Thursday, August 25, 2016

ABM Vendor Guide: State-Based Flows to Orchestrate Account Treatments

Next up in this series on ABM sub-functions described in the Raab Guide to ABM Vendors: State-Based Flows.

ABM Process
System Function
Sub-Function
Number of Vendors
Identify Target Accounts
Assemble Data
External Data
28
Select Targets
Target Scoring
15
Plan Interactions
Assemble Messages
Customized Messages
6
Select Messages
State-Based Flows
10
Execute Interactions
Deliver Messages
Execution
19
Analyze Results
Reporting
Result Analysis
16

Your first reaction that may well be, What the heck is a State-Based Flow?  That's no accident.  I chose an unfamiliar term because I didn’t want people to assume it meant something it doesn’t. The Guide states:

Vendors in this category can automatically send different messages to the same contact in response to behaviors or data changes. Messages often relate to buying stages but may also reflect interests or job function. Messages may also be tied to a specific situation such as a flurry of Web site visits or a lack of contacts at a target account. Flows may also trigger actions other than messages, such as alerting a sales person. Actions are generally completed through a separate execution system. Movement may mean reaching different steps in a single campaign or entering a different campaign. Either approach can be effective. What really matters is that movement occurs automatically and that messages change as a result.

In other words, the essence of state-based flows is the system defines a set of conditions (i.e. states) that accounts or contacts can be in, tracks them as they move from one condition to the next, and sends different messages for each condition. This is roughly similar to campaign management except that campaign entry rules are usually defined independently, so customers don’t automatically flow from campaign to campaign in the way that they flow from state to state. (Another way to look at it: customers can be in several campaigns at once but only in one customer state at a time.) Customers in multi-step campaigns do move from one stage to the next, but they usually progress in only one direction, whereas people can move in and out of the same state multiple times. Journey orchestration engines manage a type of state-based flow, but they build the flow on a customer journey framework, which is an additional condition I’m not imposing here.

This may be more hair-splitting than necessary. My goal in defining this sub-function was mostly to distinguish systems where users manually assign people to messages (meaning that the messages won’t change unless the user reassigns them) from systems that automatically adjust the messages based on behaviors or new data. This adjustment is the very heart of managing relationships, or what I usually call the decision layer in my data / decision / delivery model.

Speaking of hair-splitting, you may notice that I’m being a little inconsistent in referring to message recipients as accounts, customers, contacts, individuals, or people. A true ABM system works at the account level but messages may be delivered to accounts (IP-based ad targeting), known individuals (email), or anonymous individuals (cookie- or device-based targeting, although sometimes these are associated with known individuals). Because of this, different systems work at different levels. The ideal is for message selection to consider both the state of the account and the state of the individual within the account.

As with the Customized Message category I described yesterday, vendors who qualify for State-Based Flows fall into two broad groups: those whose primary function is cross-channel message orchestration (Engagio, MRP, YesPath, ZenIQ, Mintigo*) and those that do flow management to support delivery of messages in a single channel (Evergage, GetSmartContent, Kwanzoo, Terminus, Triblio). Marketers who are looking for a primary tool to manage account relationships will be most interested in the first group.

Differentiators to consider with this group include:

  • orchestrates activities at account level (doesn't treat each lead independently)
  • assigns Web site visitors to segments during each visit using current data
  • automated models to classify content, define segments, and select best content per segment
  • automated models to assign contacts to personas and select best content per persona
  • automated models to recommend best actions per account
  • present sets of content in sequence or all at once
  • continue same experience over time across different channels
  • prioritization to ensure highest value message is always presented
  • accounts can be in multiple programs simultaneously
  • contacts can be limited to one program at a time
  • limit number of messages sent to each contact within a specified time period
As you no doubt realize, this is the area that most directly overlaps with marketing automation and journey orchestration systems that are not ABM specialists.  They key feature to watch out for when evaluating those systems for ABM programs is the abillity to work at the account level.  That was not part of many older marketing automation systems, although several vendors have now retrofitted their products to support to some degree.
______________________________________________________________________
* via its Predictive Campaign integration with Eloqua

Wednesday, August 24, 2016

ABM Vendor Guide: Features to Customize Messages

Moving along with our series on sub-functions described in the Raab Guide to ABM Vendors, let’s take a look at Customized Messages.

ABM Process
System Function
Sub-Function
Number of Vendors
Identify Target Accounts
Assemble Data
External Data
28
Select Targets
Target Scoring
15
Plan Interactions
Assemble Messages
Customized Messages
6
Select Messages
State-Based Flows
10
Execute Interactions
Deliver Messages
Execution
19
Analyze Results
Reporting
Result Analysis
16

According to the Guide:

Vendors in this category build messages that are tailored to the recipient. This tailoring may include insertion of data directly into a message, such as “Dear {first name}.” Or it may use data-driven rules to select contents within the message, such as “show a ‘see demonstration’ button to new prospects and a ‘customer service’ button to current customers”. Systems may also use predictive models rather than rules to select the right message. Customized messages can appear in any channel where the audience is known to some degree – as an identified individual, employee of a particular company, or member of a group sharing particular interests or behaviors.

The Guide lists just a half-dozen vendors in this category. That’s not because there are so few systems that do this: to the contrary, nearly any email, marketing automation, or Web personalization tool would fit the definition. What is rare is ABM specialists who provide this function. That’s because, ultimately, message customization for ABM is pretty much the same as message customization for any other purpose. So the customization vendors in the Guide either provide customization to support a different ABM function such as display advertising (Demandbase, Kwanzoo, Vendemore) or have a broadly-usable customization tool they have targeted at ABM applications (Evergage, SnapApp, Triblio).

Some differentiators to consider when assessing a customization system include:

  • types of data made available to use in customization rules (behind the scenes) and in presentation (actually displayed).
  • ability to work with individual and account level data for rules and presentation
  • complexity of rules that can be used to create customized content
  • use of machine learning or predictive models to create customized content (either to select content directly or to use scores within rules that select content)
  • channels supported  (emails, Web site messages, display ads, etc.)
  • effort and skills needed to set up customized content
  • ability to use the same content definition in multiple locations or promotions (some systems tie the content definition directly to a single Web page location or email template; others store the content definitions separately and let any message call them).
  • generation of messages in real time during interactions, using data gathered during the interaction
  • customization level (are messages unique to each contact, same for all contacts in an account, same for all contacts in a segment such as account industry and/or contact role)
  • complexity of created content (single page, multiple pages, interactive content, etc.)
  • ability to coordinate messages received by different individuals within an account
  • ability to recognize individuals, accounts, locations, etc.
Only a few of these differentiators apply specifically to ABM. Many marketers will be able to use an existing customization system to generate their ABM messages. But for marketers whose current messaging systems lack adequate customization features, a specialized ABM customization system may make sense.


ABM Vendor Guide: Features to Look for in Target Scoring Vendors

My last post used data from our new Guide to ABM Vendors to describe differentiators among companies that provide external data for account based marketing. Let’s continue the series by looking at differentiators related to Target Scoring, the second sub-function related to the ABM process of identifying target accounts.

ABM Process
System Function
Sub-Function
Number of Vendors
Identify Target Accounts
Assemble Data
External Data
28
Select Targets
Target Scoring
15
Plan Interactions
Assemble Messages
Customized Messages
6
Select Messages
State-Based Flows
10
Execute Interactions
Deliver Messages
Execution
19
Analyze Results
Reporting
Result Analysis
16

While External Data is one of the broadest sub-functions described in the Guide, Target Scoring is one of the narrowest. Target Scoring isn’t just any use of predictive analytics, which can also include things like finding surges in content consumption (used to identify intent) or recommending the best content to send an individual. As the Guide defines it:

Vendors in this category use statistical techniques to select target accounts. The models most often predict whether an account will make a purchase, but sometimes predict events such as renewing a contract or becoming an opportunity in the sales pipeline. Scores can be built for individuals as well as accounts, although account scores are most important for ABM. Many scoring vendors gather external data from public or commercial sources (or both) to gain more inputs for their models. They may or may not share this data with their clients, and they may or may not provide net new records. Target scoring is more than tracking intent surges, which do not capture other factors that contribute to likelihood of purchase.

The vendors in this category include the specialized scoring firms (Infer, Lattice Engines, Leadspace, Mintigo, Radius) plus companies that do scoring as part of a data offering (Avention, Datanyze, Dun & Bradstreet, InsideView, GrowthIntel) or for message targeting (Demandbase, Everstring, Evergage, Mariana, MRP, The Big Willow). Beyond those fundamental differences in the vendor businesses, specific differentiators include:

  • the range of data used to build models, including which data types and how much is proprietary to the vendor 
  • amount of client data (if any) loaded into the system and retained after models are built
  • advanced matching of unaffiliated leads to accounts (an important part of preparing data for account-level modeling)
  • tracking movement of accounts and contacts through different segments over time (as opposed to simply providing scores or target lists on demand)
  • self-service model building (as opposed to relying on vendor staff to build models for clients)
  • separate fit, engagement, and intent scores (as opposed to a single over-all score)
  • range of model types created (fit, engagement, behavior, product affinity, content consumption, etc.)
  • limits on number of models included in the base fee
  • implementation time (for the first model) and model creation time (for subsequent models
  • sales advisory outputs including talking points, intent indicators, product recommendations, content suggestions, etc. 
Even though target selection is obviously a core ABM process, target scoring is distinctly optional.  Most firms already have target account lists that were built by sales teams based on their own marketing knowledge.  An ABM program can easily get started using that list.  Chances are, though, that target scoring will find some high-potential accounts that aren't on the old list and find some low-potential accounts that are on the old list but shouldn't be.  Scoring can also do a better job of prioritizing accounts within the list, often by incorporating event and intent information to uncover opportunities that would otherwise remain hidden.  So although account scoring may not be the first thing you do when setting up your ABM program, it is something to consider adding as you move along the ABM path.