Selligent Brings a New B2C Marketing Automation Option to the U.S.

I’m writing this post on my old DOS-based WordPerfect software, to get in the proper mood for discussing business-to-consumer marketing automation.* The late 1990’s were really the last time we saw major innovation in the B2C market, when vendors like Unica and Aprimo released their then-innovative systems to create lists for direct mail and, somewhat later, email campaigns. Since then, the number of independent B2C marketing automation vendors has actually dwindled** as major and not-so-major products were purchased by larger companies to bundle into integrated suites or just use internally. Even the leading survivors, including Neolane (founded 2001) and RedPoint (founded 2006) have their roots in traditional outbound campaigns, although they now support Web, social, and real-time interactions to varying degrees.

Still, I’m excited as an entomologist with a new beetle*** to see another vendor in the space. Selligent  isn't brand new – the company was founded in 2000 and its marketing automation system dates back about six years – but the company says it was built from the ground up to manage real time interactions. A deep dive left me seriously impressed.

The first thing you need to know about Selligent is that while it’s new to the U.S. market, it is well established in Europe, where it serves 400 brands in eleven countries. It’s particularly strong among retailers and publishers. This experience translates into the types of refinements that can only be based on client demands. To pick a couple more or less at random, these include a matching engine that selects among hundreds of news articles to find those most relevant to individual subscribers (a sort of dynamic-content-on-steroids that’s important to publishers) and tools to manage product give-aways and rewards for viral sharing (important for retailers). They also include standard B2C features that are still lacking in most B2B products, such as precise control over user access to specific pieces of content, data, and system functions; planning hierarchy to schedule and budget for multiple marketing programs in separate organizations; and rules to limit the number of marketing messages each customer receives.

The basic architecture of Selligent is typical for B2C marketing automation systems. That is, it attaches a marketer-friendly interface for data access and analytics to an externally-built customer database. Brave users can add new fields and even entire data tables, and can import external data directly into the system. But the data must be matched on a fixed identifier, such as account number or email address. Data standardization, identity resolution, and change history (such as new vs. old mailing address) are largely handled elsewhere.

Selligent also uses the familiar flow chart interface to design its campaigns. Beyond the superficial graphics – where Selligent is no better than average – it takes a connoisseur to spot the subtle differences among these implementations. Selligent does hit a number of fine notes, including A/B tests, option to reunite branches after a split, mix of data segmentation with marketing outputs in the same flow, ability to enter and leave flows at multiple points, option to direct customers to other flows, separate schedules for individual objects within a flow, and automated warnings of incomplete designs.  Did I mention hints of almond?

But the big differences are not visible. For example, you see that a campaign flow can include a survey.  What you can't see is that surveys go beyond progressive profiling (replacing questions as they’re answered) to include branches based on customer profile and in-session answers, ability to re-ask questions after a specified period, asking questions in different languages but storing answers in one language, using the same question on multiple surveys and storing all answers in a single location, and providing statistics on completion rates, average time to complete, and which pages have the most validation issues and drop-outs. This is definitely above average for survey features in marketing automation tools, and more than competitive with many dedicated survey systems.

Selligent flows also support business processes that include external steps, such as manual review of an order. To do this, the system assign a “state” to a customer and then reacts differently within the flow based on that state. A separate “FrontOffice” module provides an interface for call center and other agents, and can apply rules to route customers to specific centers, teams, and agents. The system supports Microsoft’s computer-telephone integration (CTI) features, but doesn’t integrate with other phone systems. Basic campaign flows can send data to external systems via file exports or by calling an Application Program Interface (API).

As these example may suggest, one key theme for Selligent flows is tight integration across all channels. Perhaps the best example of this – and something I’ve looked for in many other systems and never found – is that its flows can control movement from one Web page to another. That may not sound unique, but if you look closely at other marketing automation flows you’ll see they typically end with serving up a Web form and then start independently with events triggered by completion of a form. All well and good, but it means you need to build a separate flow for each step in a multi-stage interaction. Even the real-time interaction management systems treat each recommendation independently, so the only way to create a multi-part dialog is to build separate campaigns or to build one campaign with complex qualification rules that test for completion of each dialog step before moving to the next. Both approaches involve much painstaking labor and room for error. Selligent, on the other hand, lets users connect one page to another on the flow diagram; the system then automatically embeds URL links to make the connection. I’ve had three clients ask me for this in the past year alone.

Back in the realm of the normal, Selligent also offers a graphical email/Web page designer that supports the usual design features, personalization, dynamic content.  More impressive, it has integrated multi-variate testing and lets users convert an email to a Web page, or vice versa, by pushing a single button. A single email or Web page can be shared across multiple campaigns with  changes to the master object automatically deployed everywhere it’s used. Selligent doesn’t automatically store previous versions of contents as they’re edited and doesn’t provide formal check out/check in to avoid conflicting edits. But users can always save a backup copy and the system does issue a warning when someone opens a document someone else is already editing.

Reporting in Selligent is based on information gathered from each object in a campaign flow. The specific data depends on the nature of the object.  Users can also insert objects that capture specific information such as the number of people who have passed through. There are no standard campaign reports; instead, users build their own reports by assembling the object-level information. I found this a bit odd, but Selligent said each client wants something different and prefers to create their own. The vendor has recently added a Business Intelligence module, using Quiterian (recently purchased by Actuate and renamed BIRT Analytics) which provides ad hoc analytics and visualization. Data is exported from Selligent to the Quiterian database, but this happens automatically and selections made in Quiterian are automatically copied back to Selligent as segments for marketing campaigns.

Selligent does have its weaknesses. It doesn’t easily support some advanced queries and splits, such as finding the top 100 customers per store or selecting the highest-spending person per household. It has no built-in predictive modeling or integration with third-party modeling systems, although it can easily import externally-created model scores. Somewhat surprisingly, it lacks geographic radius selections (although Quiterian has these) and doesn’t adjust sending dates or hours for the local holidays or time zones. Apparently it hasn’t needed these in Europe, where clients run separate campaigns for individual countries, countries don’t span time zones, and local units, such as provinces, are small enough to make distance-based selections unnecessary. This will surely change as it adapts to the U.S. market.

Pricing for Selligent is based on modules used and number of unique customers. There is no separate charge based on message volume or number of users. The system can be purchased as a vendor-run service, deployed on-premise, or deployed locally with Selligent executing the emails. Price for the base system starts around $6,000 per month for 250,000 contacts.  The vendor says its cost is often equivalent to what high-volume clients are paying for email alone.  As in Europe, Selligent expects to sell primary through marketing agencies and service providers in the U.S. market. 


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* Not really. I tried, but WP.exe won’t run on my current computer. Too many bits or something.

** See my list of mid-tier B2C systems.  Seven of the twelve listed are now owned by someone else.

*** Probably more excited. New beetles are pretty common.  Nearly 250 have been discovered this year alone in New Guinea  and Central/South America.

Silverpop Announces Universal Behaviors to Provide Better Cross Channel Customer Experience

At their annual Amplify conference last week, Silverpop unveiled the culmination of a two year project that conveniently matches the Customer Data Platform (CDP) concept I’ve been describing for the past month. While the timing is just coincidental, Silverpop’s Universal Behaviors provide more evidence that a new breed of system is emerging.

Silverpop’s new features load customer behaviors from all sources into a central database, match identities to create a unified customer view, and make the resulting information available for real-time, automated interactions across all channels. The central database and cross-channel treatments are two of the three capabilities I’ve defined for a Customer Data Platform. Silverpop falls short on the third CDP function, which is integrated predictive modeling.  But it has partners who fill that gap.

Many CDPs have been quietly maturing for several years.  Silverpop's two-year gestation cycle is a good example.  I can't say precisely why so many are emerging more or less simultaneously, but suspect a combination of business conditions and ever-more-urgent marketer needs.  The long-term drivers are clear: more marketing channels make customer attention harder to attract, spread behavior across different media, and require coordinated contacts across channels. As a result, marketers need a unified customer database, unified campaigns, and way to deliver messages across whatever channels customers use now or in the future. This is what they get from a CDP.

It’s less surprising to see another CDP system than to see it coming from Silverpop.  After all, Silverpop’s twin heritages in B2C email and B2B marketing automation both use simple data models: flat lists for email and basic lead/contact/account tables for B2B marketing automation. Both types of systems traditionally merge customer data using only email address.  Neither build a company's primary marketing database or shares data with external systems. So its quite unexpected to see Silverpop ingest data from any source, cross-reference any set of individual identifiers, offer access to the data, and send messages for delivery by other systems.

So how do Universal Behaviors work?  Each Behavior is first defined in Silverpop with a fixed set of attributes. Source systems then capture Behaviors and post them via an API to Silverpop.  They are stored in MongoDB, a “NoSQL” database that supports high input volumes and multiple record structures.  This is another departure for Silverpop, which uses the Oracle database in its core systems.

Behaviors include whatever customer identifiers the source system can provide: email address, cookie ID, phone number, account number, etc. Silverpop uses matches from external systems to link all identifiers associated with an individual: for example, a Web transaction might include cookie ID and email address, while an email could contain email address and account number.  Silverpop could later take a Behavior with any one of those identifiers and associate it with the same individual.  But there are limits to Silverpop's customer integration powers: it doesn’t do “fuzzy” matching to merge similar identifiers or import third-party reference databases that contain such links.  I'm beginning to see those capabilities are specialties that are not necessarily core features of a CDP because they're best purchased from third party vendors.

The initial release of Universal Behaviors, set for July, will support predefined Behaviors from ArgyleSocial social listening, Webtrends Web site behaviors, Digby location-based marketing, Invodo video, and several as-yet unannounced vendors, as well as Silverpop’s own location-based and SMS offerings. It will later add more partners, provide a system development kit (SDK) for mobile apps, and eventually allow any company to build its own connectors.

Once Universal Behaviors are loaded into Silverpop, they become available within the system for queries, program triggers, rules within programs, dynamic content, personalization, scoring, and analysis – pretty much anything that could be done with standard Silverpop data.  Program outputs such as messages and lists can be pushed in real time to external systems to manage interactions.

Silverpop has also created native integrations with Adobe and Episerver Web content management systems.  These let those systems submit a visitor ID to Silverpop and receive Silverpop data to use in dynamic content and personalization. Connectors for other CMSs will be added as clients request them. Clients could also write their own integrations using a published Silverpop API or use tags to display Silverpop-generated content on any Web page. Currently, CMSs can access selected customer attributes but not the Universal Behavior database itself.  Silverpop plans to provide full data access in the future.

The mobile app SDK will go even further, allowing apps to execute Silverpop functions such as adding a customer to a program or sending an email.  This is in addition to the standard features of submitting Universal Behaviors, reading Silverpop data, and rendering Silverpop-generated content.

The critical point in all this is that Silverpop will integrate other customer-facing systems instead of only executing interactions itself.  The integration includes sending data to Silverpop, reading data within Silverpop, and receiving Silverpop marketing treatments.  In other words, the role of Silverpop shifts from delivering customer treatments to helping other systems find the best treatments to deliver. Of course, Silverpop still retains its original execution capabilities for email and some other channels.  But it’s perfectly conceivable that a client could hook the new Silverpop features to someone else's email delivery system (not that anyone at Silverpop mentioned the possibility).

This separation between a central data-and-decision platform and multiple independent execution systems is the core concept underlying the Customer Data Platform. I’m increasingly convinced it is the only way that marketers will be able to keep up with ever-expanding channels and customer expectations.  By developing a structure that fits the CDP model, Silverpop has responded to a pressing client need and established itself in an important new category.

HubSpot Releases Social Inbox and Reveals So Much More

I spent yesterday afternoon at HubSpot’s “Open House” in Cambridge, MA, during which they briefed the community on their business progress, introduced their new Social Inbox, described their  approach to marketing and sales alignment, explained their “culture code”, and answered questions.

The most concrete news, Social Inbox, extends existing HubSpot features by more fully integrating social media monitoring and response with the HubSpot interface. The Social Inbox presents a list of Twiter posts by user-specified individuals or containing specified key words. Users can drill into each post to see a complete profile of the poster. The big deal in HubSpot’s eyes is the profiles include all information the HubSpot database about each person, and are even color-coded with the sales lead stage. The data includes Web and email behavior captured directly in HubSpot, data imported from Salesforce.com, and whatever else the system has available. Users can respond directly, forward a post to someone else, or add the poster to a HubSpot campaign. The system can automatically alert users to new Tweets as they happen or on a regular schedule.

HubSpot said they couldn’t find any other product that combines this type of social monitoring with access to such deep profiles. I can’t immediately think of one either, although it might exist. Either way, uniqueness is less important than the value provided, which is considerable.

What’s ultimately more interesting, however, is that Social Inbox is aimed at managing one-on-one interactions between users and individual contacts.  This sort of contact management is quite different from HubSpot’s traditional focus on attracting inbound traffic or even from conventional marketing automation.

The new features came up again later in the day, when the audience asked several pointed questions about whether HubSpot would eventually add a full CRM capability. This caused by far the most discomfort of any topic addressed by a management team which provides itself on transparency. Answers ranged from a coy “we think about a lot of things” to a fairly definitive stream of conscious listing of the arguments against adding CRM.  The currently dominant line of thought seems to be that HubSpot already provides adequate features for clients who want light contact management, while adding full CRM features would only lead to a losing battle with Salesforce.com. Unstated but hovering in the background was the fact that Salesforce.com is an investor in HubSpot and might some day consider buying them to expand its own marketing scope. CRM would make HubSpot less attractive to Salesforce, since it would create a set of redundant features that need to be supported or removed.

But the most fundamental reason that HubSpot management seems genuinely disinclined to add CRM is that they see HubSpot’s mission as transforming marketing. There’s a distinctly messianic gleam in CEO Brian Halligan’s eyes when he says this and the vision is no doubt shared widely across the company. In fact, it’s arguably more surprising that HubSpot has overcome its marketing focus to introduce the contact management features already in place. My take is that customer needs – another HubSpot mantra – have driven the system in this direction despite management reluctance. The system has a will of its own.

Admittedly, I’ve been arguing this for a long time: the need for integrated customer treatments will eventually lead marketing automation, CRM, and Web content management to become a single system, or at least to share a common customer database. HubSpot’s current vision of highly personalized data-driven marketing is consistent with this. The current vision is also quite different from the original HubSpot vision of attracting traffic through huge volumes of great (but not personalized) content. But the new vision is a logical extension of the original: once you’ve attracted people and start to learn their preferences, the more you’re able to make targeted content recommendations. And, the more content you have available, the more you need those recommendation to point people at the right materials.

This brings HubSpot right back to contact management, because the same data used to recommend marketing content can, and should, be used to recommend treatments during personal interactions. It’s possible to simply push recommendations to an external CRM platform, but setting a connection for each point of contact quickly becomes a lot of work. The temptation to eliminate that work by building an integrated CRM system is hard to resist.  As I say, the system has a will of its own.

Incidentally, there is another way to look at this. The traditional view sees marketing as making automated contacts, while sales and service use human agents, supported by CRM, for individual interactions. This is why CRM seems foreign to a marketing system. But the automated-vs-human division is no longer so clear cut. Social media marketing is mostly done by humans through one-on-one messages, while many sales and service interactions are automated. In this view, HubSpot needs contact management features even if it rigorously restricts itself to serving marketers alone.

The problem with this approach is that it denies sales and service the benefit of HubSpot’s data and customer understanding – a terrible waste of corporate resources. So this view also pushes HubSpot towards a unified marketing and CRM system, or at least a database and recommendation engine that’s accessible by both HubSpot and a separate CRM. I swear I didn’t mean to end up here, but this does lead to the Customer Data Platform I’ve been discussing over the past few weeks. I don’t think HubSpot management wants to move in that direction, or even that they necessarily should.  But these things have a will of their own.

Provenir Adds Social Listening to Customer Decisions: Another Customer Data Platform

I’m still collecting examples to illustrate my new category of Customer Data Platform (CDP) systems. The latest is Provenir, a company founded in 1992 that has long sold a system to make credit risk and fraud decisions in real time. Over the past year, the company has added “social listening” capabilities and begun offering itself to marketing agencies as a customer interaction manager. It has met with good success and is now offering its “social listening platform” more broadly. *


It’s a slight stretch to call Provenir a CDP, because it doesn’t manage a permanent customer database.  Rather, like most interaction managers, it calls data from external sources during each decision.  But Provenir does have some customer matching capabilities and stores at least some information internally. Moreover, it completely meets the other three CDP criteria: predictive modeling, real-time decisions/recommendations executed through external systems, and a non-technical user interface. It’s also sold as the “glue” connecting data sources, modeling, and execution systems, which is exactly the role played by a CDP.  So, what the heck…welcome to the club!

Provenir is organized around process flows, which cover a particular task such as reacting to a Web site visit. Users define each process by building a flow chart, or, as the cool kids call them today, a graph.** These, um, graphs***, can contain branches, loops, and other advanced structures.  The nodes can also contain other graphs that define a subprocess in more detail. Nodes can perform a wide range of operations including data gathering, calculations, updates, decisions, and messages to external systems. Although setting these up is inevitably rigorous, Provenir makes it as painless as possible by providing help such as letting users draw lines to map fields from one system to another; building rules through score cards, tables and decision trees; and warning if a flow is incomplete.

Provenir relies on external systems to assemble, integrate, and store customer data.  Users can build matching processes with system graphs, although the vendor recommends connecting to other products to load reference data or do advanced "fuzzy" matching.  Provenir can monitor source systems for selected events and issue queries to assemble data as needed. The social listening features can monitor Twitter for keywords and Tweets by specified individuals.  These can trigger process flows that can retweet a message, send a direct Twitter message to the poster, or respond through another channel. The system can also monitor and post messages on Facebook. Other channels will be added over time.

Predictive modeling in Provenir is also done in external systems. The system can import PMML code or call models in SAS, R, or even Excel. Data mapping functions can automatically extract the list of required variables from PMML, do basic transformations and calculations when loading model inputs, and manage parameters, constants, and local variables.

Decisioning is Provenir’s greatest strength. The process flow…I mean graph…is inherently very flexible, and the ability to define rules as tables, trees, score cards, and other formats adds even more power. Users can set up champion/challenger tests as splits within a process flow; results are stored in a database for analysis and reporting. Users can also build simulated data sets, containing specified distributions of particular variables, and use these to forecast results of their flow designs. Such simulation is one mark of a mature decision system.

Provenir has some built-in messaging capabilities, but most decisions are executed externally.  The system has been connected with email, Web content management, call centers, campaign management, text messaging, and other execution platforms.

Pricing for Provenir’s social listening product is based on the size of the customer database. Starting price can be as a low as several thousand dollars per month. The system is usually sold on a Software-as-a-Service (SaaS) basis, but on-premise licenses are also available.


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* For extra credit, compare and contrast Provenir’s primary Web site  with the site for their listening division.

** Defined in Wikipedia as “mathematical structures used to model pairwise relations between objects”.

*** Would it be even cooler to call them grafs or, better still, grafz?

I've Discovered a New Class of System: the Customer Data Platform. Causata Is An Example.

It has taken me a while to connect the dots, but I’m now pretty sure I see a new type of software emerging. These systems that gather customer data from multiple sources, combine information related to the same individuals, perform predictive analytics on the resulting database, and use the results to guide marketing treatments across multiple channels. This differs quite radically from standard marketing automation systems, which use databases built elsewhere, rarely include integrated predictive modeling, and are focused primarily on moving customers through multi-step campaigns. In fact, the new systems complement rather than compete with marketing automation, which they treat as just one of several execution platforms. The new systems can also feed sales, customer service, online advertising, point of sale, and any other customer-facing systems.

Given how much vendors and analysts love to create new categories, I’m genuinely perplexed that no one has yet named this one. I’ll step in myself, and hereby christen the concept as “Customer Data Platform”.  Aside from having a relatively available three letter abbreviation (see Acronym Finder for other uses of CDP), the merits of this name include:

- “Customer” shows the scope extends to all customer-related functions, not just marketing;
- “Data” shows the primary focus is on data, not execution; and
- “Platform” shows it does more than data management while supporting other systems

But, you may ask, is this really new? Certainly systems for Customer Data Integration (CDI) have been around for decades: these include specialized products like Harte-Hanks Trillium and SAS DataFlux, CDI features within general data management suites like Informatica and Pentaho, and integration within cloud-based business intelligence products like GoodData and Birst. Many of those products have limited capabilities for working with newer data sources like Web sites and social networks, but the real distinction between them and CDPs is that the older systems are mainly designed to assemble data.  Some also provide analytics, but they don't extend to real-time decisions based on predictive models.

Similarly, there have long been specialized systems for real-time interaction management (such as Infor Interaction Advisor and Oracle Real Time Decisions) and for predictive modeling (SAS, IBM SPSS, KXEN). Some interaction managers do create predictive models, and the really big vendors (IBM, SAS, Oracle) have all three key components (CDI, real-time decisions, and predictive models) somewhere in their stables. But systems that closely couple just those features with the goal of feeding data as well as recommendations to execution systems? Those are something new.

By now, you’re probably wondering if I’ll ever get around to actually naming the vendors I have in mind. I’ve recently written about some of them, including Reachforce/SetLogik and Lattice Engines.  I also include RedPoint in the mix, because it has all the key capabilities (database development, predictive models, and real time decisions) even though it also offers conventional campaign management. Others I haven’t yet written about include Mintigo and Gainsight. Of course, each has a different mix of features and its own market position.  Indeed, several have specifically told me they do not compete with the others. Fair enough, but I still see enough similarity to group them together.

All this is a very long-winded introduction to Causata, yet another member of this new class. By now, you can probably guess Causata’s main functions: assemble customer data from multiple sources, consolidate it by customer, place it in an analytics-friendly format, run predictive models against it, and respond in real time to recommendation requests from other systems including Web sites, email, banner ads, and call centers. And you’d be right.

But that’s not the end of the story. With any product, it’s the details that matter. Causata is particularly strong in the data management department, accepting both batch and real-time data feeds and storing data as different types of events (email sent, Web site visit, call center interaction, etc.), each having predefined attributes. The system also has a particularly sophisticated “identity association” service, which looks for simultaneous events involving different identifiers as a way to link them, and can chain identifiers that were linked at different times. When I spoke with Causata about two months ago, the association rules were pretty much the same for all clients, but they promised users would get more control in the future. Users could already choose which types of associations to use in specific queries.

Causata stores the assembled data in HBase, a Hadoop-based database management system that is particularly well suited to large data volumes, many different data types, and ad hoc queries. In addition to the raw data, the system can store derived values such as aggregations (e.g., number of Web page view in past 24 hours) and model scores. Users can run SQL queries to extract data for analysis and predictive modeling in third-party software including QlikView, Tableau, SAS, and R. Prebuilt QlikView reports show the predictive power of different variables for user-specified events. The lack of native analysis and modeling tools creates some friction for users, but also lets them stick with familiar products. So the pros and cons probably cancel each other out.

The system’s decision tools are straightforward. For each situation, users define a “decision engine” that can select among multiple options, such as campaigns, products, or marketing content. These options can have qualification rules. To make a decision, the system can test the options in sequence and pick the first one for which a customer is qualified, or pick the option with the highest predictive model score. Users can also specify a percentage of customers to receive a random option, to gather data for future decisions. An engine can return multiple decisions for situations that require more than one option, such as a Web page with several offers. Causata has some machine learning algorithms to help with the decision process. It plans to expand these to automatically select the best option in a given situation.

Decision engines are called by external systems through a Web services API that can respond in under 50 milliseconds. This is fast enough to manage Web banner ads – something not all interaction managers can achieve. Model scores and other data are updated in real time during an interaction.

Causata can be deployed on-premise by a client or as a cloud-based service. The vendor says a typical implementation starts with three or four data sources and is deployed in about 30 days – very fast for this type of system. In February, Causata introduced prebuilt applications for cross-sell, acquisition, and return programs in financial services, communications, and digital media. These will further speed deployment.

Pricing is based on the number of data sources and touchpoints, with additional charges based on data storage. Cost begins around $150,000 per year.

Lattice Engines Automates All Steps in Prospect Discovery

There’s nothing new about using public information to identify business opportunities: it’s why lawyers chase ambulances and bankers phone lottery winners. But the Internet has exponentially grown the amount of data available and made it easily accessible. What’s needed to fully exploit this resource is technology that automates the end-to-end process of assembling the information, identifying opportunities, and delivering the results to sales and marketing systems.

Lattice Engines was founded in 2006 to fill this gap. The system scans public databases, company Web pages, and selected social networks to find significant events such as title changes, product launches, job openings, new locations, and investments. It supplements this with data from the clients' own systems including customer profiles, Web site visits, and purchases. It then looks at past data to find patterns which predict selected outcomes, such as making a first purchase, buying an additional product, or renewing. It uses these patterns to identify the best current prospects for each outcome, and makes the lists available to marketing systems or sales people. The sales people also see explanations of why each person was chosen, what they should be offered, and recommended talking points.

Each of these steps takes significant technology. Lattice Engines currently monitors Web sites of five to 10 million U.S. businesses, checking daily for changes.  The system’s semantic engine reads structured texts such as management biographies and press releases, extracting entities and relationships but not trying to understand more subtle meanings such as sentiment. Clients specify blogs to follow, which receive similar treatment. The company also monitors Twitter, Facebook company pages, Quora, and LinkedIn profiles of people within each sales person’s network. Additional data comes from standard sources such as business directories and from special databases requested by clients. Information from all these sources is loaded into a single database available to all Lattice Engine clients.

Lattice Engines also imports data from the clients own systems, although of course this isn’t shared with anyone else. Again, there’s some clever technology needed to recognize individuals and companies across multiple sources. Lattice Engines doesn’t try to link personal and business identities for individuals.

All this information is placed in a timeline so that modeling systems can look at events before and after the target activities. The models themselves are built automatically, once users specify the target activity, product, and time horizon. Users can then build a list of customers or prospects, have the model score it, and send high-ranking names to marketing or sales for further contact. Results can be exported to a marketing automation system or appear within the sales person’s CRM interface. Lattice Engines is directly integrated with cloud-based CRM from Salesforce.com, Microsoft Dynamics, and Oracle, and via file transfer with SAP CRM. Users can export lists to Excel and Marketo, with connectors for Eloqua and other marketing automation systems on the way.

The net result of this is a single system that performs all the tasks needed to exploit the wide range of information available about customers and prospects.  Marketers could theoretically use separate systems for each step in the process, and integrate the results for themselves.  But few really have the skills to do this.  And, in most cases, it would be more expensive than purchasing a single system like Lattice Engines.  It's particularly helpful that Lattice Engines supports both prospecting and customer management -- further reducing the need for multiple products, and further encouraging cooperation between marketing and sales departments. 

Pricing for Lattice Engines starts at $75,000 per year and grows based on the number of data sources and sales users. Client data volume doesn't affect the cost, since Lattice Engines’ own databases are vastly larger than any client data. The company has close to 50 deployments, nearly all at large B2B marketers including Dell, HP, Microsoft, ADP, and Staples.

Adometry Combines Attribution with Optimization

So…my last two posts on attribution systems (MMA and VisualIQ ) were among the least popular ever, right down there with Marketing Lessons from Chernobyl (which, let’s face it, was in pretty poor taste). But vox populi isn’t always vox Dei, eh? I think it’s an important topic, so here we go again.

The lucky recipient of that less-than-stirring introduction is Adometry, which in no way deserves any disrespect. From humble beginnings in click fraud prevention, they have grown in recent years to be one of the leaders in algorithmic response attribution. Their latest expansion moves them beyond digital channels to offline media including direct mail, television, and print. They have also moved from attributing past results to using predictive models to optimize current and future campaigns. Impressive.

The core of Adometry’s attribution methodology is to compile the sequence of marketing messages seen by each individual, and then compare results of individuals whose sequence differs by only one message. Any difference in results is then attributed to that message. This is conceptually simple, but requires clever treatments to handle low volumes for specific sequences and to isolate the impact of attributes such as placement, time slot, creative, and list segment. Adometry also lets users model against multiple events in the customer life cycle, such as sign-ups, first purchase, and repeat purchase. It calls these all conversions, which I personally found a bit confusing but suppose would quickly get used to.

The system also classifies each conversion as attributable, multi-touch, and multi-channel, depending on whether it was linked to at least one message (attributable), to multiple messages (multi-touch) and to messages in multiple channels (multi-channel). For each category, it shows the conversion count and revenue: so, for example, you see the number and revenue for multi-touch repeat purchases. That’s a lot of information to digest, but does give a great deal of insight into the effect of different promotions and channels on different parts of the business. This encourages marketers to look beyond any single measure, such as cost per order, that tells only a small part of the business story.

The system’s optimization process begins with the attribution analysis, but then adds auto-generated predictive models to estimate the impact of future ad plans, including interactions across channels. Users can enter scenarios with budgets for multiple channels and campaigns, and then apply other constraints such as limits on the change in spending per channel. They also define output measures for the system to optimize against: like other optimization systems, Adometry can only optimize against a single measure, but this can be a composite of several items. For each scenario, the system will determine the optimal budget allocation and show the expected results across each output measure. Users can modify the recommended plan and have the system re-forecast the results. The final plan can be output to a spreadsheet for further editing. Adometry can also be connected directly to ad buying platforms, including systems for real time bidding on individual impressions.   The company says optimization typically yields a 20% to 40% improvement in ad-to-sales ratios.

The database of marketing messages per individual can be used for other types of analysis. These include reach and frequency reports, which show the number of individuals reached in total, reached in each channel, and reached exclusively for each channel. The reports count impressions as well as individuals; show how many people were reached in each combination of channels; show the number of people with each number of impressions (one, two, three, etc.); and show the current member count in each funnel stage.

Adometry’s data comes primarily from tags embedded in advertisements, emails, and other online messages, which drop cookies to identify who sees which message. The system can also draw data from Web server logs or third party tags. Adometry can further enrich its database by appending external information about individuals, using both online and offline sources. This lets it profile the audiences associated with different events, channels, campaigns, and other attributes. Optimization models can use data that can’t be tied to specific individuals, such as weather, economic conditions,  and mass media like television and print. The system can also verify which ads were actually seen by individuals, providing more precise inputs to the attribution calculations.

Pricing for Adometry is based on the number of channels and volume of data. It starts around $100,000 per year for the smallest clients with enough volume to use the system effectively (about 30 to 50 million impressions per month). Currently, more than 50 companies use Adometry’s attribution services.