When You Centralize Data, You Too Become Central

One of the ways that bricks and clicks are starting to merge is through a technology called beacons. It’s all the rage in retail right now. Acme places specialized transmitters in each of its stores. When a customer with an Acme app on his or her phone enters the store, the transmitter can push real-time, targeted promotional messages to that customer. Even better, the customer doesn’t have to access the app – it’s designed to wake up and alert the customer.
Cool stuff, and what better time to target customers then when they are inches from your cash register. Yet, not every promotional message generates a sale. Despite your best efforts, the customer leaves your store. Now what?

This is the interesting area where a start-up called Unacast is playing. It wants to marry the data you have on the customer who just left your store to online ad re-targeting platforms, so you can continue to advertise to these customers, in the hope of making the sale. Again, cool stuff.

But Unacast is taking this a step further. It is going around to all the manufacturers of these beacon transmitters and positioning itself as a central back-end data repository for this valuable shopping data. As a central repository, Unacast can watch where else the customer is going to gain both marketing and segmentation insights. Did the customer go to a competitor? Better re-target with your best deal then. Does the customer go to discount stores or high-end retailers? A retailer can not only learn a lot more about its customers, but is better able to serve them highly customized advertising messages as well.

It’s a data bonanza that will yield endless benefits, and Unacast is moving fast to lock up this opportunity. That’s important because there’s typically only room for one central clearinghouse in a market.

This is a model you might apply to your own vertical. If you are seeing numerous companies collecting similar pots of proprietary data, chances are there is both a need and an opportunity to be the central repository. Why you? Why not? You’re established, know the data business and you’re a neutral player. Central clearinghouse opportunities typically go to the fleet of foot, especially now because the value of data is much more broadly appreciated. Do you have your running shoes on?

Reviewing the Reviewers

You are likely familiar with Yelp, the local business ratings and review platform. It's been a phenomenal success, but it also has a large number of detractors, in large part the businesses that are the subject of those ratings and reviews. Yelp is a business that, if examined dispassionately, really should never have succeeded. The primary reason was its business model: let anonymous users pick apart businesses in published reviews, then try to sell advertising to those same businesses. Yelp made this challenged business model even tougher by introducing a secretive filtering algorithm that would decide what reviews got published. The objective was to weed out spam, but all it did instead was to spur conspiracy theories among businesses that felt good reviews were being swallowed while bad reviews always seemed to get published.

Tough business, right? Well it gets tougher, particularly because Yelp showed little interest in mediating disputes (for example, there are documented cases of restaurants getting bad reviews on dishes they have never offered), essentially admitting it was too much work. Mix into this the inexperienced sales force Yelp fielded, giving rise to stories of reps offering to make bad reviews disappear  in exchange for advertising, with a raft of lawsuits claiming extortion quickly following.

Things seem to have calmed down for Yelp in the last year or so, but it's hard to imagine that the rift between the business community and Yelp has fully mended. Yelp is more powerful than ever, and can make or break a business. Yet it maintains as a core principle that it is a consumer empowerment tool, even though Yelp generates no revenue from consumers.

That's why I find it surprising that Yelp just announced the acquisition of Eat24, a service that lets people order food for home delivery. Yes, the company that controls the reputation and success of restaurants now wants to control their order flow as well. I see nothing to suggest that Yelp has become a friendly, trusted brand to the average local restaurateur. Yelp brings scale, but a lot of baggage as well.

What is the correct business model for a ratings and review business? There is no easy answer, especially as the consumers who typically provide the reviews show little appetite to pay to access them. One exception is Angie's List, which sells subscriptions, but even Angie's List now makes more money from advertising than subscriptions. Fortunately, Angie's List found a middle path that allowed this revenue pivot without compromising its credibility and integrity.

TripAdvisor is another reviews site with many of the same issues as Yelp. But TripAdvisor makes most of its money by selling eyeballs, a traditional media model. This means its doesn't have to rely on hotels for revenue, though it recently started to push in this direction.

The real estate website Zillow posts its estimate of a home's value right next to the (almost always higher) asking price. One can presume that's not helpful to making the sale. Awkward? Well, Zillow now asks consumers to rate and review the real estate agents to whom it sells advertising.

In many respects, the jury is still out on what does and doesn't work for review sites. What we've seen to date is that if you can build a big enough audience, the advertising dollars will follow, no matter how upside-down your business model. But just because they pay you doesn't mean they have to like you.  And this may come back to haunt these companies, if not in their core business, then by ultimately limiting their growth and expansion potential.

Buying Guides That Do Stuff

It’s been very interesting to watch the transition of buying guides from print to online. Print buying guides were a pretty good business, although in fact few of them were very good products. That’s because most buying guides were what I call shallow information products: they would typically list a product and the names and addresses of companies that (hopefully) made or sold the product. After that, users were on their own. This stripped-down format was in part practical, because even this limited information was hard to obtain. It was in part by design, because it encouraged companies to buy advertising next to their listings to provide additional information. There’s no room on the web for shallow information products anymore. Search engines have gotten good enough that you can find at least a few manufacturers or sellers of just about anything with very little effort. And company websites now typically contain a wealth of product information, in part because it is so cheap and efficient to do so. Overall, this leaves little room for buying guides to add value, at least in their traditional format.

So is the buying guide model dead? If you are talking about the traditional shallow information model, the answer is yes (something that the big yellow page publishers, incredibly, have still not figured out). But what is emerging in its place are a number of exciting new products that mix and match such features as:

• User ratings and reviews (and some now validate users and even confirm that they have purchased the product they are reviewing)
• Links to third-party professional reviews
• Downloadable CAD drawings
• Photo portfolios showing product applications and/or the product in use
• Strong parametric search
• Side-by-side comparison of selected products
• Guided search where instead of traditional searches, users answer a questionnaire instead
• Shared online areas where users can post products for review by co-workers
• Ability to request product samples from the manufacturer
• Integrated ordering capabilities
• Warehousing and shipping of product on behalf of manufacturers
• Product specification data, warranty data, installation instructions, manuals
• Real-time inventory information
• Real-time pricing information

In short, the list is long. And what results is a true destination purchasing research site and, increasingly, a central marketplace. Find exactly what you need and order it. That’s been the holy grail of buying guides for decades, and it’s finally becoming a reality.

The other piece of the puzzle is advertising. Because publishers are now building these true destination sites, they can also develop substantial traffic simply because they are offering utility and value. And advertisers respect these highly qualified or often quite large audiences because they are truly “in the market,” and what advertiser doesn’t want visibility when the buying decision is being made. It is, as we like to say, “data that does stuff.”

So while the approach is different, what we see with buying guides is exactly the same as what we see with other forms of data, and exemplifies infocommerce: creating a high value proposition with better, deeper data and tools to act on it.

The Hidden Data in Invoices

The data business is one of creativity, and what could be more creative than asking companies to send you their invoices and other types of billing data so you can get them into a database and sell the aggregate results back to them? Now, why would something like this ever make sense? Well, in many industries, there is nothing more useful or important than pricing information. Yet the pricing information that many companies publish (if they publish it at all) is almost always the list price. And in many industries, the list price is close to meaningless, since every customer will have a special deal and varying discount. So how do you develop a database of what companies are really paying for specific products and services? Ask to see their invoices!

There are lots of spins on this intriguing model, so let’s take a look…

The question already on your mind quite likely is, “Why would any company let me look at its invoices?” The simple answer is what I often refer to as “strength in numbers.” A company will happily give up its individual data (properly secured and anonymized) in exchange for access to the aggregate results. And they’ll pay for that access, and that’s exactly the play here.

A great example of collecting, normalizing and reporting out information drawn directly from the internal systems of advertising agencies can be found in a company called SQAD. Its NetCosts product collects data for media purchases from advertising agencies worldwide, generating what may be the only honest look at what broadcasters are charging for media buys, and even what they have charged into the future. You can immediately see how valuable this information can be.

Thomson West has a product called Peer Monitor that does the same thing in a slightly different way: rather than work with the recipients of invoices, it works with the senders of invoices, in this case law firms, to collect similar data to be used in similar ways.

If it sounds like a lot of work, it is, or probably was. That’s because SQAD now receives most of its purchase data digitally, through interfaces with client systems. And while those interfaces were doubtless painful to build, at the same time SQAD has built an almost impregnable franchise, because as long as SQAD doesn’t get greedy, nobody can justify the time, cost and pain to try to compete with them. The same holds true for Peer Monitor.

There’s also what might be called the pre-order model. Here, your objective is to gather RFPs and proposals to get a true look at what companies are proposing to charge for their products. One advantage of picking up information at this stage is that there is often a lot more detail, allowing you to collect even more granular product price data. A great example of this model is MD Buyline, a company that collects price quotes and proposals on medical equipment to build a high-accuracy pricing database.

Lots of variant models, but the objective is the same: gather data on actual prices being charged in the marketplace, the more granular the better. Your job is to aggregate, normalize, and report back to the marketplace, while protecting the anonymity of those who participate.

It’s important to note that the need for pricing data isn’t equally compelling in every market. The dynamic seems to be a reasonably large pool of both buyers and sellers, and a solid tradition of haggling over price. It won’t work for everyone, but it’s certainly worth considering if it would work in your market.

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.