Friday, November 21, 2014

Sailthru Offers End-to-End Omnichannel Personalization for B2C Marketers

I know this is blasphemy, but I’m beginning to have doubts about solution selling – the idea that marketers should describe the customer problems they solve, not the features of their products. The issue, at least in marketing technology, is that all systems address pretty much the same general problem of sending the right messages to the right customers (in the right time, place, medium, device, language, tone, etc.). This means that solution statements sound pretty much alike, even when the actual products are different. It’s left up to the poor buyer to figure out what each product does and whether that is something she truly needs.

Sailthru is a good example. The corporate home page says “Sailthru makes it easy to personalize every channel for every customer,” which is accurate enough.  But plenty of other companies also help with omni-channel personalization.   A marketer looking for personalization solutions might add Sailthru to her list of options, but wouldn’t know whether it's a good or poor fit without digging much deeper..

On the other hand, resolving that sort of ambiguity is what keeps me in business.   Perhaps I shouldn't complain.  In any event, there are several differentiators that determine whether a product like Sailthru is suitable for a particular situation.

  • customer profiles. Sailthru builds a history of information about individual customers.  You  might think that would be done by all personalization systems but it's possible to do something that can reasonably be called “personalization” using only anonymous information such as traffic source, search terms, location, or Web pages viewed during a visit. Sailthru goes beyond this to store behaviors over time.  These are linked across channels to a customer identity that is usually known at the start of an interaction. The identity might be available because the customer is interacting with a mobile app for which she has registered, is responding to an email or text message that was already tied to her identity, has logged into an ecommerce Web site, or is known through a cookie that was previously linked to her identity. Sailthru generally does not deal with anonymous customers. It can store several identifiers for the same customer, which is how it coordinates interactions in different channels. The identifiers would be linked through “hard” matches such as an email address provided when registering a mobile app or an ID number embedded in a Web link in an email.  "Fuzzy" matching, which attempts to link identifiers that have not been directly connected, is generally avoided by Sailthru.

  • data store. Sailthru stores data in MongoDB, a “No SQL” database that can handle nearly any data type and can easily add new “fields” (not really the correct term) without formally defining them in advance. This makes it extremely flexible, which is very important in the fluid world of marketing information. Mongo is also fast and scalable and good for analytical processing in general. A fair number of multi-channel personalization systems use Mongo or something similar, but many others use conventional relational databases (which are less flexible) or other data stores.

  • data sources. Sailthru gathers most of its data from its own tags placed within emails, Web pages, and mobile apps. This distinguishes it from systems that rely primarily on feeds from external systems via API connectors or batch files. Technical users can still set up a feed using API calls or JSON posts when necessary. Prebuilt integrations are available for Magento ecommerce and WordPress, with others on the way. There are no standard integrations for marketing automation or CRM. The system usually stores emails sent, Web pages visited, purchases, content read, and mobile interactions. The system can scan, classify and tag company’s marketing contents and then use the tags to build a customer’s content consumption profile. It can do similar tracking based on merchandise category tags from ecommerce systems. Users can also set up custom variables derived from original inputs.

  • predictions. Sailthru has a recommendation engine that uses customer history to suggest the product or content they are most likely to select next. It has recently released the beta version of a predictive platform that automatically generates probability scores, rankings, and estimated values for nine actions such as making a purchase within the next 24 hours, opting out of future contacts within the next week, and expected revenue within the next thirty days. These can be used in segmentation and message selection. General release of the prediction tool is planned for early 2015. Predictive models are rebuilt and records are rescored nightly, with no changes during the day in response to new customer activity. Recommendations do adjust in real time to customer behaviors. The system can create control groups to measure the impact of recommendations on long-term customer behavior. These predictive capabilities are among the biggest differentiators for Sailthru: predictions and recommendations are oriented to consumer marketing, not B2B lead scoring, and Sailthru doesn’t (yet) allow clients to choose what they wish to predict. On the other hand, the modeling is fully automated, while many other systems require at least some manual set-up for each new model.

  • message selection. Users can define lists based on any data in the system and then send a specified email or mobile message to each list. They can also export lists for Facebook promotions or to other channels.  Messages and Web pages can contain real-time recommendations and can also adjust their contents based on data and scripts written in Sailthru’s own Zephyr language.  Marketers should look closely at this aspect of Sailthru: while powerful, it's not creating rules to send different content to different segments, doesn’t send sequences of messages over time, and doesn’t support real-time interaction flows.  Be sure you're getting the personalization features you need. (Note: after I published this, the vendor told me they do indeed have these capabilities.  I think they were referring to the ability to set up separate strategies that can each send a different message to a different segment; this is not the same as creating a single strategy that controls messages to multiple segments.  But maybe they have capabilities I didn't see.  In any case, I'll circle back with them and update as necessary.  As always, buyers need to check for themselves to see what any system actually does.)

  • message delivery. Sailthru builds and delivers email, mobile, and Web messages directly, rather than sending lists or recommendations to other systems. Many marketers will like this,  since it avoids the need to integrate with another product. But marketers who have want to use other delivery platforms may not be happy.  This is one reason I haven’t classified Sailthru as a customer data platform: although Sailthru does a great job of building a unified customer database, most CDPs are specifically designed to work with other systems when sending messages.

  • data access. Sailthru lets clients export lists based on profile data, can display individual customer profiles, and provides some limited API access to the profiles. But it doesn’t support mass exports of the profiles or allow external queries of the profile database. This is the other and more important reason I don’t consider Sailthru a CDP: making the database available to external systems is the very core of the CDP concept.

  • pricing and company background. Sailthru was founded in 2008. It currently has about 400 clients, mostly in ecommerce and media. Pricing is based on the number of active profiles and (unlike many personalization products) does not increase as clients support more channels. Prices begin around $30,000 per year.

Sunday, November 16, 2014

Hushly Helps Marketers Connect With Anonymous Web Site Visitors

When this blog last left Geoff Rego in 2010, he had just sold the assets of pioneering B2B marketing automation vendor Market2Lead to Oracle. Since then, he’s been gnawing at the bone of anonymous business leads, suspecting that there’s some way to gain value from people who are interested in a product but haven’t identified themselves to vendors. Rego has shown me a couple of approaches over the past few years, none of which quite worked out. But when we saw each other at Dreamforce last month, it seemed he had settled on a keeper.

The new product is Hushly, which addresses the reluctance of prospects to provide their email address even in return for valuable content. This is the gating dilemma: more people will read your content if it’s not gated, but you don’t capture their email address unless you gate. In its current form, Hushly waits until visitors have abandoned a content form and then pops up an offer for them to download anonymously via Hushly. Visitors determined to remain unknown can view the content online, while those willing give Hushly their email address can actually download and save it.  Making the offer after the form is abandoned ensures that Hushly only captures people who would not otherwise have connected with the company directly.

Once a Hushly member has downloaded vendor content, vendors can send emails to the member via Hushly.  This allows communication without the vendor actually receiving the member's email address.  Members can block messages from a vendor if they wish.

Hushly also lets members send questions to vendors and receive answers through the system, still without revealing their identity. They can even contact competitive vendors with the same protection. The system lets members send a list of questions to multiple vendors and tabulates the results, allowing more detailed anonymous research. 

Each member gets a Hushly library to store their downloaded documents and communications. Members can grant other people access to their library for collaboration. On the vendor side, Hushly creates anonymous lead records in the client’s instance, so companies can track their interactions with anonymous prospects and keep the history once the prospect identifies herself. The system can integrate with other CRM vendors through batch file transfers.

All told, I think Hushly is a pretty clever idea. The concept takes a bit of explaining to potential members, which could be a barrier to success. There’s also a chance that people simply won’t believe Hushly’s promises to protect members’ privacy – not because of anything about Hushly but simply because they distrust of Web services in general.  Happily, both obstacles can be overcome through good marketing.  Rego reports that initial results show Hushly can confidently guarantee a 200% increase in distribution of gated content and 20% increase in identified leads. So it looks like enough potential users are receptive to make things interesting.

Hushly has been deployed in various forms by more than 200 companies since early 2014. Set-up is quite simple: users associate content with Hushly widget, which they embed in a landing page. Pricing is based on the number of form abandons, starting at $200 per month for 100 abandoners and falling on a per-abandoner basis as volumes increase.

Tuesday, November 04, 2014

Lytics Adds Marketing Recommendations to a Customer Data Platform

It’s just over one year since I first spoke with Lytics*, which at that time was (accurately) calling itself a Customer Data Platform but had not yet released a beta version of its product. The company has been busy since then, raising $7 million to supplement its initial $2.2 million funding, enrolling about 30 beta clients, releasing its initial system and a new self-service option, developing an automated process to recommend marketing programs to its clients, and abandoning the CDP label to call itself a “marketing activation platform”. CEO James McDermot said the label was changed because big companies thought a CDP sounded like an IT project, not something run by marketers. Fair enough, but Lytics still perfectly fits my definition of a CDP: a marketer-controlled system that supports external marketing execution based on persistent, cross-channel customer data.

In fact, Lytics could pretty much the poster child for the CDP concept. While many CDPs also provide some execution services, Lytics draws a sharp distinction between its core data layer, supporting analytics, and message delivery.  Data and analytics are included in the system; execution is not.  Also in the CDP spirit, Lytics makes extensive use of external products within its data and analytics layers, relying on third party systems to connect social media, email and postal identities; to import social and Web site data; for reporting; and to do natural language processing. All told, the company has prebuilt connectors with more than 80 software-as-a-service products. Execution systems on the list include, Marketo, Eloqua, Act-On, Facebook, Twitter, Youtube, Demandware, Optimizely, Adobe Target, and most major email providers.

But perhaps I’m getting ahead of myself.  I should really start with what Lytics does.  Basically, it imports data from multiple sources, builds a consolidated profile for each customer, tracks individual behavior over time, builds segments of customers with similar behaviors, and makes those segments available to external systems for marketing messaging. It uses several data storage technologies, including Cassandra, Elasticsearch, and Titan Graph DB, to handle large amounts of structured and unstructured data. It combines its own identity matching techniques with third party resources to consolidate the profiles across channels, add more data, and extract meaning from text. It lets users define and extract audience segments and can push alerts to execution systems as customers change audience segments in real time.

Lytics would be a perfectly fine CDP if it did nothing beyond what I’ve just listed. But the system actually takes two additional steps – and is tip-toeing towards a third – that make it quite exceptional.

The first step is to summarize customer behavior with scores for interaction momentum, quantity, frequency, responsiveness, and intensity. These are combined to create about thirty segments, such as “burn out” customers, defined as people with high intensity and low momentum. The segments can be further qualified based on what addresses are available (email, postal, phone, Facebook, etc.) and on other profile data specified by the user. The resulting audiences give marketers a structured way to manage customer treatments.

The second step is to actually recommend those customer treatments. Lytics has a database of marketing tactics, such as reengagement programs for dormant users or upsell programs for active users. It looks at existing audience segments and the execution tools the client has in place, and calculates which tactics to which audiences in which channels would yield the highest results. It then recommends the most promising options to the client, who can activate the suggested program with the push of a button. This isn't actual program execution: Lytics only sends the audience to the selected tool, where the client must still set up the program and its message. But it's still a big stride towards helping marketers make choices that otherwise depend entirely on their own expertise. This is important because shortage of marketers with adequate skills has been a major stumbling block for many advanced marketing technologies.

The third step, which Lytics hasn’t yet taken, is to select the content itself.  McDermot was quite adamant that the company is not in the content recommendation business, leaving that marketers’ creativity. But he did say Lytics is experimenting with a “content graph” that classifies content and shows how it is related to individuals, which suggests the system will eventually be able to make some suggestions. There are other capabilities Lytics would need to make optimal content recommendations, notably decision rules to address business goals such as selling excess inventory or satisfying unhappy customers. These don’t seem to be on the company’s radar. But they could appear as it moves ahead.

So, about that self-service option. This might Lytics’ most impressive news of all. The initial release of the system was targeted at large enterprises and relied on traditional programing to connect with external systems using APIs. McDermot and I didn't discuss pricing but you can be sure it was in the five or six figures.  The self-service version enables automatic connections to the 80+ partners already in place. Pricing is based on the number of customer profiles and channels managed and includes all the existing connectors. It starts at a shockingly affordable $1,000 per month, making Lytics an option for just about any business. Combined with the product’s predictive scoring and tactic recommendations, this could empower a huge number of marketers whose firms couldn't previously afford a powerful marketing database and the integrations needed to make it useful.  We'll see how this plays out, but Lytics could be revolutionary indeed.

* not to be confused with Lityx, which offers LityxIQ cloud-based predictive modeling and data management and is worth a look in its own right.

Saturday, November 01, 2014

Seven Marketing Automation Myths to Ignore - Illustrated Edition

I’m sad.

I’ll be giving a speech in Milwaukee next week on marketing automation myths, and early in the preparation process had the idea of illustrating it with mythical creatures from the films of Ray Harryhausen, the stop-action animation genius whose best known images are probably the skeleton warriors in Jason and the Argonauts (1963).

This led to many pleasant hours scrolling through galleries of Harryhausen images. I even found an illustration that vaguely matched the theme of each myth.

But there’s a problem. Every bit of presentation-giving advice, training, and experience I’ve ever had tells me that these illustrations will distract attention from my points rather than reinforcing them. The responsible adult inside of me knows I have to get rid of them while the fun-loving child says, Yeah, but they're just so cool. 

This blog post is my compromise: I’ll publish them here, which will make dropping them from the actual presentation much less painful. **sigh**

So, the illustrated version of my talk goes like this:

Marketing Automation Myth Busting: we start with Mighty Joe Young, Harryhausen’s 1949 tribute to King Kong. I could tell you he’s about to smash some myths, but who are we kidding? It’s just a great image. 

Trouble in Paradise: marketing automation is growing quickly but users are dissatisfied. Maybe that’s not as bad as being attacked by a giant crab, but it’s still problematic. Image from Mysterious Island (1961).

Myth: All systems are the same.  This is an easy mistake because systems all look and sound alike during the buying process. But in fact they differ greatly. The myth leads buyers to think it doesn’t matter which system they purchase, and therefore that they can buy without first defining their requirements. In fact, our research shows that unsatisfied marketers often have purchased a system that didn’t meet their needs. Conversely, the most satisfied users did select based on specific features. The image here is Cyclops from The Seventh Voyage of Sinbad (1958). He has vision problems; it’s hard to see the differences between marketing automation systems. Get it?

Myth: Integration is easy. This echoes the first: all marketing automation products integrate with CRM, so people assume they don’t have to look into the details. But products differ hugely in which systems they connect with, what data they import and export, and how much control uses have over the details. Integration is the single most commonly cited obstacle to success and is linked to the most dissatisfied users. So people really need to ensure that the system they’re buying meets their integration needs. Kali from The Golden Voyage of Sinbad (1974) coordinates fighting with six arms, so she is the goddess of successful integration.
Myth: Failure is the user's fault, not the system's. This myth follows from the first two: if all systems are the same, then failure must be fault of the user. But, as we’ve seen, systems aren’t the same and many failures result from a system that doesn’t meet the user’s needs. Other research shows that users generally overcome obstacles they can control, like organization, training, and staffing levels. Of course, system selection is itself done by users, so they do have some responsibility for any problems. Talos, an animated statue from Jason and the Argonauts, ultimately fails to protect the tomb he is built to guard, so he represents a system that doesn’t work.

Myth: New users should crawl, walk, run.  Many experts – myself included – have suggested that new marketing automation users can safely start without planning by just duplicating their existing programs like email blasts, and then add more sophisticated uses over time.  But our research found that marketers who used more features from the start were happier. My interpretation is that successful marketers took the time to plan and train before deployment, while marketers who didn’t prepare in advance never found the time to learn what they needed. It’s possible to overstate this position – even successful users will add some new features over time. But the point about preparation is important. Kraken, from Clash of the Titans (1981), is a sea monster with no legs, so he never had a chance to move beyond crawling.

Myth: Bigger companies do better.  You might expect that bigger companies would do a better job with marketing automation because they have larger and more sophisticated staffs. They do in fact select more wisely, paying more attention to features and integration than marketers from smaller companies, and less to cost and apparent ease of learning. But they also face more non-technical obstacles such as training, staffing, and organizational barriers. So their over-all satisfaction level is no higher than smaller firms. I chose the giant octopus from It Came from Beneath the Sea (1955) because it’s big – no deeper meaning is intended.

Myth: Marketing automation creates prospects and saves money.  Marketers who expect their system to generate more prospects with less effort are usually disappointed. Marketing automation is basically about nurturing existing leads, not finding new ones, and most companies add staff and budget. Medusa, from Clash of the Titans, is the boss you don’t want to give bad news about system results: her dirty look will turn you to stone.

Myth: Marketing automation has stopped evolving.  Commoditization and consolidation may make marketing automation look like a mature industry.   But there's still plenty of change: new vendors entering the space, existing vendors being bought and repositioning themselves, and expanding scope to include consumer marketing, display ads, external data, better databases, identity resolution across channels, mobile apps and formats, advanced attribution, social promotions and new types of content.  The Beast from 20,000 Fathoms (1953) is a dinosaur who hasn’t evolved one bit.
So what? That’s the end of the myths, but we need to leave on a positive note.  So I end the presentation with some sound, if predictable, advice to prepare carefully, define and select against actual requirements, test integration in advance, deploy quickly, and expect the unexpected. The puzzled look on Troglodyte’s face, from Sinbad and the Eye of the Tiger (1977), represents the confusion marketers feel when wondering what to do next..
Marketers who want help selecting a system could try blowing on a ram's horn like Calibos from Clash of the Titans. Or they can just send me an email at

*              *             *

Speaking of art that's amusing if irrelevant, here's a link to a Twilight Zone-themed introduction to a football recruiting show produced by my son Brian.  The apple doesn't fall far from the tree.

Thursday, October 30, 2014 Provides Another Choice for Automated Predictive Modeling

I’m beginning to feel like Lucille Ball in the chocolate factory: predictive modeling systems are coming at me faster than I can review them. I had already planned this week to write about and then yesterday omnichannel personalization vendor Sailthru announced their own predictive solution . Now, Sailthru is interesting in its own right – it’s a Customer Data Platform with strong decisioning capabilities – but they’ll have to wait their turn. This week, I’ll stick with

By now, you can probably recite along with me as I list the key differentiators for predictive systems. Let’s run through them with as the subject.

• inputs. connects to any system with an open API, which includes most major software-as-a-service products. Vendor staff does some basic mapping for each client, which usually takes a couple of hours at most. Most of that time is spent working with the client to decide what data to include in the feed. One important feature of is that it can handle very large numbers of inputs – hundreds or thousands of elements – so there’s not much pressure to restrict the inputs too carefully. The system can also take non-API feeds such as batch data loads, although this takes more custom work. It can handle pretty much any type of data and includes advanced natural language processing to extract information from text.

• external data. Many predictive modeling systems, especially for B2B lead scoring, supplement the client’s data with company and individual information they gather themselves from sources like social networks, Web sites, job boards, and government files. doesn’t do this.

• data management. maintains a database of information it has loaded from source systems. It can accept inputs from multiple sources in different formats. Data is stored on Amazon S3 and Postgres, allowing to handle very large volumes. But the system doesn’t link records belonging to the same individual or company unless they have already been coded with a common key.

• automation. has almost fully automated the data loading, variable selection, model building, and scoring processes. The system has sophisticated features to automatically adjust for missing values, outliers, inconsistencies, and similar real-world problems that usually require human intervention. To build a new model, users simply select the items to predict and the locations to place the results. The system’s machine learning engine automatically uses existing records in the client’s database to create the model and then places the predictions in the specified fields.

• set-up time. New clients usually have their first model within one day, assuming credentials are available to connect with source systems and the vendor and client can quickly agree on what to import. This is about as quick as it gets. While other vendors work even faster, they do this by limiting themselves to prebuilt connectors to standard systems. There’s nothing wrong with that but bear in mind that even those vendors will take longer once you start to add other inputs.

• outputs. generates predictions, confidence scores for the predictions, and lists of drivers that show the reasons for the predictions. These are loaded into client systems where they can generate reports (see below) or be integrated with CRM or customer support agent interfaces.

• self-service.  After the initial setup, clients can build new models for themselves through a simple interface that basically involves specifying the source data, item to predict, and destination for the results. Adding a new data source would take some help from the vendor but should be pretty quick unless the source lacks a standard API or export tools.

• update frequency. will load data in real time as it is updated in client systems, assuming the client system supports this. Scores will reflect the latest data. The system continuously and automatically updates its models to reflect new results.

• applications. can be used for pretty much any predictive application, but the company has focused its initial efforts on customer support and retention. This involves tasks such as identifying churn risks and assigning support cases to the proper agent.

• cost. Pricing is based on the number of predictions the system generates, whether those are support tickets, email messages, or customer lists. Enterprise edition installations start in the mid-five figures (i.e., around $50,000) and can go considerably higher. A new self-service edition is limited to specific marketing automation, customer support, and CRM systems and costs somewhat less.

• vendor. The company was launched in 2013 and has some modest venture funding (published figures range from $2.5 million to $3.5 million). It has about a dozen production clients and another two dozen or so in pilot. Client include both consumer and business marketers.

Friday, October 24, 2014

SalesPredict Offers Highly Automated, Highly Flexible Predictive Modeling

A couple of weeks ago, I wrote that “predictive everywhere” is one of major trends in data-driven marketing.  I meant both that predictive models guide decisions at every stage in many marketing programs, and that models are used throughout the organization by marketing, sales, and service.

I might have added a third meaning: that systems to do predictive modeling are everywhere as well. SalesPredict is a perfect example: a small vendor with a powerful system that just launched earlier this year. Back in, say, 2008, a product like this would be big news. Today, I simply add them to my list and try to understand what makes them different.

In this case, the main technical differentiator is extreme automation: SalesPredict imports customer data, builds models, scores current records, and deploys the results with virtually no human intervention.  This is possible primarily because the painstaking work of preparing data for analysis – which is where model builders spend most of their time – is avoided by connecting to a few standard sources, currently and Marketo with HubSpot soon to follow. Because it knows what to expect, the system can easily load customer data and sales results from those systems.  It then enhances the data with business and demographic information from public Web pages, social profiles, and third party sources including Zoominfo, InsideView, and Orb Intelligence.  Finally, it produces models that rank customers based on how closely they resemble members of any user-specified list, such as customers with deals that closed or who failed to renew.  Results appear as lists in a CRM interface or as scores on a marketing databaset. The whole process takes just a few hours from making the connection to seeing scored records, with most of the time spent downloading CRM data and scanning the Web for other information. Once SalesPredict is installed, models are continuously updated based on new CRM information and on feedback provided by users as they review the scored records. This enables the system to automatically adjust as buyer behaviors and conditions change.

User interface is a second differentiator. CRM users see a ranked list of customer records with a system-assigned persona derived using advanced natural language processing, suggested actions such as which products to offer, and the key data values that influenced the ranking.  Users can drill further into each record to see more customer and company information including previous interactions, products owned, and won or lost deals. The company information is assembled from internal and external sources using SalesPredict’s own matching methods, so results are not at the mercy of data quality within the CRM. As previously noted, users can adjust a ranking if they feel the model is wrong; this is fed back to the system to adjust future predictions. Another screen shows which data values are most powerful in predicting success.  This helps users understand the model and suggests criteria for targeting increased marketing investment. Although there’s no great technical wizardry required to provide these interfaces (except perhaps the name and account matching), they do make results more easily understood than many other predictive modeling products.

The final differentiator is flexibility.  The system can model against any user-defined list, meaning that SalesPredict can score new leads, identify churn risk, or find the most likely buyers for new products. Recommendations also draw on a common technology, whether the system is suggesting which products a customer is most likely to buy, which content they are most likely to download, or which offers they are most likely to accept. That said, SalesPredict’s primarily integration with, user interface, and company name itself suggest the vendor’s main focus is on helping sales users spend their time on the most productive lead.  This is somewhat different from predictive modeling vendors who have focused primarily on helping marketers with lead scoring.

Is SalesPredict right for you? Well, the automation and flexibility are highly attractive, but the dependence on CRM data may limit its value if you want to incorporate other sources. Pricing was originally based on the number of leads but is currently being revised, with no new details available.  However, it’s likely that the company will remain small-business-friendly in its approach. SalesPredict currently has about 15 clients, mostly in the technology industry but also with some in financial services and healthcare.

Friday, October 17, 2014

Dreamforce 2014: Process Is More Important Than Analytics

photo by Dion Hinchcliffe’s Dreamforce conference this year was the usual mix of spectacle, congestion, and heart-felt philanthropy. But the main announcements felt fairly slight: a new “analytics cloud” that is primarily about visualization and a mobile app builder for the Salesforce1 platform.

The analytics cloud* is a step forward only because Salesforce has been so far behind: it bulk loads data into a star schema relational database using inverted index for speed, which is a solid but old-fashioned approach. Of course, it’s cloud-based but so are other, newer approaches that are ultimately more flexible and scalable. Solutions to the really hard problems of entity association (matching identifiers for the same person in different systems) and predictive analytics are not included. Nor does the system handle real-time updates or allow queries by external systems for purposes like message personalization. The visualization itself is indeed fast and pretty, but it’s not obviously superior to Birst (also cloud-based), Tableau, or QlikView. The core technology was acquired when bought EdgeSpring last June.

The mobile app builder for Salesforce1** is the sort of innovation only a geek would love: after all, most people don’t think much about system building in general, let alone get excited about making it easier to build mobile apps for Salesforce. But it’s certainly the more important of the two announcements, because it illustrates how broad the scope of Salesforce has become. The most impressive demonstrations were operational processes such as remote order-taking and customer support, which are far removed from traditional sales automation. They also illustrated how absolutely central mobile devices have become to most business processes, something we all vaguely realize but are still not necessarily acting upon. Business processes need to be reimagined from a mobile perspective, taking into account the possibilities of doing things instantly while on-site at a store, a shopper’s home, traveling, or whatever. This is no longer a new thought, but few companies have actually done it. By providing a drag-and-drop mobile app builder, Salesforce opens up possibilities for companies to innovate along these lines quickly, easily, and cheaply. That’s important to everyone, not just Salesforce geeks.

In fact, the closest thing I had to a deep thought during the conference was that people put too much emphasis on distributing data for decisions and not enough about distributing processes. Demonstrations for tools like Wave always show users drilling into sales data to uncover weak pickle sales at convenience stores in Milwaukee – something that’s exciting the first time but you don’t do on a regular basis. By contrast, a distributed process like better store shelf allocations provides continuous benefits, even though it doesn’t require a human analyst to have a brilliant insight. A really good organization has smoothly running processes that handle each situation according to rules that require little or no judgment. (Of course, a certain amount of discretion by empowered employees is still necessary –but I’d argue the sorts of decisions that make for, say, a great hotel experience have nothing to do with advanced data analysis.)  People like decision management guru James Taylor  have long known this and distinguished operational decisions from strategic decisions, so I guess this isn’t really a new thought, either. But, like the growing centrality of mobile, it’s something that companies need to address by giving them resources. Winners will; losers won’t. It’s that simple.

And while I’m being blunt: two Hawaiian dances in a keynote is two Hawaiian dances too many.

_____________________________________________________________*a.k.a. “Wave”, apparently to justify many Hawaii-themed promotions and an appearance by the Beach Boys.

**called “Lighting”, which suggests it was named separately from Wave, since it's unsafe to surf during electrical storms. But nomenclature notwithstanding, the two systems do seem to work together.