Getting to the Top

It’s very gratifying to me to watch how quickly and successfully the data industry has evolved in its lead generation capabilities over the last two decades. We’ve moved from the legacy print directory model to highly sophisticated, multi-sourced signals and other inferential data to more precisely identify and pre-qualify sales leads. But where do we go from here?

I have long said that the path forward for data publishers is to move up the so-called value pyramid, from poorly differentiated “there’s a pony in there somewhere” lists that characterized the legacy print directory era to today’s evidence-based, high-confidence, highly targeted sales leads. The top of the value pyramid is actually making the sale on behalf of your customer, presumably in exchange for a sizable commission to justify the effort. Many data producers would be thrilled to shift from $100 sales leads to $10,000 commissions. But when you even scratch the surface of this idea, you see large obstacles, not the least of which is trying to scale a business model like this.

 So what’s the next highest level of value? Pre-qualifying leads. In this model, the data producer takes the leads it is generating, and further qualifies them by making direct contact, and asking, for example, “are you actively in the market for a new CNC milling machine?” If the answer is in the affirmative, you have developed information of extremely high value. A number of companies that sell technology sales leads have been doing this for a while.

 Alas, at the present time, this is a market-dependent idea. Technology marketing and sales teams tend to be highly sophisticated when it comes to lead management. But for most markets, as I’ve noted before, marketers are still out primarily looking for lists to load into automated marketing platforms, and most sales teams prefer to trust their instincts and sales prejudices over verified data, meaning great leads end up on the floor and the data producer is told its data wasn’t very good.

 All this suggests to me that for data producers to move further up the value pyramid, a lot of market education is going to be required first, and that will take a lot of time and resources. We’ll get there, but not anytime soon.

Making Introductions, Profitably

An interesting article in the New York Times highlighted a company called Legal Services Link. As you might infer from the name, the company works to connect lawyers with those who need legal services.

Lead generation? Yes, but with a twist. In this model, buyers are actively seeking sellers and the intermediary is attracting these buyers and adding value by actually matching buyer to seller. In many cases, the buyer is asked to complete a requirements survey, which is then matched to a database of qualified sellers. The intermediary (usually a data company), identifies usually from one to three vendors best qualified to help the buyer, and puts everyone in touch.

The benefit to the buyer is that a small number of pre-screened, qualified sellers make immediate contact with the buyer – enough sellers to have some choice, but not enough to be overwhelming or annoying. You may possible be surprised to learn that a hidden value-add of these matching services, is that they monitor the sellers to make sure they get in touch with the buyer quickly. Yes, even with sellers paying sometimes hundreds of dollars for a hot lead, they still manage to drop the lead on the floor!

What’s also nice about this model from the perspective of the intermediary is that there is no chance of “leakage” – a term for when buyers or sellers circumvent the intermediary, often to avoid paying a commission.

This model works well for both B2C and B2B. It seems to work best for high-value purchases that the buyer only purchases sporadically. This irregular buying pattern is key because it means the buyer can’t keep up with what seller offers what product, or even the products themselves. Markets with rapidly changing technology are especially good.

Since the buyer fills out the requirements survey with full knowledge she will be immediately hearing from salespeople, she makes an enormously high value lead. And since the seller has a good understanding of what the buyer needs before making contact, the initial conversation is more productive and the sale tends to close faster.

This is a strong model that makes more sense than ever in a world that’s rapidly getting used to apps that speed the delivery of everything. If you see the right fundamentals in your market, it’s a model that’s well worth exploring.

A Business Model Detour

TrueCar.com started out as a data and analytics company, offering insight to consumers as to the actual prices being paid for specific makes and models of cars in their local area. The idea was to aggregate multiple data sources, including actual sales data from dealers themselves to build as much precision as possible into this pricing information. In many respects, TrueCar was duplicating the approach taken by established industry powerhouse Edmunds.com. What TrueCar didn’t duplicate was the business model of Edmunds. Indeed, TrueCar took an entirely different route.

TrueCar moved beyond providing estimates of new car prices to delivering actual prices that dealers would accept. Simply hand your TrueCar number to the dealer, and the car would be yours for that price. It was a fresh approach, and particularly compelling to those consumers not fond of haggling with car dealers.

The idea took off. TrueCar signed up thousands of dealers to accept its pricing. Then, according to published reports, it started marketing itself as offering the lowest prices for new cars. Turns out, its dealers weren’t thrilled with that positioning, in large part because they weren’t offering the lowest prices, and large numbers of them canceled their affiliation with TrueCar.

TrueCar recovered from this, but in an odd way. It now represents itself as offering “fair prices” instead of lowest prices. And from a quick look at its site, you can see that it has morphed into a lead generation service for car dealers. I asked for a price on a car from dealers near my zip code and was presented with three prices from three dealers. That’s a big move away from presenting objective pricing based on aggregated sale price and other data.

So, the TrueCar value proposition has pivoted from providing objective data to providing consumers with a price in advance that certain dealers will honor, thus avoiding the stress and uncertainty of having to negotiate a price. If you look at the TrueCar website now, you’ll be repeatedly assured you are getting a fair, competitive price, but if there’s any data to back up that claim, the company’s no longer talking about it.

TrueCar claims to be responsible for 2.3% of all cars sold annually in the United States, so it seems to have tapped into a real need in the marketplace. At the same time, it’s a rare pivot away from a data-driven business model, to a model that as far as I can see doesn’t require any data at all.

Of course there’s a great lesson here in profiting from adversity, but there’s another lesson here as well: if you dive into the data business without a clear business model, you’ll probably find yourself needing to make an expensive and dangerous u-turn.