Sparklane Predict 2.0

Predict builds Ideal Customer Profiles based upon Fit (Firmographics), Need (Sales Triggers), and Behavior (Marketing Automation behavioral data) allowing customers to identify both current best-fit accounts and net-new prospects.
Predict builds Ideal Customer Profiles based upon Fit (Firmographics), Need (Sales Triggers), and Behavior (Marketing Automation behavioral data) allowing customers to identify both current best-fit accounts and net-new prospects.

French predictive analytics firm Sparklane unveiled their version 2.0 Predict platform which employs artificial intelligence (AI) and active learning to score millions of companies and determine which prospects are most likely to become net-new customers.  The Predict platform is available for the UK and French markets with localized language and datasets.  A German edition is in development.

Sparklane ingests and enriches company data, matching it against firmographics and trigger events to score millions of companies.  The system then models the Ideal Customer Profile (ICP) and Total Addressable Market (TAM).  Sparklane also identifies “sparks” (hot prospects) based upon sales triggers and delivers real-time alerts, messaging, and contacts.

Models can be deployed for both new and existing business.  New business models can be constructed from historical data (e.g. CRM win / loss flags) or estimated and refined for new market entry.  Existing business data can also be deployed for churn models to help identify companies that are more likely to drop as well as upsell and cross-sell models.

CEO Frédéric Pichard said that employing artificial intelligence to identify your next best customers “is probably the most amazing promise B2B marketing and sales tools can fulfill” as it provides “a new way of working to help our customers be more efficient and successful.”

Sparklane users begin by importing datasets from CRMs or CSV files.  Logic is employed to determine both positive and negative sample records.  For example, a CRM Win / Loss flag could serve as such an indicator.  The file is then enriched and an ICP model is constructed.  The ICP contains three types of variables: Fit (firmographic), Need (Triggers), and Behavior (Marketing Automation prospect activity).  Marketers or Sales Operations are able to view the model and adjust weights.  This model is then employed for constructing a TAM with net-new accounts which can be saved as a fixed account list or dynamic model.

Sparklane onboarded file mapping.
Sparklane onboarded file mapping.

An accuracy score helps define how well the model distinguishes between good and bad prospects.  Thus, an 80% accuracy score indicates that 8 out of 10 companies in the seed file are properly predicted by the model.

An accelerated learning option is available for new market entry.  Thus, if a seed list of good and bad prospects is not available for a new product line or market, an initial set can be manually selected from Sparklane company lists and deployed as a first generation seed list.

An active learning option allows users to perform a qualification pass on a list to help expedite model construction.  While engaged in active learning, the user is shown company profiles which include account overviews, triggers, and family trees.   The marketer can then give a thumbs up or down to each proposed account.

During active learning, sparks can be added, dismissed, or decision postponed, allowing the platform to adjust the model.
During active learning, sparks can be added, dismissed, or decision postponed, allowing the platform to adjust the model.

As output, the platform provides a set of “sparks” which are high probability accounts or contacts.  The user sets the number of sparks displayed in a spark list.  Qualified prospects can be sent to a CRM as accounts or leads.

The French dataset covers three million firms and two million contacts.  The UK universe provides 200,000 companies and 300,000 contacts.  The UK dataset focuses on large companies with sales triggers.

The French file includes 600,000 emails while the UK file supports 100,000 emails.

The firm claims that Predict increases the opportunity conversion rate by 70% and shortens the sales cycle by 30%.

Sparklane employs sixty headcount in Paris, London, and Nantes.  It invests over 20% of its turnover in R&D and has nearly 200 customers in Europe.

2016 North American Market Size

2016 North American Sales Intelligence Market Sizing Model (Excel)

The Market Size of North American Sales Intelligence Vendors. Includes vendor product features, market share, and notes. GZ Consulting Copyright 2017.

$750.00

For the past few years, I have been sizing the North American Sales Intelligence Market.  This is the largest of the markets as Europe and AsiaPac are more fragmented (the UK is the only other mature market with Bureau van Dijk, Avention UK, Artesian Solutions, and DueDil offering full solutions).

In 2016, I estimated the market at $770 million with LinkedIn Sales Navigator as the top vendor.  While new firms continue to enter, the top ten firms (now eight following the 2017 acquisitions of Avention and RainKing) earn seven of every eight dollars in the industry.

I am making my market model available for license (See PayPal button at top) as an Excel spreadsheet.  It includes revenue numbers by company along with market share, key features, and notes.

The LinkedIn Market Share Section of the 2016 North American Sales Intelligence Market Sizing
The LinkedIn Market Share Section of the 2016 North American Sales Intelligence Market Sizing

I have also broken out two sub-categories: Predictive Analytics and Tech Sales Intelligence.  Predictive Analytics vendors continue to scuffle in the marketplace.  Last September, Gartner sized the global market at between $100 and $150 million.  I have gone back and forth on whether to include them in the larger sales intelligence space, but several of the sales intelligence vendors have added light predictive tools (e.g. Avention, DiscoverOrg, RainKing) while the predictive analytics companies have moved to add enrichment and provide more insights to sales reps.  As such, I see the two product categories moving towards each other so chose to include Lattice Engines, Leadspace, and similar firms.

The Tech Sales Intelligence category (e.g. DiscoverOrg, RainKing, Aberdeen, Corporate360) continues to show strong growth and makes up just over 15% of the market.  Both DiscoverOrg and RainKing have posted remarkable growth over the past few years and merged their efforts last month.  Post acquisition, they are the number three vendor in the space and may hit $120 million in 2017 revenue.  The new powerhouse has 4,000 customers and is looking to expand beyond technology sales to become a general purpose sales intelligence solution.

Acquiring RainKing should move DiscoverOrg well past Data.com (Salesforce) which will likely see declining 2017 revenue.  Salesforce has dropped the ball on Data.com.  They overpromised and under-delivered for years, relying on their ability to bundle the offering with other SFDC products.  As of last month, they are no longer able to deliver Dun & Bradstreet content (D&B WorldBase, Hoovers, and First Research) to new customers (legacy customers retain access).  Unless Data.com has a major content partner announcement at Dreamforce, it is likely to see significant revenue declines in 2017 and 2018 as customers switch to D&B Hoovers for Salesforce and other offerings.

Dun & Bradstreet re-established itself as the #2 vendor in the space with the January 2017 acquisition of Avention and the rebranding of Avention OneSource as D&B Hoovers.  Both companies have struggled to grow revenue with Avention growing slowly over the past few years and Hoovers declining.  However, infusing Avention products with Dun & Bradstreet content both reduces the underlying cost structure of Avention offerings and improves the depth and quality of the content.  Furthermore, Dun & Bradstreet has a much larger sales force which previously has lacked a credible global sales intelligence offering.  Hoovers classic generated nearly all of its revenue in the United States.  Over the next two years, expect to see significant revenue shift from Hoovers Classic to D&B Hoovers.

Three-Toed Sloth By Stefan Laube (Tauchgurke) - Public Domain.
Three-Toed Sloth By Stefan Laube (Tauchgurke) – Public Domain.

Finally, LinkedIn Sales Navigator has established itself as the clear number one vendor in market revenue.  The product didn’t exist five years ago and its competitors still tend to dismiss this gorilla in their midst.  How can they be missing the #1 vendor in the space?  Easy — the gorilla is well camouflaged and appears to be more of a three-toed sloth sleeping in the forest canopy.  Sales reps all use the freemium version of LinkedIn so give little thought to delve further when they ask “how are you obtaining your account intelligence today?” and the response is LinkedIn.  Thus, they enter LinkedIn as the competitor into their CRM, not Sales Navigator.  A few months later when they lose the opportunity, the rep then enters “no decision” into the CRM instead of recognizing a competitive loss.  I have been warning vendors in the space for years about this phenomenon, but they have failed to understand the threat of a gorilla that looks like a three-toed sloth.


N.B. Three-toed sloths inhabit Central and South America and gorillas Central Africa.  This is a metaphor.

 

 

 

Artesian CEO on AI and RegTech / RiskTech

The Artesian Opportunity View ties together Salesforce Opportunity data by stage with Artesian Sales Triggers, helping surface key opportunity insights in context.
The Artesian Opportunity View ties together Salesforce Opportunity data by stage with Artesian Sales Triggers, helping surface key opportunity insights in context.

Recently, I had the opportunity to sit down with Artesian Solutions CEO Andrew Yates and discuss topics including artificial intelligence and risk tools they are integrating into their social selling service.  This is the second in a series of interview excerpts I am publishing this week.  On Monday, Andrew discussed Artesian’s 2016 entry to the US market.


Michael: You have recently begun to introduce AI capabilities into your platform.

Andrew: What we’ve done in our first incarnation of bot-driven AI is we’ve created something that we call an “insight agent” that, through an API into Salesforce, can build you a view of threats and opportunities within your pipeline. Which, in itself, is pretty damn useful; much more useful than a forecast report or a dashboard which is the way you see it in Salesforce today. Then we’ll lay out all of those deals by stage and value and overlay today’s new social and demographic context on top.  That’s pretty useful.

With the latest release, we’ve created a bot which literally reads and interprets the news in relation to the stage of the sales process that you’re at. And, where it sees a particular trigger that has meaning in relationship to a particular stage, it flags that. Most organizations have implemented the concepts of sale stages when they’ve implemented CRM.

Typically, when I ask somebody, “how many stages do you have?” They’ll say, “between five and seven.” The system automatically builds you a view depending on how you’re implementing Salesforce, however many stages you’ve implemented and what you call them. Then what the bot does, is it crawls all over the news looking for things that could impact those opportunities at the stage they are at.

Let’s say, I’ve got a six-stage process where stage six is closed and stage five is a negotiation.  Artesian’s insight agent finds out about a CIO who has left the business. The insight agent will notify the user that there’s a potential problem with the deal in their pipeline. The agent will tell them why there is a problem and how it’s been categorized.  There’s half a dozen next-best actions that we bundle up with the insight as we deliver it. That’s our first attempt at taking the concept of machine-based learning and natural language processing, combining it with an AI bot, and trying to make that useful for customers.

We’ve introduced the ability for the user to customize their own topics, keywords, and trigger events. We offer a bunch out of the box, and we also wrap a managed service around it and easy implementation to every customer.

We’re also seeing a lot of activity in the “RegTech/RiskTech” arena with the growth of cybercrime and terrorism, and the sensitivity around regulation of any financial, FCA [UK Financial Control Authority] regulated [business]. There are regulations that organizations need to comply with. We’re increasingly being asked by our financial services customers, particularly the banks, to get deeper into being able to provide those capabilities inside of Artesian.

Organizations want to mitigate risks. They want to fall within the arena of whatever the regulation is and comply with the law, but they also want to exploit the technology as best they can to make sure they write the best business that they can. We’re doing some work at the moment in conjunction with one of our demographic data suppliers. What we’re looking to do is extend the capabilities in Artesian to provide some of the capabilities that our customers are asking for in the RegTech / RiskTech environment. We’re going to introduce risk agents. Risk agents look at the real-time present and it looks at the past. It specifically looks at things that are in-line with the regulations and also in-line with the stated risks that the customer has mapped out.

What that translates into is a service that is not only compelling in terms of customer acquisition, customer retention, and yield, but also compelling from a kind of, you don’t go to jail if you’re using Artesian because it’s doing the regulation and risk job for you as well.

Michael: When you say risk app, are you talking more about supplier risk, compliance risk, credit, reputational?

Andrew: There are 40 or 50 pretty big companies doing this thing already. What we’re talking about is company-centric intelligence, but also the people associated with that company and the intelligence that we’ll need to derive around whether something is risky or not. It could be the performance of a business. It could be some adverse news in relation to that performance. Or it could be that an individual who has a beneficial ownership, more than a 5% stake in a business, happens to be on a naughty list in terms of the PEP [Politically Exposed Persons] or sanctions.

At the moment, we have risk triggers in the opportunity view. They’re not compliance risk triggers. If you’re going to a client, they need to know about key beneficial ownership.

Michael: Is that part of the opportunity view or is that a new type of view?

Andrew: A new type of view.  We have risk triggers in the opportunity view, but they’re not compliance risk triggers.  If you go into a bank, they need to know about beneficial ownership, adverse news going back three years, PEP, sanctions, real-time alerts from stock exchanges.  None of that is feasible within a generic instance of Salesforce.com in an opportunity view.

Michael: It sounds you’re looking to move beyond the sales and marketing teams to start to get to into things like onboarding, KYC [Know Your Customer], AML [Anti-money Laundering], PEP, and other compliance aspects that really go into monitoring of clients as well as the initial onboarding.

Andrew: Yes, if you go back to the whole customer curious mantra and deep relationship management, we like to say that we put the R back into CRM.  We are all about that relationship.

The conversations we are having with our large customers would indicate we are on the right track with that.


The interview will be continuing over the next few days with discussions of what it means to be a “customer curious” business and how Artesian maintains a very high engagement rate amongst its users.  Monday’s blog discussed Artesian’s 2016 entry into the US market.

Intent Data — Why and When?

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.

Intent Data Publisher Network and Tracked Activities (Source: Bombora)
Intent Data Publisher Network and Tracked Activities (Source: Bombora)

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.

In a blog earlier this month titled “Intent Data is Great. Except When it Isn’t,” Gartner Research Vice President Todd Berkowitz listed the following limitations concerning intent data:

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

  • Non-technology products

  • 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.

Addendum

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 €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%).

Fiind Predictive Visualization Tools

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.

Fiind Pitch Points assist with account messaging.
Fiind Pitch Points assist with account messaging.

Forums helps identify potential pain points based upon public forum discussions while case studies/success stories extract intelligence from published case studies.

Customer Complaints identify potential pain points.
Customer Complaints identify potential pain points.

Company Hiring patterns are useful for discerning the underlying technology and emerging staffing requirements at companies.

Fiind Company Hiring patterns
Fiind Company Hiring patterns

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.

InsideSales $50M Funding Round

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.

InsideSales NeuralView identifies the ”most promising leads, opportunities and accounts” for customers
InsideSales NeuralView identifies the ”most promising leads, opportunities and accounts” for customers

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.”