Poor data quality is a disease which slowly destroys the value of your marketing database. Quality is damaged through incomplete information, poor data entry, and data decay. A traditional response is to purchase new records, but this only provides a temporary (and expensive) respite from your data quality issues.
The data I’ve seen indicates that contacts decay at a 25 to 30 percent annual rate. This means that a prospect list that is 90 percent accurate today will be little more than 50% accurate two years later. Thus, a prospect list purchase strategy is like steroids, it makes your marketing database look healthier on the day the list is purchased, but it simply masks the growing disease within your database. Treating one or two symptoms does not address the underlying problem — a lack of a broad, continuous data strategy.
However, if you take a holistic view around data quality which includes continuous DaaS validation, ABM look-a-likes, web form enrichment, lead-to-account mapping, duplicate management, data standardization, and reference database appends, you will have a healthy database that ensures your MAP and CRM platforms contain the richest, most accurate data.
Vendors that support holistic data quality include ReachForce, D&B Optimizer (FKA Workbench), Zoominfo, InsideView, Oceanos, and Openprise. So if you are concerned about your ability to target, segment, pass quality leads to sales, score leads, or build predictive models, then begin with a holistic data strategy. Symptoms of poor data quality include high email bounce rates, declining email sender scores, returned direct mail, duplicate records, incomplete records, accelerating unsubscribe rates, and sales reps that ignore your marketing qualified leads.
Any firm that is adopting ABM, advanced lead scoring, a single view of the customer, or predictive analytics, should begin with a holistic data quality strategy. Otherwise, these advanced marketing strategies are bound to fail.
Predictive Analytics and Audience Management vendor Leadspace completed its Series C. The funding round was led by Arrowroot Capital and joined by JVP. The $21 million round will be used “to grow our customer team in San Francisco and Denver, and our AI and data management product teams in Israel.”
The firm is assessing additional locations, including possible offices on the East Coast and Europe, “perhaps” London.
Arrowroot has taken a seat on Leadspace’s Board. The firm wanted growth equity advisors instead of traditional VCs for Round C. “At this point the investment is not just in the idea and the team, but also the underlying metrics and performance of the business,” said CEO Doug Bewsher. “Once you have “Product/Market fit”, the kinds of questions investors ask are whether you are ready to scale; what are the opportunities for further growth; and apart from additional investment can we be an investment partner that can help you address these opportunities?”
Bewsher noted that marketing has been transformed over the past seven years since Leadspace was founded. Firms are switching from tactical demand generation programs to targeted Account Based Marketing (ABM) communications. “No longer is it OK to just send out blanket “nurture” emails to everyone and hope that will generate positive customer engagements. No longer can you rely on a single data source as the basis to know your customer. No longer is it enough for marketers to just think of leads — they need to market to accounts, and teams of people. Neither can marketers afford to ignore intelligence and information from external parties, and simply rely on the limited info they gather internally.”
Not only has the nature of B2B marketing been transformed, but “world class B2B sales and marketing organizations” need to become more like consumer companies with a deep understanding of the account at multiple levels. Echoing Sirius Decisions, Bewsher said that B2B marketers need to “really know your customer at the account, demand unit and individual level, and then target and personalize your messaging to cut through the noise. And think customer-first.”
As an analytics company, Bewsher talks up the value of AI for sales and marketing as it begins to address specific problems and workflows:
AI is everywhere. While there is no doubt that it is going to change every corner of our life, both as private users and business people, I think we will start to move from the promise to the reality in 2018. In business-to-business sales and marketing in 2017, it was enough to say: “We have a ton of great data scientists who are working on new ways to better engage your customers.”
But in 2018 customers will look to see actual results — like the 90 percent increase in email connection rates we have seen from the deployment of AI to recommend the right way to engage a specific user. This will require a maniacal focus on specific use cases from the emerging area of AI.
One area where AI will improve revenue generation effectiveness is in ABM programs which has been limited by the human ability to consume information and the historical lack of data availability. However, “AI is changing all this, with the ability to consume and understand unprecedented amounts of information and turn this into action at scale and in real time. So sales and marketing teams now have the opportunity to drive much more relevant and effective engagement programs for their entire potential target audience.”
According to Leadspace, they are trusted by over 130 B2B brands and seven of the top ten enterprise software companies. Clients include Microsoft, Marketo, Oracle, and RingCentral.
The theme of this year’s Dreamforce was The Fourth Industrial Revolution. Following after revolutions driven by steam, electricity, and information technology, the fourth industrial revolution blurs the “physical and digital worlds” creating a wave of “innovation in technology” which is transforming the economy, society, and lives while creating new jobs, industries, and opportunities. This next wave is based upon intelligence. Elements include IoT, 3D printing, biotech, robotics, autonomous vehicles, nanotechnology, and quantum computing.
“This is what we call the fourth industrial revolution,” said Salesforce CEO Marc Benioff. “There’s all these amazing new technologies, things like autonomous vehicles and artificial intelligence and nanotechnology and mobile computing and all these things are really hitting at once. And companies are really transforming themselves and bringing all these new technologies in really to connect with their customers in new ways.”
Thus, elevators loaded with sensors now communicate back to the manufacturer and predict failures, calling for service prior to trapping people. Likewise, with tires, “if the tire blows, nobody knows; but in the future, if the [smart] tire blows, everybody knows.” So, firms like Kone (elevators) and Michelin (tires) are now B2B2C companies. In the future, if a tire is about to blow, it will communicate to the autonomous vehicle to pull over.
“Every company is getting closer to their customers. We’ve been talking about this for years. It doesn’t matter if you’re a B2B company or a B2C company, everybody’s becoming a B2B2C company.”
Salesforce and its customers are “delivering personalized one-to-one engagement at scale,” said Stephanie Buscemi, EVP of Product Marketing. This is done “declaratively, with clicks and not code.” Through the Salesforce Data Management Platform, ads are customized and delivered cross-device, allowing companies to redisplay ads or present new advertisements to their customers and prospects.
Benioff cited a series of companies providing customer service and support through Salesforce platforms including Louis Vuitton, Marriot, Coca-Cola, T-Mobile, Adidas, and Ducati Motorcycles.
“Behind all these things…behind everything is a customer. And that’s what all of us do. We are working to connect with our customers in an incredible new way.”
Simplified customization, development, and branding were emphasized during the keynote. A set of customizable products provide a “smarter, more personalized Salesforce”:
MyTrailhead service supports custom branding, content, and learning paths that allows firms to onboard and train employees on desktops and phones. Tools include quizzes, reference links, trails, and badges. Salesforce Trailhead content is also available.
MyEinstein provides an artificial intelligence layer driven declaratively by “clicks, not code” supporting “smarter capabilities including bots.”
MyLightning customization provides an app builder with custom pages, a Lightning theming and design system, Lightning Flow, Components, and Bolts which operate automatically on both desktops and phones. Designers will have access to dynamic components which are conditionally displayed.
MySalesforce branded “mobile apps without code” can be uploaded to the Google Play and App Store.
MyIoT supports native integration capturing real-time events, business rule automation, and low-code orchestration.
Based upon customer feedback, SFDC has shifted from IoT as a separate platform to an integrated feature of the CRM platform which also operates “declaratively without code.”
Benioff admitted that the Fourth Industrial Revolution is creating concerns and wondered whether it is “uniting us or dividing us. Are we more connected or somehow less connected?”
He also asked whether there is more or less equality in the World.
“There is this stress being created by this fourth industrial revolution. Yes, we have this promise of this new connected World. But what is it doing to us? And what are other actors doing around the World using these technologies? Are they changing our society? Are they changing our elections? What are they doing with this technology?”
Benioff is looking at the Trailblazers attending Dreamforce as the Customer Innovators, Technology Disruptors, and Global Shapers to ensure that the next wave is directed in a positive direction. “You have all these new tools at your fingertips, these incredible new technologies, but you are doing some amazing things in the World. You are changing your companies. You are steering this technology in the right direction. I’m so confident in who you are. I’m so confident in what’s in your hearts and where we are all going.”
Benioff noted that most technology is generally neutral in it effect upon society. It is therefore incumbent upon technologists, developers, and companies to deploy technology in a socially responsible manner which promotes greater equality. Benioff called for companies to fight for equality through equal pay, investing in schools, and opposing discriminatory laws. He also noted that it is the poor who are most hurt by environmental degradation and proudly stated, “we are a net zero cloud.”
Benioff was also proud to have founded and led the leading CRM with an 18.1% market share (2016 IDC) nearly double that of Oracle (9.4%). Salesforce has the top solutions for sales (34.2%), service (33.7%), marketing (9.9%), and Platform-as-a-Service. Within the marketing cloud, Salesforce claims to offer the leading Data Management Platform and commerce Platform.
What’s more, the firm is on track to be the fastest enterprise software company to hit $12.5 billion in revenue. They hit $10 billion this year and have FY19 guidance of $12.5 billion in year 20.
One of the issues facing businesses and policymakers is an increasing skills gap. Benioff proposed MyTrailhead as one of the tools to help address the problem of workers across many industries and skill levels. MyTrailhead provides a customized, branded training platform.
TechCrunch complained that this year’s Dreamforce lacked drama as it lacked new initiatives such as the social enterprise, artificial intelligence, and IoT. “They are a company that embraces the cutting edge, but this year lacked that kind of big announcement,” complained enterprise reporter Ron Miller. To be fair, though, the company has rolled out a series of new platforms, clouds, and acquisitions over the past few years. A year with few fireworks is not necessarily a year without forward progress for Lightning, Quip, Einstein, Trailhead, and platform customization.
The conference remains a monster with 170,000 registered participants joining in San Francisco and millions of online views.
ESW Capital completed the acquisition of predictive analytics vendor Infer and will be rolling it into Ignite Technologies. Infer offers predictive lead and account scoring. Use cases include TAM identification, segmentation, account selection, demand generation, lead scoring, opportunity scoring, and upsell/cross-sell. In a September 2016 report, Gartner said that Infer pricing starts at $30,000 and increases based on the number of models. There are also charges for net-new contacts.
This summer, ESW also acquired company intelligence vendor FirstRain and rolled it into Ignite as well.
The Ignite Prime program offers clients access to additional enterprise technology once they have signed a contract for one of the Ignite Technology solutions. For example, Infer customers would have access to additional enterprise software solutions such as First Rain, ThinkVine, and Placeable equal to the value of their Infer contracts.
“We’ve been continually impressed by Ignite throughout this acquisition process. They have a strong leadership team and the right strategy that’s in line with where the future of sales and marketing solutions are going, where there’s a need to converge multiple products into a cohesive platform to drive true, full-circle customer intelligence. We’re confident this is the platform that our amazing customers will want to build on and grow, and are excited for the Infer solutions to be a part of Ignite’s Prime Program which will help customers drive 2x ROI.”
Vik Singh, Infer’s CEO
Infer was founded in 2010 and is headquartered in Mountain View, California. Infer focuses on predictive solutions for the technology sector and lists AdRoll, Cloudera, New Relic, Tableau, Xactly and Zendesk as clients. As of Q3 last year, Infer reported over 140 customers. Deal size was not disclosed.
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.
Future plans include a user interface for segmentation analysis and simplifying intent scoring to high/medium/low.
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.
One of the important recent B2B MarTech innovations is the development of intent data from vendors like Bombora. As prospects are now using the Internet to self-educate, they are reaching out to a smaller set of pre-screened vendors later in the sales cycle. But if firms are being stealthy to avoid detection during this initial phase, B2B firms have been looking to uncloak this veil of secrecy and reach out to firms during the initial phase.
One response to anonymity was content marketing which looks to deliver information (and perhaps uncover prospects) during this early phase. But it is difficult to customize messaging to anonymous individuals. Thus sprung up visitor id services such as Demandbase that map IP addresses to company firmographics in real-time. For example, a visitor from a P&C insurance IP address would be shown a website and content that speaks to their industry specific needs.
Firms also engaged in SEO and SEM to drive traffic to vertical content. While these activities were an improvement, they provided no indication concerning whether the prospect was in the market for a firm’s solutions.
Firms like Bombora and The Big Willow work with B2B media sites to map site traffic and actions (e.g. downloading white papers, webinar attendance, site searches), to specific companies. Thus, each IP address has a baseline activity trail which indicates topics of interest. Intent firms then match B2B media site visitor actions to an intent taxonomy covering thousands of topics. Of course, larger firms will leave more distinct trails and firms will display heavy footprints around their own industry and target segments. These patterns are company-specific background noise. To find the intent signals, intent vendor analytics determine which topics are surging at each company. For example, If GE has X searches per week on cloud computing, then this activity rate is general background noise. But if activity spikes to 2X, then there is likely to be some initiative underway at the firm concerning cloud computing. It is these surges that identify firms to be targeted. Intent data provides a mechanism for placing calculated bets on which accounts and prospects deserve additional resources.
Keep in mind, this activity remains anonymous. A cloud computing vendor does not know who at GE is involved in cloud computing initiatives, but they know it is the appropriate time to target GE with stepped up marketing (SEM, email, sales calls, etc.).
Thus, intent data is integrated into predictive marketing platforms such as Lattice Engines, LeadSpace, Mintigo, Everstring, and Radius.
Just this month, Everstring added Bombora’s intent data to their Audience platform. Surge data is also available for programmatic targeting on platforms such as BlueKai (Oracle), Krux, and Lotame. Thus, it is possible to target advertising for firms that have shown a surge of interest in a topic.
Like any technology, intent data has its limits. While it helps identify when to call into an account and topics of interest, it doesn’t identify whom to call and whether there is an actual initiative related to the topic. Furthermore, intent data does not indicate whether a firm is a good fit (e.g. size, industry, technographics) or how far along they are in the discovery process.
There are a large number of scenarios where intent data and models don’t add nearly as much value (if any). It’s not because the intent data is inaccurate. It’s because there is simply not enough data available to use directly or to put in models. They include:
New and emerging technology categories
Certain geographies, industries or other niches
Solutions (especially services) that can’t be easily categorized
Thus, intent data works best for well-established technology segments (versus emerging ones). Just make sure to also look at fitness indicators when building surge-based campaigns.
Within 15 minutes of posting this blog, I saw that Bombora was named a 2017 Cool Vendor by Gartner.
“We believe it’s a true milestone to be recognized by Gartner as a Cool Vendor in SaaS for 2017,” said Erik Matlick, founder and CEO of Bombora. “Our customers choose Bombora so that they may access the largest source of B2B intent data for use in their account-based marketing strategies. For us, being a ‘Cool Vendor’ serves as a validation of our ‘everybody wins’ approach to the ecosystem and the impact that our dynamic, quality intent data is having across B2B sales and marketing.”
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%).
Predictive analytics company Fiind rolled out a set of product enhancements at the beginning of March to assist with company analysis, framing discussions, and identifying potential product issues. According to the company, “Fiind helps businesses find their customers efficiently using machine learning – by enabling marketers and sellers tune into signals that customers send prior to buying. Fiind’s library of over 100 million signals serves as a Customer GPS with answers to questions such as who is likely to buy (and what and why).”
The company announced the following additional insight visualization tools for sales reps:
Pitch Points – Guidance on signals and supporting points to script effective Sales pitch.
Forum Talks – Topics (technologies, complaints, issues, etc.) classified based on the discussions in the forums.
Success Stories – Case studies/success stories on tech usage
Top Picks – Visualize corporate hiring patterns
Survey Results – Run a survey (externally) and display the key findings (e.g. DB Admin for a DB product)
The Pitch Points feature identifies company insights and frames them within the context of a company’s product offerings.
Forums helps identify potential pain points based upon public forum discussions while case studies/success stories extract intelligence from published case studies.
Company Hiring patterns are useful for discerning the underlying technology and emerging staffing requirements at companies.
Fiind predictive scores and insight visualization tools are available via browsers, email alerts, Salesforce.com, and Microsoft Dynamics. They can also provide a “personal briefing agent” within Office 365 or Google Apps.
Since yesterday I discussed SalesLoft’s funding round, I would be remiss to note that Predictive Analytics vendor InsideSales closed on a $50 million funding round which included Microsoft and the Irish government. In total, the company has raised over $250 million. The latest round, led by Polaris Capital, included Questmark Partners and the Irish Strategic Investment Fund. Also participating were existing investors Microsoft, Kleiner Perkins Caufield Byers, Hummer Winblad, U.S. Venture Partners, Epic Ventures and Zetta Venture. The latest round was flat or nominally up, allowing the firm to retain its Unicorn status.
InsideSales’ predictive Accelerate service combines predictive analytics with a phone dialer, sales gamification, and email and web interaction tracking within SFDC. Accelerate lists at $295 per user per month. An Essentials service, designed for SMBs, is priced at $25 per seat per month. The firm also offers products at several price points in between.
The company stores, anonymous, aggregated data. “We have over 120 million unique buying personas,” said CEO David Elkington. “More interestingly, I have almost a hundred billion sales interactions with those 120 million people. A sales interaction’s a conversation, an email, a response, a visit, a purchase. We’re adding roughly five billion of those a month. The reason is because it’s aggregate, it’s crowdsourced.”
Elkington emphasizes the value of data over algorithms. “We’re basically looking at the way categories of people behave within various different situations. The mistake people are making is thinking the value is in building the best algorithm. The key is in the data.”
Elkington observed a “generational transition” in sales leadership with millennials “becoming predominant quota carrying reps, taking more sales leadership roles.”
In 2015, InsideSales set out to study the “buying and selling patterns of the next generation of employees.” The firm found that over the past few years, the presence of millennials amongst buyers and sellers has nearly doubled “and their behavior is very different.”
“The way a millennial runs their day is fundamentally different than the way other generations run their day,” noted Elkington. “Millennials don’t want to sit down in their CRM. They live all over the web and move around quite a bit.”
Based on these observations, InsideSales recently released Playbooks, a browser plugin which helps sales reps “prospect, prioritize and connect without juggling multiple tools.” The Playbooks service also supports CRM synchronization and integrated telephony and emails.
InsideSales research found that the typical millennial has seventy to eighty tabs open at a time. Thus, Playbooks allows the user to leverage the intelligence in each of those tabs and immediately act on the information.
InsideSales is finding strong usage for Playbooks amongst millennials. “Reps adopt it much faster with much less training, and satisfaction seems to be higher,” said Elkington.
InsideSales has over 2,000 customers including ADP, Groupon, and Microsoft. The firm currently employs a staff of 500 located in the “Silicon Slopes” of Utah with an outpost in San Mateo.
“Our mission is to leverage big data and cloud capabilities to unlock human potential through predictive analytics and machine learning,” said Elkington. “We are building an Amazon-style recommendation engine for business — a system capable of intelligently analyzing billions of data points in real-time and recommending the optimal next steps for almost any application or business process. This lays the groundwork for a future where predictive technology can be applied, not just to sales organizations but also to government, healthcare, retail and beyond.”