by Matt Manning
The move to marketing automation platforms, “lead scoring” (prioritizing sales prospect records based on data analysis of your customer base), and predictive analytics (pure data-driven decision-making) is shifting the very foundation of the information industry. Publishers could once charge for aggregating a bunch of businessmen in a given industry around a magazine’s advertisers or an event’s sponsors. Now, they must prove to prospective customers that a positive result—increased sales—will come directly from this investment.
Creative new marketing firms (let’s call them “facilitators”) are at the forefront of this effort. They sell the results of successful marketing campaigns rather than charging for access and leaving it to their customers to close the deal.
How does this work? Instead of charging for a telemarketing campaign to recruit webinar attendees to sign up for a free trial of a product or service for which a known % of customers will purchase the product, the customer can now simply pay the facilitator for “100 paid orders.” That’s what the customer wants anyway, and it eliminates guesswork on a given campaign’s ROI.
Of course, the cost-per-order varies with the product’s quality and price. Selling an overpriced, marginal product costs a lot more per order than selling a new product or selling an existing product to a new market. The audience part of the equation—what the B2B publishing industry once delivered—is now reduced to what is essentially an algorithm and an underlying universal dataset from organizations such as InfoUSA, OpenCorporates, InsideView, Owler, etc.
This pay-for-performance shift was predicted decades ago, but now that it has arrived, let’s look again at its implications:
- Increased sophistication in competitive intelligence, with detailed feedback on product features and pricing models from facilitator survey data mapped to customer cohorts. For example, “Engineers like the export feature but most can’t sign a contract where they can be charged extra for each export.”
- Deep industry knowledge for salespeople, who will act more like market researchers or product development consultants. “I can guarantee to you 100 orders if you: lower the price by 5%/adjust your model/emphasize this particular functionality.”
- Fading importance of the monolithic reference database of organizations as core data becomes both more open and more commoditized. Precise information on intention (the date when software licenses expire; the date when a company starts its planning for large strategic initiatives; budgeting dates) will be essential to the success of sales and marketing efforts.
All this means information middlemen (publishers) need to focus even more intently on meeting the need for actionable insight and deep information, called “rich data” by some, on the end-customers of their advertisers, list renters, and event attendees (their “audience”). This fits neatly with the publisher’s traditional role of providing industry insight on new products, new companies, corporate mergers, and industry trends. It really just involves gathering additional data on end-user customers along these lines:
- What products/services do they buy?
- When do they make buying decisions?
- Who has purchasing authority and what are the limits on that authority?
- Which vendors do they currently use?
- What are their primary near-term business objectives?
Of course, this shift to gathering predictive data will itself be entirely supplanted when VRM (vendor relationship management) displaces CRM and businesspeople get comfortable with “broadcasting” their intent to buy via yet-to-emerge RFP marketplaces. At that point, vendors will know exactly what buyers in their markets want to purchase, and they’ll just need to have “sales bots” scouring the RFP boards for prospects. Salespeople can then vet these and respond to the RFPs. That will be the dawn of a truly efficient marketplace. Until then, facilitators and publishers will have to do their best to “de-risk” their customers’ marketing investments by gathering the critical data needed to make accurate sales predictions.
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