Identifying and prioritizing the market for launching a new product
Calculation of potential for individual channels and dealers
Visualization of internal data enhanced with BizMachine information
Look-alike customer base analysis for discovering additional business opportunities
Case example: Analyze car make loyalty vs. switching of fleet customers for premium car importers
Context: New dynamics identified in the premium cars fleet market. Importers questioning whether this is a one-off situation or a longer-term trend
Collect significant behaviors (switch from one brand to another, stay loyal to the same brand, enter premium market, exit from premium market, multibrand)
Identify individual companies that fit the archetypes using vehicle registry data
Identify commonalities between companies within each archetype (testing over 200 criteria) bottom up
Synthesize the commonalities into company archetypes
Find companies that fit the company archetypes, but don’t have relevant registry records, thus classifying the overall market
Case example: Identify Plastic injection molders across CEE region
Context: Successful raw material (plastic compounds resin) manufacturer and reseller wanted to expand internationally. Target = plastic molders across CEE region.
Get learning set of existing local customers from the client
Analyze learning set for content vectors, keywords and phrases using NLP methods
Develop automated web search scraper/ API connector (Google search, Bing) and run the keywords and phrases through it
Classify the results into end customer websites, aggregators, catalogs, irrelevant
Run BizMachine smart crawler in-depth on pre-selected sites and targeted crawls for all domains in the specific countries (.hu, .pl., etc)
Mine and store important information from the sites, such as phone numbers, emails, machine brands, company HQ address, etc. using libraries of patterns
Case example: PMM repricing for a leader in parcel logistics
Context: A major player in parcel logistics acquired a competitor. The pricing across their B2B customers had to be consolidated. The question? How to do it without angering the customers and leaving money on the table.
Collect the acquirer’s 12 price lists and transform them into mathematical formulas
Collect and clean the underlying data for all parcels sent by the customers of the acquired company (more than 2 million items)
Build an algorithm to compute the price for each item using each of the 12 tariffs (tens of millions of values) and add them all up for each customer into the pro-forma bill
Build an algorithm to calculate the difference between real and pro-forma billing select the most suitable tariff set for each customer
Provide the results to sales reps as a basis for negotiations
Case example: Build AI-enabled data pipeline for a B2B marketplace with construction materials
Context: A major traditional wholesaler of construction materials decided to disrupt the market by building a digital marketplace. This required processing of tens of thousands of disparate SKUs and classifying them neatly into understandable catalog.
Collect the “zillions” of input sources (ERP feeds, material databases, pictures, .pdf documents, printed paper pages, ...)
Create the target SKU template catalog & determine all the mandatory parameters for each SKU (e.g., length, weight, ...)
Create a library of patterns for SKUs and their parameters
Build loaders and parsers, to ingest and transform the source data using the pattern library and load them into the catalog
Build a supervisor app to highlight issues and enable overrides
Wrap it all up in an AI “feedback loop” that learns from the supervisor actions
Build REST API to publish the results to the outside world via a marketplace
Case example: Assess and test digital savviness of medical doctors for a large pharma company
Context: It is difficult, if not impossible, to talk to MDs in person during a pandemic. Which of them will respond to digital communication? And where doesn’t make any sense to even try?
Collect and compile industry sources (public lists of MDs, practices and licenses, scientific journals, conferences, ...)
Collect digital footprint of medical practices using BizMachine smart crawler and automated web search scrapers
Match and validate digital footprint and publications with medical practices and individual MDs (manual sample validation + automatic validation based on manually identified systemic issues)
Construct digital affinity and influence score
Test response rates of various affinity and influence segments to digital communication by automated non-intrusive probing
Case example: Build data-driven outbound sales machine for the partner channel of a top-tier tech giant
Context: Every company might need digital services. But which ones exactly and when is the best time to talk about it to maximize uptake and value of the relationship?
Analyze tens of thousands of B2B customers (using internal billing and behavioral data as well as external company data)
Build a predictive model of purchase intent and volume based on 600+ tested parameters for over 300,000 companies collected using BizMachine smart crawler and other tools.
Setup integration with client CRM and BizMachine Prospector company intelligence tool for call agents, including reasons to call (traits relevant for the given prospect) and routing logic to third-party sales
Build evaluation engine for effectiveness of third-party sales (engagement speed, conversion rate, time-to-close, size deal, etc.)
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