Projects: Individual B2B market analysis
If our products are not enough for you, we can also help you with a tailored market analysis. Get to know and understand the market you are in. Let us prepare a tailored market and business opportunity analysis.
You get data and project support and all the experience of our B2B business consultants, data and development team
We use the most advanced statistical and analytical methods to offer comprehensive analyses
Together, we invest in building precise market knowledge, sales processes and technology
What we count among tailor-made projects
Cleaning and enhancing the customer database
Identifying and prioritizing the market for launching a new product
Visualization of internal data enriched with BizMachine data and signals
Calculation/modelling of business potential for individual channels and merchants
Look-alike analysis of the customer base for new customer acquisition and new market segment validation
We can help you find answers to your questions about the B2B market
Here is a selection of projects that we have already successfully managed together with our clients
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
BizMachine approach
The first step is to find out how customers behave
Are they switching from one brand to another? Are they loyal to the same brand when renewing their fleet? When do they choose to buy premium cars and when do they stop? Do they only have one premium car brand in their fleet, or are there more than one?
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
The final step was to find all the individual companies that matched the archetypes of premium company car customers but did not have the relevant entries in the vehicle register. We thus classified the entire market for premium company cars.
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.
BizMachine approach
We analyze the resulting learning set to extract content vectors, keywords and phrases using NLP methods.
First, we obtain data from the client about their existing local customers.
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
We extract and store important information from end customers' websites, such as phone numbers, emails, brands of machines used, company headquarters address, etc., using pattern libraries.
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.
BizMachine approach
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
Postavit algoritmus pro výpočet rozdílu mezi skutečným
a pro forma vyúčtováním tak, aby vybrala nejvhodnější sadu tarifů pro každého zákazníka.
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.
BizMachine approach
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?
BizMachine approach
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?
BizMachine approach
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.)
Find out more about our work
(most of the articles on our blog are Czech only)
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