0 Salesforce + ExactTarget vs. SAP + hybris: Two Paths to Customer Management

Fresh on the heels of Tuesday's blockbuster ExactTarget / Salesforce.com deal, SAP Wednesday announced acquisition of e-commerce vendor hybris software.  Since Salesforce said that other companies also wanted to buy ExactTarget, it seemed possible that SAP had lost the deal and purchased hybris as a second choice. After listening to the analyst conference call (available at (303) 590-3030 passcode 4623918), I still can't say.

The SAP and hybris managers unfairly implied during their call that ExactTarget does nothing but email (without mentioning Salesforce.com or ExactTarget by name).  But as Salesforce.com made clear in its own call yesterday, they were most attracted by ExactTarget's multi-channel marketing capabilities.  It's possible SAP wanted ExactTarget for the same reasons and would have described it differently had they been the winning bidder.

In any case, SAP did tell a good story: real-time interactions seamlessly presenting customers with consistent information, dialogues, and purchases across all channels, with a central role for the Web.  This is certainly the long term goal for most marketers, although few are close to delivering it.  As SAP pointed out, it's a customer-centric view of the world, quite different from the operational focus of traditional CRM.  SAP does have some unique assets to support this vision, including back-office systems with sales, inventory, costs, and other data needed to fully inform customer treatments, and the in-memory HANA database to make this data immediately available for real-time interactions.  I haven't done enough research to judge whether SAP can effectively combine these pieces, but they're making the right promises.

I still wouldn't be as dismissive of the Salesforce / ExactTarget combination as the SAP managers.  People integrate CRM with back-office systems all the time.  You can also build great customer experiences with little or no back office integration.  ExactTarget does have some Web personalization features (from its iGoDigital acquisition), although I don't know how well they're integrated with the rest of the system.  Similarly, it has claimed to support real-time interactions in its Interactive Marketing Hub, but I don't know how well that works.  What I do know is that Salesforce and ExactTarget have a reasonable idea of what's needed and the resources to build it.  How well and how quickly they execute remains to be seen -- but you can say the same for SAP.

Incidentally, the common thread for these acquisitions is that both vendors are moving into direct B2C marketing.  It's a big new market for each of them, and makes both much more interesting competitors to IBM, Oracle and Adobe.  Perhaps that's the most important news here.

It would be misleading to give the impression that SAP and Salesforce are equivalent.  The two deals highlight some very fundamental differences:

- SAP is a full enterprise system; Salesforce is about CRM. The SAP managers made the point most clearly when they discussed that their appeal is targeted at the boardroom level: they are selling to companies who want to build their entire infrastructure on SAP's system.  Salesforce is now, finally, adding serious marketing to its CRM system (although there are still some gaps such as media buying), but even so its vision is still limited to customer management, and it is selling at the level of sales, service, and marketing departments -- rarely in the boardroom.  Note that the original concept of CRM already encompassed those departments, so this is less an expansion than a filling of gaps.

- SAP is a suite; Salesforce is a platform.  Indeed, SAP is the ultimate suite: every enterprise function running on a single, tightly integrated system.  I've long argued that the fundamental rule of software marketing is that "suites win", meaning most companies will choose an integrated suite over multiple best-of-breed point solutions.  SAP's success is Exhibit A in my evidence for this, but you could argue it's actually so large that companies might be just as happy with several smaller suites instead (e.g., one for CRM and one for back-office).   This would still let them avoid doing most of the integration work, while not forcing them to commit totally to one vendor's system. 

Salesforce is also an integrated suite, although limited to CRM.  But it has also embraced (and I think invented) the idea of an open platform: a foundation system that can be supplemented by attaching other vendors' products.  This provides easy integration without limiting users to capabilities provided by the suite vendor.  The model has been tremendously successful for Salesforce, particularly at letting it offer advanced functions to its clients without having to pay for developing those functions.  ExactTarget has embraced a similar model, incidentally.

- SAP is largely on-premise software; Salesforce is Software as a Service (SaaS).  It's true that SAP now offers SaaS options, but it was built as on-premise software and its large enterprise clients still mostly run it that way.  hybris also offers both options but runs mostly on-premise (typical for Web content management).  Salesforce of course is the granddaddy of all SaaS companies.

- hybris runs Web sites; ExactTarget is still primarily about email.  The obvious point of this is that Salesforce still needs serious Web site management to provide comprehensive customer treatments.

But the difference goes deeper.  Web sites are inherently real-time systems, while email is inherently batch processing.  This was the essence of SAP's comments today, and while they may understate ExactTarget's abilities, there is a kernel of truth.  Web systems are engineered from the start for high-speed processing, and the e-commerce features of hybris also mean it was engineered from the start to interact with individual customers, not just serve generic Web pages.  Email systems were originally engineered for batch processing, not individual interactions.  Mobile and social messages, which ExactTarget also supports, can also be handled quite well in batch.  I don't know to how far ExactTarget has evolved towards supporting real-time interactions, but its heritage lies elsewhere.

- hybris has 500 customers; ExactTarget has 6,000.  The revenue difference is much less: $100 million for hybris and nearly $400 million for ExactTarget.  What this reflects is that hybris' clients are mostly large enterprises, while ExactTarget has a broad mix of large and small companies.  Each each a good match for the core business of its new owner: SAP also focuses on large enterprises, while Salesforce sells to pretty much everyone. The broad reach of ExactTarget was certainly part of the reason that Salesforce wanted it, but Salesforce already has well over 100,000 clients, so the net increase isn't all that important.

What all this means, I think, is that SAP and Salesforce represent very different approaches to customer management: SAP proposes a single, tightly integrated, highly responsive real-time system where everything is connected and optimized.  Salesforce offers a looser set of connections with less control but more room for variety, change, and innovation.  SAP will sell more to the boardroom while Salesforce will sell to sales and marketing departments.  I frankly expect that both will succeed; it's a big market and each approach will appeal to different customers.  What I really hope is that both will show the market how to do integrated, cross-channel customer management: that way, everybody wins.

Circling back to the original question: I still don't know whether SAP tried to buy ExactTarget.  Based on the what I wrote above, hybris was a better fit.  But the SAP managers spent so much time disparaging email in their call that I thought I smelled sour grapes. Or was it just competitive vitriol?

0 Neolane Interaction Tightly Integrates Real-Time and Outbound Marketing Campaigns

As I mentioned last week, there haven’t been many new B2C marketing automation products in recent years. But this doesn’t mean the industry has been stagnant. New developments have come from established vendors who are steadily expanding their products.

Neolane has been one of these, growing from its roots in email to encompass other outbound and inbound channels, and more recently with a slew of social and mobile marketing features. One of its offerings, originally launched in 2009, is its real-time interaction manager, Neolane Interaction.

Interaction performs the same basic functions as other interaction managers: it receives a request from an external touchpoint that is engaged with a customer, selects the best treatment, and returns the recommendation to the touchpoint.  However, it differs in several key details:

- it supports both batch (outbound) and real time interactions. This is unusual, because efficient processing usually takes different data structures and methods for batch vs. real time. But Neolane cites one customer delivering 10,000 Web recommendations per minute and another sending ten million customized emails per week, so it has apparently found a way to handle both. Supported channels include email, Web, social, mobile, call center, point of sale, and SMS.

- it uses the same offer library for real-time and outbound campaigns. This means that offers can be used interchangeably in both types of campaigns – something that considerably simplifies program design and analysis. Each offer can include separate content for the different channel formats. Users specify which offers are available in which channels, to ensure an offer isn't recommended where it shouldn't be.

- it draws data from the Neolane marketing database as part of its offer selection rules. This is different from most interaction managers, which query external systems rather than maintaining their own persistent customer database. (Neolane could also do external queries.)  Again, this approach raises a technical eyebrow, since traditional marketing databases are not structured for real-time interactions. But Neolane seems to pull it off.  I suppose it helps that Neolane can attach to any data structure, so clients who want real-time interactions presumably build a real-time-friendly database.

- it supports both anonymous and identified customer interactions. Most real-time interaction managers make customer-specific recommendations, although Web recommendation engines are often designed for anonymous visitors. It makes sense for Neolane to support both, again serving the greater goal of providing one marketing system to meet as many needs as possible.

These items all grow out of the fundamental fact that Interaction is a module within the larger Neolane system, rather than a separate product. Other features of the system are more typical of dedicated interaction managers:

- eligibility rules for each offer. There are also eligibility rules for offer categories, which save effort by applying the same rule to multiple offers.

- offer arbitration (i.e., choosing which of several eligible offers to return). Offers can be ranked using fixed weights assigned to each offer; by calculating weights with a formula that draws on customer data; or with an unusual “autolearn” function that adjusts the weights to ensure that the offer will be seen. The system does not incorporate any of predictive modeling, although model scores built elsewhere could be used in weighting formulas.

- each recommendation is independent of a larger dialogue flow. At best, marketers wishing to deliver a sequence of treatments could create eligibility rules that check for previous treatments. This limitation is typical of real-time interaction managers.

- a simulation function that estimates how often each offer would be presented to an audience with specified characteristics. This helps marketers see the results of different eligibility rules and offer weights, which can be difficult to estimate in advance. It’s found in some but not all interaction managers.

The downside of Neolane’s approach is that customers who only want an interaction manager must still purchase the full Neolane system. This isn’t necessarily cost-prohibitive: the company says its average deal for the base system plus Interaction is around $350,000. Actual fees depend on the number of interactions processed. The system is available for on-premise, hosted, or “hybrid” deployment (on-premise installation with Neolane-hosted email delivery). Neolane says about 60% of its clients choose an on-premise option.

To end on a positive note: Neolane sold 22 new Interaction installations in 2012, up from 17 the year before. This makes it one of the company’s fastest-growing products.

0 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?

0 IBM Interact Adds Interactions to Enterprise Marketing Management

My continuing tour of real time interaction managers landed with the good folks at IBM two weeks ago, where I caught up with what’s now IBM Interact. The product was originally launched more than a decade ago by Unica as Affinium Interact.*

The concept of Interact has stayed quite consistent over the years, although the underlying technology has been overhauled several times. The general trend of the changes has been closer integration with other components of the IBM/Unica marketing suite. For example, the original Interact had its own flow chart interface, but the system now uses the same segmentation interface as IBM Campaign. The two modules can also share segment definitions, offers, and interaction history. There’s also some integration with other IBM marketing products, notably the Product Recommendation component inherited from IBM’s CoreMetrics acquisition.

Interact's concept is the same as other interaction managers: touchpoints send it data about a current interaction; the system uses rules, models and data to select one or more offers; and the offers are sent back to the touchpoint for delivery. The differences among these systems are matters of nuance: Interact stores its own permanent customer profiles, while some other systems must re-load data from external systems during each interaction.  Interact assigns fixed scores to offers within each segment definitions, while other systems use scoring formulas shared across segments (although Interact can do that too).  Interact can create self-training predictive models, not all competitors have this option.


A couple of other features seem more or less unique. Interact determines whether customers are eligible for an offer using either qualification rules or Campaign-generated “white lists” and “black lists”; other systems use rules alone. Interact can also assign offers at global, segment, or individual levels, while other systems don’t provide all those choices.

It’s unlikely that any of these differences make Interact significantly more powerful or easier to use than competitors. In practice, the system’s major appeal will be its close integration with Campaign and other IBM products. It is now part of the IBM’s Enterprise Marketing Management (EMM) group, which includes both Unica and Coremetrics, both acquired in 2010. This group supports IBM’s larger strategy of selling systems that use huge quantities of data to run all aspects of large organizations. The company has identified marketing organizations as a major potential market within this strategy and is spending aggressively to both develop that market and take advantage of it.

You might think that Interact plays a central role in IBM’s marketing ecosystem: after all, real-time interactions are the epitome of data-driven marketing. But just a tiny fraction of IBM’s 2,500 EMM customers use Interact (actual figures are confidential) and most deployments seem to be focused on specific -- dare I say tactical? -- applications in one or two channels. The company’s EMM focus seems to be more on analytics and outbound marketing: for example, its most recent EMM acquisitions were Tealeaf Technology (Web experience analysis)  and DemandTec (merchandising analysis) . But it does report increasing interest in Interact among its clients, and high hopes for future growth.



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*A year’s free AARP membership to everyone who remembers the Affinium brand and can sing the jingle.**

** Okay, just kidding.  There never was an Affinium jingle, so far as I know.

0 A Framework for Real Time Decision Management: How SAS RTDM Fits In

I’ve had a couple of consulting projects recently that involve real-time decision systems (a.k.a. real time interaction managers), which are used to select the best treatment during a Web visit, telephone call, or other interaction.  This type of software has been around for two decades or more and repeatedly proven its value, but still has relatively few implementations.

There are many possible reasons for the slow adoption.  Maybe marketers don’t realize how much  improvement they get from driving recommendation with predictive models rather than simple rules.  Perhaps the decision capabilities built into delivery systems are already adequate.  The delivery systems are controlled by Web and call center managers who are not incented to generate revenue and may not be interested in a shared decision engine to coordinate customer treatments. Maybe each of these plays a role.

Still, the interest among my own clients has been enough to spur a fresh look at the vendors in this field.  To gather this information systematically, I need a framework that lists standard features and options within those features.  This makes it easy to isolate critical differences among the products.

For real-time decision managers, the framework includes:

• connecting to external systems.  This includes the touchpoints (customer-facing execution systems), such as Web sites and call centers, and other systems with relevant data, such as order processing and marketing databases. Connections to touchpoints are typically through Web Services calls; connections to other sources are usually made through API calls and SQL queries. The connections are set up during system implementation and then used in real time to look up information about a specific individual during an interaction. One important difference among real time decision systems is whether they look up information each time they are asked for a decision, or whether they look it up once at the start of an interaction and then retain it in a session until the interaction is complete.  The session reducing workload and helps to run multi-step dialogues. Another difference is whether the system maintains its own permanent database of individual profiles and contact history or must query external systems for all data.

• making decisions based on rules and predictive models. Rules are always available; systems differ greatly in how hard they are to build and maintain. Predictive models are optional.  They be built outside of the system, built within the system in periodic (batch) processes, or built and updated automatically. Systems also differ considerably in how they choose among competing treatments, a process called “arbitration”. The ranking may be as simple as picking the offer most likely to be accepted, or it may involve complex user-specified considerations such as offer value, sales targets, and business priorities. Some systems let users apply weights to multiple factors.

• integration with campaign and content systems. Early decision systems were not connected to outbound campaign managers or to content stores. But today they are often part of a larger marketing suite that includes an outbound campaign manager. The decision system may share campaign flows, offer definitions, customer data, analytics, and other features with the campaign manager. This simplifies training and facilitates integrated, cross-channel customer treatments. But even the unified systems typically run the outbound campaigns and real-time decisions on separate engines, each optimized for its particular type of processing. Regarding content: the decision systems traditionally returned a content ID that the execution system converted to actual content internally. When the decision manager is part of a marketing suite that includes a content repository, it can return the content itself.

• deployment model. Most real-time decision managers are deployed the old-fashioned way, as on-premise software. This gives clients the greatest control over security and performance. Some are cloud-based or vendor hosted (not precisely the same thing, but close enough), which simplifies deployment. Several vendors offer both options.

With that framework in mind, let’s take a look at another product in this group: SAS Real Time Decision Manager (RTDM).

RTDM meets all the framework requirements: it receives a Web Services request from an external system for a decision, runs the request through rules and models, and returns one or more choices. The results are usually displayed in a slot on a Web page or call center screen, although they could also be presented in an email, mobile device, or other channel.

RTDM leans toward the simpler end of most framework options. Each request loads fresh data from the touchpoint and other source systems, even within a multi-step interaction. At best, users can create continuity by storing a session token at the end of one interaction and retrieving it at the start of the next interaction. The systems returns tags, IDs or URLs but not actual content.

Decisions are based primarily on rules. These can incorporate predictive models, but the models themselves are built outside of the system, using SAS or other products, and do not self-adjust based on results. The system can select among multiple results by sorting on one or more user-specified variables, although any more complex arbitration requires custom coding in the SAS language.  Such  formulas could be registered in the system and reused across campaigns. Users can define a group of treatments, called a “campaign set”, that share a single set of eligibility rules. Individual treatments can also have their own eligibility rules that are applied whenever the treatment is used.

RTDM is tightly integrated with SAS’s campaign management system, SAS Marketing Automation. It shares the same campaign flow interface, treatment library, and database of contacts and responses. Predictive models built with SAS tools are also available to both.  Both use other SAS platform components including data structures, reporting tools, and other general functions. RTDM can be installed on-premise or hosted by SAS.

RTDM has been around in some form since 2008, although integration with the Marketing Automation treatment library is more recent. The system has sold more than 50 licenses, although fewer than half have been deployed. SAS says most deployments have been single-channel, single-purpose projects. Deployment has come slower where RTDM is part of a larger multi-channel deployment involving other SAS marketing products.  The other components must be put in place before the client is ready for RTDM.

Pricing of the system is based on the number of decisions processed or call center seats.  Cost starts around $150,000.

0 Infor Epiphany Marketing and Interaction Advisor: Good Examples of B2C Marketing Automation

Epiphany was one of the high-fliers of an earlier marketing automation boom: launched in 1997 with an initial public offering in 1999, it traded stock for a full suite of marketing and CRM systems before its price collapsed. The remains were scooped up in 2005 by SSA Global, which was itself purchased in 2006 by enterprise software vendor Infor. Through all this, the company’s products continued to sell with little change. The crown jewel turned out to be RightPoint, a pioneering real-time interaction manager now called Interaction Advisor.

Infor has recently renewed its commitment to the Epiphany line, increasing investment in the product and its marketing. Recent improvements include a unified interface for inbound and outbound campaigns, tighter integration among its components, greater scalability, support for more channels, and pre-packaged solutions for specific applications. The vendor has integrated with Orbis Global for marketing resource management and has an AppExchange integration with Salesforce.com. A new user interface is planned for next year.

As I noted in yesterday’s post, B2C marketing systems like Epiphany have actually been more popular acquisition targets than B2B products. Since many readers of this blog are unfamiliar with the B2C products, it’s worth taking a detailed look at Epiphany’s components.

Let’s start with the marketing automation product, Infor Epiphany Marketing. This sits on a marketing database built outside of the system; part of the set-up is mapping that to Epiphany. This is already a contrast to B2B marketing automation, where the database is part of the system and structures are largely limited to contacts, accounts, and marketing interactions.

In addition to the external data, Epiphany does maintain its own database of operational components. These are arranged in a standard model including programs, which can contain multiple campaigns, which in turn can have multiple communications (messages) and cells (contact groups). Communications can be shared by multiple cells, and one cell can use multiple communications. Each communication may contain one or more creatives, which are specific bits of marketing content. Campaigns, communications, and cells can all be assigned to output channels.

The model also contains segments (sets of customers or prospects), events (campaign triggers, which can be based on time, channel, behavior, queries, or feeds from external Web services), and packages (sets of campaigns used for Interaction Advisor). Campaigns can be divided into waves, each with its own schedule. The schedules can have a fixed date, recur at fixed intervals (from minutes to weeks), or be triggered by events.

Events can be captured as they happen, but the system still pushes the responses to a queue to batch the replies.  The queue might be cleared as often as each minute for near-real-time messaging such as a purchase confirmation email. It might wait longer for media such as direct mail, where there are significant economies of scale.  Bear in mind that this limit applies only to outbound campaigns: Interaction Advisor provides true real-time response to inbound interactions.

Users can also create global marketing rules that apply across campaigns.  These help to enforce regulatory constraints, such as age restrictions or opt-out compliance, or company policies such as limits on the number of messages within a time period.

The structure I've just described is substantially more complicated than most B2B marketing automation systems. That complexity adds some cost, but it also lets users can more easily manage shared components and analyze results by communication, channel, program, segment, and other groupings. This is hugely important in managing marketing programs with hundreds or thousands of components, a typical B2C requirement.

Epiphany campaigns are set up by assembling segments in a hierarchical tree, splitting them into cells if desired, and assigning a communication to each cell. Rules and segmentations are built with a powerful query builder that can read any data in the system, including transaction details, and supports relative dates, value ranges, events, ranking (e.g. 100 highest-revenue customers), and negatives (e.g., has not bought a specific product). Again, this is typical of B2C systems, while B2B query builders are sometimes more limited.

Beyond campaign management, Epiphany Marketing provides integrated data mining and predictive models; advanced reporting and visualization, including use of report cells as campaign segments; an executive dashboard; global permissions and security management; and the Orbis Global integration for marketing calendars, workflow, digital asset management, and financials. These are rarely available in B2B marketing automation systems, although exceptions exist.

Epiphany Marketing has its own email engine.  It actually supports two kinds of dynamic content.  One is your everyday dynamic content, where rules within the email determine what’s shown to each recipient. The other, which Infor calls “true” dynamic content, can change the contents after a message is delivered. It does this by calling back to Interaction Advisor for a selection based on current information. Neat trick.

On the other hand, Epiphany Marketing currently lacks an end-user tool to build emails or landing pages. This is one feature found in even the mostly lowly B2B systems. But gap exists because Epiphany and other B2C systems were designed primarily for large organizations where content is created by full-time designers or external agencies. Infor plans to add a content builder next year, and other B2C vendors will probably do the same if they haven't already.

So much for Epiphany Marketing. It’s neither the best nor the worst B2C marketing system.  But it’s a good example of what those products provide and how they differ from B2B marketing automation products.

Interaction Advisor, on the other can at least make a plausible claim to leading its industry. With nearly 200 installations, it may well have more clients than any competitor – systems including Oracle Real Time Decisions, IBM Unica Interact, Pega Next-Best-Action Marketing, SAP Real-Time Offer Management, and a host of others.

These systems all work roughly the same way. They connect with external customer-facing platforms, usually Web sites or call centers, which alert them when a customer or prospect starts an interaction. The systems pull information about the customer from the external system and other sources, apply rules and predictive models to recommend a treatment, and send the recommendation back to the customer-facing system for delivery.

The connections with external systems are made through Application Program Interfaces (APIs) provided by those systems, so the interaction managers handle those pretty similarly – although there are some differences in the types of connections they support.  The more important variations are in their internal decision-making process. Specific considerations include the complexity, scope and difficulty of creating rules; the predictive modeling methods and user requirements; and how the system reconciles conflicting priorities in making its final recommendation.


Interaction Advisor handles all these quite nicely. The vendor divides the process into four steps: dynamic profiling (gathering the data); business rules (selecting options to consider); real-time analytics (self-managing predictive models); and arbitration (selecting the best option based on user objectives).

In practice, the process starts with a call from an external system. This could be triggered by a tag embedded in Web page or call center screen. The call contains information about the current customer and the context. It carries the identity of an Interaction Advisor event, which tells the system what campaigns to apply and how many recommendations to return. The system might need several responses to display multiple ads on a Web page or to give a call center agent some choices.

When the call is received, Interaction Advisor creates a session that will remain open until the interaction is complete. It stores the data it received in memory and then queries other systems, such as the company’s marketing database, customer files, and inventory systems, to assemble whatever other data it needs. This is also stored in memory: there is no persistent customer profile within Interaction Advisor although some information generated during the interaction will be stored permanently.

The system then executes the rules, which first activate the specified marketing campaigns and then determine which offers within those campaigns are available to this customer in this situation. (For example, the system might exclude offers within the campaign for products the customer has already purchased or recently rejected.) There might be additional constraints such as ensuring that offers relate to the contents of the originating Web page or don’t include conflicting products.

Once the eligible offers are identified, the system activates its predictive models. Interaction Advisor supports two types of predictions: Bayesian models that estimate the likelihood of the customer responding to one specific offer, and collaborative filtering that identifies offers most commonly selected together. Both are self-generating and self-tuning, so human model-builders are not needed. The Bayesian models do provide reports on which attributes have the most influence on the likelihood score.  These provide useful business insight and let users check that the models are reasonable.  The system keeps a record of offers made and accepted, which is essential for the Bayesian modeling technique.

The final step is arbitration. This looks at the likelihood scores for the eligible offers, the value (revenue or profit) from each offer, and perhaps other considerations such as inventory levels or sales quotas. Users set up arbitration rules depending on their priorities: they might want to make the offer most likely to be accepted, the offer with the highest value, or the offer with the highest expected value (i.e., likelihood x value). One arbitration scheme can apply across multiple events, even if they involve different campaigns and offers.

Once the offers are chosen, the system passes the customer-facing system a content ID that tells it what to display. Interaction Advisor could also store the content internally and send it instead of an ID.  But most users prefer to let the customer-facing system to manage its own content.

The call-and-response cycle just described applies to a single interaction. Interaction Advisor doesn’t execute multi-step dialogs like a call center script or sequence of Web pages. Users could accomplish that indirectly by creating different events that call different rules, or by creating eligibility rules that take into account previous activities. Since sessions remain active for a user-specified period of time, previous events within the sequence are all immediately available. However, sessions are channel-specific, so a customer simultaneously looking at a Web page and talking to a call center agent would have two independent sessions active. At best, the data in those sessions could be shared by posting it to an underlying database. Posting also makes the interaction history available to in the future.

Products like Interaction Advisor often generate substantially more revenue than manual recommendations.  They are priced accordingly: a single-channel Interaction Advisor installation starts around $150,000 and could run much higher. Fees are usually based on the number of users in a call center or sales agent environment, or the number of recommendations or visitors in an automated environment like a Web site. Most clients install InteractionAdvisor on-premise, although hosted options are available.