The industry is buzzing these days with marketers eager to test and prove the latest predictive marketing data. The idea is simple: By targeting companies that have a record of investing in a product category, you benefit from greater efficiency, a higher close rate, and shorter sales cycle.

That type of predictive-driven targeting manifests itself in the form of a "Named Account" list. It's usually based on user or company activity, and used either programmatically through systems like Demandbase or as a shared list given to third-party media vendors with the instruction of "only send leads who work at these companies."

Such specific targeting often results in restricted supply, naturally drives a higher price point for execution. The gamble for the marketer is that this price increase is well worth the conversion rate increases and sales acceleration.

According to what we hear and see as evidence, this gamble appears to be paying off. The question then becomes a matter of how to use all aspects of the marketing industry to develop the most effective account-based marketing (ABM) lists.

Letting Yesterday's Behavioral-Data Buying Inform Today's ABM Strategies

The idea of interest-based marketing is far from new. Audience relevance is the cornerstone of effective marketing.

Not long ago in B2C marketing, behavioral- and audience-based buying was all anyone could talk about. Every week, a team somewhere would launch a new audience-targeting technology, and every agency promoted the idea to its brands as a way to set itself apart.

Today's predictive bubble is eerily reminiscent of that former phenomenon, from which we can learn a lot. During that time, several things were discovered that most likely stand to repeat themselves today as ABM takes B2B marketing by storm.

1. Behavioral- and audience-data buying was, at best, "generally accurate." Behavioral and audience targeting was only as good as the data that informed it. The methods in which the data was captured and distributed defined the increase in relevance.

What this means for ABM: Data integrity is paramount. If the performance data that informs your predictive models and account lists is skewed by inaccurate prospect data, the entire ABM program can come crumbling down.

2. Retargeting worked. Aggregating cookie pools and retargeting those consumers who had shown interest held far more value than wasting efforts and media budget on new audiences. And what held true in display, search, and social holds true in today's B2B demand gen world.

What this means for ABM: This is one of the main, yet often-neglected benefits of ABM. Cross-selling and upselling an existing customer is much easier than identifying, engaging, and winning new accounts. Once captured, a contact at a named account can be nurtured into a sale in a much more predictable and efficient manner.

3. Balancing targeting costs and benefits was crucial. Spending on audience and behavioral data when I was paying on a per-conversion metric (CPA or CPL) didn't makes sense. For example, you don't see an online membership brand (such as HauteLook or Netflix) adding $2 CPM behavioral data (such as pixels on users who've shown interest in online fashion retail or on-demand TV) for their CPA campaigns that they run with their affiliates (but you do see it on a riskier CPM basis).

The onus for conversion shifts towards the publisher/affiliate as the payment event shifts toward the bottom of the conversion funnel. In other words, the conversion cost (risk) is built into the publisher's set CPA. There's no reason to pay extra to decrease that risk because the marketer is already paying it in the form of the publisher's higher CPA price.

What this means for ABM: Balancing ABM targeting approaches is just like knowing when to pay for audience or behavioral data. For example, if you're running a named-account content syndication program targeting 20 companies, that is driving up lead costs substantially while eliminating the chance of gaining quality leads you may not have been aware of.

The value of ABM is efficiency... but becoming too focused can result in increasing costs, fewer opportunities, and overall decreased return on your ABM investment.

4. The best targeting resulted from widely tested performance data. My favorite strategy in my previous life was to use the campaigns I paid on CPA/CPL model to gather audience data, create retargeting cookies, and run A/B testing. I would let that data fuel my CPC- and CPM-based buys.

I wouldn't run search until I had proved my best creative and landing pages from my affiliates. I wouldn't run display until my retargeting pool had grown enough from my CPA and CPL. The results were always that I achieved much more with much less.

What this means for ABM: Jumping headlong into a named-account program is a bad idea. You only learn from the data that enters your funnel. So, if the top of your funnel is narrow from the start, your predictive model won't be nearly as effective as you hope. Take time to learn from an open demand-gen strategy; allow it to inform a sophisticated ABM program that will pay off in huge returns later down the road. And even after this point, return to an open demand-gen program from time to time to ensure a continuously evolving account-targeting strategy.

5. Data expired quickly. Behavioral and audience data had especially short life spans. A consumer's interest in a new car is arguably shorter than a business's interest in a new server, but both surge and both fade.

What this means for ABM: Account data expires quickly as contacts leave companies, budgets shift, industries change focus, and competitors move in. This is just another reason to always keep the door open to a wider demand-gen program. Doing so will prevent "ABM tunnel vision" and ensure your predictive models stay fresh as data expires and industries, companies and personas evolve. It also ensures that you're capturing new interests and markets rather than only targeting companies already speaking to your competitors.

Part of a Complete marketing strategy

Account-based marketing is a strong, proven strategy, but it's not everything. And it doesn't work well in a vacuum.

Just like any engine, ABM requires fuel, the data gained across all marketing efforts that inform your predictive models. If you choke out all your open demand marketing programs to focus solely on ABM, you'll eventually run out of the predictive data the fuels your account-targeting efforts.

The marketing organizations that reap the greatest rewards will be those that harness wide-open campaigns side by side with their ABM campaigns. That will ensure your ABM program remains accurate and that you continue to identify new opportunities.

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image of Bodhi Short

Bodhi Short is senior vice-president of product for Integrate, a provider of demand marketing software.

LinkedIn: Bodhi Short

Twitter: @bodhishort