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by Matt Manning

The intent to purchase has always been a critical piece of information to b-to-b marketers and the information services that serve them. Intent comes in many forms:

  • Past purchases: The core of any direct marketing campaign is a list of people who have bought a related product/service with the same price point as your offering.
  • Emerging needs: If your firm just bought a commercial printing press then it is likely to buy ink for that press. Looking for ‘symbiotic’ purchases is a high-percentage marketing approach.
  • Long-term requirements: A company that retains a realtor to look for commercially zoned land will probably need architects and general contractors in a matter of months. The firm that gets a ‘tip’ on this indicator early stands a strong chance of winning the business.
  • Financial events: The value of pitching prospective customers right before or after a major funding event (investment round, IPO, sale of a business unit) is that they tend to buy everything more readily. The converse also applies for companies restructuring their debt, laying off employees, etc.
  • Professional motivation: Executives spend more freely during their ‘honeymoon period’ at a new firm. Pitch them early on the products/services most closely aligned with the stated goals for their employer and the odds are in your favor.

Over time, more and more of these kinds of intent data have become available to the commercial marketplace, but historical purchasing data is the trickiest to obtain. It is private, for one, being owned by the vendors to the company you’re targeting, so it’s hard to get outside of direct research (i.e., telephone surveys asking about products/services used) or a Data Cooperative model (where group of sellers pool anonymized/aggregated customer data for insights into their best clients).

That said, it’s normally easier/cheaper to divine intent via ‘data exhaust’ metadata. For instance:

  • Job postings: Volume indicates general corporate growth; skillset definitions in job postings show intent to tackle certain types of projects with specific tools in mind.
  • Attendance at particular events: Long-term intent to deploy people and tech to expand into specific areas.
  • Vendors’ public customer lists/endorsements: Insight into installed technology at those firms. Announcements of large new clients point to growth.
  • Public RFPs/Government records/Legal settlements: Resolution of legal disputes, the granting of patents, the registration of domain names, and the levying of government fines are all examples of the types of news events that indicate a corporate reaction is imminent.

However it’s done, the effort to read the data tea leaves to figure out what products and services organizations will buy is a never-ending pursuit that will always pay dividends to those who crack the code.


posted by Shyamali Ghosh on January 10, 2018

by Matt Manning

Some 18 years ago I spoke at the European Association of Directory Publishers conference. This was at the very height of irrational exuberance over online information services. The conference attendees were all very interested in transactional business models such as those floated by VerticalNet. The idea was that you could create a buyer’s and seller’s marketplace on your web site (a buyer’s guide combined with an RFP and ecommerce mechanism) and use a business model where the buyers and sellers would compensate the platform’s owner via a percentage of the deal transacted.

At the time, I certainly understood why the platform owners would like that model, but I saw no reason why the buyers and sellers would agree to participate under those terms. Yes, sellers will pay for advertising and ‘preferred placement’ in search results to reach buyers without any guarantee of return on that investment, but the idea of paying a big commission (a small % but large dollar amount) on a guaranteed order was still something most firms would only entrust to their in-house sales teams. And, of course, there was the obvious issue that users could choose a vendor, agree to terms, and simply do the deal offline.

Almost two decades later, we have not advanced much further toward this exact transactional model, but there are a couple of variant business models that are taking hold and showing the path forward.

The first is BuyerZone’s model, which uses sophisticated SEM strategies coupled with a short survey to identify people looking to purchase a particular product right now. They do this by buying web traffic for specific keywords and displaying a link to a brief survey in the search results. The survey refines the search request and gathers data on the requester. Vendors agree in advance to pay $X for each qualified buyer delivered, so the end result is that BuyerZone can sell vendors a list of specific people interested in buying a very specific product (i.e., not a printer but an HP Envy 401X printer) and they can even take it a step further and provide retailers with lists of people in their geographic area who want to buy their products now.

Another more hands-off approach is the “deal room” model where two parties to a large transaction agree to pay flat fees for access to a collaborative workspace powered by third-party software and relevant data. It works like co-working space where the buyers and sellers can pay a flat fee and get in and out with no long-term commitments. This software-first model has a low barrier to entry and delivers the kind of ROI that buyers instinctively understand and appreciate.

Both of these models for enabling transactions involve transparent pricing and convenience to the firms buying the service – the two aspects missing from the ‘wishful thinking’ transactional business model of 1999. I expect these to evolve in 2018, along with other creative approaches to inserting ‘middlemen’ layers into b-to-b buyer and seller transactions, eclipsing the traditional industry-specific buyer’s guide model completely in the not-too-distant future.


posted by Shyamali Ghosh on December 11, 2017