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by Matt Manning

New product development for information services is a risky business. It takes deep customer insight, rigorous data analysis, management buy-in, and no small amount of gumption for a new information service to succeed.

The variables involved in success include:

  • An unmet need: The solution to one or more of a businessperson’s more vexing professional problems.
  • A barrier to entry: Unique, exclusive, or hard-to-find information.
  • A compelling user experience: The form and function of the service.
  • Anchor tenants: A guaranteed set of early-adopters eager to purchase your new service.
  • Timing: A salubrious economic climate puts wind in your sails while an untimely recession can scuttle even the best of launches.

Due diligence should allow you to ‘run the table’ on all of these variables (apart from the blind luck of timing and the dart art of pricing), but there is one guaranteed way to reduce the risk of a product launch: Keep the cost of the new product as low as possible.

Skimping on marketing outlays is a potentially fatal error that falls into the “pennywise but pound foolish” category, while keeping your product creation costs low is something that can make a huge difference. For example, if a product’s break-even cost is 250 subscribers, then getting 500 subs is a runaway success. If break-even is at 750 subs then you may be updating your LinkedIn profile earlier than you thought when only 500 seats get sold.

So how do you keep content creation costs low? Building an extremely fresh database and designing a low-cost and low-maintenance data-updating process is not something that too many publishers, let alone outsourced vendors, can do. IEI, however, was built for this challenge and we have a track record of successful product launches for our customers to prove it.

When you get wind of ‘the next big thing’ come ask us what it will take to get to a minimally viable product with a solid data supply chain in place. With a strong initial customer user experience you, too, may be able to drive early renewals and thus guarantee the longevity of your new service.


posted by Shyamali Ghosh on February 20, 2018

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