It’s Hard to Trust This One

Recently, ADP (the association of yellow page publishers, not the payroll company) announced something called “Trusted Local Directory,” an online directory of “Trusted Local Businesses.” To become a Trusted Local Business, a company is “thoroughly investigated” and if worthy receives both a Trusted Local Business seal for its use, along with a listing in the Trusted Local Directory.

I give ADP kudos for trying to find ways to breathe new life and relevance into the yellow page directory business, but I have to admit some skepticism as well.

First, this model is not a new one, and the track record of third-party trust evaluators isn’t a good one. Trust is hard. Perhaps more to the point, trust is expensive. And to a great extent, trust is in the eye of the beholder – simply defining how a company can objectively prove it is trustworthy is remarkably challenging. That’s why this is one tough model.

 Consider as a case study the Better Business Bureau (BBB). They’ve been providing assurances of trust for over 100 years. But they’ve come be viewed as a consumer advocacy organization when in fact they are supported by their business members, setting up all sorts of inherent conflicts. Moreover, new BBB business members automatically receive a top rating upon joining. The rating may then be reduced over time depending on how the business handles its complaints. That’s a loophole that scammers can drive a truck through. Moreover, BBB has set itself up to process and resolve mountains of consumer complaints, something it doesn’t get paid to do. More fundamentally, BBB has a pay to play business model. It makes no money unless a business becomes a member, and once a member the business automatically receives a top rating from BBB.

If BBB has trouble with this model, consider that ADP has the additional hurdle of being an unknown brand. Moreover, rather than leveraging the directories of its members, ADP has created the Trusted Local Directory as a new directory site that will need to build usage from scratch, a daunting task at this late date. And lest you think that the Trusted Local Directory is a directory of trusted local businesses, be advised that it appears to be a national directory of all businesses, one that offers no more than business name, address and phone.

An online directory of trusted local businesses could be a good and useful product. But the business model inherently fights you every step of the way. A directory like this needs a critical mass of businesses to be useful and viable. But assessing trust at anything more than a cursory level is slow, manual, expensive and difficult to scale. So you can’t do it for free. But by charging for inclusion, fewer businesses will want to be included. To combat this you can reduce your price, which means a less rigorous assessment, which in turn limits the value of the product. Alternately, you can give the impression of a rigorous review without actually doing the work, but that is more likely to lead to court than to success.

Crowdsourced reviews have come closest to making the third-party review model work. They are low cost and do readily scale, but many suffer from gaming and have credibility issues of their own. To succeed, they need a lot of policing and quality control, and that quickly gets complex and expensive, and there only a few examples (TrustPilot is one good one) of meaningful monetization with this model. 

Again, kudos to ADP for thinking outside the box, but it doesn’t seem to me they’ve cracked the code on this inherently challenging business model. And for anyone else considering this model, trust me, it’s hard.

Variable Pricing, Data-Style

Variable pricing is a well-known pricing strategy that changes the price for the same product or service based on factors such as time, date, sale location and level of demand. Implemented properly, variable pricing is a powerful tool to optimize revenue.

The downside to variable pricing is that it has a bad reputation. For example, when prices go up at times of peak demand (which often translates into times of peak need), that’s variable pricing. Generally speaking, when you notice variable pricing, it’s because you’re on the wrong end of the variance.

Variable pricing lends itself nicely to data products. But rather than thinking about a traditional variable pricing strategy, consider pricing based on intensity of usage.

Intensity of usage means tying the price of your data product to how intensely a customer uses it – the greater the use, the greater the price. Intensity pricing is not an attempt to support multiple prices for the same product, but rather an attempt to tie pricing to the value derived from use of the product, with intensity of usage a proxy for value derived from the product.

For data producers, intensity-based pricing can take many forms. Here are just a few examples to fuel your thinking:

1.         Multi-user pricing. Yes, licensing multiple users and seats to large organizations is hardly a new idea. But it’s still a complex, mysterious thing to many data producers who shy away from it, leaving money on the table and probably encouraging widespread password sharing at the same time. The key to multi-user pricing is not to try and extract more from larger organizations simply because “they can afford it,” (a contentious and unsustainable approach), but to tie pricing to actual levels of usage as much as possible.

2.         Modularize data product functionality. Not every user makes use of all your features and functionality. Think about identifying those usage patterns and then re-casting your data product into modules: the more modules you use, the more you pay. We all know the selling power of those grayed-out, extra cost items on the main dashboard!

3.         Limit or meter exports. Many sales-oriented data products command high prices in part because of the contact information that they offer, such as email addresses. Unfortunately, many subscribers still view data products like these as glorified mailing lists to be used for giant email blasts. This is a high intensity use that should be priced at a premium. A growing number of data producers limit the number of records that can be downloaded in list format, charging a premium for additional records to reflect this high-intensity type of usage. It’s similarly possible to limit and then up-charge certain types of high-value reports and other results that provide value beyond the raw data itself.

4.         Modularize the dataset. Just as few users will use all the features available to them in a data product, many will not use all the datamade available to them. For example, it’s not uncommon for data producers to charge more for access to historical data because not everyone will use it, and those who do use it value it highly. Consider whether you have a similar opportunity to segment your dataset.

While your first consideration should be revenue enhancement, also keep in mind that an intensity-based pricing approach helps protect your data from abuse, permits lower entry-level price points, creates up-sell opportunities, and properly positions your data as valuable and important.

There are competitive considerations as well. When you are selling an over-stuffed data product in order to justify a high price, the easiest strategy for a competitor is to build a slimmed-down version of your product at a much lower price – Disruption 101. You simply don’t want to be selling a prix fixe product in an increasingly a la carte world (look at the cable companies and their inability to sustain bundled pricing even with near-monopoly positions).

The Power to Destroy

In 1819, Supreme Court Chief Justice John Marshall penned the famous phrase, “the power to tax involves the power to destroy.” This insightful commentary came as part of the Court’s ruling in the case of McCulloch v. Maryland. The case involved a move by the State of Maryland to favor in-state banks by taxing the bank notes of the federally chartered Bank of the United States. In a unanimous decision, the Court ruled that Maryland couldn’t try to run the federal bank out of town through clever tax schemes.

This famous phrase pops into my head every time I see data and software companies get too arrogant or too greedy and start to abuse their market dominance. This is because software and data companies that dominate their markets have some of the same coercive power as governments with their ability to make rules and set prices.

I got a direct taste of this earlier in the week when an email arrived from QuickBooks to tell me that they were more or less doubling my annual subscription fee. The rationale for this massive increase? The folks at Intuit (parent company of QuickBooks) feel they work very hard and deserve more money. You may recall that Intuit has recently been hard at work with its fleet of lobbyists trying to get legislation passed to prohibit the IRS from offering an online tax filing service. In its annual report, Intuit specifically calls out the threat of federal and state “encroachment” on its business. A touch of entitlement, perhaps?

My email from QuickBooks was followed by an email from DropBox announcing a 20% price increase. At least DropBox doubled my online storage in exchange, not that I really needed it.

It’s not just in the software industry where market power is being abused. As just one example, StreetEasy, the dominant real estate listing platform in New York City, stopped accepting automated listings feeds from several major real estate brokers in a fit of arrogance and competitive gamesmanship. Try not to laugh when you read StreetEasy’s justification for suspending automated feeds:

“Sending a feed sounds simple and seamless. It’s not. Continuing to receive listings in such an inefficient way wasn’t doing anyone — agents or consumers — any favors. So, we innovated.”

StreetEasy’s innovation? Data entry screens that require brokers to re-enter all their listings … manually. You can’t make this stuff up.

Often what damages or even kills great data and software companies with dominant market positions is the abuse of their market power. They forget why they exist and who the customer is. In many cases they get lazy, finding it easier to raise prices than continuing to innovate. Sometimes these companies impose big price increases, as in the case of QuickBooks, simply because they can.

Market dominance creates coercive power that can destroy. With taxation, the party that can be destroyed is the taxpayer. But with private companies, coercive power comes with the ability to destroy … themselves.

Data.Gone

On May 4, 2019 it’s official: data.com connect is shutting down. You may remember data.com connect in its original incarnation as Jigsaw.com. Salesforce.com acquired Jigsaw in 2010, paying huge dollars to kick-start an ambitious plan to not only be a software platform to manage sales activities, but to help companies maintain and grow their sales leads as well.

Lest you think Salesforce was lacking in ambition, it then acquired the data.com domain name for $1.5 million. Jigsaw moved over to data.com, and Salesforce began to execute on its vision of a data marketplace, where its software users could discover, purchase and seamlessly import third-party data into Salesforce. It was a big, slick and arguably brilliant idea.

But an idea falls far short of a successful strategy, and data.com never appeared to be much more than an idea, or more accurately, a series of ideas. And Salesforce, for all its success, never figured out decisively what it wanted data.com to be when it grew up. Add in competing corporate strategies, office politics, a high-growth core business and a go-go culture, and it’s perhaps not surprising that data.com quickly became a corporate orphan.

 More fundamentally though, we see once again that software companies – despite lots of brave talk – just don’t “get” data. In particular, a good database needs care and feeding using processes and techniques that are messy, imperfect, never-ending and perhaps most importantly of all, impossible to simply automate and forget.

 Jigsaw probably looked like a light lift to Salesforce. After all, the brilliance of Jigsaw was it was crowd-sourced data. The people using the data committed to correcting it and adding to it. On the surface, it probably looked like a perpetual motion machine to Salesforce. But that perception couldn’t be farther from the truth. Crowdsourcing is an intensely human activity, because you have to motivate and incent users to keep working on the database. You have to construct a structure that rewards top producers and pushes out bad actors. You have to relentlessly monitor quality and comprehensiveness. It’s endless fine-tuning, lots of trial and error, and a deep understanding of how to motivate people.

This is where Salesforce failed. It either didn’t understand the commitment required or didn’t want to do the work required. And just as a crowdsource database can grow quickly, it can also decline quickly.

I’ve said it before: I see more success among data providers that develop software around their data than software companies trying to develop their own databases.

Choose Your Customer

From the standpoint of “lessons learned,” one of the most interesting data companies out there is TrueCar.

Founded in 2005 as Zag.com, TrueCar provides consumers with data on what other consumers actually paid for specific vehicles in their local area. You can imagine the value to consumers if they could walk into dealerships with printouts of the lowest price recently paid for any given vehicle. 

The original TrueCar business model is awe-inspiring. It convinced thousands of car dealers to give it detailed sales data, including the final price paid for every car they sold. TrueCar aggregated the data and gave it to consumers for free. In exchange, the dealers got sales leads, for which they paid a fee on every sale.

 Did it work? Indeed it did. TrueCar was an industry disruptor well before the term had even been coined. As a matter of fact, TrueCar worked so well that dealers started an organized revolt in 2012 that cost TrueCar over one-third of its dealer customers.

The problem was with the TrueCar model. TrueCar collected sales data from dealers then essentially weaponized it, allowing consumers to purchase cars with little or no dealer profit. Moreover, after TrueCar allowed consumers to purchase cars on the cheap, it then charged dealers a fee for every sale! Eventually, dealers realized they were paying a third-party to destroy their margins, and decided not to play any more.

TrueCar was left with a stark choice: close up shop or find a new business model. TrueCar elected the latter, pivoting to a more dealer-friendly model that provided price data in ways that allowed dealers to better preserve their margins. It worked. TrueCar re-built its business, and successfully went public in 2014.

A happy ending? Not entirely. TrueCar, which had spent tens of millions to build its brand and site traffic by offering data on the cheapest prices for cars, quietly shifted to offering what it calls “fair prices” for cars without telling this to the consumers who visited its website. Lawsuits followed.  

There are four important lessons here. First, you can succeed in disrupting an industry and still fail f you are dependent on that industry to support what you are doing. Second, when it comes to B2C data businesses, you really need to pick a side. Third, if you change your revenue model in a way that impacts any of your customers, best to be clear and up-front about it. In fact, if you feel compelled to be sneaky about it, that’s a clue your new business model is flawed. Fourth, and I’ve said it before, market disruption is a strategy, not a business requirement.