Friday, July 25, 2014

LinkedIn Buys Bizo and Oracle Adds Database Services: Everything Is Going According To Plan

The past week brought two industry announcements: acquisition of Bizo by LinkedIn and new “Data as a Service” offerings from Oracle. Both illustrate the continuing evolution of marketing technology towards a data-centric world.

The Bizo purchase, priced at $175 million, makes perfect sense.  It gives LinkedIn more tools to expand its marketing offerings and lets Bizo use LinkedIn data to improve targeting within its own products. Some eyebrows were raised by a statement on LinkedIn’s blog that it will sell off Bizo’s Data Solutions business, which markets Bizo’s 120 million name database of business contacts. But LinkedIn doesn't need that data: it already has vastly better information in its own files.  Retaining Bizo's data business would only have raised questions about whether LinkedIn data was somehow leaking into the marketplace through Bizo. Many LinkedIn customers would have considered this unacceptable use of their profiles, regardless of whether LinkedIn’s privacy policy actually allows it (which my quick reading suggests it does). The more interesting question is who, if anyone, will buy the business from Bizo.

The Oracle announcement provided unintentional symmetry with Bizo: as LinkedIn was leaving the customer data sales business, Oracle was expanding its offerings. Arguably Oracle’s announcement was little more than relabeling of the BlueKai data management platform it purchased in February. But Oracle presented it in terms that make clear it sees a new, central role for data in the marketing technology stack – a view I share wholeheartedly.

In fact, Oracle’s discussion made almost exactly the same points I’ve been making about Customer Data Platforms: that marketers need a shared customer database which integrates information about each individual and makes the consolidated information easily available to analysis and execution systems. The key notion is that this consolidated database has its own very high value, apart from the value of any applications that use it. Oracle is supporting this vision by ingesting data from hundreds of partners; doing advanced quality assurance, identity matching, and “signal extraction” from unstructured data (i.e., intent, sentiment, themes, topics, entities, etc.); and providing connectors to dozens of ad targeting, site customization, testing, and analysis systems. It also highlights functions to manage data access rights in compliance with privacy, regulatory, and contractual obligations, something that's also important even though I haven’t given it quite as much attention.

While this is quite similar to what BlueKai did before Oracle bought them, it’s a big difference to have Oracle’s muscle behind the vision of making it easy for marketers to access to a rich, powerful customer database. Among other things, the Oracle product will set a benchmark for pricing of similar services by other vendors.  I didn't see a price announcement, but if Oracle prices aggressively and executes well, it will be much harder for smaller vendors to compete. The likely result is to switch the focus of competition from assembling data and providing a database to making clever use of the data through things like advanced analytics. That’s really where smaller vendors can shine and, from some lofty cosmic viewpoint, the world is better off if the smart people focus their creative energies on that rather than on duplicating the basic data assembly capabilities.

Back to that question of who will buy Bizo’s data business: I wouldn’t be at all surprised to see Salesforce.com take it over, since it would supplement their existing Data.com business and give an advertising-oriented data management platform to balance against Oracle/BlueKai. In the on-going tit-for-tat competition between Salesforce and Oracle, that is probably reason enough for Salesforce to do the deal.

Friday, July 18, 2014

Are Millennial Marketers More Analytical?

I had an interesting conversation this week with a vendor of marketing measurement systems on the question of why more marketers won’t buy his type of software. After all, surveys often show that marketers and CEOs alike rate better measurement as a high priority. Yet actual measurement techniques don’t improve much from year to year: to cite the most recent report to cross my desk, the 2014 State of Marketing Measurement Survey Report from Ifbyphone found that 45% of marketers are measuring Return on Investment in 2014 vs. 40% in 2013 -- a gain that is probably within the survey's margin of error.  Other, simpler measures are more common and growing more quickly, but that’s exactly the point: marketers don’t invest in meaningful performance measures like ROI.


My vendor friend’s suspicion was that marketers don’t buy better measurement because, whatever they say in surveys, they really don’t want to be measured. My own opinion, based on comments from marketers over the years, is they don’t have time to put advanced measurement systems in place.

Of course, time is a matter of prioritization, so this really means that marketers think the time spent on an advanced measurement project will produce less value than if that time were spent on something else.  In other words, marketers don’t invest in advanced measurement because they don’t think the resulting information will drive enough improvement in their marketing results.  That's not an unreasonable belief: much ROI information is in fact interesting but not actionable and, therefore, adds no business value.  Further evidence: the advanced measurement techniques that have been widely adopted, like marketing mix models and multi-touch attribution, all have proven bottom-line impact. The impact of marketing ROI, on the other hand, is often less clear.

Then our conversation took an unexpected turn: the vendor speculated that younger marketers might be more analytical and hence more inclined to ROI measurement.  This was a new thought to me and offered the cheery prospect of an actual change from the long-term status quo. But neither of us had seen any research on the topic, so we couldn’t judge whether it was likely to be true.  End of discussion.

I’ve since had time to look into this more deeply. There’s plenty of research on millennials’ (currently 19-34 years old) in general and a fair amount on their behavior in the workplace. Most of it reinforces familiar stereotypes: millenials are collaborative, tech-savvy, results-focused, fast-working, multi-tasking, anti-hierarchical, socially-conscious, company-disloyal, and of course digitally connected. But none of the research shed much light on whether they’re more or less analytical than older generations: since they’re skeptical of authority, you can expect them to be more open to challenging past assumptions, but this doesn’t necessarily mean they rely on data to resolve those challenges. They could just as easily rely on what feels right to them, even though they’ve had little time to sharpen their intuitions on the stone of reality.  Even their presumed affinity for digital media, which is certainly more measurable than traditional media, doesn’t necessarily translate to an interest in ROI measurement. Indeed, most digital measurements such as Web traffic and social media interactions have almost nothing to do with ROI.  Finding that millennials rely heavy on them would bode poorly for advanced measurement methods.

But all of this is just speculation, and I am definitely a fact-based kinda guy. Has anyone seen any information on how marketers’ behaviors differ by generation? If not, would you find it an interesting topic for a survey?

Thursday, July 03, 2014

StrongView Moves Beyond Email to Real-Time, Contextual Marketing

Today's email vendors face an interesting business challenge. On one hand, building a high-volume email engine is a lot harder than you’d think, so business is strong despite near-commodity status. But marketers want to integrate email with other messaging channels, so a stand-alone email platform is increasingly unattractive. The obvious solution is to add other channels and, even more important, features to control the decisions of when, how, and to whom messages are sent. This puts vendor at the center of the marketing operations, helping them to retain clients and charge higher fees.

Indeed, this strategy has succeeded for many vendors: ExactTarget, Responsys, Silverpop, and Neolane all grew from mostly-email to broader systems that were purchased by larger vendors as the foundation of an all-compassing marketing suite. Other email providers have remained independent but still expanded their scope to remain competitive and grow.


StrongView, which was original email specialist StrongMail, has followed this course. Rotating banners on the company Web site position StrongView as a “product platform” and “marketing cloud” as well as mentioning “cross-channel lifecycle marketing”, “present tense marketing”, “true one-to-one communication” and “the first customer insight solution supporting unlimited cross-channel interaction data”. This may set a world record for buzzword intensity, but that’s okay so long as the underlying product matches the implied promises. On the whole, I’d say it does.

The key to all this is the one unfamiliar phrase on the previous list: “present tense marketing”. This is StrongView’s own coinage, intended to describe their proprietary view of context-based marketing (which strikes me as plenty buzzy all by itself, but then I have a low tolerance for such things). The gist of contextual marketing, in StrongView's definition, is to tailor customer treatments to the current situation (location, device, environment, past behaviors, etc.), so those treatments lead the customer in a profitable direction. StrongView sees this as the future of marketing and has defined its strategy as helping marketers make the transition by providing the necessary technology and supporting services.

StrongView earns considerable credit in my book for articulating a proper strategy: one that defines not just a goal (helping marketers make the transition) but also the method for achieving that goal (by providing the necessary technology and services). Still, articulation is just a first step. The next is even more important: implementing the method effectively.

StrongView has identified several key implementation requirements. Necessary technologies include real-time analytics to select best treatments; dynamic message assembly to construct those treatments; and multi-channel routing to deliver the treatments via email, SMS, mobile apps, Web pages, social media, and display ads.

Of these technologies, content creation, dynamic assembly and delivery are extensions of the company’s original email functions. StrongView has supplemented them by building an impressive campaign flow designer that handles complex, multi-step programs.  Predictive analytics rely on external modeling tools, but the vendor will compensate with prebuilt models and with services to create custom models. There’s also a methodology to guide creation of campaigns and models.

All these functions require a much larger, more flexible data environment than a traditional email system.  StrongView has stepped up to the challenge by building a data store using Amazon RedShift.  This closes a critical gap faced by email vendors trying to reach the next level.  StrongView has also knitted together everything from content creation and campaign design to execution and reporting in a tightly integrated user interface, another requirement in providing the speed and efficiency needed for a “contextual” approach.

Finally, we come to services.  StrongView recognizes that many marketers will need help in making the transition towards more advanced marketing techniques, so it is offering marketing strategy, analytics, technical development, campaign design, creative, production and delivery.  These are sold on project or retainer basis as appropriate. StrongView is clear that these services are intended to help marketers supplement their own resources, not to convert the business into a service agency.

All told, this is a pretty complete package. Although StrongView’s vision is far from unique, they have carefully worked through the implications to define and deliver a complete solution.  This should be enough to get their customers started.  Results will determine what happens next.

Friday, June 27, 2014

Fliptop: A Customer Data Platform for Predictive Lead Scoring, Pure and Simple

It’s been a while since I wrote about Customer Data Platforms, but only because I’ve been distracted by other topics. The CDP industry has been moving along nicely without my attention: new CDPs keep emerging and the existing vendors are growing.

Fliptop wasn’t on my original list of CDPs, having launched its relevant product just after the initial CDP report was published. But it fits perfectly into the “data enhancement” category, joining Infer, Lattice Engines, Mintigo, Growth Intelligence (which I’ve also yet to review) and ReachForce. Like all the others except ReachForce, the company builds a master database of information about businesses and individuals by scanning the social networks, company Web pages, job sites, paid search spend, search engine page rank, and other sources. When it gets a new client, it loads that company’s own customer list and sales from its CRM system, finds those companies and individuals in the Fliptop database, enhances their records with Fliptop data, and uses the combined information to build a predictive model that identifies the likelihood of someone making a purchase. This model can score new leads and classify existing opportunities in the sales pipeline.

So what makes Fliptop different from its competitors? The one objective distinction is that Fliptop is publicly listed on the Salesforce.com App Exchange, meaning it has passed the Salesforce.com security reviews. Not surprisingly, the company’s Salesforce connector is very efficient, automatically pulling down leads, contacts, accounts, and opportunities through the Salesforce API and feeding them into the modeling system. New clients who import only Salesforce data can have a model ready within 24 hours, which is faster than most competitors. But data from other sources may require custom connectors, slowing the process.  Fliptop is also able to model quickly because it defaults to predicting revenue: in other systems, part of the set-up time is devoted to deciding what to model against.


Once the model is built, Fliptop scores the client’s entire database and assigns contacts, accounts, and opportunities into classes based on expected results. A typical scheme would create A, B, C, and D lead classes, where A leads are best. Reports show the percentage of records in each group and the expected win rate, which in turn relates to expected revenue. A typical result might find that the top 10% of contacts account for 40% of the expected revenue or that the top 40% of contacts account for 95% of the revenue. Clients can adjust the breakpoints to create custom performance ranges. Reports also show which categories of data are contributing the most to the scoring models: this is more information than some systems provide and is presented quite understandably.  (Incidentally, Fliptop reports it has generally found that "fit" data, such as company size and industry, is more powerful than behavioral data such as email clicks and content downloads.)

Fliptop scores are loaded into a CRM or marketing automation system where they can be used to prioritize sales efforts and guide campaign segmentation. There are existing connectors for Salesforce.com, Marketo, and Eloqua and it’s fairly easy to connect with others. New leads can be scored in under one minute or in a few seconds if the system is directly connected to a lead capture form. Clients can build separate models for different products or segments and receive a score for each model. The system automatically checks for new sales results at regular intervals and adjusts the models when needed.

At present, Fliptop only sends scores to other systems. (Infer takes a similar approach.) The next release of its Salesforce.com integration is expected to add top positive and negative factors on individual records.  The company is considering future applications including campaign optimization, pipeline forecasting, and account-level targeting. But it does not plan to match competitors who offer treatment recommendations, sell lists of new prospects, or provide their own behavior tracking pixels.

Pricing for Fliptop is based on data volume and starts at $2,500 per month. The company offers a free 30 day trial – unusual in this segment and possible because set-up is so automated. After the trial, clients are required to sign a one-year contract. The system currently has about three dozen paying clients and a larger number of active trials.

Bottom line: Fliptop does a very good job with predictive lead scoring. Marketers looking for a broader range of applications may find other CDPs are a better fit.

Sunday, June 22, 2014

NextPrinciples Offers Integrated Social Marketing Automation

Social marketing is growing up.

We’re seen this movie before, folks. It starts when a new medium is created – email, Web, now social. Pioneering marketers create custom tools to exploit it. These are commercialized into “point solutions” are perform a single task such as social listening, posting, and measurement. Point solutions are later combined into integrated products that manage all tasks associated with the medium. Eventually, those medium-specific products themselves become part of larger, multi-medium suites (for which the current buzzword is “omni-channel”).

But knowing the plot doesn’t make a story any less interesting: what matters is how well it’s told. In the case of social marketing, we've reached the chapter where point solutions are combined into integrated products. The challenge has shifted from finding new ideas to meshing existing features into a single efficient machine. More Henry Ford than Thomas Edison, if you will.



NextPrinciples, launched earlier this month, illustrates the transition nicely. Originally envisioned as a platform for social listening and engagement, it evolved before launch into a broader solution that addresses every step in the process of integrating social media with marketing automation. Functionally, this means it provides social listening for lead identification, social data enhancement to build expanded lead profiles, social lead scoring, social nurture campaigns, integration with marketing automation and CRM systems, and reporting to measure results.

It’s important to clarify that NextPrinciples isn’t simply a collection of point solutions. Rather, it is a truly integrated system with its own profile database that is used by all functions. It could operate without any marketing automation or CRM connection if a company wanted to, although that doesn’t sound like a good idea.  Its target users are social media marketers who want to work in a single system of their own, rather than relying on point solutions and social marketing features scattered through existing marketing automation and CRM platforms.

The specific functions provided by NextPrinciples are well implemented. Users set up “trackers” to listen to social conversations on Twitter (today) and other public channels (soon), based on inclusion and exclusion keywords, date ranges, location, and language. Users review the tracker results to decide which leads are of interest, and can then pull demographic information from the leads’ public social profiles. Leads can also be imported from marketing automation or CRM systems to be tracked and enhanced. Trackers can be connected with lead scoring rules that rate leads based on demographics and social behaviors, including sentiment analysis of their social content. Qualified leads can be pushed to marketing automation or CRM, as well as entered into NextPrinciples’ own social marketing campaigns to receive targeted social messages. Campaigns can include multiple waves of templated content. The system can track results at the wave and campaign levels. It can also poll CRM systems for revenue data linked to leads acquired through NextPrinciples, thus measuring financial results. Salespeople and other users can view individual lead profiles, including a “heatmap” of topics they are discussing in social channels.

If describing these features as “well implemented” struck you as faint praise, you are correct: as near as I can tell, there’s nothing especially innovative going on here. But that’s really okay. NextPrinciples is more about integration than innovation, and its integration seems just fine. I do wonder a bit about scope, though: if this is to be a social marketer’s primary tool, I’d want more connectors for profile data such as company information and influencer scores. I’d also want lead scoring based on predictive models rather than rules. And I want more help with creating social content, such as Facebook forms, sharing buttons to embed in emails and landing pages, multi-variate testing and optimization, and semantic analysis of content “meaning”.

NextPrinciples is working on at least some of these and they’ve probably considered them all. As a practical matter, the question marketers should ask is whether NextPrinciples’ current features add enough value to justify trying the system. In this context, pricing matters: and at $99 per month for up to 100 actively managed leads, the risk is quite low. For many firms, the lead identification or publishing features alone would be worth the investment. Remember that NextPrinciples is only the next chapter in an evolving story.  It doesn’t have to be the last social marketing system you buy, so long as it moves you a bit further ahead.

Thursday, June 12, 2014

B2B Marketing Automation Vendor Strategies: What's Worked and What's Next

I recently did a study of the strategies of B2B marketing automation vendors. Of the two dozen or so companies in the sample, six were clearly successful (defined as achieving major share within their segment), seven had failed to survive as independent companies and sold for a low price, and the rest fell somewhere in between.

The research identified 28 different strategies which fell into six major groups. Some approaches definitely had better track records than others, but it’s important to recognize that the market has changed over time, so past performance doesn’t necessarily indicate future success. What I found most intriguing was the sheer diversity of the approaches, showing that vendors continue to explore  new paths to success.

The table below shows results for each strategy for each set of vendors, grouped by the major strategy categories. Most vendors used more than one strategy. Shading indicates the relative frequency of each strategy.


In general, the winners have focused on two of the major strategy groups: reducing sales barriers and expanding distribution. This made considerable sense in the early stages of a new market, when building awareness and market share was critical.

Within these categories, some strategies have worked better than others.  Freemium has been particularly unsuccessful, while low price, ease of use, limited features, and agency versions have been applied by vendors with all types of results. Winning vendors were most distinguished by user education, reseller networks, and heavy spending to grow quickly.  There is certainly some chicken-and-egg ambiguity about whether the companies were successful because of their strategies or were able to adopt those strategies after some initial success.  One thing that doesn't show up on the chart is that some successful vendors have shifted strategies over time, generally moving away from low prices to higher prices and from small businesses to mid-size and larger.

As the market matures, I’d expect different strategies to become more important. Established vendors will need to focus on increasing client success in order to retain the clients and will want to expand their footprint to leverage their installed base, especially through setting themselves up as platforms. Those two shifts are well under way.  Smaller vendors will find it harder to challenge the leaders, especially if they lack heavy financing.  But they may be able to thrive in niches by focusing on narrow market segments or meeting special client needs.

The chart below shows the same data as the table but in a more visual format, for all you right-brainers out there.

Wednesday, June 04, 2014

Marketing Automation Buyer Survey: Many Myths Busted but Planning is Still Key to Success

The marketing automation user survey I mentioned last March has finally been published on the VentureBeat site (you can order it here). At more than 50 pages and with dozens of graphs and charts, it’s not light reading. But it’s still fascinating because the findings challenge much of the industry’s conventional wisdom.

For example, industry deep thinkers often say that deployment failure has more to do with bad users than bad software. The underlying logic runs along the lines that all major marketing automation systems have similar features, and certainly they share a core set that is more than adequate for most marketing organizations. So failure is the result of poor implementation, not choosing the wrong tools.

But, as I reported in my March post, it turns out that 25% of users cited “missing features” as a major obstacle – indicating that the system they bought wasn’t adequate after all. My analysis since then found that people who cited “missing features” are among the least satisfied of all users: so it really mattered that those features were missing. The contrast here is with obstacles such as creating enough content, which were cited by people who were highly satisfied, suggesting those obstacles were ultimately overcome.*



We also found that people who evaluated on “breadth of features” were far more satisfied than people who evaluated on price, ease of learning, or integration. This is independent confirmation of the same point: people who took care to find the features they needed were happy the result; those who didn’t, were not.



But the lesson isn’t just that features matter. Other answers revealed that satisfaction also depended on taking enough time to do a thorough vendor search, on evaluating multiple systems, and (less strongly) on using multiple features from the start. These findings all point to concluding that the primary driver of marketing automation success is careful preparation, which means defining in advance the types of programs you’ll run and how you’ll use marketing automation. Buying the right system is just one result of a solid preparation process; it doesn't cause success by itself.  So it's correct that results ultimately depend on users rather than technology, but not in the simplistic way this is often presented.

I’d love to go through the survey results in more detail because I think they provide important insights about the organization, integration, training, outside resources, project goals, and other issues. But then I’d end up rewriting the entire report. At the very least, take a look at the executive summary available on the VentureBeat site for free. And if you really care about marketing automation success, tilt the odds in your favor by buying the full report.

__________________________________________________________________________
* I really struggled to find the best way to present this data.  There are two dimensions: how often each obstacle was cited and the average satisfaction score (on a scale of 1 to 5) of people who cited that obstacle.  The table in the body of the post just shows the deviation of the satisfaction scores from the over-all average of 3.21, highlighting the "impact" of each obstacle (with the caveat that "impact" implies causality, which isn't really proven by the correlation).  The more standard way to show two dimensions is a scatter chart like the one below, but I find this is difficult to read and doesn't communicate any message clearly.


Another option I tried was a bar graph showing the frequency of each obstacle with color coding to show the satisfaction level.  This does show both bits of information but you have to look closely to see the red and green bars: the image is dominated by frequency, which is not the primary message being communicated.  If anyone has a better solution, I'm all ears.