Your Biggest Competitor is No Decision

Back when I was a product manager, I used to conduct sales training classes.  I often opened up the session by asking the question, “Who is your biggest competitor?”  The reps invariably listed a company or two they had heard over the prior day and a half of training.  Even seasoned reps would answer the question incorrectly.

Unless you are in a duopoly or there is a competitor that controls half the market, your biggest competitor is probably NO DECISION.  Either the purchasing decision is kicked down the road or no funding is found.  It may also be that the opportunity was poorly qualified to begin with.

Sales reps no longer control the conversation due to the informed buyer who leverages the Internet and social media in order to research vendors prior to contacting them.  This is one of the reasons that marketing is looking at digitally influencing anonymous individual on the web via Visitor ID, SEO, SEM, and Programmatic.  Sales reps are also confounded in their sales efforts by a second change in purchasing patterns.  B2B budgetary decision making processes have become more complex.

Budgetary centralization and committee-based buying decisions have increased the number of decision makers in the purchasing process, resulting in a greater likelihood of no decision.  According to a Forrester survey of IT sales reps, 43% of lost deals weren’t to competitors but to a category titled “lost funding or lost to no decision: customer stopped the procurement process.”

Furthermore, the rise of cloud computing has shifted budgetary decision making authority away from the CIO to the heads of various functional departments.  Purchasing decisions are being compared to a broader set of non-related purchases from across the organization.  It is therefore critical that sales reps “understand and navigate complex agreement networks and processes within the buying organization that span different altitudes and functional roles,” blogged Forrester Sales Enablement Analyst Mark Lindwall.  “Because decisions are more cross-functional, every dollar is compared against how it could add value in potentially completely non-related areas of investment.”

Thus, sales reps need better tools for identifying who to engage and when best to engage.  They also need to be better informed about companies, individuals, and the industries into which they sell.  In short, they need to know who to call, when to call, and what to say.  They need to quickly navigate what Forrester calls agreement networks to establish relationships across multiple levels and job functions at the organization.

Fortunately, Sales 2.1 tools provide rich biographies and full family trees for navigating these networks.  Users can target specific job functions and levels across the corporate hierarchy, research the appropriate individuals, and reach out to them via social media, email, or phone.

Newer ABM tools help identify the Ideal Customer Profile (ICP), score leads based on the ICP, and call out similar accounts and contacts that are not on the company’s radar.  Thus, it’s not just about selling more intelligently based on insights, but targeting and prioritizing one’s sales efforts more effectively.

Sales triggers assist with identifying executive changes, M&A events, product launches, and other reasons for reaching out to individuals.  Triggers can also indicate an expanding opportunity or that a proposal is potentially at risk due to company or market dynamics.

And yes, sales reps should research both the company and the executive.  They need to understand the key trends in the prospect’s industry, why their last quarter was soft, and what does the executive muse about on social media.  While such facts may not be immediate hooks, they provide context and potential talking points down the road.  It also shows that the rep is willing to invest time in understanding the exec, her company, and the environment in which she is making decisions.

There is an opportunity cost to poor targeting, prioritization, and account planning. It shows up as No Decision in your CRM, slow deal velocity in your pipeline metrics, and disappointing sales growth.

RampedUp Sales Intelligence & Win Stories

RampedUp Win Story Questionnaires are customized for each company.
RampedUp Win Story Questionnaires are customized for each company.

Launched in 2015, RampedUp offers a sales intelligence solution for browsers and Salesforce. Company and Lead Prospecting is managed from the browser with the option to send one or multiple records to SFDC. Along with standard firmographic selects, prospecting supports technographics and sales triggers. The database is gathered from Synthio and other vendors and spans 6 million global companies and 180 million contacts.

From within Accounts, Leads, and Opportunities, sales reps have access to the following Battle Card intelligence across five tabs:

  • Company: Contact information, firmographics, social media links, mined business descriptions, and recent news stories
  • Contacts: Title, location, email, and phone. The service also indicates whether the contact has been previously loaded into SFDC.
  • Customers: Similar companies with win stories.
  • Competition: Peers based on firmographics and keywords.
  • Technologies: Products for complementary and competitive targeting

A distinguishing feature of RampedUp is a custom tool for recording and sharing customer wins. Win Stories allow reps to understand how their peers closed deals in similar situations.

Sales Ops or Marketing define the key questions for a Win Story template and sales reps enter their responses. Of course, sales ops or marketing can conduct an interview to gather this information with the custom questionnaire operating as a survey template.

The wins are then published as email announcements and available as mini-case studies for other reps within SFDC. Thus, if prospect X has a competitor that has a win story, the competitor’s win is made available in the RampedUp i-frame. The browser version supports a searchable Win Vault.

Relevant Win Stories are delivered in the Customers tab within SFDC.
Relevant Win Stories are delivered in the Customers tab within SFDC.

RampedUp’s Home Page provides a set of gamification elements including recent case studies and a leader board.

RampedUp front page with win stories and a Leader Board.
RampedUp front page with win stories and a Leader Board.

While the most recent company new stories are displayed within SFDC, users must pop out to the RampedUp browser application to view a deeper set of news or filter by event category and date. Triggers are searchable by company, URL lists, or broadly. Bing-based sales triggers go back several years and are tagged by company and topic.

RampedUp does not yet provide sales trigger alerts, but they are on the company’s roadmap.

RampedUp also provides data enrichment functionality via a Clean Tool. The service charges $1,000 for every 10,000 companies and contacts cleaned during an initial batch cleanse. There is also an on-demand batch Clean process which flags inactive contacts, updates company and contact information, and adds additional contacts. Clean includes a unique contact update feature – not only does it flag departed Contacts, but it indicates where execs moved to as new SFDC Lead records.

RampedUp is priced at $1,000 per month for up to twenty users with unlimited access. Additional users are sold in twenty user bands.

RampedUp has fifty clients and is based in Norcross, Georgia.

SaaS Market Valuations

Venture Capital and Private Equity firms place a higher valuation on companies with recurring revenues. In Q1, software companies with a SaaS model received multiples of seven times revenue while other software companies received a multiple of 6.1.

“Any firm with recurring revenue is extremely attractive to investors,” said Rohit Kulkarni, head of research at SharesPost. “The subscription model translates to greater visibility of revenues, less volatility.”

According to PitchBook Data, Software-as-a-Service deals grew 217% between 2010 and 2016.

“SaaS is a more predictable and reliable revenue stream than if you had to go out and sell the software — the perpetual license model,” said Peter Fair, managing director at Golub Capital LLC.

Michael Larsen of Cambridge Associates said that SaaS models provide a “better measuring stick” as “these companies are moving toward more attractive, more readily transparent ways of selling products and they have attractive, meaningfully recurring revenues.” Employing a SaaS model does not prevent firms from failing but “it creates a more intensely analytical and measurable way of determining how a company is doing.”

For example, subscription firms that employ discounted offers to lure new customers may suffer from churn and see their business model unravel quickly. Subscription length needs to be carefully factored into valuing a firm and estimating its viability.

DueDil Posted Strong 2016 Turnover Growth; Positions Itself for European Expansion

DueDil's new credit risk filter allows users to filter prospects by the degree of trade credit risk.
DueDil’s new credit risk filter allows users to filter prospects by the degree of trade credit risk.

European private company information platform DueDil recently filed its 2016 financials with Companies House. Revenue increased from £1.21M to £2.25M. The startup continues to run in deficit (£6.17M), but that should be anticipated for an information services startup that is rapidly expanding its content and functionality. DueDil averaged 75 employees last year, up from 46 in 2015.

DueDil’s losses expanded last year as it pursued the European company intelligence market which it believes is becoming more hospitable.

“In the last 12 months, our competitive landscape has changed dramatically, Bureau Van Dijk got sold for $3.3 billion to Moody’s, and Dun & Bradstreet divested its Benelux division. This, along with our much better unit economics, gave our board and management team a clear signal to grow the product and the team and start to compete directly with them. The higher costs are the result of growing our product coverage from the UK and Ireland to pan-European, soon to cover 100 million+ companies.”

  • CEO Damian Kimmelman

Because subscription services are ratable, growth in revenue lags billings. Although enterprise sales grew 100% last year, much of this growth won’t show up in the top line until 2017. DueDil had its best quarter in Q1 and then grew 60% in Q2.

“Over the last 6 months, we have grown sales 5% week over week and are continuing to do so,” said Kimmelman.

To fund growth until its next equity round, the firm recently issued a £900,000 convertible note and plans to issue a £1.1 million convertible note. “I think you can assume that we wouldn’t be so bullish on a convertible if there hadn’t been an immediate liquidity event in sight,” Kimmelman told Business Insider. “I am not trying to be coy but that is all I can say.”


In product news, DueDil added Credit Risk and Ownership filters to its list building module. Sales and Marketing can employ the credit risk filter to remove prospects that are unlikely to pass credit checks or to target higher risk companies. The filter can also be used by credit risk and procurement teams during the onboarding process allowing them to streamline the processing of low-risk companies.

Ownership search filters assist with targeting firms with concentrated shareholdings which are “ripe for takeover.”  New Ownership screens include Total Shareholding Count, Individuals Count, Companies Count, and Shareholder Name.

Along with credit scores and ownership filters, DueDil supports a broad set of financial screening filters spanning 40 million companies in the UK, Ireland, France, Germany, Benelux, Norway, and Sweden with additional European countries in queue.

DueDil added four new shareholder filters for identifying firms "ripe for takeover."
DueDil added four new shareholder filters for identifying firms “ripe for takeover.”

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.