 3,000 B2B media sites), and HG Data (technographics). The Lattice Data Cloud covers over 200 million global companies, 21,000 buying signals, 100 million tracked domains, and over one billion daily interactions. Internal content spans transactions, CRM, marketing behavioral data, usage data, and support services.
“Predictive analytics is one of the few types of marketing technology that has the ability to solve issues at every step of the funnel, because it aligns sales and marketing against the right targets, and provides them with the right data to create targeted campaigns. By infusing fit and intent data into our models we enable teams to have a complete understanding of their ideal customer profile, which enhances the programs teams orchestrate against their targets.”
Director of Corporate Marketing Caitlin Ridge.
Firms can build multiple models to support various geographies, product lines, and scenarios (e.g. win/loss, upsell/cross-sell, renew/churn). Lattice scores and modeled data are integrated with many of the key SalesTech and MarTech platforms:
Ads/Web: DemandBase, Oracle Data Cloud, doubleclick (Google), AdRoll, Facebook
CRM: Salesforce, MS Dynamics, Oracle Sales Cloud, SAP
This platform coverage enables Omni-channel ABM campaigns across programmatic platforms, email, direct mail, and field marketing. Scores, insights, and recommendations are provided to sales reps within CRM i-frames.
“Lattice remains the most visible “face” of the market,” said Gartner analyst Todd Berkowitz in September 2016. “With its focus on security, level of integrations and ETL tools, the company is a fit for enterprise clients (both in high-tech and other industries) and/or companies planning to deploy in multiple regions. Gartner clients report that the company’s go-to-market approach is unique in the way it addresses complex problems and help customers operationalize the insights from the models. Lattice is one of the few vendors that can recommend key plays at both the lead and account level across the entire funnel.”
According to Lattice, customers enjoy a broad set of improved metrics:
2X Higher Conversion
3X Greater Pipeline
35% Higher Deal Sizes
6% Increase in Quota Attainment
85% Rise in Revenue per Customer
20% Reduction in Customer Churn
The firm sells broadly across B2B sectors. Customers include Amazon, Dell, PayPal, Staples, and SunTrust Bank.
In the late 1980’s, when my career was first beginning, I worked on a technology helpdesk for an insurance agency automation system (Aetna’s Gemini platform). Many of the calls were routine with an easily road mapped set of resolution steps. So the firm decided to invest in artificial intelligence (AI) and began interviewing its most seasoned experts to identify the problem resolution path.
After several months of development, an AI module would be unveiled that walked the user through problem resolution. It was basically a set of if-then-else and case statements providing pre-coded branching logic. Support reps started with a category and were walked through a set of questions to ask and resolution steps to convey over the phone.
The solution was expensive and lacked the ability to learn. Thus, if new problems arose or the problem resolution changed due to new hardware or software being introduced, the rules no longer applied.
It was far from intelligent. Heck, I’d coded a twenty-questions game in a first semester programming class that was more intelligent than the service. At least my Q&A game had the ability to learn new questions to ask without requiring an expensive consultant.
Finally, it was only used by new hires as much of the routine steps were just that — routine.
Solutions like this quickly proved that Artificial Intelligence wasn’t intelligent and after a few years, the term AI fell from favor and returned to the realm of sci-fi killer robots.
Nearly three decades later, the term AI is once again being rolled out. But now it does convey an impressive level of intelligence which makes our devices feel smart. It’s why we call them smartphones. They are able to leverage vast amounts of data and make decisions in the blink of an eye. Whether it is asking Siri a question or having Google map the best route to a location subject to current traffic patterns and transportation mode, we expect our devices to be intelligent.
AI represents a massive change in technology. You might call it a “paradigm shift” or “disruption” or we could just stick with “massive change.” What we’re trying to say is, AI is kind of a big deal. And just like the arrival of the personal computer, cloud computing, and the mobile smartphone, AI is going to fundamentally change the way things work, forever.
So it was with a smile that I saw the term AI being used by Salesforce in positioning their new Einstein service. Each year at Dreamforce, CEO Marc Benioff discusses a new underlying technology or cloud. Most recently it has been Lighting (UI and workflows), Wave (analytics), and the Internet of Things Cloud. At Dreamforce 2016, it is Einstein, their artificial intelligence platform to assist with sales, marketing, and service.
Salesforce presents AI simply as
Lots of data + cloud computing + good data models = smarter machines
So while much of this technology has been provided as consumer applications for over a decade, businesses have been lagging behind when the scope goes beyond a mobile app or e-commerce portal.
Shouldn’t the full transactional and service history be available to help understand past purchases, preferences, and potential cross-sell and upsell opportunities?
Wouldn’t we want it delivered no matter the touch point?
That is the type of intelligence that Einstein is looking to bring to Salesforce customers. Einstein is “the world’s first comprehensive artificial intelligence platform for CRM. I’ve never been more excited about the innovation happening at Salesforce,” said Benioff.
Einstein is available both programmatically (for developers) and “declaratively for non-coders,” said Benioff. It is integrated directly into the SFDC platform and available across all of the clouds. For example, an Einstein widget displays a set of insights identifying competitor news, recommended actions, and account intelligence.
Einstein Insights alerts widget.
Einstein Insights surfaces insights both programmatically for developers and data scientists and declaratively for end users. It can even infer competitors from emails and deliver alerts within SFDC widgets.
Einstein builds models with no coding or initial training by users. For example, the system is able to determine which trigger events are important to sales reps and surface news about competitors without asking “who are your competitors?” The system also can make recommendations concerning high-scoring leads based upon both fit (firmographics, biographics) and behavior (e.g. recent viewing of a demo).
Einstein recommends actions to sales reps. In this case, it is suggesting an email requesting a meeting with the VP of Sales at a high scoring lead who recently viewed a product demo on the website.
Not only does the system recommend activity, but it then offers recommended email copy including a proposed call time.
The platform is built on a series of recent acquisitions including RelateIQ (rebranded SaleforceIQ), MetaMind, Implisit, PreductionIO, and TempoAI. The firm now has a team of 175 data scientists “stitching together this amazing platform,” said Benioff.
“The new platform will “democratize artificial intelligence” and “make every company and every employee smarter, faster and more productive,” continued Benioff. “This is going to be a huge differentiator and growth driver going forward as it puts us well ahead of our CRM competition once again.”
The new platform infuses their sales, cloud, and marketing platforms with AI capabilities for “anyone” regardless of their role or industry. According to Salesforce, Einstein lets employees “use clicks or code to build AI-powered apps that get smarter with every interaction.”
Einstein is positioned as having your own data scientist focused on applying AI to customer relationships. Einstein has access to a broad set of intelligence including CRM data, email, calendar, social, ERP, and IoT to “deliver predictions and recommendations in context of what you’re trying to do. In some cases, it even automates tasks for you. So you can make smarter decisions with confidence and focus more attention on your customers at every touch point.”
Several predictive analytics companies used the launch to shout, “hey wait, we’ve already mastered AI for sales and marketing.” LeadSpace CEO and former Salesforce CMO Doug Bewsher stated, “B2B marketers need a complete solution that works across multiple channels, in their existing marketing stack.”
“Bad data is the Achilles heel of AI,” continued Bewsher. “AI is only as good as the data available to it. Marketers who want to get the full benefit of AI need to address their data problems first, or they’ll see the same diminishing returns as with traditional marketing automation.”
Shashi Upadhyay, CEO at Lattice Engines was a bit more diplomatic in welcoming Einstein. “After having led the market for several years, we are really excited to see the mainstream attention shifting towards AI-based solutions for marketing and sales. The Einstein announcement from Salesforce is a great step forward, as it will serve to educate the market and signal that predictive solutions are here to stay.”
