Software Has Stopped Eating the World

This August will see the tenth anniversary of Marc Andreessen’s famous claim that software is eating the world. He may have been right at the time but things have now changed: the world is biting back.

I’m not referring to COVID-19, although it’s fitting that it took an all-too-physical virus to prove that a digital bubble of alternate facts could not permanently displace reality. Nor am I juxtaposing the SolarWinds hack with the unexpectedly secure U.S. election, which showed a simple paper trail succeed while the world’s most elite computer security experts failed.

Rather, I’m looking at the most interesting frontiers of tech innovation: self-driving vehicles, green energy, and biosciences top my list. What they have in common is interaction with the physical world. By contrast, recent years haven’t seen radical change in software development. There have certainly been improvements in software, but they’re more about architectures (cloud, micro-services) and self-service interfaces than fundamentally new applications. And while most physical-world innovations are powered by software, the importance of those innovations is that they are changing physical experiences, not that they are replacing them with software-based virtual equivalents.

Even the most important software development of all – artificial intelligence – measures much of its progress by its ability to handle physical-world tasks such as image recognition, autonomous vehicle navigation, and recognizing human emotion. Let’s face it: it’s one thing for a computer to beat you at Go, but quite another for it to beat your dance moves.  Really, what special talent is left for humans to claim as their own?

The shift is well under way in the world of marketing. One of the more surprising developments of the pandemic year was the boom in digital out-of-home advertising, which includes outdoor billboards and indoor signage. The growth seemed odd, given how much time people were forced to spend at home. But the industry marched ahead, spurred in good part by increased ability to track devices as they move through the physical world. It’s a safe bet that out-of-home ads will grow even faster once people can move about more freely.

Indeed, the industries hit hardest by the pandemic – travel and events – also show that virtual experiences are not enough. Whatever their complaints before the pandemic, almost everyone who formerly traveled for business or attended business events is now eager to return to seeing people and places in person. The amount of travel will surely be reduced but it’s now clear that some physical interaction is irreplaceable.

In a similarly ironic way, the pandemic-driven boost to ecommerce has been accompanied by a parallel lesson in the importance of physical delivery. Almost overnight, fulfillment has gone from a boring cost center to a realm of intensive competition, innovation, and even a bit of heroism. Software plays a critical role but it’s a supporting actor in a drama where the excitement is in the streets.

Still closer to home for marketers, we’ve seen a new appreciation for the importance of customer experience, specifically extending past advertising to include product, delivery, service and support. If the obsession of the past decade has been targeted advertising, the obsession of the next decade will be superior service. This ties into other trends that were already under way, including the importance of trust (earned by delivering on promises through fulfillment, not making promises in advertising) and the shift from prospecting with third party data to supporting customers with first party data. Even at the cutting edge, advertising innovation has now shifted to augmented reality, which integrates real-world experiences with advertising, and away from virtual reality, which replaces the real world entirely.

This shift has substantial implications for martech.

- The endless proliferation of martech tools may well continue, especially if the definition of “tools” stretches to include self-built applications. But the importance of tools that only interact with other software will diminish. What will grow will be tools that interact with the real world, and it’s likely those tools will be harder to find and (at least initially) take more skills to use. It’s the difference between building a flight simulator game and an actual aircraft. The stakes are higher when real-world objects are involved and there’s an irreducible level of complexity needed to make things work right.

- As with all technology shifts, the leaders in the old world – the big software companies and audience aggregators like Facebook and Google – won’t necessarily lead in the new world. Reawakened anti-trust enforcement comes at exactly the worst moment for big tech companies needing to pivot. So we can expect more change in the industry landscape than we’ve seen in the past decade.

- New skills will be needed, both to manage martech and to do the marketing itself. The new martech skills will involve learning about new technologies and tighter integration with non-marketing systems, although fundamentals of system selection and management will be largely the same. The marketing skill shift may be more profound, as marketers must master entirely new modes of interaction. But, again, the marketer’s fundamental tasks – to understand customer motivations and build programs that satisfy them – will remain what they always were.

It’s been said that people overestimate short-term change and underestimate long-term change.  The shift from software to physical innovation won’t happen overnight and will never be total. But the pendulum has reversed direction and the world is now starting to eat software. Keep an eye out for that future.

Salesforce Buys Evergage But Not For CDP

The CDP Institute published its semi-annual Industry Update report today, which you download here for free. Although every word and image in the report is a jewel, there’s no question that the main story in this edition is CDP industry consolidation. Events in the past six months (stretching a bit to include early January 2020) include seven new funding rounds, three acquisitions of CDP vendors, four acquisitions by CDP vendors, and four asset sales by CDP companies.  Asset sales aside, these are all ways for companies to strengthen their business more quickly than organic growth permits.

What’s particularly intriguing is the industry position of the firms in these deals.  Using our best guess at CDP employment for each vendor, only one of the twelve vendors involved funding or either side of an acquisition is among the industry’s five largest (SessionM, bought by Mastercard). The rest all ranked within the fairly narrow band from number eight to number thirty (of 101 total). That is, they were bigger than most but not the industry's largest.  I interpret this to mean that these vendors were either adding resources for a push to reach the industry top tier or have already decided they need to be part of something else.  

Notably, the firms that engaged in asset sales were much smaller: only IgnitionOne would have fallen within the top thirty and their deal might be considered more of an acquisition, since Zeta Global is apparently still selling the product.

Careful readers will have already noticed that this chart includes one other deal: acquisition of Evergage by Salesforce, announced on Monday.  Evergage fits into the size range of the other deals and the sale can certainly be seen as an escape from the crowded campaign CDP space. But the purchase is otherwise atypical because Salesforce has stressed that they are primarily interested in Everage for real time interaction management and personalization.  Of course, Salesforce is already far along in work on its own CDP, the Customer 360 Audiences component of Customer 360 Truth, which is due for general release around June. So this deal has little to do with Evergage as a CDP.  It's about closing a gap elsewhere in the Salesforce product line, not a sudden acceleration of Salesforce’s entry into the CDP space.

Consumers Aren't As Into Personalization As You Think, and Other Survey Results

I see a lot of surveys -- easily a dozen each week.  Mostly they go into a big file which I mine occasionally for factoids to spice up a paper or presentation.  Sometimes I take a more thorough tour to look at some bigger issues.  Today is one of those days.

Specifically, I was prepping for a presentation in Amsterdam, which meant I needed to present general industry trends and then see what is different in Europe.  This turned out to be pretty interesting.  But I assembled vastly more data than I could include in any presentation where the audience was not chained to their seats (frowned upon by EU regulators).   So I'm sharing it all here with you instead.

(I've also packaged it all in a paper for the Customer Data Platform Institute, available here.  Much more convenient than copying this blog post if you want a reference copy.)

Note that there's more information on sources at the end of this post.  For now, let's just get to the good stuff.

Consumer Attitudes: Personalization

If marketers hold any truth to be self-evident, it’s that today’s consumers want and expect personalization. The reality is a bit different and depends greatly on the definition of “personalization”. The majority of consumers believe they receive personalized service, but many fewer expect personalized experiences. What they do expect is consistent service, shared information, and being identified as repeat customers. In other words, they expect you to know who they are and to use that data to serve them – for example, by being aware of past purchases and problems. But they don’t necessarily expect you to make personalized offers or otherwise personalize their experience.

We do see quite consistently that European consumers have lower expectations for all kinds of personalization.

Whether or not consumers expect personalization, it can still be a competitive advantage to provide it. The majority of consumers do say they’re more loyal to brands that understand them and provide good service, and more likely to stop doing business with brands with poor service. But, again, the focus seems to be more on service than proactive personalization: barely one quarter of consumers said that anticipating needs is the most important part of personalization. This may come as a shock to marketers who have put anticipating needs at the top of their list of reasons to do personalization.

These results shouldn’t be read as a reason to ignore customer needs. Companies get more revenue when they offer customers what they want, whether or not the customer expects it.

We again see that European consumers place slightly less weight than U.S. consumers on personalization, although the difference is less pronounced than with expectations. One interpretation would be that European consumers don’t expect personalized treatments and thus don’t factor it into their behavior.

 
Consumer Attitudes: Privacy

Marketers know they need to balance personalization against privacy. We’ve just seen that consumer interest in personalization may not be quite as high we thought. By contrast, consumers show great interest in privacy, both in general and specifically in relation to marketing. More than three-quarters don’t want companies to market to them based on personal data. Fewer than half would trade their data for personalized service, even though that’s the reason most companies give for collecting it. Although European consumers show slightly less concern about privacy in general, they are more opposed than U.S. consumers to letting companies use their data for marketing. This is consistent with the personalization results: if European consumers place less value on personalization, it makes sense that they’d be less willing to share their personal data to enable personalized treatments.

Looking beyond personalization to the broader question of trust, we again see that Europeans place less trust in business than U.S. consumers. An astonishing 68% believe brands sell their data. This may reflect the attention drawn to data sharing by the European Union’s General Data Protection Regulation (GDPR). Europeans' lack of trust in most business may also explain why they are more likely to support brands that do show high purpose.

Marketing Technology

Now let’s turn to marketers. Most European marketers will tell you that their region is behind the U.S. in adoption of advanced marketing technology. European consumer perceptions of less personalization support this. The data here do show that European marketers use fewer data sources and channels for most purposes, although the figure for inputs to attribution is higher. The differences are relatively small with the significant exception that Europeans report using personalization in 20% fewer channels (4.1 vs 5.1) than U.S. marketers.

The gap is larger when we focus specifically on data integration. European marketers are much more likely to cite challenges with linking multiple data sources, more likely to see linking data as the reason to deploy a Data Management Platform, and more likely to avoid a DMP because the technology is too complex. While integration is a substantial problem for many U.S. based marketers, it’s clear the pain is greater in Europe – despite having slightly fewer data sources to integrate.

The same pattern holds for marketing technology in general. European marketers spend a slightly smaller share of their marketing budget on martech and a slightly smaller share of their martech budget on data and analytics. But while those differences are fairly small, U.S. marketers expect much higher growth in their 2019 martech budgets. This is a significant indicator of attitudes regardless of what actually happens. Similarly, European marketers show consistently but slightly lower adoption of advanced marketing systems such as DMP, cross-channel engagement, and flexible attribution models.

Marketing Maturity

Looking beyond technology, we see that U.S. and European marketers share a high level of belief in personalization. But European marketers rank lower on other measures that indicate maturity. It’s particularly intriguing that European marketers are less likely than U.S. marketers to be prioritizing first party data, even though GDPR is generally assumed to make first party data more important.

In sum, the belief that European marketers are using less advanced technology than U.S. marketers appears to be correct.

 
Leaders vs Mainstream

What separates the most successful marketers from the rest? This data, all from the same survey, found that high performing marketing departments were twice as likely to be responsible for technical activities related to customer data: operations, governance, security, and schemas. This suggests that marketers do in fact get better results when they have more control over their customer data. By contrast, leading and mainstream departments had similar responsibility levels for traditional marketing activities such as automation rules, data acquisition, and analytics.

It’s important to qualify this message. Even among leading marketing departments, the majority do not have technical responsibilities. So clearly success is possible under other arrangements. It’s also important to recognize that marketing and IT will almost always share some responsibilities. And they should.

Other leader vs mainstream comparisons provide more insight into the challenges faced at different maturity levels. Mainstream marketers are more likely than leaders to cite disparate technology as their biggest martech challenge: this suggests that is the first hurdle to cross. Leaders, having started to knit together their systems, are likely to run into organizational barriers next. Once they resolve organizational problems, they can deliver results such as a single customer view and quantifying the benefits of personalization and real time marketing. Few mainstream marketers, still fighting technical and organizational battles, are able to accomplish these.

Some markers show much less correlation with leadership. Mainstream marketers are nearly as likely as leaders to lead customer experience initiatives and to run real time interactions in at least one channel. Note that single channel real interactions do not require unified customer data or any type of shared systems. So they are not by themselves an indication of maturity.


Customer Data Platforms

Finally, we’ll look at some information specifically related to Customer Data Platforms. The table below compares CDP selection priorities for enterprise vs mid-tier buyers. It supports the common belief that these groups have different concerns. Enterprise marketers give higher priority to data security and integrating data from many sources, including third party data. Mid-market buyers also rank security as their top concern but then look for help with internal data and for data analysis tools. These are probably problems that enterprises have already solved. One implication is that CDP vendors may find themselves specializing in one or the other type of buyer so they can optimize their systems for the different needs.

I also have several surveys that asked about CDP deployment. Answers vary greatly although the general result suggests that CDP adoption is getting close to DMP adoption. The very low figure from Heinz Marketing reflects the nature of its survey, which asked B2B marketers about tools for marketing analytics and pipeline management. The audiences for the other surveys were more representative but the figures still seem much higher than likely. The CDP Institute’s own estimate is that market penetration for CDPs at the end of 2018 was around 15%.

Note on Sources

This paper draws from surveys with different audiences, survey methods, and sample sizes. The origin of each item is indicated by a number that relates to the list of surveys below.  This list provides some information about each survey, as presented in the survey report.
Data from the original surveys has been processed in several ways:

• Questions have been paraphrased for brevity and clarity.
• European results are averages of country results, which have been weighted in different cases by national population, sample size, or not at all. Different surveys included different countries.
• Some U.S. results include data from all of North America.

Readers should be able to track down the original survey reports on the Internet. I haven't published links because links change too often to be useful.

1 Acquia, Closing the CX Gap: Customer Experience Trends Report 2019. More than 5,000 consumers and 500 marketers.
2 AdRoll, The State of Marketing Attribution, 2017. 987 respondents recruited by email and social media. Majority at director/manager level.
3 Aspect, 2017 Aspect Consumer Experience Index. Online survey with 1,000 aggregate U.S. sample and similar in Germany, Spain, United Kingdom.
4 Econsultancy, The Customer Data Imperative, 2018. 509 online survey respondents, primarily at large B2C brands. Mix of marketing, IT, and operations.
5 Edelman, 2018 Edelman Trust Barometer. 33,000_ online survey respondents across 28 countries.
6 ExchangeWire, Adoption vs Execution: How Media Agencies Across the Globe Are Making the Most of their DMP’s Capabilities, 2017. 470 agency professionals.
7 Frost & Sullivan, The Global State of Online Digital Trust, 2018. 990 survey responses.
8 Gemalto, Data Security Confidence Index, 2018. 1,050 IT decision makers from organizations with perimeter security systems.
9 GlobalWebIndex, Trends 19, 2019. 91,913 Internet users aged 16-64.
10 Harvard Business Review Analytics Services, The Age of Personalization, 2018. 625 responders from audience of Harvard Business Review readers. Primarily executive/senior management at large enterprises.
11 Heinz Marketing, State of Revenue Marketing, 2018. 241 B2B marketing executives, primarily small to mid-size companies.
12 Infosys, Endless Possibilities with Data, 2018. 1,062 senior executives from organizations with annual revenues exceeding $1 billion.
13 Ipsos+Medallia, The Customer Experience Tipping Point, 2018. 8,002 consumers in U.S., UK, France, Germany.
14 Mulesoft, Consumer Connectivity Insights 2018. 650 IT decision makers at organization with 1,000+ employees.
15 Relevancy Group, CDP Buyers Guide 2018. 406 executive marketers.
16 Salesforce Research, Fifth Edition State of Marketing 2019. 4,101 responses from full-time marketing leaders, primarily mid-size organizations. Mix of B2B and B2C.
17 Sizmek, Marketers Survey Results 2018: An Insider’s Look at Data, Walled Gardens, and Collaboration. 522 B2C brand marketers.
18 Spiceworks, 2019 State of IT, IT Marketing. 780 business technology buyers.
19 Walker Sands, State of Marketing Technology 2018. 300 marketing professionals. Primarily small to mid-size companies.
20 WE Communications, Brands in Motion 2018. Online interview of consumer survey panel totaling 11,000+ in U.S., U.K., and Germany.
21 Winterberry Group, Know Your Audience: The Evolution of Identity in a Consumer-Centric Marketplace, 2018. Online survey of more than 400 advertisers, marketers, fundraisers, publishers, technology developers and marketing service providers.

Not the CDP Daily News

The World Health Organization has just declared that video addiction is a real disease but they've missed something even more insidious: the dangers of newsletter publishing. The CDP Institute Web site has been down for two days now (hopefully it will be back up by the time you read this and test that link), which means I haven't been able to publish the Institute's daily newsletter. (Yikes -- was my authorship a secret?)  This turns out to be very stressful for me, especially since I feel obligated to write the newsletter anyway so I'm ready whenever the site reappears. Gives a whole new meaning to the term "news junkie".

But, like the gun in a Chekov play, any copy that's created is begging to be used. So I'll post yesterday and today's items here for your enjoyment and my relief.  If you don't already subscribe and like what you see, visit the Institute site (once it's running) and join.

June 19, 2018

Google Invests $550 Million in Chinese E-Commerce Merchant JD.com
Source: GlobalNewswire
Just in case you had doubts that Google is serious about competing with Amazon in retail, consider this: Google just invested $550 million in Chinese e-commerce merchant JD.com. Google doesn’t do much business in China so this is about expanding in other markets and listing JD.com as a seller in Google Shopping. Google also announced several enhancements last week that help retailers display their inventory on-line and drive traffic to local stores. See this from The Street for more thoughts on the JD.com deal.

Adobe Expands Attribution Features
Source: Adobe
Adobe has expanded its attribution capabilities with Attribution IQ, an enhancement to Adobe Analytics that estimates the impact of campaigns in all channels on purchases. The offering includes ten different attribution models and lets users drill into results by customer segments, campaigns, and keywords.


IBM Computer Competes Effectively with Human Debaters
Source: CNET
I could tell you about Tru Optik’s Cross-Screen Audience Validation (CAV) service,
which draws on Tru Optik’s 75 million household database of smart TV viewers to give advertisers detailed information on audience demographics, reach and frequency by audience segment. But I doubt you care. So instead, ponder this: an IBM computer is now competing effectively with human debaters, showcasing skills like marshalling facts and choosing the most effective arguments. In other words: you’ll soon be able to argue with Alexa and lose.

June 20, 2018

RichRelevance Launches Next-Generation AI-Based Experience Personalization
Personalization vendor RichRelevance has launched its next generation of AI-based personalization tools. Key features include dynamic assembly of individual experiences, real-time performance tracking and continuous optimization. A helpful “Experience Browser” overlays the client’s Web site to display data, rules, and results for each decision in context. Marketers can set business rules to constrain the AI decisions and data scientists can draw on system data to define custom personalization strategies.


Automated Data Management: Immuta Raises $20 Million and Crate.io Raises $11 Million
Compared with AI-based personalization, automated data management gets relatively little attention, at least in martech circles. But its potential for solving the data unification problem is huge. Immuta, which marshals sensitive data for machine learning projects, just raised a $20 million Series B.
And Crate.io, an open source SQL database to manage feeds from machines and IoT devices, raised an $11 million Series A.  Now you know.

Mobile Phone Operators Take Baby Steps to Protect Location Data
I have a slew of other items about AI being used for cool things including seeing around corners, rendering 3D objects from photos, and delivering packages via two-legged robots (creepy!).  But let’s get back to reality with a report that several mobile operators were recently caught selling location data with little control over how it was used. The good news is that Verizon, AT&T and Sprint have shut off access to the two companies that were identified as misusing it. The bad news is, they’re still selling it to pretty much anyone else. Apple also recently changed App Store rules to limit apps publishers' access to people’s iPhone contact lists.  So maybe this is progress.

Collapse of Civilization Makes Marketers' Jobs Harder

Political situations come and go but trust is the foundation of civilization itself. So I was genuinely shaken to see a report from the Edelman PR agency that trust in U.S. institutions fell last year by a huge margin – 17% for the general public and 34% for the “informed public,” placing us dead last among 27 countries. All four measured institutions (business, media, government, and non-governmental organizations) took similar hits, although government fell the most.

You won’t be surprised to learn that concerns about fake news and social media are especially prominent. What’s less expected is that trust in traditional journalism actually increased in the U.S. The over-all decline in media trust resulted from falling confidence in news from search engines and social media. Similarly, world-wide trust increased in traditional authorities such as technical, academic, and business experts.  So there are rays of hope.

Digging deeper, the sharp fall in U.S. trust levels follows two years when levels were exceptionally high. The current U.S. trust level is roughly the same as the four reports before that. Maybe you shouldn't head for that doomsday cabin quite yet.

Still, other reports also show tremendous doubts about basic questions of truth. A Brand Intelligence study comparing brand attitudes of Democrats vs. Republicans found that eight of top 10 most polarizing brands were news outlets. World-wide, 59% of people told Edelman they were simply not sure what is true and just 36% felt the media were doing a good job of guarding information quality.

The social implications of all this are sadly obvious.  But this blog is about marketing. How can marketers adapt and thrive in a trust-challenged, politically-polarized world?

• Protect privacy. Consumers can be remarkably cavalier in practice about protecting their data: this McAfee report found 41% don’t immediately change default passwords on new devices and 34% don’t limit access to their home network at all. But they are adamant that companies they do business with be more careful: Accenture studies have found that 92% of U.S. consumers feel it’s extremely important for companies to protect their personal information while 80% won’t do business with companies they don’t trust. Similarly, a Pega survey found that 45% of EU respondents said they would require companies to erase their data if they found it had been sold or shared with other companies. 

• Personalize wisely. Accenture also found  that 44% of consumers are frustrated when companies don’t deliver relevant, personalized shopping experiences and 41% had switched companies due to lack of personalization or trust. So there’s clearly a price to be paid for not using the data you do collect. Similarly, an Oracle report found 50% of consumers would be attracted to offers based on personal data while just 29% would find them creepy.  In fact, consumers have a remarkably pragmatic attitude toward their data: 24[7] survey found their number one reason for sharing personal information is to receive discounts. This has two implications: ask consumers whether they want personalized messages (or any messages), and be sure the value of your personalization outweighs its inherent creepiness. 

• Use trusted media. Consumers’ attitudes towards media in general, and social media in particular, are complicated. We’ve already noted that Edelman found growing distrust in online platforms. Other studies by Kantar and Sharethrough found the same. But consumers still spend most of their time on search engines and social media, which GlobalWebindex found remain by far the top research channels. Yet when it comes to building awareness, a different GlobalWebIndex report found that social ranked far behind search engines, TV, and display ads. Further muddying the waters, social and ecommerce companies (Facebook, Amazon, and eBay) topped NetBase’s list of most loved brands while Google ranked just 29th. But love isn’t the same as value: a LivePerson survey of 18-to-34 year olds – presumably the most enthusiastic social media users – found most would delete social apps from their phones before they'd give up practical apps for banking, ride-sharing and shopping. Similarly, The Verge found that Amazon and Google were significantly better liked and trusted than Facebook or Twitter. Taken together, this suggests that marketers need social channels for scale but can’t rely on them for credibility. Indeed, that’s precisely the conclusion of this Trusted Media Brands report about branded video.

• Consider brand safety. The problems with social and display channels extend beyond general mistrust to actively offensive environments. GumGum found that 68% of brands knew their ads have been placed in objectionable environments, with fake news, divisive politics, and disasters heading the list. A Dun & Bradstreet report on programmatic B2B ads similarly found that 66% of brands have found brand safety increasingly important.  Ad fraud is also a major concern – the two are related because they both reflect brands’ loss of control over their ad placements. This Forrester report on mobile advertising found 69% of marketers felt at least 20% of their budgets were exposed to found mobile ad fraud. Yet all three studies found marketers were plunging ahead with just limited efforts at brand safety and fraud prevention. In a world where consumer trust is tenuous to begin with, this is a very high-stakes gamble.

• Be careful about politics.  Edelman found that 64% of people want business CEOs to lead change rather than waiting for government to impose it. Sprout Social reported a similar finding:  65% of U.S. consumers felt brands should take a stand on social/political issues and 59% felt CEOs in particular should step in. But Euclid found the opposite: 78% said brands should avoid making political statements. Even more extreme, Bambu found just 2.3% of consumers said posting political content would make them more likely to buy from a salesperson while 34.9% said posting political content was a deal breaker, regardless of whether they agreed.  Still, the real danger is taking a position the customer dislikes: Bambu, Euclid and Sprout all found consumers are likely to boycott firms based on their positions. Sprout noted some compensating gain from people who agree but the net benefit is questionable at best: people are slightly more likely to praise a brand when they agree (28%) than criticize when they disagree (20%). But fewer will recommend it (35%) than warn friends and family (38%) and, most critically, fewer will purchase more (44%) than purchase less (53%).   In short, the data here are wildly conflicting: people want businesses to lead but they’ll punish behaviors they don’t like as often as they’ll reward choices they agree with. Of course, widely popular positions are still safe but many issues today have large numbers of people on both sides.  And remember it’s still possible to annoy everyone: Brand Intelligence found that Democrats, Independents, and Republicans all had Diet Pepsi (Kendall Jenner commercial, presumably) and Diet Mountain Dew (I don’t know why) on their most disliked lists. The ultimate result is probably that business leaders can justify being as active or inactive as they personally prefer.

So there you have it. Assuming we avoid complete social collapse, marketing in today’s polarized, anxiety-ridden world poses unprecedented challenges. Ironically, the loss of trust is happening at the precise moment when physical products are being replaced by trust-based services such as subscriptions and automated recommendations. The stakes couldn’t be higher.  Choose carefully and good luck.

SessionM Expands from Loyalty to Full Customer Engagement Management

SessionM launched in 2012 as a platform that increased user engagement by adding gamification and loyalty rewards to mobile apps. The system has since expanded to support more channels and message types. This puts it in competition with dozens of other customer engagement and personalization systems. Compared with these vendors, SessionM’s loyalty features are probably its most unusual feature.  But it would be misleading to pigeonhole SessionM as a system for loyalty marketers. Instead, consider it a personalized messaging* product that offers loyalty as a bonus option for marketers who need it.

In that spirit, let’s break down SessionM’s capabilities by the usual categories of data, message selection, and delivery.

Data: SessionM can gather customer behaviors on Web and mobile apps from its own tags or using feeds from standard Web analytics tools. It can also ingest data from other sources such as a Customer Data Platform or CRM system. Customer data is organized into profiles and events, which lets the system store nearly any type of information without a complex data model.  SessionM can also accommodate non-customer data such as lists of products and retail stores. It can apply multiple keys to link data related to the same customer, but requires exact matches. This works well when dealing with known customers, who usually identify themselves when they start using a sytem. Finding connections among records belonging to anonymous visitors would require additional types of matching.

Message Selection: SessionM is organized around campaigns.  Each campaign has a target audience, goal (defined by a query), outcome (such as adding points to an account or tagging a customer profile), message, and “execution” (the channel-specific experience that includes the message). SessionM describes the outcome as primary and the message as following it: think of notification after you've earned an award. Non-loyalty marketers might think of the message as coming first with the outcome as secondary. In practice, the order doesn’t matter.

What does matter is that campaigns can include multiple messages, each having its own selection rules. Message delivery can be scheduled or triggered by variables such as time, frequency, and customer behaviors. This means a SessionM campaign could deliver a sequence of messages over time, even though the system doesn’t have a multi-step campaign builder.  Rules can draw on machine learning models that predict content affinity, churn, lifetime value, near-time purchase, and engagement. Clients can use the standard models or tweak them to fit special needs. Automated product recommendations are due later this year.  Messages are built from templates that can include dynamic elements selected by rules or models.

Delivery: Campaign messages are delivered through widgets installed in a Web page or mobile app, through lists sent to email providers or advertising Data Management Platforms (DMPs), or through API calls from other systems such as chatbots. Multiple campaigns can connect through the same widget, which raises the possibility of conflicts.  At present, users have to control this manually through campaign and message rules. SessionM is working on a governance module to manage campaign precedence and limit the total number of messages.

The system can generate presentation-ready messages or send data elements for the delivery system to transform into the published format. It supports real time response by loading customer profiles into memory, limiting itself to information required by active campaigns. External systems can access the customer profiles directly through JSON API calls or file extracts, but not through SQL queries.

About that loyalty system: it’s sold as a separate module, so only people who need it have to pay for it. It includes the features you’d expect: points, promotions, awards, status tiers, reward redemption, and so on.  SessionM added the ability to deliver and redeem personalized coupons through retail Point of Sale systems when it bought LoyaltyTree in December 2016,

SessionM has about 70 clients. The company originally sold to large enterprises, which are still about half its customer base. It is now pursuing mid-market clients more actively. The company has raised $73.5 million in funding.


_______________________________________________________________________
* You’ll note that I’m using “customer engagement”, “personalization”, “messaging”, and other system categories interchangeably. It’s probably possible to distinguish among them, but, in practice, all assemble a customer profile, use rules to select messages for individuals, and deliver those messages through execution systems such as Web sites. Most marketers will want to pick just one system to do this sort of thing, so they’ll evaluate vendors from all those classes against each other. This makes distinguishing between them largely an academic exercise.

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.

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.

BlueVenn Bundles Omnichannel Journey Management, Personalization, and Single Customer View

BlueVenn has only been active in the U.S. market only since March 2016, although many U.S. marketers will recall its previous incarnation as SmartFocus.* The company offers what it calls an omnichannel marketing platform that builds a unified customer database, manages marketing campaigns, and generates personalized Web and email messages.

The Venn in BlueVenn

The unified database process, a.k.a. single customer view, has rich functionality to load data from multiple sources and do standardization, validation, enhancement, hygiene, matching, deduplication, governance and auditing. These were standard functions for traditional marketing databases, which needed them to match direct mail names and addresses, but are not always found in modern customer data platforms. BlueVenn also supports current identity linking techniques such as storing associations among cookies, email addresses, form submits, and devices. This sort of identity resolution is a batch process that runs overnight.  The system can also look up information about a specific customer in real time if an ID is provided. This lets BlueVenn support real time interactions in Web and call center channels.

Users can enhance imported data by defining derived elements with functions similar to Excel formulas. These let non-technical users put data into formats they need without the help of technical staff. Derived fields can be used in queries and reports, embedded in other derived fields, and shared among users. To avoid nasty accidents, BlueVenn blocks changes in a field definition if the field is used elsewhere. Data can be read by Tableau and other third-party tools for analysis and reporting.

BlueVenn offers several options for defining customer segments, including cross tabs, geographic map overlays, and flow charts that merge and split different groups.  But BlueVenn's signature selection tool has always the Venn diagram (intersecting circles).  This is made possible by a columnar database engine that is extremely fast at finding records with shared data elements. Clients could also use other databases including SQL Server, Amazon Redshift (also columnar), or MongoDB, although BlueVenn says nearly all its clients use the BlueVenn engine for its combination of high speed and low cost.

Customer journeys - formerly known as campaigns - are set up by connecting icons on a flow chart. The flow can be split based on yes/no critiera, field values, query results, or random groups. Records in each branch can be sent a communication, assigned to seed lists or control groups, deduplicated, tagged, held for a wait period or until they respond, merged with other branches, or exit the flow. The “merge” feature is especially important because it allows journeys to cycle indefinitely rather than ending after a sequence of steps. Merge also simplifies journey design since paths can be reunified after a split. Even today, most campaign flow charts don’t do merges.

BlueVenn Journey Flow

Tagging is also important because it lets marketers flag customers based on a combination of behaviors and data attributes. Tags can be used to control subsequent flow steps. Because tags are attached to the customer record, they can be used to coordinate journeys: one application cited by BlueVenn is to tag customers for future messages in multiple journeys and then periodically compare the tags to decide which message should actually be delivered.

Communications are handled by something called BlueRelevance. This puts a line of code on client Web sites to gather click stream data, manage first party cookies, and deliver personalized messages. The messages can include different forms of dynamic content including recommendations, coupons, and banners. In addition to Web pages, BlueVenn can send batch and triggered emails, text messages, file transfers, and direct messages in Twitter and Facebook. Next year it will add display ad audiences and Facebook Custom Audiences. The vendor is also integrating with the R statistical system for predictive models and scoring. BlueVenn has 23 API integrations with delivery systems such as specific email providers and builds new integrations as clients need them.

All BlueVenn features are delivered as part of a single package. Pricing is based on the number of sources and contacts, starting at $3,000 per month for two sources and 100,000 contacts. There is a separate fee for setting up the unified database, which can range from $50,000 to $300,000 or more depending on complexity. Clients can purchase the configured database management system if they want to run it for themselves. The company also offers a Software-as-a-Service version or hybrid system that is managed by BlueVenn on the client's own computers.  BluyeVenn has about 400 total clients of which about two dozen run the latest version of its system. It sells primarily to mid-size companies, which it defines as $25 million to $1 billion in revenue.

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*The original SmartFocus was purchased in 2011 by Emailvision, which changed its own name to SmartFocus in 2013 and then sold the business (technology, clients, etc.) but kept the name for itself. If you’re really into trivia, SmartFocus began life in 1995 as Brann Viper, and BlueVenn is part of Blue Group Inc. which also owns a database marketing services agency called Blue Sheep. The good news is: this won't be on the final.

ABM Vendor Guide: Features to Customize Messages

Moving along with our series on sub-functions described in the Raab Guide to ABM Vendors, let’s take a look at Customized Messages.

ABM Process	System Function	Sub-Function	Number of Vendors
Identify Target Accounts	Assemble Data	External Data	28
	Select Targets	Target Scoring	15
Plan Interactions	Assemble Messages	Customized Messages	6
	Select Messages	State-Based Flows	10
Execute Interactions	Deliver Messages	Execution	19
Analyze Results	Reporting	Result Analysis	16

According to the Guide:

Vendors in this category build messages that are tailored to the recipient. This tailoring may include insertion of data directly into a message, such as “Dear {first name}.” Or it may use data-driven rules to select contents within the message, such as “show a ‘see demonstration’ button to new prospects and a ‘customer service’ button to current customers”. Systems may also use predictive models rather than rules to select the right message. Customized messages can appear in any channel where the audience is known to some degree – as an identified individual, employee of a particular company, or member of a group sharing particular interests or behaviors.

The Guide lists just a half-dozen vendors in this category. That’s not because there are so few systems that do this: to the contrary, nearly any email, marketing automation, or Web personalization tool would fit the definition. What is rare is ABM specialists who provide this function. That’s because, ultimately, message customization for ABM is pretty much the same as message customization for any other purpose. So the customization vendors in the Guide either provide customization to support a different ABM function such as display advertising (Demandbase, Kwanzoo, Vendemore) or have a broadly-usable customization tool they have targeted at ABM applications (Evergage, SnapApp, Triblio).

Some differentiators to consider when assessing a customization system include:

• types of data made available to use in customization rules (behind the scenes) and in presentation (actually displayed).
• ability to work with individual and account level data for rules and presentation
• complexity of rules that can be used to create customized content
• use of machine learning or predictive models to create customized content (either to select content directly or to use scores within rules that select content)
• channels supported  (emails, Web site messages, display ads, etc.)
• effort and skills needed to set up customized content
• ability to use the same content definition in multiple locations or promotions (some systems tie the content definition directly to a single Web page location or email template; others store the content definitions separately and let any message call them).
• generation of messages in real time during interactions, using data gathered during the interaction
• customization level (are messages unique to each contact, same for all contacts in an account, same for all contacts in a segment such as account industry and/or contact role)
• complexity of created content (single page, multiple pages, interactive content, etc.)
• ability to coordinate messages received by different individuals within an account
• ability to recognize individuals, accounts, locations, etc.

Only a few of these differentiators apply specifically to ABM. Many marketers will be able to use an existing customization system to generate their ABM messages. But for marketers whose current messaging systems lack adequate customization features, a specialized ABM customization system may make sense.