Sunday, August 20, 2017

Treasure Data Offers An Easy-to-Deploy Customer Data Platform

One of my favorite objections from potential buyers of Customer Data Platforms is that CDPs are simply “too good to be true”.   It’s a reasonable response from people who hear CDP vendors say they can quickly build a unified customer database but have seen many similar-seeming projects fail in the past.  I like the objection because I can so easily refute it by pointing to real-world case histories where CDPs have actually delivered on their promise.

One of the vendors I have in mind when I’m referring to those histories is Treasure Data. They’ve posted several case studies on the CDP Institute Library, including one where data was available within one month and another where it was ready in two hours.  Your mileage may vary, of course, but these cases illustrate the core CDP advantage of using preassembled components to ingest, organize, access, and analyze data. Without that preassembly, accessing just one source can take days, weeks, or even months to complete.

Even in the context of other CDP systems, Treasure Data stands out for its ability to connect with massive data sources quickly. The key is a proprietary data format that lets access new data sources with little explicit mapping: in slightly more technical terms, Treasure Data uses a columnar data structure where new attributes automatically appear as new columns. It also helps that the system runs on Amazon S3, so little time is spent setting up new clients or adding resources as existing clients grow.

Treasure Data ingests data using open source connectors Fluentd for streaming inputs and embulk  for batch transfers. It provides deterministic and probabilistic identity matching, integrated machine learning, always-on encryption, and precise control over which users can access which pieces of data. One caveat is there’s no user interface to manage this sort of processing: users basically write scripts and query statements. Treasure Data is working on a user interface to make this easier and to support complex workflows.

Data loaded into Treasure Data can be accessed through an integrated reporting tool and an interface that shows the set of events associated with a customer.  But most users will rely on prebuilt connectors for Python, R, Tableau, and Power BI.  Other SQL access is available using Hive, Presto and ODBC. While there’s no user interface for creating audiences, Treasure Data does provide the functions needed to assign customers to segments and then push those segments to email, Facebook, or Google. It also has an API that lets external systems retrieve the list of all segments associated with a single customer.  

Treasure Data clearly isn’t an all-in-one solution for customer data management.  But organizations with the necessary technical skills and systems can find it hugely increases the productivity of their resources.  The company was founded in 2011 and now has over 250 clients, about half from the data-intensive worlds of games, ecommerce, and ad tech. Annual cost starts around $100,000 per year.  The actual pricing models vary with the situation but are usually based on either the number of customer profiles being managed or total resource consumption.



Friday, July 14, 2017

Blueshift CDP Adds Advanced Features

I reviewed Blueshift in June 2015, when the product had been in-market for just a few months and had a handful of large clients. Since then they’ve added many new features and grown to about 50 customers. So let’s do a quick update.

Basically, the system is still what it was: a Customer Data Platform that includes predictive modeling, content creation, and multi-step campaigns. Customer data can be acquired through the vendor’s own Javascript tags, mobile SDK (new since 2015), API connectors, or file imports. Blueshift also has collection connectors for Segment, Ensighten, mParticle, and Tealium. Product data can load through file imports, a standard API, or a direct connector to DemandWare.

As before, Blueshift can ingest, store and index pretty much any data with no advance modeling, using JSON, MongoDB, Postgres, and Kafka. Users do have to tell source systems what information to send and map inputs to standard entities such as customer name, product ID, or interaction type. There is some new advanced automation, such as tying related events to a transaction ID. The system’s ability to load and expose imported data in near-real-time remains impressive.

Blueshift will stitch together customer identities using multiple identifiers and can convert anonymous to known profiles without losing any history. Profiles are automatically enhanced with product affinities and scores for purchase intent, engagement, and retention.

The system had automated predictive modeling when I first reviewed it, but has now added machine- learning-based product recommendations. In fact, it recommendations are exceptionally sophisticated. Features include a wide range of rule- and model-based recommendation methods, an option for users to create custom recommendation types, and multi-product recommendation blocks that mix recommendations based on different rules. For example, the system can first pick a primary recommendation and then recommend products related to it. To check that the system is working as expected, users can preview recommendations for specified segments or individuals.

The segment builder in Blueshift doesn’t seem to have changed much since my last review: users select data categories, elements, and values used to include or exclude segment members. The system still shows the counts for how many segment members are addressable via email, display ads, push, and SMS.

On the other hand, the campaign builder has expanded significantly. The previous form-based campaign builder has been replaced by a visual interface that allows branching sequences of events and different treatments within each event.  These treatments include thumbnails of campaign creative and can be in different channels. That's special because many vendors still limit campaigns to a single channel. Campaigns can be triggered by events, run on fixed schedules, or executed once.


Each treatment within an event has its own selection conditions, which can incorporate any data type: previous behaviors, model scores, preferred communications channels, and so on. Customers are tested against the treatment conditions in sequence and assigned to the first treatment they match. Content builders let users create templates for email, display ads, push messages, and SMS messages. This is another relatively rare feature. Templates can include personalized offers based on predictive models or recommendations. The system can run split tests of content or recommendation methods. Attribution reports can now include custom goals, which lets users measure different campaigns against different objectives.

Blueshift still relies on external services to deliver the messages it creates. It has integrations with SendGrid, Sparkpost, and Cheetahmail for email and Twilio and Gupshup for SMS. Other channels can be fed through list extracts or custom API connectors.

Blueshift still offers its product in three different versions: email-only, cross-channel and predictive. Pricing has increased since 2015, and now starts at $2,000 per month for the email edition version, $4,000 per month for the cross-channel edition and $10,000 per month for the predictive edition. Actual fees depend on the number of active customers, with the lowest tier starting at 500,000 active users per month. The company now has several enterprise-scale clients including LendingTree, Udacity, and Paypal.

Friday, July 07, 2017

Lexer Customer Data Platform Grows from Social Listening Roots

Customer Data Platform vendors come from many places, geographically and functionally. Lexer is unusual in both ways, having started in Australia as a social media listening platform. About two years ago the company refocused on building customer profiles with data from all sources. It quickly added clients among many of Australia’s largest consumer-facing brands including Qantas airlines and Westpac bank.

Social media is still a major focus for Lexer. The system gathers data from Facebook and Instagram public pages and from the Twitter follower lists of clients’ brands. It analyzes posts and follows to understand consumer interests, assigning people to “tribes” such as “beach lifestyle” and personas such as “sports and fitness”.  It supplements the social inputs with information from third party data sources, location history, and a clients’ own email, Web site, customer service, mobile apps, surveys, point of sale, and other systems. Matching is strictly deterministic, although links based on different matches can be chained together to unify identities across channels.  The system can also use third party data to add connections it can’t be made directly.

Lexer ingests data in near-real-time, making social media posts available to users within about five minutes. It can react to new data by moving customers into different tribes or personas and can send lists of those customers to external systems for targeting in social, email, or other channels.  There are standard integrations with Facebook, Twitter, and Google Adwords advertising campaigns. External systems can also use an API to read the Lexer data, which is stored in Amazon Elastic Search.

Unusally for a CDP, Lexer also provides a social engagement system that lets service agents engage directly with customers. This system displays the customer’s profile including a detailed interaction history and group memberships. Segment visualization is unusually colorful and attractive.

Lexer has about forty clients, nearly all in Australia. It is just entering the U.S. market and hasn’t set U.S. prices.

Monday, July 03, 2017

The Personal Network Effect Makes Walled Gardens Stronger, But There's Still Hope

I’m still chewing over the role of “walled garden” vendors including Google, Amazon, and Facebook, and in particular how most observers – especially in the general media – fail to grasp how those firms differ from traditional monopolists. As it happens, I’m also preparing a speech for later this month that will touch on the topic, which means I’ve spent many hours working on slides to communicate the relevant concepts. Since just a handful of people will see the slides in person, I figured I’d share them here as well.

In pondering the relation of the walled garden vendors to the rest of us, I’ve come to realize there are two primary dynamics at work. The first is the “personal network effect” that I’ve described previously. The fundamental notion is that companies get exponentially increasing value as they capture more types of information about a consumer. For example, it’s useful to know what’s on someone’s calendar and it’s useful to have a mapping app that captures their locations. But if the same company controls both those apps, it can connect them to provide a new service such as automatically mapping out the day’s travel route.  Maybe you even add helpful suggestions for where to stop for fuel or lunch.


 In network terms, you can think of each application as a node with a value of its own and each connection between nodes having a separate additional value. Since the number of connections increases faster than the number of nodes, there’s a sharp rise in value each time a new node is added. The more nodes you own already, the greater the increase: so companies that own several nodes can afford to pay more for a new node than companies that own just one node. This makes it tough for new companies to break into a customer’s life. It also makes it tough for customers to break away from their dominant network provider.

My best visualization of this is to show the applications surrounding an individual and to draw lines showing how many more connections appear when you add nodes.  If it looks like the customer is trapped by those lines, well, yes.



The point that’s missing from the discussions I’ve seen about walled gardens is that personal networks create a monopoly on the individual level. Different companies can coexist as the dominant networks for different people.  So let’s assume that Google, Facebook, Amazon, and Apple each manage to capture one quarter of the population in their own network. If each member spends 100% of her money through her network owner, the over-all market share of each firm would be just 25%. From a classical viewpoint, that’s a highly competitive market. But each consumer is actually at the mercy of a monopolist.  (If you want a real-life example, consider airline hub-and-spoke route maps.  Each airline has an effective monopoly in its hub cities, even though no airline has an over-all monopoly.  It took regulators a long time to figure that one out, too.)  

In theory the consumer could switch to a new network. But switching costs are very high, since you have to train the new network to know as much about you as the old network. And switching to a new network just means you’re changing monopolists.  Remember that the personal network effect makes it really inconvenient to have more than one primary network provider.

The second dynamic is the competition among network providers to attract new customers. As with any network, personal networks hold a big first mover advantage: whichever provider first sells several apps to the same consumer has a good, and ever-growing, chance of becoming that consumer's primary network.

Once the importance of this becomes clear, you can recognize the game of high-stakes leapfrog that network vendors have been playing for the past two decades. It starts with Amazon in 1994, intercepting buyers before they can reach a physical retailer. A few years later, Google starts catching buyers in the browser, making searches before they’re ready to buy through Amazon. Then Facebook shows up, first with a social network where people discuss their purchases before they make a Web search, and later with a mobile app that bypasses the Web browser altogether. A decade after that, Amazon strikes back with voice search on Alexa, which can happen even before someone types in a social post.

Remember, this isn’t just about selling advertising. Vendors can share that pie. What they can’t share is control over one consumer’s personal network. Since that, in turn, gives control over actual purchases, it’s a much bigger prize and, therefore, worth a great deal more effort to win. Now you see why Amazon has put so much effort into hardware over the years.  It's not just that Jeff Bezos likes cool gadgets.

At this point, you might pause to wonder what happens next.  Is there something that can intercept consumers before they say what they're thinking?  AI- and/or implant-enabled mind reading are certainly possibilities.  But the next frontier right now is subscriptions, which let purchases happen without any specific action for a voice system to intercept.


This is exactly why subscriptions are getting so much attention right now.  (I do need to admit that Dollar Shave Club, Blue Apron, and Birchbox messed up my chronology by launching around 2011, several years before Alexa).

Of course, there’s nothing to prevent the network vendors from launching subscription services. In fact, the price of Blue Apron’s IPO was depressed precisely by the fear that Amazon would enter its business through the Whole Foods acquisition.

But what’s really interesting about subscriptions is they’re less subject to the personal network effect than other types of purchases. A subscription company comes to understand its customers’ needs in one particular area very deeply.  Potentially, it can fulfill those needs better than a company working from less detailed data gathered in other domains.

Certainly a great deal depends on execution.  But if I’ve trained my wine-by-mail company to understand my precise tastes, I’ll probably buy through them when I’m stocking up for my next party, even though Amazon knows I’m planning to have people over and has some general idea that my friends like to drink.

In short, the walled gardens are not impregnable.  Subscriptions might offer a way to help customers escape. But marketers are going to have to work harder than ever to create relationships strong enough to pull their customers away from the networks. All I can do here is to clarify the issues so marketers can better understand the tasks ahead.

Sunday, June 18, 2017

Amazon Buys Whole Foods: It's Not About Groceries

Most of the comments I’ve seen about Amazon’s acquisition of Whole Foods have described it as Amazon (a) expanding into a new industry (b) continuing to disrupt conventional retail and (c) moving more commerce from offline to online channels. Those are all true, I suppose, but I felt they missed the real story: this is another step in Amazon building a self-contained universe that its customers never have to leave.

That sounds a bit more paranoid than it should. This has nothing to do with Amazon being evil. It’s just that I see the over-arching story of the current economy as creation of closed universes by Amazon, Facebook, Google, Apple, and maybe a couple of others. The owners of those universes control the information their occupants receive, and, through that, control what they buy, who they meet, and ultimately what they think. The main players all realize this and are quite consciously competing with each other to expand the scope of their services so consumers have less reason to look outside of their borders. So Amazon buys a grocery chain to give its customers one less reason to visit a retail store (because Amazon’s long-term goal is surely for customers to order online for same-day delivery). And, hedging its bets a bit, Amazon also wants to control the physical environment if customers do make a visit.

I’ve written about this trend many times before, but still haven’t seen much on the topic from other observers. This puzzles me a bit because it’s such an obviously powerful force with such profound implications. Indeed, a great deal of what we worry about in the near future will become irrelevant if things unfold as I expect.

Let me step back and give a summary of my analysis. The starting point is that people increasingly interact with the world through their online identities in general and their mobile phones in particular. The second point is a handful of companies control an increasing portion of consumers’ experiences through those devices: this is Facebook taking most of their screen time, Google or Apple owning the physical device and primary user interface, and Amazon managing most of their purchases.

At present, Facebook, Apple, Google, and Amazon still occupy largely separate spheres, so most people live in more than one universe. But each of the major players is entering the turf of the others. Facebook and Google compete to provide information via social and search. Both offer buying services that compete with Amazon. Amazon and Apple are using voice appliances to intercept queries that would otherwise go through to the others.

Each vendor’s goal is to expand the range of services it provides. This sets up a virtuous cycle where consumers find it’s increasingly convenient to do everything through one vendor. Instead of a conventional “social network effect” where the value of a network grows with the number of users, this is a “personal network effect” where the value of a vendor relationship grows with the number of services the vendor provides to the same individual.

While a social network effect pulls everyone onto a single universal network, the personal network effect allows different individuals to congregate in separate networks. That means the different network universes can thrive side by side, competing at the margins for new members while making it very difficult for members to switch from one network to the other.

There’s still some value to network scale, however. Bigger networks will be able to create more appealing services and attract more partners, The network owners will also provide sharing services that make it easy for members to communicate with each other (see: Apple Facetime) but harder to interact with anyone else. So the likely outcome is a handful of large networks, each with members who are increasingly isolated from members of other networks. Think of it as a collection of tribes.

Even without any intentional effort by the network owners, members of each network will have shared experiences that separate them from outsiders: try asking an Android user for help with your iPhone. The separation will become even more pronounced if the network owners more actively control the information their members receive – something that’s already happening in the name of blocking terrorists, bullies, and other genuinely bad actors. Of course, people who prefer a particular world view will be able to form their own networks, which will be economically viable because the personal network effect with outweigh the social network effect. These splinter networks might be owned independently (it’s easy to imagine a Fox News tribe) or owned by a bigger network that just gives each tribe what it wants. Either way, you have a society whose tribes that are mutually unaware at best and actively hostile at worst.

Let’s put aside the deeper social implications of all this, in the best tradition of “other than that, how did you like the play, Mrs. Lincoln?” My immediate point is that marketers and technologists should be aware of these trends because they help to explain much of what’s happening today in our industry and help to prepare for what might happen tomorrow. Here are some things to keep an eye on:

- Growth of Voice. As I’ve already mentioned, voice interactions are an alternative to conventional screen interactions. What’s important is the voice interaction often happens first: it’s easier to ask Alexa or Siri to do something than to type that same request into Google, Facebook, or Amazon. This means whoever owns the voice interaction can intercept customer behaviors before anyone else. So pay close attention to voice-based systems: far from a gimmick, they could be keys to the kingdom.

- Owning the Pipes. Network owners want above all to keep their customers’ data to themselves. This will make them increasingly interested in owning the pipes that carry that data and in blocking anyone else from tapping those pipes. Don’t be surprised to see the network owners take an interest in physical networks (cable and phone companies) and alternative connections (community wifi). Also expect them to argue that physical network owners shouldn’t be allowed to use the data they carry (an argument they just lost in Congress but will likely resurrect on privacy grounds) and that they should be able to buy preferential access (the “network neutrality” debate they are now winning in that same Congress). Could a pipe owner grow its own network? The folks at Verizon apparently think so: that’s why they bought AOL and Yahoo!

- Data Motels. It goes pretty much without saying that network owners are eager to take data from other companies, but stingy about sharing their own. So they’re happy to import other companies’ customer lists and serve them ads, conveniently getting paid while gaining new information. But they’re less interested in exporting data about those same common customers. It’s the information version of a roach motel: data checks in but it can’t check out.

- Expanding Services. We’ve already covered this but it’s so important that it bears repeating: network vendors will continue to extend the services they offer, tightly integrating them to increase the “personal network value” of their relationships. Watch carefully and you’ll notice each new service gives customers a reason to share more data, gives the network owners still more information to better personalize customer services. Everybody wins, although the networks win more.

- The AIs Have It. The networks’ ultimate goal is to handle all their members’ purchases. The best way to do this is to have members delegate as many decisions to the network as possible, starting with things like subscriptions for restocking groceries and on-demand transportation. This saves the effort of making individual sales and, more important, eliminates opportunities for members to leak out of the system. Delegation requires the members to trust the network to make the right decisions on their behalf. Gathering more data is one key to this; artificial intelligence to make good decisions with that data is another. So if you’re thinking the networks are investing in AI only because they’re nerds who like science projects, think again.

- Trust. Arguably, trust is the result of experience, so making good decisions for members should be enough to earn permission to make more decisions. But in practice it will be impossible for consumers to know if the network is really making the best possible choices. So building trust through conventional branding and relationship management will be critical skills for the network marketers, especially when it comes to recruiting new members. (Of course, with network usage starting somewhere around age 2, membership is likely to be more hereditary than anything else.) Data and AI systems will help network marketers know the best way to build trust with each individual, but human marketing skills will also be needed – at least for now.

- Marketing to Networks. If the networks really do take control of their members’ commercial lives, the role of marketers at non-network companies is much diminished. This is already happening: every dollar spent on pay-per-click search or social advertising is essentially a dollar the network spends on the owner’s behalf, based on data only the network possesses. Today, non-network marketers still set budgets, write copy, and select keywords. But those tasks are well on their way to being automated and it won’t matter much whether the automation runs on a machine at the network or the non-network company. The role of the non-network marketer in this world is to market to the network itself. This is already a reality: search engine optimization is really marketing to network search algorithms. It will be even more important when the member isn’t directly involved in the purchase process. No doubt there will be a certain amount of “incentivizing” of the network to pick a particular product, some of it under the table. But there will also be competition to build products and services that best meet member needs and to create brands that members are pleased to have chosen on their behalf.

- Whither MarTech? Marketers at non-network companies will still have jobs whether or not they sell directly to their customers. But martech vendors could face a threat to their existence. Simply put, if the networks capture all direct customer interactions and don’t share their data with outsiders, the market for customer data platforms, journey orchestration engines, predictive analytics, content management systems, and other martech mainstays will vanish. This probably overstates the problem: presumably companies will still interact directly with people after have made a purchase, even if the purchase itself is managed by the network. But the majority of marketing technology is used for customer acquisition, and much of that could become obsolete.

- Alternate Routes. Like Dickens’ Ghost of Christmas Future, I’m only showing you what might be. Non-network marketers have a strong incentive to preserve direct access to their current and future customers and many suppliers have ways to help. Non-network advertising media are first in line, of course, although they’ve been losing ground at an alarming rate. But many other companies are finding creative ways to capture customer data and attract customers’ attention. Location data and mobile apps are especially contested territory because they let firms reach customers directly in ways that customers find highly valuable. The lowly mobile wallet, if it remains outside the networks’ control, could be an alternative channel for reaching a mass audience. Telecommunication providers, with their deep pockets, broad reach, physical access to mobile devices, and vast government relationships, are probably a better bet. Of course, the telcos would probably rather join the network oligopoly than break it. But the broader point is there are still many players in the game and the outcome is far from decided. I hope this helps you make a little more sense of what's happening on the field.


Saturday, June 10, 2017

Cheetah Digital Debuts in Las Vegas

I spent the latter part of last week still in Las Vegas, switching to the client conference for Cheetah Digital, the newly-renamed spinoff of Experian’s Cross Channel Marketing division. Mercifully, this was at a relatively humane venue, the big advantage being I could get from my hotel room to the conference sessions without walking through the casino floor or a massive shopping mall. But it was still definitely Vegas.

The conference offered a mix of continuity and change. Nearly every client and employee I met had been with Cheetah / Experian for at least several years, so there was a definite feeling of old friends reconnecting. Less pleasantly, Cheetah’s systems have also been largely unchanged for years, something that company leaders could admit openly since they are now free to make new investments. Change was provided by the company’s new name and ownership: the main investor is now Vector Capital, whose other prominent martech investments include Sizmek, Emarsys, and Meltwater. There’s also some participation from ExactTarget co-founder Peter McCormick and Experian itself, which retained 25% ownership. The Cheetah Digital name reflects the company’s origins as CheetahMail, which Experian bought in 2004 and later renamed, although many people never stopped calling it Cheetah.

Looking ahead, newly-named Cheetah CEO Sameer Kazi, another ExactTarget veteran, said the company’s immediate priorities are to consolidate and modernize its technology. In particular, they want to move all clients from the original CheetahMail platform to Marketing Suite, which was launched in 2014. Marketing Suite is based on the Conversen, a cross-channel messaging system that Experian acquired in 2012. Kazi said about one third of the company’s revenue already comes from Marketing Suite and that the migration from the old platform will take four or five years to complete.

Longer term, Kazi said Cheetah’s goal is to become the world’s leading independent marketing technology company, distinguishing Cheetah from systems that are part of larger enterprise platforms. Part of the technical strategy to do this is to separate business logic from applications, using APIs to connect the two layers. This will make it easier for marketers to integrate external systems, taking advantage of industry innovation without requiring Cheetah to extend its own products.

Cheetah will also continue to provide services and build customer databases for its clients. Products based on third party data, such as credit information and identity management, have remained with the old Experian organization.

With $300 million in revenue and 1,600 employees, Cheetah Digital is already one of the largest martech companies. It is also one of the few that can handle enterprise-scale email. This makes it uniquely appealing to companies that are uncomfortable with the big marketing cloud vendors. The company still faces a major challenge in upgrading its technology to optimize customer treatments in real time across inbound as well as outbound channels.  It's a roll of the dice.

Wednesday, June 07, 2017

Pega Does Vegas

I spent the first part of this week at Pegasystems’ PegaWorld conference in Las Vegas, a place which totally creeps me out.* Ironically or appropriately, Las Vegas’ skill at profit-optimized people-herding is exactly what Pega offers its own clients, if in a more genteel fashion.

Pega sells software that improves the efficiency of company operations such as claims processing and customer service. It places a strong emphasis on meeting customer needs, both through predictive analytics to anticipate what each person wants and through interfaces that make service agents’ jobs easier. The conference highlighted Pega and Pega clients’ achievements in both areas. Although Pega also offers some conventional marketing systems, they were not a major focus. In fact, while conference materials included a press release announcing a new Paid Media solution, I don’t recall it being mentioned on the main stage.**

What we did hear about was artificial intelligence. Pega founder and CEO Alan Trefler opened with a blast of criticism of other companies’ over-hyping of AI but wasn’t shy about promoting his own company’s “real” AI achievements. These include varying types of machine learning, recommendations, natural language processing, and, of course, chatbots. The key point was that Pega integrates its bots with all of a company’s systems, hiding much of the complexity in assembling and using information from both customers and workers. In Pega’s view, this distinguishes their approach from firms that deploy scores of disconnected bots to do individual tasks.

Pega Vice President for Decision Management and Analytics Rob Walker gave a separate keynote that addressed fears of AI hurting humans. He didn’t fully reject the possibility, but made clear that Pega’s official position is it’s adequate to let users understand what an AI is doing and then choose whether to accept its recommendations. Trefler reinforced the point in a subsequent press briefing, arguing that Pega has no reason to limit how clients can use AI or to warn them when something could be illegal, unethical, dangerous, or just plain stupid.

Apart from AI, there was an interesting stream of discussion at the conference about “robotic process automation”. This doesn’t come up much in the world of marketing technology, which is where I mostly live outside of Vegas. But apparently it’s a huge thing in customer service, where agents often have to toggle among many systems to get tasks done. RPA, as its known to its friends, is basically a stored series of keystrokes, which in simpler times was called a macro. But it’s managed centrally and runs across systems. We heard amazing tales of the effort saved by RPA, which doesn’t require changes to existing systems and is therefore very easy to deploy. But, as one roundtable participant pointed out, companies still need change management to ensure workers take advantage of it.

Beyond the keynotes, the conference featured several customer stories. Coca Cola and General Motors both presented visions of a connected future where soda machines and automobiles try to sell you things. Interesting but we’ve heard those stories before, if not necessarily from those firms. But Scotiabank gave an unusually detailed look at its in-process digital transformation project and Transavia airlines showed how it has connected customer, flight, and employee information to give everyone in the company a complete view of pretty much everything. This allows Transavia to be genuinely helpful to customers, for example by letting cabin crews see passenger information and resolve service issues inflight. Given the customer-hostile approach of most airlines, it was nice to glimpse an alternate reality.

The common thread of all the client stories (beyond using Pega) was a top-down, culture-deep commitment to customer-centricity. Of course, every company says it’s customer centric but most stop there.  The speakers’ organizations had really built or rebuilt themselves around it.  Come to think of it, Las Vegas has that same customer focus at its core. As in Las Vegas, the result can be a bit creepy but gives a lot people what they want.  Maybe that's a good trade-off after all.

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* On the other hand, I had never seen the corrugated hot cup they had in the hotel food court. So maybe Vegas is really Wonderland after all.

** The solution calculates the value a company should bid to reach individual customers on Facebook, Google, or other ad networks.  Although the press release talks extensively about real time, Pega staff told me it's basically pushing lists of customers and bid values out to the networks.  It's real time in the sense that bid values can be recalculated within Pega as new information is received, and revised bids could be pushed to the networks.