An area of martech that is returning to prominence is signal and intent information gleaned from off-site (or un-owned) behaviors of audience members. This resurgence is partially due to the growth of Account Based Marketing as well as applications of Adtech to demand generation needs. One of the leaders in this space is TechTarget, a top provider of technology product content and intent information.

Recently, I spoke with the team at there including John Steinert, CMO and his colleagues: Josh Garland, VP of Product Marketing and Ben Bradley, Director of Client Consulting. Our conversation included details of their Priority Engine™ service and their partnership with DiscoverOrg.

Josh: What are some of the ways your clients are leveraging high resolution behavior and driver data with Sales?

Ben Bradley (TT): There are a variety of ways sales teams can use high resolution data to enhance their outreach and drive faster paths to revenue. With TechTarget specifically, the Priority Engine™ tool gives direct and highly-flexible access to the information present in high resolution data.

The most common use of this data for sales is for reps to use the account RANKING ability built into to understand WHICH accounts to engage and WHEN – refreshed with new activity every week. The tool also provides contacts, so they know WHO to engage.

At the account level, the intent data shows exactly what the account is researching – the topics, the vendors, etc… — so Sales can understand specific pain points and entry points and personalize their outreach (from here it is easy to find the specific assets being consumed as well). Through our partnership with HG Data we also include install base information.

For example, in 90 seconds, I can find the account I want to call, identify what matters to them, and tailor my pitch to their needs and preferences. Further, our new DiscoverOrg partnership exposes relevant contacts to a specific deal that may not be exhibiting intent but are typically associated with such a purchase.

There are a number of other ways reps can use the data we provide. A core capability in the tool is the ability to load a Named Account List. This can be any list of accounts (territory, industry, closed/lost, customers, ABM lists, etc.). After a list is uploaded, the Priority Engine rankings will change to reflect only the accounts that match the list. This can be a huge value, because while named accounts offer more focus, at any given point and time, only a small set of your target accounts will actually be in market. Priority Engine will identify the accounts from your list that are in market right now.

Additionally, through our Qualified Sales Opportunities service, we provide full purchase intent insight about fully verified deals within specific technology segments. Every Qualified Sales Opportunity is a report detailing an upcoming technology purchase we have verified with someone on the buying team and includes the following sales intelligence:

• Top drivers for the purchase
• Product feature criteria
• Vendors being considered
• How the account plans to use the new technology
• Current tech install
• Project insider contact info
• Reformatted reports give sales teams the keys to landing appointments
• Suggested “icebreakers” and calling guidance

This service helps sales teams increase appointment rates by focusing on accounts with confirmed projects together with a “blueprint” of actual deal requirements and guidance (sales enablement) on how to engage in order to win the deal.

Josh: What are some of the ways your clients are leveraging high resolution behavior and driver data with Marketers?

Ben Bradley (TT): We generally see that there are five roles within in an organization who use the high-resolution behavior and purchase driver data from Priority Engine. Sales is one of those teams, which we just talked about, the other three generally fall into the broader Marketing organization. On the marketing side, we have the other four teams: Demand Generation, Marketing Ops, Field/Channel, and Product.

• Demand Generation is the most common and fastest growing user group, and the most common use case is building content marketing lists to generate MQLs. Demand Marketers use the account data, ranking data, and contact level data to deploy nurture streams to these accounts/contacts and produce MQLs. In short, they pull the most active accounts/contacts researching a given topic each week and act on them in very specific ways according to the nurture stream.

There are many variations of how you can deploy the active contacts into nurture. Our data can be segmented using many variables and this sophisticated segmentation capability creates opportunities to pursue a host of use cases. Priority Engine subscriptions begin at a top-level segment of the enterprise tech space, e.g. Endpoint Security. You can then dig deeper into subtopics within endpoint security around what the desired accounts are researching most (IE Android Malware, Botnet, Intrusion Detection, etc.). With this very granular topical capability, an advanced marketer can deploy highly segmented nurture streams focused on continuous engagement or highly focused conversion drivers based on specific behaviors.Additional segmentation occurs around which vendors target accounts are researching, verticals, company sizes, install base, and what level of engagement they have with a customer’s brand. And these in turn spawn new opportunities around use case ideas/messaging ideas, from competitive conquesting, to vertical marketing plays.

• Priority Engine gives Field/Channel users the ability to segment the audience based on location to help support event recruitment and related highly-geographic efforts. This use case has been a particularly strong asset for teams in EMEA, where regional events remain very popular. With the named account feature we have seen teams load a list of booth visitors post-event to help prioritize follow up efforts. Alternatively, you can support geo-specific field marketing efforts with this feature. Another common use case of this group is to help support channel partner efforts by providing them target lists of accounts to focus on based on active demand. If you have a channel partner focused on a vertical (or geo), you can pull (or automatically push) weekly lists of top ranked accounts to supply them.
• Marketing Operations is another user group that has seen success with Priority Engine. The use cases here are a bit less intuitive, but when applied it can be really powerful. Perhaps the most common use case is loading a named or active account list. In this case, loading a list of accounts that have visited your website for appending with actionable prospects. With Priority Engine, you can add to the depth of insight you have on your website visitors and you can augment a visiting account with more prospects that may not have come inbound to you. You can see the entire buying team and also rank the accounts visiting by their activity on the TechTarget network to get better insight into who to prioritize. Marketing Operations teams are also realizing that their scoring methodology can be much more productive when 3rd party insights are layered in (see the related webcast we did with SiriusDecisions). Many marketers use Priority Engine to augment or even override the scoring that is occurring within their own inbound or outbound activities.
• Product marketers use all three of TechTarget’s IT Deal Alert intent-based offerings – our buyer pre- and post research (also used by other strategic players in the org), Priority Engine for targeting, content effectiveness, competitive insight, messaging/positioning and our Qualified Sales Opportunities for deep insight into consideration rates, win/loss, more buyer’s journey analysis, etc.

These are just some of the ways different teams or users from inside an organization can use the high-resolution behavior and data from Priority Engine and the ITDA family as a whole. There are more, which we can discuss in more detail and even customize based on a sales/marketing needs. 

Josh: A few weeks ago, DiscoverOrg and RainKing anncounced a partnership, and then TechTarget made them a core component of PriorityEngine™. Help me understand more about the new partnership.

John Steinert (TT): Priority Engine already delivers direct, real-time access to the most active accounts and named prospects conducting purchase research. We do this across hundreds of market segments and within key geographies.  With the integration of DiscoverOrg’s trusted contact data, we are able to expand coverage of key stakeholders and buyers who are involved in the final purchase decision at target accounts. The combination of TechTarget’s insights into account purchase intent and active prospect activity and DiscoverOrg’s deep reach into an account’s extended buying team in one unified solution is very powerful for our customers because it provides the opportunity to make large productivity gains by reducing the number of accounts and contacts to those who really matter.

These new enhancements to Priority Engine accelerate sales conversions by providing more of the essential information that B2B sales people need to penetrate and drive engagement with accounts that are truly in an active buyer’s journey. The platform provides direct access to buyers at the right time by ranking accounts based on various purchase intent intensity attributes within their very specific technology segment. It helps the sales team focus on the accounts that matter most and, critical to effectiveness, it helps them identify and reach the essential Target Buying Team involved in the purchase, including active researchers and relevant stakeholders. With enhanced title coverage inside the account and rich behavioral insight to personalize outreach, sales teams are able to convert prospects into customers at a higher rate.

As part of this partnership, TechTarget-only customers (those without a DiscoverOrg license already) will be able to access up to 10 Recommended Contacts from DiscoverOrg per account, selected according to title/role relevance and seniority at no additional cost. TechTarget customers who already subscribe to DiscoverOrg will have unlimited access to DiscoverOrg’s entire database of contacts at the accounts Priority Engine identifies as being in-market. With improved audience targeting, deeper sales intelligence and new insights, our customers can achieve vastly improved ROI for ABM, demand generation and sales enablement.

Josh: How do you help potential clients understand how TechTarget is different than Paid content? Why not invest in Owned content promotion?

John Steinert (TT): TechTarget is the leading content provider for enterprise technology buyers. It is the behavior of the registered users on our 140+ independent editorial sites that generate our exclusive intent data.  The usefulness of this content for purchase decisions is what continues to build our membership and helps us identify when and where technology projects are happening. Our enterprise tech vendor customers understand that it’s our independent content and the trust we’ve built with over 19M technology buyers that helps them reach the right people and expand their influence in the markets that matter to them.

Many customers today still leverage the TechTarget network to host and promote their own content to generate leads. While we’ve never allowed vendors to pay for editorial coverage, we do offer many exclusive editorial alignment opportunities to boost their brand recognition across extremely targeted technology topics.

We also have a large custom content division that can develop custom white papers, sites, display and native advertising sponsorships for our customers. All this content is clearly labeled as sponsored as to not confuse our members.

Tech marketers need to continue investing in a lot of owned content, but to make sure they reach as much of the active demand in their market as they possibly can, the need to “take that content on the road” by syndicating it to where their audience is when it is not interacting with them or doesn’t even realize that it should be.

Josh: My readers care a lot about operationalization of tools like TechTarget. Can you share more about how your customers plug PriorityEngine™ into their martech stacks?

Josh Garland (TT): There are really two ways our customers currently plug Priority Engine™ into their stacks and it really comes down to inputs and outputs.

Inputs: Priority Engine™ allows users to upload custom account lists directly into the tool and prioritizes them based on what TechTarget knows about their recent purchase behavior. For example, let’s say a customer is using a predictive solution to identify a target account or look-alike list.

Customers can upload the list directly into Priority Engine™ and the tool can tell them which accounts they should focus on right now based on if TechTarget sees the account is currently active in their market. Critically important is the fact that Priority Engine can identify the actual people they should focus on at these accounts based on their individual research behaviors.

Output: This is extremely powerful because one of the most common issues our customers experience with both predictive and ABM lists is they don’t know whom to focus on at their target accounts. This leads to “carpet-bomb” advertising approaches and can cause exhaustive BDR call-downs, which causes a bad customer experience: bad for their brand and not efficient at all.  Priority Engine will take the list, re-rank it based on what we see and then identify the best prospects to email and call. This makes it easier for sales to focus their attention on the right people and break through.

We see a lot of customers leverage the Priority Engine custom list upload feature across many areas of their stack. They’ll upload Salesforce opportunity or territory lists, lists from events they’ve attended, site visitor lists, lead lists etc. The bottom line is Priority Engine can take what you’re doing with other tools and make it better by helping you identify the right accounts to focus on and providing the names, contact information and behavioral data of the right people at those accounts.

The other way we plug into the stack is via direct integrations with the leading marketing automation systems and salesforce. All prospects that Priority Engine discovers can be easily exported directly into marketing automation systems on a weekly basis. This includes TechTarget members and additional contacts from DiscoverOrg.

All a customer needs to do is set up a Target Profile once, which is basically a segmented audience list, and Priority Engine will export those prospects directly into their systems every Sunday. This makes it extremely easy for marketing teams to take this data and action on it for nurture campaigns, event promotion, lead generation etc. Even if they just add these prospects to their standard nurture tracks they will immediately see higher conversion rates and increased pipeline.

As a user, you will see a screen like this, which allows you to select subscribed topics, regions and target specific audiences.

Finally, Priority Engine™ account data can integrate directly into Salesforce (see image below). Salespeople can access TechTarget’s exclusive account intent data, TechTarget prospects, DiscoverOrg contacts and HG Data installed technology information right from their Salesforce account views. This makes it easier for salespeople to leverage this data within their current workflow. This data will only populate for accounts where Priority Engine is seeing pre-purchase activity, so it helps sales people quickly determine if there is a potential opportunity arising from one their accounts.

Josh: Demand generation marketers probably think of TechTarget as a lead generation channel, not an ABM player. How, and why, has TechTarget shifted to ABM?

Josh Garland (TT): I would not say TechTarget has shifted to ABM, I’d suggest that we have always focused on helping our customers target the accounts that matter. What has shifted is the market. In the past, marketers were heavily focused on generating massive amounts of leads from specific buyer personas, even though the sales organization was typically focused on accounts and territories.

What’s changed is that marketing teams are starting to align closer to Sales as more and more KPIs shifted away from front end metrics (CTR, open rate, downloads, leads) and moved to revenue focused KPIs (meetings, pipeline, opportunities, closed deals, upsell). This is a pretty significant change for marketing organizations that in the past were goaled on scale and now are judged by the individual deals they influence. I think this is why we are now all talking about ABM.

What’s interesting is that while we are hearing a lot of customers talk about testing ABM, many are struggling with the implementation of it. Some tools are helping them identify accounts they should go after, but not telling them whom to target. Other tools can give them every contact in the world, but don’t help them figure out where to start and who to focus on. In the worst cases, they lean on the sales team to generate an ABM list and find out months into a campaign that there are no opportunities at any of the accounts they’ve invested in.  While not advertised as such, we believe Priority Engine is a full end-to-end ABM platform that addresses these key challenges.

First, marketers can use Priority Engine to create ABM lists. Every week it will tell you who are the top 200 accounts most likely to purchase solutions in your market. You also can upload a custom ABM list into the tool and it will rank those accounts for you as well. To do this, you can use pre-set identifiers or ask your TechTarget service representative to assist. The screen looks like this:

Next, it will tell you whom to contact at ABM accounts. Because Priority Engine uses user-level data to determine account ranking, we always know who the key researchers are at target accounts. Priority Engine ranks accounts by looking at individual buyer behavior and rolls that up to team behavior and finally to account behavior. This is very different than other intent providers who use account-level IP data to determine rankings. The IP approach makes it extremely hard to understand who to target at key accounts. We believe our bottom-up methodology provides a cleaner intent signal with the added benefit of always being able to track intent back to the individual researcher.

For example, if we see five people from Bank of America in a Boston office doing research on Flash Storage, it’s a signal that in that location, for that account, something is happening. This is exactly the same methodology we’ve used for over 10 years to deliver thousands of leads a day for our customers. This is why I believe TechTarget has always done ABM, we just didn’t call it that.

TechTarget Prospect Types include:

• Leads: opt-in members who interacted with a customer’s sponsored content.
• Active Prospects: opt-in members who have downloaded relevant content in the target technology market segment in the past 90 days.
• Suspects: opt-in members who have downloaded content that’s related to the target technology market segment.

Our new partnership with DiscoverOrg helps us fill out the target buying team even more. Now we can identify not only the active researchers at ABM accounts, but we can provide additional senior decision makers who may not be conducting the research, but will most likely have influence over the final purchase.  These DiscoverOrg types can include:

• Recommended Contacts:
  • DiscoverOrg contacts Priority Engine identified as related to the technology topic based on job title and seniority.
    • Non DiscoverOrg customers will receive up to 10 DiscoverOrg recommended contacts for all ranked accounts
    • DiscoverOrg customers will see all the recommended contacts at ranking accounts based on their DiscoverOrg subscription.
  • Contacts: if you are also a DiscoverOrg subscriber, Priority Engine will deliver all additional non-recommended account contacts available based on a customer’s DiscoverOrg subscription. The added benefit of this is you’ll see all TechTarget and DiscoverOrg contacts in a single screen.

Finally, we make it very easy for both sales and marketing teams to break into ABM accounts. All prospects can be easily exported into marketing automation systems to run targeted ABM email or programmatic display campaigns.

The export profile will include 37 unique fields that should be carefully mapped to your Marketing Automation Platform and CRM to ensure proper signals reach Sales. Signals in the fields will include other Vendors the Lead looked at; Core Research Topics; Buying Team; and contact details.

Marketo users can expect standard contact fields will map easily. The integration is through an API rule and API User to create a custom service. Once the field mapping is complete, you can setup related smart lists and workflows. Other MAPs work similarly, including Integrate. [JDH: normally I link you to the documentation, however, it isn’t available to non-subscribers].

Account details, including install data from HG Data can be found in a single Web dashboard or can be integrated into Salesforce. Through our own studies, we’ve learned that sales people who leverage our intent data to have better conversions with ABM prospects generate more appointments, which leads to more closed opportunities. Priority Engine’s all-in-one account dashboards make it easy for sales people to have those intelligence driven sales conversations.

Many of our Priority Engine customers also still leverage our lead generation and display services in addition to their Priority Engine subscription. These tactics are proven to help warm up the accounts and open doors before marketing and sales engages them directly. Customers who run fully integrated ABM programs that include targeted lead generation, display, and Priority Engine see more success and faster conversions.

Josh: Let’s talk about the last mile – getting the signal and intent information into a salesperson’s hands. Once the data is flowing from Priority Engine through the MAP to a CRM, what should the Salesperson do? I’ve been on a lot of cold calls where the salesperson opened with, “So, I see you downloaded our whitepaper…” That’s really off putting. Is there a better way you recommend to your clients?

Josh Garland (TT): I agree. The information from Priority Engine is intended to help Sales prioritize contacts and Accounts. It’s also intended for salespeople to spend time reading and understand the signals to prepare the right questions for a call. This information collected helps accelerate the conversation rather than asking basic questions that will irritate most buyers. We recommend these steps:

• Train the team on what each signal means. TechTarget can also provide these training sessions for you.
• Have a script or preparation sheet to explain how a conversation could go if the interest is confirmed.
• Have an initial script that inserts the topics the prospect appears to care about, but avoid mentioning specific papers downloaded or other things we already know.

Here’s an example of how Priority Engine intent data appears to the salesperson in the dashboard. All of these data points, including installed technologies, can be leveraged to have better conversations with prospects.

 

Josh: I see. Essentially, the Salesperson can look at this screen and prepare for a conversation that addresses competitive issues that focus on Ransomware and Cyberattack instead of things the Prospect isn’t interested in. (I wrote about this recently). Thank you again for the interview and deep explanation of PriorityEngine™ and the DiscoverOrg partnership. I know I learned a lot about how to use the signals and integrate it into a martech stack effectively.

Interested in more product discussions like this? Let me know in the comments below.

I had the opportunity to discuss the use of predictive scoring with Infer’s Nikhil Balaraman, Director of Product Marketing and Sean Zinsmeister, VP of Product Marketing. I asked them to challenge my thinking on the adoption of Predictive tools as well as explain more about how Infer and predictive can help firms improve marketing ROI.

Josh: In the Martech Maturity Model I wrote about, I placed Predictive tools at Stage 6 – the very end of the 24-36 month implementation timeframe for firms to build out martech. Do you agree or disagree, and why?

Waiting until the end of a martech implementation is certainly one approach to adopting predictive tools, however, I’d argue that in most cases there’s no need to wait that long before getting a leg up on your competition. In fact, many of our customers start with predictive fit scoring prior to implementing marketing automation (MAP). Here are a few key use cases for predictive that we’ve seen at early stages of the Martech Maturity Model:

• Stage 0 (Marketing Transformation): Most companies don’t start building their sales and marketing stack by selecting a MAP vendor — their first step is usually to purchase a CRM system like Microsoft Dynamics or Salesforce to store sales data. At this juncture, the business challenge is to filter and prioritize leads so that sales knows which ones to work, which is a great use case for a predictive solution like Infer. As long as a company has captured sales data on at least 100 or so conversions in their CRM system, we can build and deploy a statistically accurate model for them that same day. Additionally, we can build Market Development Models for companies. These models are based solely on lists of their target companies, and helps them more efficiency enter new markets or roll new products out to market. In both scenarios, adoption is usually quite fast, since Infer Scores can be easily integrated into pre-existing CRM workflows, such as lead assignment and routing.

• Stage 1 (Automation): Once a company has started the marketing transformation process and adopted a MAP as system of record, predictive behavior models can accelerate the impact and simplify the rest of the stages by providing a system of intelligence with insights and actionable intelligence for reps and marketers. These predictive models assign an immediate quantitative measure of value to each lead and account based on a machine learned model and trained on historical data; therefore, the score not susceptible to human bias in the same way as rules-based scores. This intelligence should be a considered part of every decision a company makes across their funnel.

• Stage 2 (Lead Quality Management): At this stage, we’ve seen great results from predictive with customers like Nitro. The company had a “champagne problem” of so many leads that they were breaking their marketing automation system. Since their reps could only work a tiny percentage of their leads, Nitro needed to implement predictive scoring immediately so that they weren’t wasting time chasing low quality leads. Infer also helped the company determine which leads to keep in their marketing automation system.

• Stage 3 (Nurturing in Sales Context): Here, companies should use predictive fit scoring to identify which prospects are not a good fit for their business, and won’t convert into revenue. These types of leads can be funneled into low touch nurture tracks. In addition, predictive behavior scoring can help monitor all prospects in these nurture tracks and push highly engaged prospects back into sales reps’ hands.

We don’t believe predictive is a single point solution to only be implemented at the end of a 3-year marketing transformation.

Josh: Interesting. While I agree that predictive can support Nurturing, I’ve found firms in these Stages aren’t ready to consider powerful tools because they are still learning how to use Marketing Automation, Nurturing, and sales-marketing alignment.

Josh: How does Infer think about ABM+Predictive? What’s different about ABM+predictive vs. statistically correlated lead scoring?

Account-based marketing and predictive are highly complementary strategies for go-to-market. ABM is all about targeting key accounts, but the reality is that reps today are typically assigned hundreds of accounts. This begs the question: how do you build those lists in the first place, and how do reps know where to start? At the end of the day, ABM needs to begin with predictive, otherwise you are choosing accounts in the dark.

With predictive fit scoring, you can begin by scoring all the accounts and showing reps those that are most likely to generate revenue. With account-based fit models, you first select a target outcome—such as “Accounts with closed won opportunities”— then a tool like Infer will build a predictive model using thousands of data points such as semantic analysis of the website, annual revenue, or technologies in use at each account.

Even if a company does not have enough data to build a predictive fit model, say for a new vertical or market, that company can still benefit from predictive by using a Market Development Model to identify its top account list. From there, many companies layer on account-based behavior scoring, which uses MAP data to show reps which of their accounts are most engaged with the account-based campaigns that marketing is running. In addition, predictive sales intelligence tools (like Infer Glance) provide sales context directly to reps within Salesforce across the Lead, Account, Contact and Opportunity levels. This saves them valuable time since they don’t need to open multiple tabs to do research on their prospects.

Josh: What are the common issues your customers come up against when implementing ABM/Predictive? What do they wish they knew before they started?

We see a broad range of sales and marketing challenges that our customers are looking to solve with ABM and predictive.

Challenges stem from simply not knowing where to begin. Sometimes, there might be a fear of wasting resources on something so new because the opportunity cost is not known (e.g. pipeline generated by events, email campaigns, virtual events, etc.). Without a good initial sense of how much impact ABM will drive, our customers tend to approach this framework cautiously. To find clarity, we instruct them to begin by answering the following questions:

• How should I score accounts to determine their fit for my business?
• Do I have a way to measure both lead and account engagement to understand which accounts are activated?
• Do I have a way to acquire net-new contacts and accounts that look like my best customers?
• Do my reps have the right information on these accounts, and where are the gaps?

For these reasons, Infer customers like Yesware, Nitro, InsightSquared, Rapid7 and Druva infused predictive intelligence into their ABM programs to accomplish the following goals:

• To find an objective way to identify top accounts in their system, and assign them to reps in a fair and transparent manner
• Identify and target high-potential accounts in new regions and verticals where they did not have enough data for a predictive scoring model
• Align sales and marketing on the top accounts based on an objective measure to ensure everyone agreed on the ranking mechanism
• Measure the impact of their ABM programs and campaigns in real-time by tracking engagement scores to ensure that their dollars are being well spent

We often hear early on how helpful it can be to take a moment to zoom out and understand which issues your business is trying to solve. Many companies get caught up in the buzz of ABM, but there might not always be a “there there.” Once you’ve identified the market problem and honed your focus, it’s important to audit your tech stack to determine whether your backend, operations, and workflows will actually support your ABM strategy. For example, do you have a way to A) automatically match leads to their respective accounts, then B) score those leads?

To get started, we recommend our customers use an ABM framework to guide their approach: start by mapping your current database, adding new leads, and completing your total addressable market (TAM) analysis and target account list. Prioritize and activate your best accounts, then measure success with predictive behavior scoring.

Josh: When vendors discuss ROI of their product, they speak of the vision that could be attained, rather than the time savings involved with, say, automation of lead routing. As a buyer of ABM tools, what would you really want to hear about the ROI of ABM?

We see the most important ROI measures for ABM and predictive as those that have a clear impact on the bottom line. For example, do your strategies result in a measurable increase in deal sizes or ACV? Infer customers such as ZipRecruiter have told us that when they focus their effort on the prospects (or accounts, leads, contacts) with the highest fit scores, they increase deal sizes and conversion rates as a result.

Pipeline velocity is another important metric for sales teams, since increased velocity allows the team to work more accounts and close more deals with the same number of people. With account-based behavior scoring, Infer surfaces the most engaged accounts to the reps, which allows them to reach out to prospects when they look like they’re ready to make a purchasing decision, rather than waiting around for the prospect to call. By working accounts when they’re ready to convert, time to close goes down.

For example, if a rep is able to identify when an account is showing heightened interest before they even engage with an inbound channel, the rep can prospect into the account and proactively begin a conversation. As a result, engagement happens earlier than it typically would in a standard sales process because the rep no longer has to wait for the lead to first ‘raise their hand.’ In this case, the rep is able to close this deal faster than they would otherwise, then quickly move onto the next account in their target list that is showing interest.

Josh: Which types of firms are most ready for predictive tools? Which are not?

We have seen predictive platforms adopted by organizations across the spectrum — from eight-person companies that don’t even have a CRM to multinational companies that run their entire business through products such as Salesforce and Marketo. Even when companies think their process of passing good leads to sales is as efficient as possible, we’ve seen gains from sales intelligence tools that helps reps plan their conversations. Additionally, customers with “dirty” or incomplete data are also a great fit for predictive solutions like Infer Glance and Infer Smart Signals, which can take an input such as email address and identify the company that the prospect works for, then provide the rep with valuable firmographic and technographic signals directly in Salesforce.

For example, we’ve found that basic data such as a company name, website, and email address tends to be fairly complete and high quality across systems. For the most part, data quality tends to decrease around data points that reps are expected to proactively gather and manually enter themselves, including fields like employee count, revenue, or complementary technologies in use. To remedy this pain point, we use matching abilities to further expand the account record. With only a company name, website, or email address, we’re able to determine additional details about a company, such as website technologies in use or social media presence, as well as standard firmographic signals. Then, we push the data back into the customer’s system or record at the Lead, Contact, Account and Opportunity levels where it is continually kept up-to-date with Infer’s data cloud.

The most important trait for a company interested in predictive is a desire to harness advances in technology in order to accelerate growth. This typically requires a corporate culture that rewards those who take intelligent risks, is data-driven, or even is simply trying to do more with less. Businesses that find the most success with predictive tend to have a single pain point that can be alleviated with a predictive use case like filtering leads, prioritization, demand generation, or nurturing. This is because they grasp the value of predictive even before the model is live in their system, and are able to champion the solution across their company.

Josh: Thank you Nikhil and Sean for your time and viewpoint.

In 2015, I released the Martech Maturity Model™ to explain what I was seeing happen with companies adopting marketing automation platforms. Since then, I’ve continued to use the model as an explanatory method and have found quite a few other organizations are using it, too. This past September, I visited the Corporate Executive Board, which has used the Martech Maturity Model™ in some of their marketing councils. And I’ve heard from a few martech vendors that they are using the model to evaluate if an account is ready for their services.

Today’s update explores three key areas:

1. How buyers and vendors can use the Model
2. How ABM works with the Model
3. How more recent survey data proves the Model reflects reality.

Buyers: How to Use the Martech Maturity Model™

The Model can help you chart your course through the adoption of marketing technology tools. Many firms attempt to go too fast, due to vendor generated excitement or internal promises. As a roadmap, the Model is designed to rein in this excitement and keep teams focused on where your firm is now versus where you want to be. The timeframes are based on experience; they should help create the proper expectations with executives, avoiding disappointment and lost jobs.

This roadmap should be used with vendors to determine where the vendor is in their use of their tools, as well as what the vendor can help you with. For example, if an attribution vendor comes along, you can see clearly that they are in Stage 5, while you may have only finished Stage 2. Building out attribution and funnel data is a good idea, but if it’s a distraction from Stage 3 and 4, it’s a good idea to ask that vendor to come back in about nine months, maybe longer.

Vendors: How to Use the Martech Maturity Model™

The Model is helpful in understanding where your firm fits into the overall martech adoption lifecycle. And once you know where your tool fits, you can approach prospects who are in that stage, or approaching that stage. I know of one vendor that uses the Model to go after Stage 3+ prospects. Their tool enables deeper persona and content matching to accelerate deals. Stage 3 focuses on nurturing, better sales context, while Stage 4 and 5 look at funnel conversion and pipeline attribution. If you are selling improved outcomes for pipeline, why would anyone buy your tool if they can’t prove pipeline conversion changed? Match your promised outcome to firms that can prove it works.

Let’s discuss the latest on the Stages.

Account Based Marketing & Sales with the Martech Maturity Model™

I bet quite a few people said, “Josh, this is great, but ABM clearly upends your fancy Model. ABM is fresh and new, and it flips the funnel.”

No, no one ever said that to me. Just in case you were thinking about it, however…

First, the only flipping is in the Target Account selection process. Yes, you should do that early on in your marketing strategy. And if anything, flipping the funnel should be about changing the stage names to the Buyer’s Point of View.

Second, ABM doesn’t inherently change the Model. After a lot of thought and a little preview in the  Mintigo-Engagio webinar, the Model supports an “ABM range.”

This means that martech starts to support Account based systems around Stage 2 and beyond. When you are at Stage 0 or Stage 1, you are still getting a handle on the user of the systems, along with alignment. If you decide to add ABM (especially ABM, not ABS) before Stage 2, I would expect you to fail the entire project. When firms start in Stage 0 or 1, the CRM and sales mentality are also in a lead centric view and sales isn’t aligned closely on systems and marketing SLAs. So if you start ABM processes this early, you will likely experience a higher hurdle to success with all martech.

Now, you could argue that an Account based firm can easily pull in Engagio, Outreach, or Salesloft and see success early on without any sort of marketing automation platform. Maybe. That could be a place we go in the next three years, so I would challenge doubters to send me example case studies where the ABM range could be across all Stages in this Model. However, Marketing is still going to be unable to leverage Account based martech until at least Stage 2, regardless of what Sales is up to.

Thus, ABM is not a Stage, per se, it’s a mindset that will only show up in technology adoption around Stage 2 in the Model.

Stage 0: Marketing Transformation

The pre-requisite to success with a marketing automation platform (MAP) is to undergo a transformation, which usually involves:

• Sales-Marketing Alignment (ABM requiring much deeper alignment)
• Inbound vs. Outbound framework.
• Funnel terminology adoption.
• Content marketing as the basis for marketing communications.

Many firms are still struggling to master Content that consistently drives conversions.

Stage 1: Automation

This stage involves taking what you learned in Stage 0 and then finding the martech tools to automate it. Typically, this means a MAP selection process. It could mean marketing becoming more involved in the CRM. To some degree, this is where things like Lead Scoring, Lead Quality, and service level agreements become fleshed out, or even operationalized in preparation for Stage 2.

Stage 2: Lead Quality Management

Some firms may experience Stage 1 and 2 at roughly the same time. Be careful that you plan for the quality management before you do the vendor selection. Stage 2 is about turning the whiteboard session into a real-life system; you’ll take your business process alignment and build it out in the MAP and CRM. You will also do the following:

• Score leads by behaviors, often for the first time.
• Hold back non-MQLs from Sales.
• Route Leads to Sales based on new things like behavior, and without human intervention.
• Continually review lead quality with sales to see what is working and what is not.

You will almost certainly begin to ask questions about Sales Context and conversion rates, but do not try to solve them all at once!

Stage 3: Nurturing and Sales Context

This stage is where most firms get stuck. They may sort of have a basic lead lifecycle, but it’s about routing, not conversation rates (Stage 4). They start to build a few drip campaigns, but nothing that matches “lead nurturing.” Some firms will try to move into Stage 4 and may or may not have success there, but eventually, they all have to go back to Stage 3. A few times I’ve seen firms reach Stage 4 or even into Stage 5, but demand generation never did much in Stage 3, so there is a giant gap in program capability. Avoid this pit and think hard about nurturing. Marketing should build the capability to automate the company’s story. Your team has solid automation skills, now build out drips.

If you feel you only have above average skills, I would argue that’s about where Nurturing pros are. They understand the logic and system enough to whiteboard nurturing and plug in content. Marketing will get better at it, so don’t fret about AB testing on your first five tries.

What happens in Stage 3 is that Nurturing programs get developed and Marketing Ops should provide increasing amount of information to sales on what a lead is doing before MQL and afterwards. Team alignment discussions should focus on how to explain lead behavior to salespeople and what types of long term programs Marketing has setup. The more of that you can automate, the better.

Marketo users wonder why “sales context” is in Stage 3 when Marketo Sales Insight is part of the implementation package that normally happens in Stage 1. What I’ve found is that many firms never really adopt Sales Context tools at all. Sometimes an add-on like MSI is not setup right. Sometimes Sales just never gets training. Sometimes Sales won’t use what they didn’t buy themselves. A few reps like the LinkedIn plugin, a few just want Task notifications, and others love whatever it is they understand best.

Marketing operations should be ahead of the curve here because of tight alignment on Sales’ needs. The explosion of sales automation or sales-tech is just beginning and sales isn’t waiting around for us to offer help.

Stage 4: Funnel Visibility & Lead Lifecycle

This Stage is, for many of us, the vision that we were sold during our MAP purchase. And this is the part that everyone wants, but so few attain. It is possible to achieve, for the patient.

When you are feeling pretty good about the automation of your company’s story, then it’s time to worry about Funnels and Lead Lifecycles. Most firms will have some sort of lifecycle at this stage.

Stage 4 is hard to achieve and takes patience and due diligence, which is why many firms still feel that MAPs are a letdown. It takes about 2 years to reach this point if you want to trust the funnel data. Only at this point will your business have a good understanding of the stages and data required. Only now will you have a marketing ops pro who can handle the challenge.

(And for the 92% of respondents who thought training was a low priority, good luck with this one).

Stage 5: Attribution and Allocation

Only in Stage 5 do we consider the true nirvana: Knowing which content is working on which channel to drive accounts through the funnel. True attribution is difficult but not impossible. But there’s no point in doing this until your firm has funnel data to use. And if you have funnel data, you probably have a team that can build and understand attribution systems.

It is interesting that vendors who once pushed for revenue performance have dropped this in favor of the next shiny toy: ABM. Perhaps revenue attribution is too hard?

Not at all. It’s completely attainable for those firms that take the time to get there and keep their expectations in check.

Etumos has mastered this for clients and has created a Lead Source Setup Guide to help you master this stage.

[Update: March 5, 2017 – see David Raab’s piece summarizing martech failure survey data – master the basics to achieve ROI visibility. It’s a big desire, but have you finished the other pieces?]

Stage 6: Predictive

I’ve discussed if firms should adopt Predictive tools earlier than Stage 6. Clearly, vendors disagree with me. And Sirius Decisions asserts that firms should skip the “MAP based” scoring and go straight to Predictive. While I agree that a statistically based scoring method is desirable, what I’ve seen is that it doesn’t matter which model you use – it matters if Sales believes the model works. The only way Sales believes in the model is if they were in the room when it was made and if they are continuously consulted. One expert I know had a semi-monthly “Lead Scoring Congress” to review the data and scoring model.

Most firms ignore the model after it is setup and eventually the model is out of step with reality. Thus, Predictive can be done at Stage 1, however, it will almost certainly fail if sales-marketing alignment isn’t continuous. The Maturity of the organization in using Martech is what matters here, and I urge firms to avoid Predictive scoring tools until they have a strong maturity across the organization. I highly doubt this is attainable until Stage 6.

Considerations

The Martech Maturity Model™ explains what’s happening in the marketplace and within firms. The adoption cycle of new martech follows the technology adoption lifecycle, which is also seen as Martec’s Law. No one should be surprised at this. What is surprising is how little progress has been made in nearly two years. Walker Sands’ survey was cited by Scott Brinker as progress for firms fully leveraging their stacks. Yet, in that survey, 56% of respondents saw the “landscape” change rapidly, while only 24% thought their company’s martech changed at the same rate. (Slide 10). The survey data out there appears to support the idea that martech vendors are very far in front of their clients. An adoption curve by Industry organization vs. employees would be very interesting. Already it seems that tech startups are the primary early adopters, with early adopter staff gravitating toward the same (Walker Sands).

Ascend2’s 2016 survey appears to show a huge jump in adoption: 9% of respondents with a fully used stack to 54% of respondents with a fully used stack as evidence martech is widely adopted from 2015 to 2016. While this is promising for widespread adoption of martech, it may not be as effectively used. I want you to understand that adoption within the firm, or marketplace, is not the same as a firm or team achieving the vision they were sold. Firms may have martech and may think they are “fully using it,” but nearly all appear to have not attained full lead lifecycles and revenue attribution.

To me, all of this means there is a still a long road to adoption and training for marketers and organizations. Vendors have promised much, but firms found it too difficult to realize the promise. I wouldn’t say vendors failed Marketers, but they do (and did) raise expectations to a very high level. As a salesperson or martech vendor, I would use the Model to manage objections about adoption and the onboarding process to prevent churn. As a Buyer, I would use the Model to better manage my martech stack.

Here’s an overview:

Interested in the full deck and analysis? Signup for a free download.

Today, I spoke with Frannie Danzinger, Head of Market Development for 6sense, a Predictive intelligence martech vendor.

Josh: In the Martech Maturity Model I wrote about last summer, I placed Predictive tools at Stage 6 – the very end of the 24-36 month growth cycle for firms. Do you agree with this? Where do you see Predictive being the most useful?

Frannie:

I agree and disagree here. This is a nice, linear model. And in many ways, Predictive is in the same place that MAPs were six years ago – still being tested and adopted. If a firm is sophisticated in their use of martech, then yes, Predictive has certain benefits. And if a firm is not at Stage 6, and they already bought Predictive, then their mindset is more advanced and ready to use Predictive.

Where I disagree with this model is that the people involved do not always need the technology assumed by the model. As times evolve, marketers are increasingly ready to open the floodgates of predictive scoring without a full martech stack. At Enterprise and Mid-market firms, where most have a CRM system and likely have a MAP (marketing automation platform), marketers’ primary focus is on net-new acquisitions. Our product 2sense focuses on net-new with web analytics and limited internal data. This gives us the ability to drive ABM strategies without input from all of the other martech datasets.

Thus, it is entirely possible to use Predictive much earlier in the martech lifecycle than you suggest.

[Josh – I’ve been hearing this from predictive vendors, so I’d love to hear from customers – is this your experience too?]

Josh: How does 6sense think about ABM+Predictive? What’s different about ABM+predictive vs. statistically correlated lead scoring?

Frannie:

For leading B2B organizations of all sizes, Account Based Marketing (ABM) has become the dominant strategy to ensure high-value target accounts are being marketed to in a way that is highly personalized and specific. As ABM adoption has soared, with more than 70% of B2B companies focused on implementing ABM, the selection process of identifying target accounts has become the most critical component of effective strategies. The introduction of predictive intelligence has allowed organizations to build lists not simply on gut instinct and static look-alike demographics, but on buyer activity that demonstrates a business need and propensity to purchase.

ABM strategies are built on a company’s ability to create target account lists based on the business needs and challenges they are uniquely positioned to address. Having access to buyer activity through predictive intelligence allows marketers to identify accounts with greater precision and confidence. Now, enterprise and mid-market customers have the ability to create targeted account lists on continuously monitored intent data, enabling them to track the needs and trends driving the evolution of their customers and iterate accordingly

Josh: Which firms are most ready for predictive tools? Which are not?

Frannie:

Forward thinking firms will always be more ready to embrace predictive tools. Are the marketers ready to be uncomfortable with new technology as well as challenge assumptions real data brings? Companies that are more open to data-driven marketing and believe in decision-making powered by data will better reap the advantages predictive offers because of how they respond to the results. Companies with data-driven cultures are more receptive to applying and integrating predictive cross-functionally and are quicker to transition quantitative results into more efficient and effective real-life practices.

The most sophisticated and informed companies are increasingly using analytics to explain and predict performance and facilitate their decision making. As organizations place more of a focus on analytics, sales and marketing can align and concentrate on the data to improve business processes and overall team performance. Businesses that optimize analytics and predictive to the fullest will continuously rely on the numbers to help drive change, seek new opportunities and identify the best ways to improve efficiency and results.

In terms of the minimum volume of data required to build predictive models, they’re not just volume-based, but instead rely on the quality of the data signals. The ideal is two years of historical data.

Josh: If you had budget for just 3 tools, which would you purchase to run ABM?

Frannie:

• A DMP: (e.g.: Oracle DMP (BlueKai))
• ABM Tool: (e.g: Madison Logic, Terminus, Kwanzoo)
• A DSP: (e.g.: Media Math, Turn)