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.

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