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3 Ways to Supercharge Your Conversational Marketing Chatbots with Behavioral Intent Data

Jul 26, 2019 | Author Yvonne Haendel

TL;DR - Conversational marketing seeks to make interactions more pleasant, efficient and productive for prospects, customers and salespeople. Realizing that requires knowing what’s motivating and informing a visitor’s actions. Contact-level intent data brings rich, granular insight and enables targeting of sophisticated playbooks that will resonate and convert.

Inviting chatbots to the conversation

Chatbots are all the rage. They promise to streamline prospect interactions by (dis)qualifying leads and moving deals to sales meetings faster, all while actually improving the prospect experience and delivering relevant content.

What’s not to love?

The real challenge is to implement conversational marketing and chatbots at scale without the robotic, generic feel.

Drift has pushed the dots really close together for those of us in the B2B marketing space. Its content illustrates, coaches and evangelizes how companies can personalize their conversational marketing and imbue chat automation with a vibrant brand. They’ve clearly walked their walk.

But there’s more to it than a liberal dose of emojis, website photos of actual people and some edgy copy. The key is to personalize the experience to the visitor. That requires more than knowing their company’s name and key components of their tech stack.

This is especially true at the intersection of conversational marketing and ABM where it’s necessary to create awareness and develop need among a variety of stakeholders.

If you’re interested in the demand gen opportunities at the intersection of these three trends - intent data, account-based marketing and conversational marketing - then you might be interested in a recent Drift webinar with Gar Smyth, ITSM’s Rob Leavitt, and IntentData.io’s Ed Marsh.

The benefit and limitations of company-level intent data

Account-Based Marketing best practice is to implement a scoring model by putting targets into categories. Engagio suggests three tiers of 20 to 50, 200, and thousands of accounts each.

One-to-one personalization is reserved for the smallest group. Engagio suggests persona and industry-level personalization for the second group, and limited personalization for the third, which is a reflection of reality and resource constraints.

But how do you know when one of the middle tier (200) accounts is moving into the market? With multiple stakeholders in each, it’s unlikely that your sales team will happen to communicate with the right person at the perfect time — and happen to ask the right question. It’s clearly impractical with the thousands of accounts in the highest tier.

Account-level intent data can help solve for this.

Observing “surges” of activity or multiple instances of purchase intent indicating action at the company level allows revenue teams to focus their marketing and sales resources on the accounts where there’s likely some activity.

That’s valuable, but it’s limited. With average buying teams of 6.8 people, each of whom has different perspectives and priorities, it’s unrealistic to assume that the same marketing message will resonate appropriately with each person. After all, some probably prefer a competitor and some may advocate for allocating the resources to completely different projects more aligned with their priorities.

Highlighting areas of activity at the account level doesn’t get companies closer to authentic, personalized, individual conversational experiences. At best, this allows chatbot playbooks to be displayed to those companies (by reverse IP lookup) and to incorporate some reference to active interest. That’s better than a generic bot, but it’s a long way from the aspiration of conversational marketing.

And this is precisely where contact-level™ intent data is valuable. Beyond identifying account-level interest, contact-level data can support conversational marketing demand generation and ABM plays in three important ways.

  1. Identify those actively researching
  2. Understand their buyer journey and priorities
  3. Competitive positioning

Contact-level intent data for individualized chatbots

First, think of how differently you’d approach the creation of a conversational marketing playbook for an anonymous visitor versus a known (by email pixel or website cookie) visitor.

The anonymous visitor might be put at ease or intrigued by a reference to their company name, industry, tech stack and page/context of their visit. In contrast, known visitors might be greeted by their assigned rep, engaged based on number/frequency of visits and their journey along a path of content reflecting a stage in the buying journey, and inferences which can be drawn about the problems they hope to solve or outcomes they seek to achieve.

Let’s shift our thinking even further.

Identify Active Contacts for ABM Playbooks

One of the biggest hurdles in ABM at scale is the ability to identify who the active players are. Verified databases and diligent research and relationship building helps to populate a CDP (customer data platform) with the names of 15 or 20 likely key players based on job title. That’s often vastly different than understanding who’s initiating, justifying, expanding or even potentially resisting an initiative.

Frequently, initiatives originate at execution levels in organizations (think of the recent surge in freemium and product-led marketing models). The instigators often aren’t particularly visionary or event-focused on a specific vendor; they’re simply trying to reduce the friction in performing their day-to-day job or looking for a boost to hit an aspirational KPI.

Great marketing and sales organizations empower these prospects. They provide enablement content which helps to clarify the need and quantify the benefit, to build the justification and to productively engage their colleagues and management.

That’s been the allure of inbound marketing — the opportunity to identify early-stage prospects. Statistically, though, that probably yields 1%-2% of opportunities. (30% CTR on non-branded organic terms * 3% site conversion rate = .9%!)

Identifying the people that never searched that term — the 70% that didn’t click it, and the 97% that didn’t convert — is the challenge. Conversational marketing often doubles conversion rates. That’s a lift. From .9% to 1.8%. There’s still a lot of opportunities.

Contact-level intent data identifies the actual people (and provides their details and contact information) who are taking action that indicates research intent - wherever on the public web they happen to take it.

This is like rocket fuel for ABM. Now the list of potential stakeholders can be supplemented with actual, active prospects.

Know what they’re thinking

We all know why we think someone should buy our product or service. Our opinion is irrelevant. What matters is why a prospect thinks they should — the problem they’re trying to solve or the outcome they’re trying to achieve.

This is where contact-level intent data can really turbocharge chatbots.

Rather than “topics” with an opaque taxonomy, a bespoke intent data algorithm provides rich, nuanced and contextual detail that helps to understand:

  • Where a prospect is in their customer journey
  • By virtue of the seniority of folks involved, also where the account is in their journey
  • The problem they’re trying to solve and/or outcome they’re trying to achieve

This information is pure marketing & sales gold. Segmenting with these details allows you to personalize chatbots at scale. Overlay this individual insight on top of the firmographic and technographic information available (e.g. through Drift Intel), and it’s suddenly possible to create a set of situational chatbots which engage prospects around what’s important to them rather than what we guess or hope might be.

That’s the pinnacle of personalization at scale — the ability to intuit what someone’s thinking and meet them with the right insight/content/approach accordingly.

Competitive positioning

We all say that competition is good because it makes us stronger. And most of us wish we had less of it. It’s a reality though, and one that we can more effectively deal with when we know precisely what we’re up against.

Account-level data can alert you to the fact that an anonymous individual, among hundreds or thousands in a company, has taken some action with a competitor. Contact-level buyer intent data, however, tell you which person (along with the important context of their title) has engaged in which way (e.g. social following or followed, or engaged with content about the competitor) with which competitor.

Interpolating that with additional signals around the problem they’re trying to solve or the outcome they seek allows you to draw on your product marketing battle cards to understand precisely how to position your product for their requirements.

It even allows you to be a bit more direct — although subtlety is important. Imagine a chatbot that welcomes a visitor whose colleague you know has engaged with a competitor.

“Thanks for visiting again! We happened to think that some of your colleagues might be suggesting <competitor name> as an option, so we pulled together some comparison info that you might want to share with them. Sound good?”

How powerful is that?

Of course, it’s only possible when you know what person is actually taking action — and their relationship to the visitor on your site. That provides the framework of targeting information necessary to show these powerful, contextually appropriate chatbots to the right people, at the right time.

BDR / SDR Playbooks

There’s a lot to manage here, particularly managing the differences in approach between anonymous visitors and known visitors, and further distinguishing between those from accounts that are known to be active.

Then there’s another layer of understanding the action they’re taking, relative to colleagues, and interpreting that according to their job function and relative seniority.

It’s complex stuff.

We believe that activation and orchestration are as important as having the right data. That’s why we offer bolt-on programs including conversational marketing implementation along with our contact-level intent data. Learn more here.

Some of it you can automate — like targeting playbooks based on the intent data signal regarding the desired outcome and competitor interactions.

You can build processes around some — like adding target accounts to lists for platform alerts to sales reps.

Some will require BDR/SDR playbooks — like proactively emailing active observed prospects as part of an outbound sales sequence with a secondary goal of using a tracking pixel to identify the prospect when they visit the site….precisely to be able to deliver a contextually optimized conversational marketing experience.

Later in the process, reps can incorporate the blended first-party data of observed interactions with third-party data to target other players as part of their outbound and opportunity sales playbooks.

That brings us to alignment. Contact-level intent data is an incredibly powerful tool with use cases across the customer lifecycle.

It can support enterprise sales and success teams as a bonus for companies that originally see it as a demand gen or ABM tool. Fully unlocking the potential requires a well-orchestrated effort across functions.

Turning banter into conversation

The bottom line here is the opportunity to take the casual, chatty approach that characterizes conversational marketing and turn it into an interactive conversation that speaks directly to the visitor’s personal and corporate situation.

That’s like the difference between the vacuous but comfortable banter you might have with a gregarious stranger in a bar and the deeper, insightful conversation you experience with a colleague brainstorming a challenge over lunch.

Which is more productive?

Should we meet at Hypergrowth?

We’ll be at Drift’s Hypergrowth 19 on September 3 in Boston. If you’re going to be there, let’s put a meeting time and place on the calendar to make sure we connect.

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