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.”
A November study by Demandbase and Wakefield Research of 500 B2B marketers (250+ employees) found that while marketers are confident that Artificial Intelligence (AI) will reshape marketing by 2020, they lack confidence in how to implement the new technology. According to Demandbase, “80 percent of all marketing executives believe AI will revolutionize marketing over the next 5 years, but only 26 percent are very confident they understand how AI is used in marketing and only 10 percent of marketers are currently using AI today.”
Marketers had numerous concerns about implementing AI, including
Integrating AI into their existing technology (60%)
Training employees (54%)
Difficulty interpreting the results (46%)
Implementation costs (42%)
On the benefits side, marketers listed
Better insights into accounts (60%)
More detailed analysis of campaigns (56%)
Identifying prospective customers (53%)
Expediting daily tasks (53%)
“As someone who has been studying AI for many years, I’ve recognized the promise of AI and B2B marketing for some time, which makes it really rewarding to see this vision is now shared by marketing executives,” said Aman Naimat, SVP of Technology at Demandbase. “This data reveals that in order to be successful, marketing leaders need to lead the charge and present opportunities for AI instruction and experience for their teams, to ensure implementing it into their B2B technology stacks is effective.”
In a November Harvard Business Review article titled “How Artificial Intelligence Will Redefine Management,” (Vegard Kolbjørnsrud, Richard Amico, and Robert J. Thomas), the authors offered a set of best practices for managers. Noting that managers spend 54% of their time on administrative tasks such as scheduling, monitoring, and reporting, they suggest that managers transition administrative tasks to AI. Instead managers should focus more on judgment work which combines rules with “their knowledge of organizational history and culture, as well as empathy and ethical reflection.” Thus, there will be a greater emphasis upon “judgment-oriented skills” such as “creative thinking and experimentation, data analysis and interpretation, and strategy development.”
The authors also suggested viewing AI as a trusted colleague instead of a “race against the machine.” Thus, managers can merge judgment with AI-based decision support, simulations, and search and discovery activities. A full 78% of managers believe they will trust the advice of intelligent systems. Furthermore, because AI will be approachable through voice and other intuitive interfaces, AI will be their “always-available assistant and adviser.”
Another recommendation was harnessing the creativity and ideas of co-workers and team members. With time freed from administrative tasks, there is more time for synthesizing multiple ideas and formulating new products and processes. “Manager-designers bring together diverse ideas into integrated, workable, and appealing solutions. They embed design thinking into the practices of their teams and organizations.”
Finally, managers will need to hone their social skills with an emphasis on networking, coaching, and collaborating.
The authors concluded that “writing earnings reports is one thing, but developing messages that can engage a workforce and provide a sense of purpose is human through and through. Tracking schedules and resources may soon fall within the jurisdiction of machines, but drafting strategy remains unmistakably human. Simply put, our recommendation is to adopt AI in order to automate administration and to augment but not replace human judgment.”
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.
For those of you trying to get a handle on all of the new terms in Sales Tech and Marketing Tech, DiscoverOrg just published a short glossary on their website.
While Martech has been receiving significant investment over the past half decade, the past year or two has seen a growth in sales tech investment. A few years ago, this sector was labeled Sales 2.0 and basically consisted of sales intelligence products, lead prospecting datasets, and presentation tools for sales reps. But now,
The Sales Intelligence vendors are moving into marketing hygiene with MAP connectors, data enrichment, segmentation analysis, look-a-like prospecting, and TAM analysis.
Predictive Analytics, which originally focused on marketing, is equally focused on the sales function.
New social selling and trigger selling tools continue to appear on the market.
Account Based Marketing has become the rage with vendors now repositioning their offering under the ABM banner and adding features to assist ABM programs across the funnel.
The Account Based Sales Development function has been professionalized with the introduction of a series of ABSD tools providing a “tip of the spear” toolset for ABM.
In short, it is a dynamic time for the sales tech industry even as the distinction between SalesTech and MarTech continue to blur.
As technology continues to plunge sales and marketing professionals further into transformative innovation and new opportunities, we must define the new terms taking us there. Considering the breakneck speed of tech advancement, it’s not uncommon for terms, which were merely a blip on the radar a year ago, to become part of our everyday vernacular.
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.