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!

AlternativeTo
AppAppeal
BestVendor
Chief MarTech
Cloudswave
Credii
DiscoverCloud
GetApp
G2 Crowd
Gartner
Gleanster
Growthverse
Luma Partners 
MarTech Advisor
Osalt
IT Central Station
Serchen
Social Compare
Software Advice
Software Insider (formerly FindTheBest)
Terminus
TopAlternatives
TrustRadius

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 have can work with Bombora, The Big Willow, TechTarget, IDG, and DemandBase.





Friday, July 31, 2015

VEST Report: Competition in B2B Marketing Automation Isn't About Features

Yesterday I released the mid-year edition of the VEST Report on B2B marketing automation vendors, thereby meeting my self-imposed deadline of July 31. Look here for more information or to make a purchase.

Updating the report gives a nice overview of recent industry developments. Here are some observations:

- market positions are pretty stable. The only new vendor to make a splash recently has been SharpSpring, which went from zero to 500 agency clients in the past year. This puts them among the top 3 leaders in the small business sector. Otherwise, the top players have remained the same: Infusionsoft, HubSpot, Act-On, Salesforce Pardot, Marketo, Oracle Eloqua, and Adobe. Maybe RedPoint has crept up to a leader position, but they don’t share enough business information for me to know. Open source vendor Mautic has some interesting potential but it’s too soon to see any actual impact.

- products are pretty stable, too. The VEST entries showed very little change in the features reported by the various vendors since the last report. This isn't bad: it's simply that the standard features are now widely understood and vendors have had time to add them. The only major changes captured in the new report are the custom table abilities added by Marketo and Ontraport.

- the real action is outside the products. Probably the most interesting trend is integration of marketing automation with retargeting and display ad vendors, which has been announced in various forms by Marketo, Oracle, and Adobe. That, of course, relates to the convergence of martech and ad tech into “madtech” that I've written about before. The other big trends are systems for marketing agencies (either focused products like SharpSpring or added features and partner programs by the major vendors) and education programs for users (something that major vendors have long done but that others like Autopilot* and Mautic are also expanding). Both agencies and education are ways to support industry growth by overcoming the lack of marketers who can effectively use marketing automation systems.

- the really real action is elsewhere. Lest you think I’m just plain cranky, be assured that I see lots of exciting things happening in predictive analytics, data aggregation and enrichment, automated intelligence, and other areas. Even B2C marketing automation is showing some interesting new life. But even though B2B marketing automation revenues are still growing nicely**, the industry itself is looking pretty stable these days.



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*Not in the VEST by their choice.
** I'm estimating 40% in 2015, although it’s now harder to know because so much revenue is hidden inside companies like Oracle, Salesforce, and IBM that don’t report it separately.

Thursday, July 23, 2015

Design Your Best Marketing Technology Stack and Plan the Transition: Sneak Peek at FlipMyFunnel Conference

Picture posted by Terminus

I can’t decide which is more exciting about next month’s FlipMyFunnel conference in Atlanta (register here and use the code DR50 for a 50% discount): the opportunity to interact with a great collection of speakers and attendees or seeing what the conference organizers at Terminus do with the notion of MarTech Stack Jenga. Based on one cryptic Twitter picture, they’re up to something big.

My own contribution will be a presentation on designing your marketing stack. This is something I’ve done for years as a consultant but it’s now an especially hot topic. Here are some of the key points I’ll be making:

- the stack is based on your business and marketing strategies. I’ve described the importance of strategy before but have now refined my explanation to show how marketing programs, business requirements, and functional requirements connect over-all marketing strategy with martech. The picture below also highlights the importance of planning for future business, marketing, and martech developments.


And I’ve provided a sample template for organizing your requirements by system.

- a winning stack is efficient as well as functional. I'll present a checklist for evaluating your stack design along those dimensions.

- how you draw the stack makes a difference. I’ll argue that a diagram which shows relationships between systems is more helpful than one that simply lists the different components. In the example below, the flow highlights the isolation of sales and service from the rest of the stack – a critical weakness that isn’t apparent when you look at the systems only.


- transition planning must be systematic as well. Companies struggle with transition planning even more than they struggle with stack design.  The goal is to sequence the stack changes so that each new system adds the greatest value with the least disruption. This requires understanding which system changes support each improvement.  This lets you figure out which improvements would be supported by changing any one system, what would then be possible after changing a second system, what is possible after changing a third system, and so on.  The worksheet lets you explore different sequences so you can pick the best one.

This will be easier to understand in person than in writing. Don't take my word for it: join us in Atlanta and see for yourself.

Friday, July 17, 2015

Predictive Analytics: Should Automated Content Selection Work by Segment or Individual?

Two vendors made the same point with me this week, which is reason enough for a blog post in mid-July. The point was the difference between basing content selection on individuals and on segments. I have never considered the distinction to be especially important, since segment membership is determined by individual behaviors and individual-level decisions are guided by behavior patterns of groups. But the two vendors in question (Evergage and Jetlore) and another I spoke with earlier (Sailthru) were downright religious about the superiority of their approach (individual-level selections in every case). So I thought it worth some discussion.

First, let’s clarify the topic. The distinction these vendors were making is between selecting content separately for each individual and selecting the same content for all members of a segment. Of course, customers are assigned to segments based on their individual behavior and other attributes, but once someone is in a segment, the segment-based system ignores individual differences. Among segment-based systems, collaborative filtering uses product selections almost exclusively: this is the classic “customers who looked at this product also considered these products” approach, which doesn’t take into account other aspects of the customer’s history. Other methods build segments based on customer life stage, demographics, and similar broad attributes. It’s possible to build segments based on very detailed behavioral differences, but that’s likely to create too many segments to be practical.

At an operational level, the individual-level systems use automated analytics to rank all possible content choices for each individual using that individual’s data. Segment-based systems either use rules to select content for each segment or use automated analytics to rank content choices for the segment as a whole. The individual-level approach makes the most sense when there are many content choices to consider, as with retail merchandise or entertainment (books, music, movies, etc.). Those are cases where getting precisely the right content in front of the customer is much more effective than offering everyone the most commonly-selected items. Retail and entertainment marketers also usually have detailed history which supports accurate predictions of what the customer will want. Segment-based systems work best when only a few choices are available.  This means a separate segment can be created for each item or, more realistically, segments come first and items are created to serve them.

So does the entire debate really come down to using individual-level systems when there are lots of choices and segment-based systems when there are only a few? Not really: collaborative filtering can also handle massive numbers of options with great accuracy. The difference is that collaborative filtering doesn’t really consider much beyond a particular product choice, while a sophisticated individual-level system will consider other factors including the current context and the customer’s history. Done correctly, this should yield more appropriate selections. On the other hand, individual-level approaches require more data and more complex analytics, so there will be cases where a segment-based method is ultimately more appropriate.

Moreover, the two approaches are as much complementary as competitive. A segment can indicate customer state, such as just-acquired, satisfied, or churn-risk, which constrains the contents considered for offer by the individual-level system.* Or a segment-level system could chose the type of message to send but let the individual-level system pick the specific contents. Dynamic content within email campaigns often works exactly this way.

In fact, I’d argue that state-based segmentation is essential for individual-level optimization because states provide a framework to organize the masses of detailed customer data. Without tagging the customer’s current state during each event, it would be very difficult for even the most sophisticated analytical system to see the larger arc of the customer life cycle or to understand the relationship between specific offers and long-term outcomes.

All this has practical implications for marketers considering these systems.

- for individual-level systems, make sure they can look beyond predicting the highest immediate response rate to measuring impact on long-term objectives such as conversion or lifetime value.

- for segment-level systems, make sure they can take into account the customer’s past behaviors and attributes, not just the products they have recently purchased or considered

- for all types of systems, assess how they track and guide the customer through the stages of her long-term journey

You'll want to consider other differences between content selection systems, such as number of items they can manage, how quickly they return selections, how they incorporate items without a sales history, and what data they consider in their analysis. Just remember that making selections isn’t an end in itself: you want to make choices that will create the greatest long-term value. To do that, it’s not a choice between individual and segment level analysis. You need both.

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* See my June 25 post for a more detailed discussion of state-based campaigns.