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.”

ABM and High Growth Companies

Account Based Strategy Adoption Rates (DiscoverOrg and Smart Selling Tools)
Account Based Strategy Adoption Rates (DiscoverOrg and Smart Selling Tools)

A joint study by DiscoverOrg and Smart Selling Tools of 200 sales and marketing organizations found that high growth companies with at least 40% growth over the past three years are 2.5 times more likely to have adopted an Account Based Marketing (ABM) strategy.  Furthermore high growth companies are twice as likely to have successful cold calling programs and are more likely to have a dedicated outbound prospecting team.  High growth firms are also more likely to hire sales reps based upon their “tech-savvy” than experience and have adopted twice as many sales technologies than their slower growth brethren.  With respect to MarTech, high-growth companies have adopted 24% more marketing solutions.

The study also found that fast growth companies provide at least three hours of coaching or training per week to their sales teams.  At slower growth companies, training appeared to have less of an effect.  According to the report, “While an increase in training hours correlated with a rise in growth rates for the high growth group, it did not with low growth companies. This suggests that training may not in of itself cause growth, but it is critical in sustaining it. Fast growing organizations need to train constantly to maintain momentum and enable teams to perform at a high level. Companies that err on the side of less training and coaching do not appear to set their teams up for the same level of success.”

“The findings clearly demonstrate that achieving fast growth is not as simple as having a great product and hiring experienced sales reps.  Sales and marketing teams that are true revenue-generating engines take risks and do the hard things – like cold calling, focusing on data quality, and heavily aligning sales and marketing teams across account-based strategies.”

– DiscoverOrg CEO Henry Schuck

“Technology proliferation in the sales and marketing industry is both a challenge and an opportunity,” added  Nancy Nardin, CEO of Smart Selling Tools. “The fastest growing companies are investing in technologies that make their sales and marketing teams more productive and more insightful, while recognizing it is equally as important to have highly trained team members who know how to leverage that technology to its fullest power.”

The primary inhibitor of even faster growth at high growth companies was data quality issues concerning accounts and contacts.

The top technology available to sales reps were CRM (52%) and LinkedIn (free LinkedIn was deployed at 45% , premium LinkedIn at 33%, and Sales Navigator at 27% of sales teams).  Pipeline and Opportunity Management software was third at 42%.  Rounding out the top five were compensation/commission software and sales intelligence, both with a 38% deployment rate.  Surprisingly, 37% of sales teams still employ account and contact data providers / list providers.  As sales intelligence vendors support list building along with sales intelligence (and some also data hygiene), there are likely ongoing opportunities to move sales teams up the value chain from list purchases.

Predictive analytics / predictive intelligence placed 36th out of 37 technologies with only a 5% deployment rate.  As Gartner estimated the total global market for predictive analytics technology to be between $100 and $150 million, this low penetration rate should not be overly surprising.

The study, conducted in November, used 40% growth between 2013 and 2016 (estimated) as the high growth cutoff as it is represents the recent growth floor for Inc. 5000 membership.  Of the 200 firms studied, 17% fell into the high-growth category, 69% fell into the low-growth category (1-39%), 13% had flat revenue, and 1% had declining revenues.  The survey was over weighted to technology companies with software, IT Services and Telco as the top three industries surveyed.  82% of the firms were B2B and 85% were headquartered in the US.

Market Insights News

Market Insights Newsletter (January 22, 2017)
Market Insights Newsletter (January 22, 2017)

One of the services I provide to vendors is a weekly newsletter called Market Insights which covers the Sales Intelligence, Data as a Service (DaaS), Data Hygiene, and Predictive Analytics markets.  I’ve been writing it since mid-2012 so have built up a significant archive on these topics.

Year one, I had four clients, all located in the United States.  Three were in the Sales Intelligence space and one was in Data Hygiene so my focus was on those segments plus DaaS, a key delivery channel.  But predictive analytics was beginning to compete with the SI firms so I folded it into my coverage in 2013.

By 2015, Account Based Marketing and Account Based Sales Development were hot topics so they joined my topic list.  I was also covering many more sales intelligence companies outside of the United States.  On the DaaS side, Marketing Automation Platform and Chrome Connectors have become much more prominent in my coverage.

And interest in my little newsletter has grown to over twenty paid clients including firms in the UK, France, Israel, and India.  This list now includes content vendors that market their databases to the sales intelligence, hygiene, and predictive analytics vendors.

What I’m most proud of is that eight of the top nine sales intelligence vendors in North America are now newsletter clients along with three of the top four UK vendors.

Please contact me If you are interested in a free trial subscription to Market Insights.

With AI, It is Garbage In, Garbage Out

scrapyard-70908_960_720

A few weeks ago, Salesforce announced its new Artificial Intelligence (AI) functionality called Einstein.  The new features promise to provide improved decision making based upon predictive scores and recommendations to sales, marketing, service, and other functions.  Likewise, Microsoft announced yesterday that they have formed a dedicated AI group working on infusing Microsoft products with intelligent capabilities.

However, as AI and Predictive Analytics become key technologies for companies, it is important to remember the old GIGO maxim:

Garbage In, Garbage Out

These tools simply won’t work well if your information is inaccurate, out of date, or incomplete.  Best case, bad data results in weak predictions that aren’t trusted.  Worst case, they provide a false confidence that wastes resources and misdirects corporate activities.

John Bruno, an analyst at Forrester, described this problem well in a recent blog:

The future analytics-driven sales processes is bright, but the path ahead is not without its challenges. Current and potential Salesforce customers should be mindful that intelligent recommendations require a large volume of quality data. If poor data goes in, poor recommendations will come out. Cleansing data and iterating the fine-tuning of recommendations will be vital to long-term success.  Another major hurdle is adoption. Many sellers still lack trust in “intelligent” recommendations. You will need to handhold these sellers until they form trust. This means starting with small recommendations and scaling from there.

The good news is that many of the sales intelligence companies are now offering data hygiene services for lead, contact, and account records.  The processing can be performed via CRM or MAP connectors or by uploading files to their cloud services.  The vendors match sales and marketing files against their reference datasets and then augment the files with firmographics, biographics, technographics, etc.  Matching can be done both in real-time to support both list uploads and web forms and via batch processing to support on going maintenance of corporate data.

While no company and contact database is 100% accurate, they are far more accurate than most marketing automation platforms and CRMs.  Furthermore, they have better field fill rates, standardized values (important for segmentation and analytics), and more rapid update cycles.

The predictive analytics companies are also beginning to provide enrichment services.

 

 

SFDC Einstein: Once Again We’re Discussing AI

einstein-artificial-intelligence-in-business

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.

AI is not killer robots. It’s killer technology.

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 Widgets provide intelligence both programmatically for developers and data scientists and declaratively for end users.
Einstein Insights alerts widget.

 

Einstein can surface competitor mentions even if the end user hasn't trained it to do so.
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 be setup with the VP of Sales at a high-scoring account.
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.”

Image Credit: Salesforce.com