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Does Actionable Intent Data Mean Integrated?

Sep 01, 2020 | Author Ed Marsh

Tl;dr - It's common to conflate intent data sophistication with the GUI, platform, interface and integration. That's a mistake for two reasons. First, integration often precludes the really important analysis of aggregate data that's invaluable. Second, simplifying activation isn't the same as supporting effective activation.

Can You Ask Them to Create a Salesforce Report?

You've heard that before.

After all, the enormous, complex beast that is Salesforce.com...in addition to just replacing Exxon Mobile on the Dow Jones...has spawned massive ecosystems of consultants and developers. That's not because it's exactly user friendly.

It's so hugely powerful, that it's not easy to use. Not for administrators, not for managers, and not for sales people.

And that makes it a black hole for information. Sure, your database is enormous. Since they sell seats (common for CRM) vs. contact counts (common for marketing automation) there's no downside to accumulating leads, contacts, accounts, tickets, opportunities, etc., etc.

Any routine cleanup typically focuses on the forecasting pipeline - much of the rest of the data lies there buried and forgotten.

The result is that data analysis is impaired. Analysis, after all, is a creative process. "I wonder if/what/who/when/why/where?" is the question that normally launches it. One question leads to another and another. It's a journey.

It's one thing to have a set of established KPIs for an individual, role, department or company. It's another to massage data to learn its story.

And that brings us to the Salesforce report.

You have an idea that you want to dig into. You submit a request. 2 days? 7 days? later you hear your report is ready to run. Excited, you run it....and then you realize the developer misunderstood, or you explained poorly what you wanted, and they built something different; or you hadn't fully formed your question and didn't think through what you needed; or there's a fascinating insight into which you want to dig deeper.

And so you wait more days and repeat this process.

Guess what - analysis loses. You're too busy anyway. Those 4 hour time blocks that you set aside to think strategically only arise every couple months.

And once again, you're defeated by the Salesforce.com beast. (And even if you put data into Marketo or HubSpot or other marketing automation - even though it's easier to segment data yourself, the contact centric design of those systems also limits analysis.)

Buyer Intent Data Must Be Actionable AND Accessible

American Pickers is a guilty pleasure of mine. OK. Not really guilty, but simple entertainment.

I am fascinated at how they select key items from massive collections.

The thing about the show is that it really focuses on one step of three. The picking.

But before they can pick they have to find collections that are likely to yield value, and sellers that are open to parting with some of their treasures. And once they've picked they have to monetize what they've acquired.

Monetization isn't unusual. Lots of folks have made serious coin selling on eBay or opening an antique store in a tourist town. Some are more skilled than others for sure. Merchandising is part art and part science. Nevertheless it's a reasonably common skill.

Picking is different. The picking itself is built on years of experience. It's based on curiosity, deep and broad knowledge, gut feeling, and a willingness to get sweaty and dirty crawling over, under and around. It's opening every box to find the jewell that's hidden. Not many can "pick" well.

Hint - purchase intent data is similar. It takes strong sales and marketing operations, good sales enablement and skilled sales teams to fully activate it.

But activation is largely a mechanical process.

To fully leverage intent data you have to be willing to crawl through the virtual barn, brush off cobwebs that stick to your sweaty face, and spend time digging through piles of average stuff to find real valuable pieces.

Most people aren't willing to do that work. Some can't do it at scale because of systems limitations. And if you've simply integrated your data with your CRM or marketing automation, you'll almost certainly never have the opportunity to do so anyway.

Activate is Different than Integrate

I listened to an interesting webinar recently which featured Eric Wittlake (@wittlake) of TOPO. He's obviously a smart person with extensive familiarity with intent data. He made an assertion, however, that belied his perspective as a researcher/analyst vs. practitioner.

Eric explained that some intent data still relies on CSV files for delivery, and that companies researching data should look for more robust integrations that facilitate activation of the data.

I strenuously disagree for several reasons:

  1. Once you integrate data, it gets lost. Hopefully you have decent enablement to surface specific insights for BDRs, but you lose the ability to understand data in aggregate.
  2. Integration with Salesforce makes it very hard to watch data at the account level for trends. (Worse in Marketo and other marketing automation.)
  3. Integration with marketing automation often results in inflated contact costs.
  4. Some of the tightest integrations (e.g. Account-Based Marketing software partner integrations) restrict users to a single demand gen use case and ignore sales and success applications.

Why not do both?

In our experience the far better answer is a hybrid. We insist on launching with CSVs because even if someone has Tableau, Domo or similar tools, it's so easy to build pivot tables to understand what's happening. Later, once everyone agrees on how to interpret data, what signals are important, and how to track the intersection of account fit and action with contact fit and action, then we often integrate a portion of the data.

That might mean campaigns for custom audience creation to support paid ads - integrated with ad platforms. It might mean target account activity integrated with the CRM while sales management receives a pivot table report. It might also mean certain types of signals which are particularly significant.

And some kinds of data might specifically NOT be integrated. A common example is a Predictive™ Media Scan campaign which surfaces active journalists and writers for proactive outreach (vs. blind pitching.) That doesn't probably belong in Salesforce.com.

The payoff is huge. We routinely see clients stunned at persona, market and competitor insights which we quickly surface through very agile analysis of aggregated spreadsheet data. They're able to understand complex buying team activity with a glance. And product marketers love the insights.

CSVs may sound unsophisticated compared to pricing based on scales of API calls, and if all someone does is take a series of CSVs to turn around and import then into Salesforce, that's silly. But consider for a moment how many F1000 CFOs use spreadsheets to manipulate and understand data extracted from ERP systems.

Spreadsheets are a powerful tool. For corporate finance and for buyer intent data.

Integrating data hides but doesn't solve a point of frustration.

The Role of a Data Orchestration Platform

powerful insights lie deep in intent dataThat being said, there is a better - albeit rare - solution.

Eric also spoke about how complicated advanced intent data activations are.

He's right. Monitoring 1st, 2nd and 3rd party intent data at scale, providing real-time enablement coaching and notifications, building effective and accurate propensity to buy models, and building the integrations with ad platforms, sales acceleration platforms (for tailored cadence triggering), chatbots (for contextual site experience), CMS (for dynamic site content), dimensional mail, etc. is really hard. (Do you know someone that's doing this kind of exciting work? Then nominate them for a Marketing Data Master Distiller Award!)

An account with an occasional signal in 3rd party data several months ago is very different than one from which you've recently converted a 1st party lead with a couple other anonymous users from the same account and several contemporaneous signals from other buying team members in 3rd party data. But how will you know? Especially at scale?

Simply integrating 3rd party purchase intent data into a CRM isn't going to achieve the first part, much less the second and third.

The solution is a data orchestration platform. Whether it's a homegrown collection of python scripts, an independent CDP, or even the Evergage plugin to Salesforce, fully activating intent data requires orchestration that transcends any single salestech or martech platform.

A Cautionary Activation Tale

Picking isn't enough. You have to be able to sell the stuff. And while the key to activation isn't an integration, activation also takes deliberate, skilled effort.

Monetizing treasures takes certain skills. Similarly with intent data you need the copywriting, systems, sales people and expectations to actually convert when signals point you to opportunities. These are the lead bullets, as Ben Horowitz calls them, of intent data.

That's not a given.

I recently spoke with a well connected Bay area CEO who's a veteran of a couple successful exits, well networked with boards and VCs, and a driven business person.

He was annoyed that our data "didn't work." Naturally intrigued, I asked for more detail.

"Well I know" he said, "that you're finding the accounts where there are projects happening. I've confirmed that through my network in the area. But my sales people haven't booked any meetings with any of the contacts you've identified."

First, a quick refresher. Unlike account level and surge type data we don't watch for account level signals. We observe individuals taking action publicly. We then resolve their public profile to who they are, filter for the functions, seniorities and titles that clients tell us are important, and append contact details including account information. So account level activity is observed through the aggregation of contact level activity.

That means that in this case the account level activity which he was able to confirm was observed based on the actions taken by the people we observed and reported - all of whom fit the ICP persona.

In other words, the activity of these people was material and indicative of projects at their companies. They were the right contacts for the company to sell to. The sales people couldn't get any meetings.

Our data isn't perfect. No data is.

But that's not a data problem.

In fact, when people ask how accurate intent data is, one of the areas of inaccuracy that I suggest they consider (in addition to their market understanding as Eric Wittlake highlighted as a common gap in the same recent webinar) is how accurate their sales team's activation is.

Tight technical integration of data doesn't solve for analysis or activation. It's an alternative to importing.

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