Wednesday, May 24, 2017

Coherent Path Auto-Optimizes Promotions for Long Term Value

One of the grand challenges facing marketing technology today is having a computer find the best messages to send each customer over time, instead of making marketers schedule the messages in advance.  One roadblock has been that automated design requires predicting the long-term impact of each message: just selecting the message with the highest immediate value can reduce future income. This clearly requires optimizing against a metric like lifetime value. But that's really hard to predict.

Coherent Path offers what may be a solution. Using advanced math that I won’t pretend to understand*, they identify offers that lead customers towards higher long-term values. In concrete terms, this often means cross-selling into product categories the customer hasn’t yet purchased.  While this isn’t a new tactic, Coherent Path improves it by identifying intermediary products (on the "path" to the target) that the customer is most likely to buy now.  It can also optimize other variables such as the time between messages, price discounts, and the balance between long- and short-term results

Coherent Path clients usually start by optimizing their email programs, which offer a good mix of high volume and easy measurability. The approach is to define a promotion calendar, pick product themes for each promotion, and then select the best offers within each theme for each customer. “Themes” are important because they’re what Coherent Path calculates different customers might be interested in. The system relies on marketers to tell it what themes are associated with each product and message (that is, the system has no semantic analytics to do that automatically). But because Coherent Path can predict which customers might buy in which themes, it can suggest themes to include in future promotions.

Lest this seem like the blackest of magic, rest assured that Coherent Path bases its decisions on data.  It starts with about two years’ of interactions for most clients, so it can see good sample of customers who have already completed a journey to high value status. Clients need at least several hundred products and preferably thousands. These products need to be grouped into categories so the system can find common patterns among the customer paths. Coherent Path automatically runs tests within promotions to further refine its ability to predict customer behaviors. Most clients also set aside a control group to compare Coherent Path results against customers managed outside the system. Coherent Path reports results such as 22% increase in email revenue and 10:1 return on investment – although of course your mileage may vary.

The system can manage other channels than email. Coherent Path says most of its clients move on to display ads, which are also relatively easy to target and measure. Web site offers usually come next.

Coherent Path was founded in 2012 and has been offering its current product for more than two years. Clients are mostly mid-size and large retailers, including Neiman Marcus, L.L. Bean, and Staples. Pricing starts around $10,000 per month.

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* Download their marketing explanation here or read an academic discussion here.

Saturday, May 20, 2017

Dynamic Yield Offers Flexible Omni-Channel Personalization

There are dozens of Web personalization tools available. All do roughly the same thing: look at data about a visitor, pick messages based on that data, and deploy those messages. So how do you tell them apart?

The differences fall along several dimensions. These include what data is available, how messages are chosen, which channels are supported, and how the system is implemented. Let’s look at how Dynamic Yield stacks up.

Data: Dynamic Yield can install its own Javascript tag to identify visitors and gather their information, or it can accept an API call with a visitor ID. It can also build profiles by ingesting data from email, CRM, mobile apps, or third party sources. It will stitch data together when the same personal identifier is used in different source systems, but it doesn’t do fuzzy or probabilistic cross-device matching. Data is ingested in real time, allowing the system to react to customer behaviors as they happen.

Message selection: this is probably where personalization systems vary the most. Dynamic Yield largely relies on users to define selection rules. Specifically, users create “experiences” that usually relate to a single position on a Web page or single message in another channel.  Each experience has a list of associated promotions and each promotion has its own target audience, content, and related settings. When a visitor engages with an experience, the system finds the first promotion audience the visitor matches and delivers the related content.

This is a pretty basic approach and doesn’t necessarily deliver the best message to visitors who qualify for several audiences. But dynamic content rules, machine-learning, and automated recommendations can improve results by tailoring the final message to each individual. In addition, the system can test different messages within each promotion and optimize the results against a user-specified goal.  This lets it send different messages to different segments within the audience.

Product recommendations are especially powerful.  Dynamic Yield supports multiple recommendation rules, including similarity, bought together, most popular, user affinity, and recently viewed.  One experience can return multiple products, with different products selected by different rules.  In other words, the system present a combination of recommendations including some that are similar to the current product, some that are often purchased with it, and some that are most popular over all. 

Channels: this is a particular strength for Dynamic Yield, which can personalize Web pages, emails, landing pages, mobile apps, mobile push, display ads, and offline channels. Most personalization options are available in most channels, although there are some exceptions: you can’t do multi-product recommendations within a display ad and system-hosted landing pages can’t include dynamic content.

Implementation: this also varies by channel. Web site personalization is especially flexible: the Javascript tag can read an existing Web page and either replace it entirely or create a version with a Dynamic Yield object inserted, without changing the page code itself. Users who do control the page code can insert a call the Dynamic Yield API.  Email personalization can also be done by inserting an API call, which lets Dynamic Yield reselect the message each time the email is rendered. The system has direct integration with major ad servers and networks, letting it send targeting rules with different ad versions for each target.

Dynamic Yield’s multi-channel scope and easy deployment options will be appealing to many marketers. The company has more than 100 customers, primarily in ecommerce and media. Pricing is based on the number of unique user profiles managed and on system components. A small client might pay as little as $25,000 per year, although larger companies can pay much more.

Sunday, May 14, 2017

Will Privacy Regulations Favor Internet Giants?

Last week’s MarTech Conference in San Francisco came and went in the usual blur of excellent presentations, interesting vendors, and private conversations. I’m sure each attendee had their own experience based on their particular interests. The two themes that appeared the most in my own world were:

- data activation. This reflects recognition that customer data delivers most of its value when it is used to personalize customer treatments. In other words, it’s not enough to simply assemble a complete customer view and use it for analytics.  “Activation” means taking the next step of making the data available to use during customer interactions, ideally in real time and across all channels. It’s one of the advantages of a Customer Data Platform, which by definition makes unified customer data available to other systems. This is a big differentiator compared with conventional data warehouses, which are designed primarily to support analytical projects through batch updates and extracts.  Conventional data warehouse architectures load data into a separate structure called an “operational data store” when real-time access is needed. Many CDP systems use a similar technical approach but it’s part of the core design rather than an afterthought. This is part of the CDPs’ advantage of providing a packaged system rather than a set of components that users assemble for themselves. CDP vendors exhibiting at the show included Treasure Data, Tealium, and Lytics.

- orchestration. This is creating a unified customer experience by coordinating contacts across all channels. It’s not a new goal but is standing out more clearly from approaches that manage just one channel. More precisely, orchestration requires a decision system that uses activated customer data to find best messages and then distributes them to customer-facing systems for delivery. Some Customer Data Platforms include orchestration features and others don’t; conversely, some orchestration systems are Customer Data Platforms and some are not. (Only orchestration systems that assemble a unified customer view and expose it to other systems qualify as CDPs.) Current frontiers for orchestration systems are journey orchestration, which is managing the entire customer experience as a single journey (rather than disconnected campaigns), and adaptive orchestration, which is using automated processes to find and deliver the optimal message content, timing, and channels for each customer. Orchestration vendors at the show included UserMind, Pointillist, Thunderhead, and Amplero.

Of course, it wouldn’t be MarTech if the conference didn’t also provoke Deeper Thoughts. For me, the conference highlighted three long-term trends:

- continued martech growth. The highlight of the opening keynote was unveiling of martech Uber-guru Scott Brinker’s latest industry landscape, which clocked in at 5,300 products compared with 3,500 the year before. You can read Brinker’s in-depth analysis here, so I’ll just say that industry growth shows no signs of slowing down.

- primacy of data. Only a few presentations or vendors at the conference were devoted specifically to data, but nearly everything there depends on customer data in one way or another. And, as you know from my last blog post, the main story in customer data today is the increasing control exerted by Google and Facebook, and to a lesser degree Amazon, Apple, and Microsoft. If those firms succeed in monopolizing access to customer information, then many martech systems won’t have the inputs they need to work their magic. That could be the pin that bursts the martech bubble.

- new privacy regulations. As Doc Searles (co-author of The Cluetrain Manifesto) pointed out in the second-day keynote , new privacy regulations also threaten to cut off the data supply of marketing and advertising systems, creating an “extinction level event”. Searles announced a “customer commons” that lets consumers share data on their own terms . It’s an interesting concept but I suspect few consumers will put that much work into personal data management.

My initial inclination was to agree with Searles about the implications of new privacy rules, but I’ve since adjusted my view.  It’s just inconceivable that an economic force as powerful as Internet marketing will let regulations put it out of business. It's much more likely that companies like Google and Facebook will learn to work within the new regulations, which after all don’t ban personal data collection but merely require consumer consent. Surely firms with products that are literally addictive can gain consumer consent in ways that will satisfy even the most determined regulators. More broadly, big companies in general should be able to make the investments needed to comply with privacy regulations with minimal harm to their business.

Small businesses are another matter.  Many will lack the resources needed to understand and comply with new privacy regulations.  In other words, privacy regulations will have the unintended consequence of favoring big businesses – which can afford to find ways to comply – over small businesses – which won’t.   Google and Facebook will spend whatever they must to protect their businesses, in the same way that auto manufacturers found ways to comply with safety and pollution regulations. Indeed, as the auto industry illustrates, the actual cost of compliance is likely to be slight and may even result in better, more profitable products. The impact on small businesses will be to push them to use packaged software – yes, including Customer Data Platforms – that have regulatory compliance built in by experts. The analogy here is with financial and human resources packaged software, which similarly provides built-in compliance with government and industry standards.

Of course, if Google, Facebook, and a handful of others take near-total control over access to customers, there won’t be much data for anyone else to manage. But it seems likely that companies will find ways around those toll booths, especially when dealing with customers who have already purchased their products. Ironically, this would return marketers to the situation that existed before the Internet, when data on prospects was limited but customers could be reached directly. That might put a small crimp in martech growth but would still leave plenty of room for innovation.

Saturday, May 06, 2017

Martech Vendors Can't Avoid Ad Audience Battles

It’s been said that sports are soap operas for men. You can see business news the same way: a drama with heroes, villains, intertwining story lines, and endless plot twists. One of the most interesting stories playing out right now is online advertising, where the walled gardens of Google, Facebook, and other audience aggregators are under assault by insurgent advertisers who, like most rebels, aspire as much to replace their overlords as destroy their power. What they’re really fighting over is control of the serfs – oops, I meant consumers – who create the empires' wealth.

Recent complaints about ad measurement. audience transparency, and even placement near objectionable Web content are all tactics in the assault, aimed both at winning concessions and weakening their opponents. More strategically, support for letting broadband suppliers resell consumer data is an attempt create alternative suppliers who will strengthen the insurgents’ bargaining position.

Yet another front opened up last week with an announcement from a consortium of adtech vendors, including AppNexus, LiveRamp, MediaMath, Index Exchange, LiveIntent, OpenX, and Rocket Fuel, that they had created a standard identity framework to support personal targeting of programmatic ads. The goal was to strengthen programmatic’s position as an alternative to the aggregators by making programmatic audiences larger, more targetable, and more unified across devices.

The consortium was quite explicit on this goal. To quote the press release:

"Today, 48 percent of all digital advertising dollars accrue to just two companies – Facebook and Google," said Brian O'Kelley, CEO of AppNexus. "That dynamic has placed considerable strain on the open internet companies that generate great journalism, film, music, social networking, and information. This consortium enables precision advertising comparable to that of Google and Facebook, and does so in a privacy-conscious manner. That means better outcomes for marketers, greater monetization for publishers, and more engaging content for consumers."

But behind the rallying cry, the alliance between advertisers and programmatic ad suppliers is uneasy at best. After all, programmatic threatens the core ad buying business of the agencies and faces its own problems of measurement and objectionable ad placement. How the two groups cooperate against a common enemy will be a story worth watching.

Martech vendors have so far remained pretty much neutral in the ad wars, feeding audiences to both sides with the pragmatic indifference of merchants throughout history. But the new ad tech consortium brings the battle closer, since it involves the personal identities that have been the martech vendors’ stock in trade. In particular, LiveRamp (which links anonymous cookies to known identities) belonging to the consortium creates a connection that will likely pull in other martech players. Of course, the convergence between adtech and martech has long been predicted – it's more than two years since I oh-so-cutely christened it “madtech”  and the big marketing clouds started to  purchase data management platforms and other adtech components even earlier.  The merger is probably inevitable as programmatic advertising looks more like personalized marketing every day.  Martech vendors have growing reason to side with the programmatic alliance as it becomes clear that audience aggregators could threaten their own kingdoms by cutting off access to personal data and taking control of contact opportunities.

In short, what seems like a remote, and remotely entertaining, conflict in adland is more closely connected to the central martech story than you may think. So it’s worth watching closely and deciding what role your business will play when they call your cue
.



Thursday, April 27, 2017

Infusionsoft Announces Freemium Marketing Automation to Expand Its User Base

Infusionsoft has always presented combined methodical management of its own business with evangelical cheerleading for its small business clients. The contrast was even greater than usual at the company’s annual ICON conference in Phoenix this week. For Infusionsoft managers, the big news was a new product called Propel, which delivers prepackaged programs for business owners who don’t want to get involved in the details of marketing. For attendees, who were a largely partners and power users, the most exciting announcements were improvements to the current product such as a vastly better Web form builder. Propel will let Infusionsoft serve business owners who find the current product too complicated or expensive. Pretty much by definition, those people weren’t at ICON. So while Infusionsoft managers and some far-sighted partners were almost giddy about the growth that Propel could create for their businesses, the larger audience was more interested in ICON’s usual training sessions and inspirational hoopla.

Propel addresses a fundamental problem that has limited the growth of all small business sales and marketing systems: the vast majority of small business owners don’t have the time, money, skills, or interest to use them well.  Vendors have addressed this either by reducing the required effort through easier-to-use interfaces, content templates, and prebuilt campaigns, or by providing services that do the work on business owners’ behalf.  Infusionsoft has done both and also used a relatively expensive mandatory start-up package ($999 or higher) to screen out buyers who aren't serious about using the system. This has worked well for Infusionsoft – the company has grown steadily, although it no longer releases client counts as it positions itself for an as-yet-unscheduled Initial Public Offering. But it also limits the market to the most aggressive small business owners.

Infusionsoft sees Propel as a third way to serve less-ambitious businesses: not just by making the product simpler to use, but by removing some tasks altogether. For example, prebuilt campaign templates typically require users to create or customize the actual content, and often require them to set up campaign flows following cookbook-style directions. Propel will include default content tailored to a particular industry or product. It will automatically scrape a client’s Web site to find a logo and brand colors and apply them. When customization is unavoidable, Propel will let campaign designers build wizards that ask users key questions. The system will then automatically adjust the campaign by inserting relevant information or changing the campaign flow. The goal is campaigns that can be set up in a few minutes with no training and deliver immediately visible benefits. Infusionsoft hopes these will entice business owners who don’t want to commit from the start to a long-term marketing plan.

The success of this approach is far from certain.  Business owners must still take some initial steps that could be daunting. Infusionsoft managers are acutely aware of the issues and doing everything they can to remove start-up barriers. This includes making it easy to import existing email addresses from phone contact lists, personal email accounts, spreadsheets, accounting systems, or elsewhere. More radically for Infusionsoft, there will be a free version of the system and no start-up fee. This will clearly attract a new set of less-committed users. Delivering enough value for these to stick with the system will be difficult. So will making the system so easy to use that customer support costs are close to zero. It could be hard for Infusionsoft’s entrepreneur-loving staff to limit the help they give to new clients.

Infusionsoft also announced two other major changes during the conference. The simpler one was “partner first”, which translates to relying more on partners to train new clients and provide on-going support.  This will let Infusionsoft support more customers without expanding its internal staff and help to attract more partners.  Propel supports "partner first" by letting partners build their own packaged campaigns and sell them directly to their own clients or in a marketplace to all Infusionsoft clients. Although “partner first” would make sense even without Propel, leveraging partners is a key way for Infusionsoft to grow quickly while keeping costs down and margins high. The company said the proportion of new clients trained by partners has already moved from 20% to over 80%.

The second change was technical: Infusionsoft has built a new data structure and services oriented architecture that can more easily synchronize data with other systems, especially in real time. The slogan for this is “from all-in-one to one platform”. (Infusionsoft loves its slogans.)  Infusionsoft has always been clear that it won’t build a truly complete set of functions: for example, it limits its ecommerce to a relatively simple shopping cart and doesn’t provide its own Web content manager. So the real change is that the underlying data model now includes data expected from external systems. This will simplify integration with new systems and make the imported data easily available for Infusionsoft campaigns and reporting. The goal is for Infusionsoft to be the “one place” that users look for all their data, making life simpler for users and, of course, ensuring that Infusionsoft has a central role their business operations. The new platform also supports Propel by exposing more functions to build into packaged campaigns and adds some partner-friendly features such as unified access to all instances belonging to a partner’s clients.

Infusionsoft staff made scattered references to using artificial intelligence inside Propel to make recommendations and implement some changes automatically. That would be the ultimate in work reduction.  But they didn’t talk about AI in any detail, perhaps because it’s still in early stages and perhaps because it could scare some users and partners. Or both.

Current customers will have some access to Propel in May or June, and new customers will be placed on the new platform starting mid-summer. Existing customers will be migrated to the new platform in stages through the end of 2018.


Wednesday, April 19, 2017

Here's Why Airlines Treat Customers Poorly

Last week’s passenger-dragging incident at United Airlines left many marketers (and other humans) aghast that any company could purposely assault its own customer. As it happens, airline technology vendor Sabre published a survey of airline executives just before the event. It confirms what you probably suspected: airline managers think differently from other business people.  And not in a good way.


The chief finding of the study is that the executives rated technology as by far their largest obstacle to improving customer experience. This is very unusual: as I wrote in a recent post, most surveys place organizational and measurement issues at the top of the list, with technology much less of an issue. By contrast, the airline executives in the survey– who were about 1/3 from operations, 1/3 from marketing, sales, and service, and 1/3 from other areas including IT and finance – placed human resources in the middle and organizational structure, consensus, and lack of vision at the bottom.  The chart below compares the two sets of answers, matching categories as best I can.



It would be a cheap shot to point out that the low weight given to “lack of vision” actually illustrates airline managers’ lack of vision. Then again, like everyone else who flies, I’ve been on the receiving end of many cheap shots from the airlines. So I’ll say it anyway.

But I’ll also argue that the answers reflect a more objective reality: airlines are immensely complicated machines whose managers are inevitably dominated by operational challenges. This is not an excuse for treating customers poorly but it does explain how easily airline leaders can focus on other concerns. Indeed, when the survey explicitly asked about priorities, 51% rated improving operations as the top priority, compared with just 39% for aligning operations, marketing and IT, and only 35% for building customer loyalty.

There’s a brutal utilitarian logic in this: after all, planes that don’t run on time inconvenience everyone. The study quotes Muhammad Ali Albakri, a former executive vice president at Saudi Arabian Airlines, as saying, “Two aspects generally take precedence when we recover irregular operations [such as bad weather]: namely crew schedules and legality and aircraft serviceability. Passengers’ conveniences and connecting passengers are also taken into consideration, depending on the situation.” In context , it’s clear that by “situation” he means whether the affected passengers are high-revenue customers.

But as you may remember from that college philosophy course, most people reject pure utilitarianism because it ignores the worth of humans as individuals. Even if you believe businesses have no ethical obligations beyond seeking maximum profit, it’s bad practice to be perceived as heartless beasts because customers won’t want to do business with you. So airlines do need to make customer dignity a priority, even at the occasional cost of operational efficiency. Otherwise, as the United incident so clearly illustrates, the brand (and stock price) will suffer.

If you’re a truly world-class cynic, you might argue that airlines are an oligopoly, so customers will fly them regardless of treatment. But it’s interesting to note that the Sabre paper makes several references to government regulations that penalize airlines for late arrivals and long tarmac waits. These factors clearly influence airline behavior. There's even a (pitifully slim) chance that Congress will respond to United's behavior. So the balance between operational efficiency and customer experience isn’t fixed. Airlines will react to political pressures, social media, and even passenger behaviors. The fierce loyalty of customers to airlines that have prioritized customer experience, such as Southwest and Virgin America, should be lesson to the others about what’s possible. That those airlines have had very strong leaders who focused on creating customer-centric cultures highlights the critical importance of “vision” in producing these results.

In short, the operational challenges of the airline industry are extreme but they’re not an excuse for treating customers poorly. Visionary leaders have shown airlines can do better. Non-visionary leaders will follow only when consumers demand better service and citizens demand governments protect them.


Thursday, April 13, 2017

Monetate Adds Machine-Learning Based Real Time Ecommerce Personalization

Monetate is one of the oldest and largest Web testing and personalization vendors, founded in 2008 and now serving more than 350 brands. Its core clients have been mid-to-large ecommerce companies, originally in the U.S. and now also in Europe. I’ve been meaning to write about them for some time but when we finally connected late last year they had a major launch coming this April, so it made sense to hold off a little longer.

That day has come. Monetate last week announced its latest enhancement, a machine-learning-powered “intelligent personalization engine” that supplements its older, rules-based approach. Machine learning by itself isn’t very exciting today: pretty much everybody seems to have it in some form. What makes the launch so important for Monetate is they had to rebuild their system to support the kind of machine learning they’re doing, which is real-time learning that reacts to each visitor’s behaviors as they happen,

Montetate now holds its data in a “key-value store” (meaning, instead of placing data into predefined tables and fields, it stores each piece of information with one or more identifiers that specify its nature). This is a “big data” approach that lets the system add new types of information without creating a new table or field. In practical terms, it means Monetate can give each client a unique data structure, can rapidly add new data types and individual pieces of data, and can maintain a complete, up-to-the-moment profile for each customer. These are all essential for real-time machine learning. (Of course, the system still has some standard events shared by all clients, such as orders and customer service calls. These are needed to allow standard system functions.)

Important as these changes are, the basic operation of Monetate is still the same. First, it builds a database of customer information. Then, it draws on that database to help test and personalize customer experiences.

The database is built using Monetate’s own Javascript tags to capture behavior on the client’s ecommerce site. Users can also add other first- and third-party data through file uploads, by monitoring real-time data streams, or by querying external sources on demand. Monetate stitches together customer identities across sources and devices to create a complete profile. It can also build a product catalog either by scraping product information directly from the Web site or by importing batch files. Customer browsing and purchase behavior are matched against this catalog.

Testing and personalization rely on Monetate’s ability to modify each visitor’s Web experience without changing the underlying Web site. It achieves this magic through the previously-mentioned Javascript tag, which can superimpose Monetate-created components such as hero images, product blocks, and sign-up forms. Users manage this process by creating campaigns, each of which contains a user-specified target audience, actions to take, schedule, and metrics. Users can designate one metric as the campaign goal; this is what the system will target in testing and optimization. They can track additional metrics for reporting purposes.

The campaign audience can be based on Monetate’s 150 standard segments or draw on Web site behaviors, visitor demographics, local weather, imported lists, customer value, or other information derived from the database. Actions can virtually insert new objects on a Web page, or hide or edit existing objects. Users can build content with Monetate’s own tools or import content created in other systems. The content itself is dynamic so it can be personalized for each visitor. Actions can be reused across campaigns and campaigns can contain rules to select different actions in different situations. The new intelligent personalization engine automatically picks the best available content for each customer, drawing on both individual and group behaviors. Users can also embed split or multivariate tests within a campaign. The system will reallocate traffic to better-performing options while the test is running and switch all traffic to the winner when enough information is available.

In other words, this is a very powerful system.  The user interface is also remarkably, well, usable: some training is certainly required but no deep technical skills are needed.

Monetate’s intelligent personalization is currently limited selecting content for Web interactions. The company plans to add product recommendations later this year (finding the best product among thousands is a different challenge from finding the best content among dozens or hundreds). It will add support for other channels next year.

Pricing for Monetate has also changed with the new product. It was previously based on page views but is now based on unique visitors and number of channels. This reflects a desire to stress customer value over individual decisions. Fees start around $100,000 per year for a small to mid-size company.