Tuesday, October 06, 2015

Marketers Are Struggling to Keep Up Customer Expectations: Here's Proof

How pitiful is this: My wife left me alone all last weekend and the most mischief I could get into was looking for research about cross-channel customer views. The only defense I can make is I did promise a client a paper on the topic, which I finished Sunday night. But then I decided it was way too wonky and wrote a new, data-free version that people might actually read.

But you, Dear Reader, get the benefit of my crazy little binge. Here’s a fact-filled blog post that uses some my carefully assembled information. (Yes, there was actually much more. I’m so ashamed.) 

Customer Expectations are Rising

Let's start with a truth universally acknowledged – that customers have rising expectations for personalized treatment. Unlike Jane Austen, I have facts for my assertion: e-tailing group's 7th Annual Consumer Personalization Survey found that 52% of consumers believe most online retailers can recognize them as the same person across devices and personalize their shopping experience accordingly. An even higher proportion (60%) want their past behaviors used to expedite the shopping experience, and one-third (37%) are frustrated when companies don’t take that data into account.

Switching to customer service, Microsoft’s 2015 Global State of Multichannel Customer Service Report  found that 68% of U.S. consumers had stopped doing business with a brand due to a poor customer service experience and 56% have higher expectations for customer service than a year ago. So, yes, customer expectations are rising and failing to meet them has a price.

Marketers Know They Need Data

Marketers certainly see this as well. In a Harris Poll conducted for Lithium Technologies, 82% of 300 executives agreed that customer expectations have gotten higher in the past three years.  Focusing more specifically on data, Experian's 2015 Data Quality Benchmark Report, which had 1200 respondents, found that 99% agreed some type of customer data is essential for marketing success. Marketers are backing those opinions with money: when Winterberry Group asked a select set of senior marketers what was driving their investments in data-driven marketing and advertising, the most commonly cited reason was the need to deliver more relevant communications and be more customer-centric. .

Few Have the Data They Need

But marketers also recognize that they have a long way to go. In Experian’s 2015 Digital Marketer study, 89% of marketers reported at least one challenge with creating a complete customer view.

Econsultancy’s 2015 The Multichannel Reality study for Adobe found that just 29% had succeeded in creating such a view, 15% could access the complete view in their campaign manager, 14% integrate all campaigns across all channels, and 8% were able to adapt the customer experience based on context in real time.  In other words, the complete view is just the beginning.  In other words, marketers are nowhere near as good at personalizing experiences as consumers think.

Real-Time Isn't a Luxury

Given the challenges in building any complete view, is real-time experience coordination too much to ask? Customers don’t think so; in fact, as we've already seen, they assume it’s already happening. Marketers, of course, are more aware of the challenges, but they too see it as the goal. In a survey of their own clients, marketing data analysis and campaign software vendor Apteco Ltd found that 12% of respondents were already using real-time data, 31% were sure they needed it and 37% felt it might be useful. Just 17% felt daily updates were adequate.

Real-Time Must Also Be Cross-Channel

It’s important to not to confuse real-time personalization with tracking customers across channels or even identifying customers at all.  In a survey by personalization vendor Evergage, respondents who were already doing real-time personalization were most often basing it on immediately observable, potentially anonymous data including type of content viewed, location, time on site, and navigation behavior.  Yet the marketers in that same study gave the highest importance ratings to identity-based information including customer value, buying/shopping patterns, and buyer persona. It’s clear that marketers recognize the need for a complete customer view even if they haven't built one.


What are we to make of all this, other than the fact that I need to get out more?  I'd summarize this in three points:

- customer expectations are truly rising and you'll be penalized if you don't meet them
- marketers know that meeting expectations requires a complete customer view but few have built one
- the complete view has to be part of a real-time integrated, real-time to deliver the necessary results

None of this should be news to anyone. But perhaps this data will help build your business case for investments to solve the problem.  If so, my lost weekend will not have been in vain.

Tuesday, September 29, 2015

How Many Ads Do You See Each Day? Fewer Than It Seems (I Think)

My cliché detector starts chirping as soon as anyone says today’s marketers face more competition than ever before. Sepia-toned glasses notwithstanding, marketers have had competitors at least since the railroads (or maybe canals) made it practical for customers to shop outside the local village – and for even longer if you lived in a city. So competition has been tough for as long as anyone now living can remember.

I thought I’d found a legitimate exception when writing a recent paper for QuickPivot (available here) about the continued value of direct mail marketing. My core argument was that direct mail is needed to cut through the clutter created by the increase in advertising messages. Surely it’s self-evident that people get more advertising today than ever before, right?

Well, I certainly thought so when I wrote the paper. But I recently searched for some validation of the claim about more clutter.  Turns out that shockingly little research has been published on the question of how many ads people actually receive. The most authoritative-looking study I found was from Media Dynamics in 2014, which found almost no change since 1945 in ads people were exposed to, despite a near-doubling in the time spent with media (defined as TV, radio, Internet, newspapers and magazines).

Really? Just 362 ads per day? That certainly seemed low to me, even after recognizing that the study reports on paid advertising, as opposed to the brand logos you see on everything from football stadiums to a biker’s tattoo. The 362 is nowhere near the widely cited figure of 5,000 per day, although the origins of that are mysterious at best.

I looked further but couldn’t find any better data. Now I was really frustrated and pondered what it would take to make a crude estimate of my own as a simple sanity check. This ultimately led to the bright idea of using total ad spending and cost per thousand impressions to calculate total ad impressions per year. Once you have that number, it can easily be converted to ad impressions per person per day. What I like about this method is avoids any need to estimate how many ads an individual sees per minute of media time or how many ads are theoretically available to be viewed. On the other hand, it assumes that advertisers get exactly the impressions they pay for, neither more nor less.  This is certainly not true in an absolute sense, but I'm willing to trust that the correlation between actual and purchased impressions is close enough to give an answer that's approximately correct.

The table below shows my results. Figures for advertising are easy: eMarketer publishes them all the time.  CPMs are harder to find.  I ended up using figures from sources including INFOACRS (which itself quotes eMarketer, although from 2008) and MonetizePros. There’s a bit of body English in there as well.  I do think the results are either reasonable or low – another widely quoted authority, Augustine Fou, shows generally higher CPM figures than the ones I used, which would result in estimating even fewer impressions from the same spending.

Bottom line, my numbers show 264 impressions per person per day.  That's a little lower than Media Dynamics, but in the same ballpark. Interesting.

I will admit that I’m inordinately pleased with this methodology, which I suspect is roughly correct despite many flaws in the details. One virtue is it sheds some light on the original question of whether people are seeing more ads: since we know that total media spending is rising slightly and the mix is shifting towards the low-CPM digital channel, the number of impressions is almost certainly on the rise. You'd have to adjust any trend calculations for changes in population and in channel CPMs to know for sure. 

On the other hand, the shifts are fairly gradual so it's probably wrong to claim that we're facing a sudden flood of increased advertising.  I don't have the necessary data readily available to do more detailed calculations, but if anyone out there does have the data and the time to do the math, I’d love to see your results.

(And if you're wondering: there will be about 80 billion pieces of advertising mail delivered this year, which comes to about 0.7 per person per day.)

Thursday, September 10, 2015

Data Plus MarTech: HubSpot and Demandbase Join the Race

There were two industry announcements this week that were unexpectedly related. The first was HubSpot’s announcement yesterday that its CRM offerings would now include access to a 19 million account prospecting database. The second was Demandbase’s acquisition of data-as-a-service vendor WhoToo, which offers its own set of 250 million profiles relating to 70 million business processionals.

The WhoToo acquisition marks a big step in the continued evolution of Demandbase, since it's a change from targeting companies to targeting individuals (although DeamdBase still won’t sell you their names). More precisely, WhoToo aggregates audience data from multiple sources and makes it available for selections based on company and individual attributes. The company does know the identity of some individuals and will use these to target email and Web advertising to names you provide. It will also let you market to audiences in those channels without providing their names. This is a nice extension of Demandbase’s existing account-based marketing capabilities. What makes WhoToo really special is it has the technology to access its data with the split-second speed needed to purchase display and mobile ads in real time.

The addition of individual-level targeting puts Demandbase on a more even plane with LinkedIn, which of course already sells advertising to its own huge database of more than 350 million profiles. The WhoToo deal won’t fully close that gap, but it does help to keep Demandbase competitive. (I’m sure Demandbase would argue it has its own advantages over LinkedIn.)

In this context, the HubSpot announcement is interesting mostly because it too recognizes the importance of giving marketers audience lists without acquiring the names for themselves. You could argue this makes HubSpot a player in the super-hot Account Based Marketing category, although they didn't use the term.  If they are, it's ABM-lite, in the sense that HubSpot will give CRM users basic profile information, usually including a phone number, but doesn't offer contact names or email addresses. It also pulls recent news stories.  This is pretty consistent with HubSpot's historic aversion to unsolicited outbound contacts.  The company does approach the line by giving enterprise users an option to find other people in their company who have a contact at target accounts and ask for a warm introduction. 

On the other hand, HubSpot also announced integration with LinkedIn for paid ad campaigns and said a Google AdWords integration is in beta, which are definitely in outbound territory. Naturally, HubSpot says its LinkedIn and Google campaigns will be giving potential buyers information they want, so they are not at all like that bad old interruptive advertising that HubSpot has always opposed. No, not one bit.

Anyway, the point here is that both HubSpot and Demandbase are adding data to their marketing technology, something we’ve seen in other deals like Oracle buying Datalogix. There are still plenty of stand-alone data vendors, especially when it comes to B2B prospecting lists. And there are plenty of vendors who combine prospect data with predictive – including LinkedIn itself since its recent FlipTop acquisition. But I think we can add “data plus tech” to the tote board of martech horse races.

Thursday, August 27, 2015

LinkedIn Buys Fliptop: Why Account Based Marketing and Predictive Analytics Are a Natural Fit

Predictive analytics vendor Fliptop today announced its acquisition by B2B social network LinkedIn.  It's an interesting piece of news but I'm personally disappointed at the timing because I have been planning all week to write a post about the relationship between predictive analytics and account-based marketing (ABM).  I would have looked so much more prescient had they announced the acquisition after I had published this post!

The original inspiration for the planned post was a set of three back-to-back conversations I had last Friday with one ABM vendor and two predictive analytics companies (none of which were Fliptop or LinkedIn).  The juxtaposition highlighted just how much predictive and ABM complement each other.  In fact, the relationship is so obvious that it almost seems unnecessary to lay it out: predictive vendors help marketers find accounts to target; ABM helps marketers reach target accounts.  You can safely assume that both sets of vendors have noticed the relationship and that many are working to combine the two techniques.  The Fliptop/LinkedIn deal is just more evidence of the connection.

To move past the very obvious, ABM vendors – whose basic business is selling ads targeted to specific companies – could also use predictive analytics to refine their ad targeting.  This could mean selecting the best people to reach within targeted accounts or selecting the most effective ad placements to reach those accounts.  This requires integration of predictive analytics within the ABM product, not just using predictive before ABM begins.  I expect LinkedIn will use Fliptop's capabilities in these ways among others.

But, getting back to last week's conversations, what really struck me was a less obvious connection of ABM and predictive to content.  Two of the vendors described using their systems to select which content to send to specific accounts or individuals.  These selections are based on previous behavior, something that certainly makes sense.  But I don't generally recall hearing ABM or predictive vendors discussing as one of their applications.  It's an important idea because it promises to improve results by delivering more relevant content for the same price.  The same data gives marketers insights into broader trends in the types of content that buyers find interesting.

Content analysis requires the ABM or predictive system to be aware of the topics of the content being consumed.  This is only possible if someone specifically goes to the trouble of tagging the content and capturing the tags.  So content analysis is not quite a natural byproduct of the ABM or predictive analytics: it takes some intentional effort.  A corollary is that not all ABM and predictive systems can deliver this benefit.  So it's something to specifically ask prospective vendors about if you think you'll want it.

To put things in a still broader perspective, targeting content with ABM and predictive systems is part of a broader trend of using advanced technology to help marketers create, manage, and optimize content.  This is something that vendors like Captora, Persado, and Olapic do in terms of content creation, and Jivox, OneSpot, Triblio, and BloomReach do in terms of personalized content creation.  I've been looking at a lot of those systems recently although I haven't written much about them here.  New targeting technologies create unprecedented demands for more content, which only new content technologies can meet.  So you can expect to hear more about technology-based content creation, whether I write about it or not.

Friday, August 21, 2015

Landscape of MarTech Vendor Directories

I'm making a presentation on marketing technology selection at B2BLeadsCon in New York next week, and had thought to start with the usual Oh-My-God-There-Are-So-Many-Vendors slide to get everybody's attention.  This would ordinarily be Scott Brinker's popular Chief MarTech Landscape but I've recently seen so many variations on the theme that I put together a composite slide instead.  This includes Scott's slide plus versions from Luma Partners, Gartner, MarTech Advisor, Terminus/FlipMyFunnel, and Growthverse.

I considered labeling this a "landscape of landscapes" but quickly realized that (a) it's not all that witty and (b) six vendors isn't enough.  But on further reflection, I recognized that these landscapes are really a type of directory that helps marketers find available products.  This led me to consider other types of online directories, of which there are many.  So I did end up producing a landscape that still isn't as crowded as Scott's but does show the number of information sources available.

As you see, this contains four sets of products: the original six landscapes, divided between the static images and the two interactive options (both very cool).  In addition, there are two directories with analyst ratings, from Gleanster and TopAlternatives.  But the biggest category is the community review sites, of which the best known among marketers are probably G2 Crowd, TrustRadius, and Software Advice.  Because the purpose here is to list tools that help marketers find systems to purchase, I didn't extend the landscape to business directories like Crunchbase, VentureBeat's VB Profiles and Owler.

I did look at every vendor shown in the graphic and can affirm that each includes at least some marketing systems.  There are some interesting differences in approach but, like any good landscape creator, I'll simply give you a set of logos and let you research from there. Again following the tradition of landscape publishers, I make no claims about the completeness of my list or the quality of any of the companies listed.  But I will make your life a bit easier by listing all the links below.  Enjoy!

Chief MarTech
G2 Crowd
Luma Partners 
MarTech Advisor
IT Central Station
Social Compare
Software Advice
Software Insider (formerly FindTheBest)

Sunday, August 16, 2015

New Paper -- Which Social Media Marketing Methods Are Best For You?

I’ve long been intrigued by the notion that complex business decisions can be reduced to relatively simple rules of thumb.   Perhaps I shouldn’t admit this, at least when it comes to decisions I might be paid to make as a consultant. But I think I may safely say that simple rules can often provide a default decision that still needs confirmation by an expert. This gives people a clearer picture of the factors that go into making a choice without anyone getting hurt.

The good people at CommuniGator, a UK-based marketing automation system, were kind enough to sponsor a paper along those lines.  It's a do-it-yourself guide to picking social media marketing methods. You can download the actual paper here.  In this post, I’ll just outline the general approach – something I think can be applied to other situations as well.

The approach is built around three factors: the marketing methods available; the marketer's goals; and the marketer’s "situation", an umbrella term that includes resources, technology, and industry. It’s balancing  goals against the situation that's tricky: if resources were not a problem, you’d just use whatever methods fit your goals, and if goals didn’t matter, you’d just pick whatever methods fit your situation. No doubt plenty of companies have made exactly these errors. One virtue of a systematic approach is that it forces marketers to consider both dimensions.

For the social media marketing paper, we defined eight goals: attract attention, build brand reputation, generate qualified traffic, generate qualified leads, nurture relationships with leads, retain and grow existing customers, provide customer support, and gather market intelligence. You’ll notice these map nicely to the usual customer lifecycle stages.

We considered seven social media methods: monitoring social behavior, generating publicity, pushing content, making content sharable, promoting content on social bookmarking sites like StumbleUpon and Reddit, working with influencers, and managing reviews on sites like Yelp! and TrustRadius.

Finally, we have the situation. Resources include ability to generate large volumes of content, ability to generate high quality content, existing social media traffic, available support staff, and budget. Technology includes available execution tools and measurement tools. Industry considers whether customers are highly engaged in the products, how easily they can find product information, and whether the product is a locally-created service such as a restaurant or home repairs.

The actual mechanics of relating these factors are something you’ll best understand by downloading the paper. Suffice it to say that we assigned points to show which goals were more or less suited to each method, which let us identified the most appropriate methods for the marketer's goals.  We then used another set of points to find which resources, technologies, and industry characteristics are most important for each method’s success. This let us judge which of the previously selected methods were most likely to be executed successfully.  And that's what we wanted to know.

I felt the results were pretty reasonable, but then I’m biased. Please take a look and let me know what you think.

Saturday, August 08, 2015

Intent Data Basics: Where It Comes From, What It's Good For, What To Test

Intent data is a marketer’s dream come true: rather than advertising to mass audiences in the hope of getting a handful of active buyers to identify themselves, just buy a list of those buyers and talk to them directly. It lops a whole layer off the top of the funnel and finally lets you discard the wasted half of your advertising.

But intent data is a complicated topic. It comes from different places, has different degrees of accuracy and coverage, and can be used in many different ways. Here’s a little primer on the topic.

What is intent data? It’s data that tells you an addressable individual is interested in your product. Ideally, that individual will be identifiable, meaning you have an email address, postal address, device ID, mobile app registration, phone number, or other piece of information that tells you who they are and lets you communicate with them directly. But sometimes you may only have an anonymous cookie or segment identifier that lets you reach them through only one channel and not by name.

Where does it come from? Lots of places. Behavior your company captures by itself is first-party intent data.  This is probably the most reliable but only applies to people you already know. Behavior captured by others is third-party intent data.  It's the most interesting because it provides new names or new information about names already in your database. Search queries are an obvious indicator of intent, but search engine vendors don’t sell lists based on query terms because they’d rather sell you ads based on those terms. So most third-party intent data is based on visits to Web pages whose content attracts prospective buyers of specific products. A smaller portion comes from other behaviors such as downloads, email clicks, or social media posts.

How is it sold? There are two primary formats. The first is ads served to people who have shown intent; this is generally called retargeting. The people are identified by cookies or device IDs and can be shown ads on any Web site that is part of the retargeter’s network. The original action could happen on your own Web site – the classic example is an abandoned shopping cart – or on some other site. Intent-based ads on social networks work roughly the same way, although the users are known individuals because they had to sign in. Retargeting is arguably a form of advertising and therefore not intent “data” at all. But I’m including it here because so many retargeting vendors describe their products in terms of intent. The second format is lists of email addresses. These are clearly data.  The addresses are usually gathered through user registration with the Web site.  Alternatively, the email can be derived from the cookie or device through matching services like Acxiom LiveRamp.

How reliable is it? Good question. Hopefully everyone reading knows that data isn’t perfect. But intent data is especially dicey because it comes from many different sources, some of which may be stronger indicators of intent than others. Users generally can’t see the original source. In addition, the data is aggregated by assigning the original Web content to standardized categories.  These may not precisely match your own products, or they may be grouped together so that some highly relevant intent is mixed with a lot of less relevant intent.  These problems are especially acute for B2B marketers, whose products may have a very narrow focus.  There’s also an issue of freshness: intent can change rapidly, either because someone already made their purchase or because their interests have shifted. So behavior that isn’t gathered and processed quickly may be obsolete by the time it reaches the marketer.

How complete is it? It’s worth distinguishing completeness from reliability because completeness is a big problem on its own. Intent vendors won’t necessarily capture every person in market for a particular product. In fact, depending on the situation, they may capture just a small fraction. Some people may not visit any site in the aggregator’s network; some may not visit often enough to register the required level of interest; some may decline to provide their identity or delete their cookies. In some businesses, reaching only a small fraction of interested buyers is still very useful; in others – especially where there are relatively few buyers to begin with – the marketer may be forced to run her own outreach programs to capture as many as possible.  In that case, the intent vendor's list would probably not include enough additional new names to be worth buying. 

How do you use it? More ways than you might think. It’s tempting just treat intent-based lists as sales leads.  But often the quality isn't high enough for that.  So the intent lists are often considered prospects to be touched through email, targeted advertising, and other low-cost media. Similarly, retargeting ads can be used to make hard sales offers or to more gently present brand messages and name-capturing content. Other applications include using presence on an intent list as a data point in a lead score, reaching out to dormant leads or current customers who suddenly register on intent lists, and tailoring messages based on the which topics the intent vendor finds an individual is consuming.

How do you test it? On the simplest level, you just apply the intent data to whatever type of program you’re testing (sales qualification, prospecting, lead scoring, reactivation, personalization, etc.) and read the results. Where things get a little tricky is figuring out which of the names would have registered as leads through some other channel, since they should be excluded from the analysis.  Similarly, you need to carefully test how new programs like lead scoring, reactivation, or topic selection programs would have performed without the intent data – it may be the good idea was the program itself. As a general rule of thumb, expect your own data to gain power as you build a longer history, so intent data is most likely to prove valuable on names early in the buying process.

Who sells it? Consumer marketers have a wide variety of intent sources, including Nielsen eXelate, Oracle Datalogix, and Neustar for lists and AdRoll, Retargeter, Fetchback, Chango and Magnetic for retargeting. B2B marketers can work with Bombora, The Big Willow, TechTarget, IDG, and Demandbase.