InfoCommerce Group Blog


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Shine a Light on Your Hidden Data

If you watch the technology around sales and marketing closely, you’ll know that beacon technology is all the rage. Stores can purchase beacon broadcasting equipment, and when shoppers enter their stores with beacon-enabled apps, the apps will respond to the beacon signals – even if not in use. Stores see nirvana in pushing sale offers and the like to customers who are already on the premises. And of course, it is expected that some mainstream apps (Twitter is often cited, though this is unconfirmed) will become beacon-enabled as well.

Beacons represent a concrete manifestation of the larger frenzy surrounding geolocation. Everyone wants to know where consumers are at any given moment, as epitomized by big players such as Foursquare, which has evolved from its gimmicky “check ins” to become more of a location-driven discovery service.

That’s why I was so intrigued by Foursquare’s most recent product announcement called Pinpoint. Shifting its focus from where people are now, Pinpoint is going to mine valuable insights around where people have been and let companies use it for precise ad targeting.

Details about Pinpoint are scarce right now, but Foursquare is smart to start mining its historical data. At the lowest level, it means that Foursquare can help, say, Starbucks target lots of Starbucks customers. Useful, but not too sophisticated. If Pinpoint can roll up businesses by type (such as pet food stores), it starts to get a lot more interesting. But the real home run would be to be able to divine purchase intent. If someone visits three car dealers in a short period of time, you suddenly have an amazingly valuable sales lead. And mining insights like this is now practical with Big Data tools.

But the real insight here is that your history data isn’t just ancient history: it provides the multiple data points you need to find patterns and trends. Knowing that a company replaces its CEO every 18 months or so is a hugely valuable insight that you can identify simply by comparing your current data to your historical data. At a minimum, you’ve got a powerful sales lead for recruiters. But that level of volatility might be a signal of a company with problems, thus creating useful insights in a business or competitive intelligence context. We’ve all heard about the predictive powerful of social media sentiment analysis. You may have equally valuable insights lurking in your own data. All you need to do is shine a light on them.

How Starbucks in Mall of America looks to Foursquare

How Starbucks in Mall of America looks to Foursquare

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The Award for Outstanding Performance Goes to Internet Movie Database

We awarded the Internet Movie database a Model of Excellence  in 2003, and it is still a standout in terms of innovation and best practices.  

The Internet Movie Database (often called by its acronym IMDB) originally started in the UK as a non-profit undertaking, and it may well be the earliest and most successful example of crowdsourcing – well over a decade before the term was even coined. Very simply, the IMDB was a site for movie buffs worldwide to build an enormously detailed database of every movie ever made. And we are talking about a serious level of detail. Want to know who was the hairstylist for the co-star of an obscure French drama from the 1950s? Well, IMDB was the go-to source. What also made IMDB interesting was that from its inception it was a true database, and despite the inherently unruly nature of crowdsourcing, there were enough committed volunteers to take on the unsexy work of removing duplicate entries and normalizing the data.

In 1998, IMDB was quietly acquired by Amazon and turned into a for-profit company. There are some great best practices to be observed here. Taking over and commercializing a site built by tens of thousands of unpaid, die-hard movie fans was a risky proposition. The backlash could have killed the business in short order. But Amazon left IMDB alone, infusing it with editorial resources so the database got bigger and better every year. Better data, less work and all free. Not much here to get upset about!

But Amazon (surprise!) wasn’t in this to be charitable. First, it started marketing to the substantial audience of IMBD users with links to its site. Like the movie? Great. Amazon can sell you a copy.
Amazon’s next move was sell sponsorships to movie studios eager to promote upcoming releases. From there, Amazon launched a subscription-based Pro version of the database that offered enhanced searching and even deeper content to movie industry professionals for research purposes. The core site remained free, meaning Amazon was a pioneer with the freemium model, well before that term had become popular. 

Is Amazon now resting on its laurels? Absolutely not. To support both its Kindle and Amazon Prime offerings, Amazon has launched a service called X-Ray, powered by IMDB. Amazon also selectively licenses this new data capability. What X-Ray does is link movies to the IMDB database, so users can visually identify actors in the film, find movie trivia, explore the movie soundtrack and much more, right while watching the movie.  It’s not all software magic, by the way. Amazon is doing a lot of the necessary linkages manually, but it already has thousands of movies coded. Also of interest, it’s touting its “X-Ray Enabled” badge that if it plays its card right, could someday become a differentiator for new movie releases.

Endless innovation. Strong support of its core e-commerce platform. Deft handling of often prickly enthusiast community. Endless monetization. This is where data is going!

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User Interface Design: No Small Matter

In advance of big changes to the way pensions are managed, the UK government set up a quasi-independent service called Money Advice Service (MAS). MAS has the worthy goal of trying to improve financial literacy, particularly among those about to retire.

As part of its program, MAS set up an online directory of financial advisors, just launched in beta. Given its high profile and semi-official status, the MAS directory has come under a lot of scrutiny, particularly from the financial advisors it lists, all of which are keen to be highly visible in this important new directory that anticipates very heavy use. But let’s look at it from a user’s perspective to see some important lessons on how not to create an online directory.

Sample Directory Listing

Sample Directory Listing

The directory database itself is quite mundane. It presents such information as advisor name, contact details, certifications (if any), and the types of services it provides (from a fixed list of categories). But here’s how a seemingly basic directory quickly becomes complicated.

First, it encountered the issue of business locations. It’s easy to list ABC Advisors at its headquarters address in London. But what if ABC Advisors has 400 branch offices scattered around the country? Do they each get individual listings? Even more confusing, how do you properly represent advisory firms that have independent advisors, many of whom work from home? What about advisory firms that are affiliated with other advisory firms? You may think all of this is annoying, but not a huge deal. But it becomes a huge deal when the user interface is location-centric.

As it happens, the MAS directory is location-centric. It uses a postal code to do a search to return results based on proximity. But depending on how you handle the entity issues described above, ABC Advisors might appear 100 times in results of a specific search (with each of its offices or advisors appearing as a separate listing), or not at all (because only the headquarters location was listed and it wasn’t anywhere nearby). This can be very confusing to users (who often see the multiple records as annoying duplicates and the absence of major companies as questionable data quality). And if you are selling paid participation or paid enhancements in the directory, this can cause an advertiser revolt.

The MAS directory also lets you search by specialty service. Here, results are not returned by proximity, and because there is no secondary sort on distance, the first search result may list a firm 500 miles away, while a firm 1 mile away appears on page three of search results.

Perhaps the biggest issue of all is that searches tend to return hundreds of listings, and the thin dataset gives the user very little information or tools to differentiate or compare them. Apparently, the plan is to add fees and charges in the near future to build out the database. In the meantime, users struggle with a marginally useful directory. Governments can get away with this.  But those of us in the business know how to do it a lot better – or at least we should. User interface design starts with the design of the database itself, which is in turn informed by the user needs and problems you are trying to address. Shortcuts in the design phase mean expensive additional work later, and can potentially endanger the success of your data product.

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Does Co-Dominance Spur Disruption?

Outspoken Zillow CEO Spencer Rascoff made headlines this week by using an industry event to publicly describe his arch-rival, Murdoch-owned Move Inc., as “a crappy company.”

There’s no love lost on the Move side either. Move, which operates the Realtor.com real estate listings site, has previously cut off listing fees to Trulia right after Trulia was acquired by Zillow, and now has Zillow in court over its merger with Trulia.

Certainly, the stakes in the real estate listings data business are huge, so bare-knuckle competition isn’t surprising. What is surprising is that both companies are finding success with radically different business models.

Realtor.com has what I view as a very conventional “just the facts ma’am” user interface. It offers basic parametric search, with listings displayed as summary listings, each offering fast access to listings detail. Real estate agents can pay to advertise themselves or highlight specific listings, and are provided with sales leads as well.

Zillow, as you may recall, burst onto the scene with its “Zestimates,” its estimate of the value of every home in the country. This got Zillow immediate interest and tons of traffic, and it quickly became a major player in the market. Zillow also distinguishes itself with a map-based user interface and somewhat different listing detail than Realtor.com. But the “Zestimates” that helped Zillow rocket to the big time are a two-edged sword. Sellers almost always feel they should be higher, and buyers tend to assume they are much more authoritative than they really are. Zillow also sells advertising to real estate agents with essentially the same suite of offerings as Realtor.com.

Does it make sense that both can thrive? Certainly, we see examples of “co-dominance” in many very large BTC markets simply because they are so large. But while more subtle, it appears that the biggest weakness of both sites – neither has 100% of all listings – may be a strength. That’s because lots of people use both products, leaving real estate agents uncertain about where to place their advertising dollars.

It’s the same situation we saw play out in the heyday of the yellow pages industry. Independent yellow pages directories sprung up everywhere as lower-cost competitors to big, established telephone company directories. But advertisers, rather than cheering and running to advertise in the new, cheaper upstarts, found themselves confused and fearful. Which directory did their customers use? Did they use both? Well, the safest course for many advertisers was to advertise in both directories, meaning their cost to reach the same market went up significantly. Not surprisingly, advertisers were not happy with this outcome.

There are rumblings of discontent in the real estate market as well. Indeed, a new initiative called National Broker Portal Project, meant to be run by and for real estate agents and brokers, is gaining steam. It wants to create a major site that will be both dues-funded and run according to rules developed by the brokers themselves. It’s a long shot to be sure, but it shows once again that being the dominant player in a market is tricky, and sharing that dominance is even trickier. We must all remember that disruption in any industry is not inherently a one-time event.

Upping the Data Ante

Step back a bit from the fray and you’ll see an interesting evolution in the world of data: from providing lists of people or entities that might be prospects, to lists of people or entities that should be prospects, based on something they have done (think sales triggers). Now we’re beginning to move squarely into what used to be the realm of science fiction: identifying prospects before they have done anything at all.

We’re blazing new trails here, and pre-prospecting (for lack of a better name) depends heavily on lots of input data and Big Data analytics. The 800-pound gorilla in this space right now is a company called InsideSales that calls its analytical secret sauce “Neuralytics.”

All hype, you say? Well some level of hype is a given these days, but the company has raised over $139 million to date, and Salesforce.com in particular has fallen hard for the company’s pitch, and actually led its most current funding round, that also included Microsoft.

I don’t have any inside knowledge of what InsideSales is up to, but from the tantalizing tidbits that have surfaced in the press, it seems to be a combination of obvious inputs such as social media feeds, plus less intuitive things such as weather patterns and sports team scores. I can only guess that you’re a somewhat better prospect if it’s sunny out and your team won last night, but perhaps these data are being used in a more subtle and sophisticated way.

The other hint I picked up is that InsideSales depends on “email and phone records” to perform its analytical alchemy. Needless to say, these tend not to be public records, so to deliver the holy grail of sales prospecting, InsideSales apparently depends on the holy grail of input data as well!

I’m not dismissing InsideSales, primarily because I am doing some big league speculating here. But I will say there are data sources available today that get us a long way towards the notion of pre-prospecting. What excites me the most is what is going on today with online ad re-targeting. Ad re-targeting is based on what might be described as networked cookies. Visit a site, and a common cookie is placed on your computer. As you move to other sites that are part of the network, ads can be displayed based on sites you’ve previously visited. More importantly, your travels around the Internet can be centrally stored, creating a wealth of information about you, your interests, your habits and much more. While not easy, it is a straightforward leap to start learning about not only what interests you but also what are the early signs that you are beginning to contemplate a purchase.

Privacy isn’t the issue in re-targeting (at least for now), because nobody needs to know who you are for re-targeting to work. But as your movements around the Internet are recorded and analyzed, it is entirely possible that we’ll someday know when you’re thinking about buying something, and perhaps even a little before.

The next generation of sales insights likely isn’t all that far away, so now is a good time to do some pre-pondering on what it might mean to you and your business.