Every marketer is familiar with the concept of reaching the right audience, with the right message, at the right time – it’s our mantra. Across paid, earned, owned or shared media channels, we always strive to make our messaging relevant and on-point.
One of the toughest lessons that we learn as marketers, however, is that we’ll very rarely get our targeting strategy right the first time. Even with the best analytics tools, years of training and educated perspectives, our assumptions have the potential to be wrong.
We can’t let these moments discourage us. Instead, we need to embrace the fact that our brains are the smartest of algorithms. We train ourselves by hitting dead ends, taking wrong turns and making mistakes.
For many marketers, data, and even targeting itself, is new terrain. When we’re trying to reach audiences at scale, there’s no way to see which people are on the other side of the computer screen (sans Skype)—that is, until we see our reporting.
What better way to fast-track this learning process than to learn from the experiences of our fellow marketers? We asked three marketing leaders about their targeting mistakes, and they shared more than misadventures. They explained how they turned their mistakes into strong relationship-building lessons.
Lesson 1: Be skeptical of your assumptions
At IT company Monitoring Management, Inc. (MonMan), VP of marketing Ryan Hulland’s role is to help his team identify the right buyers. MonMan specializes in market entry, sales channel development and service of efficient solutions for mission critical facilities. Accordingly, Hulland assumed the right buyers to target would be facilities, engineering and IT directors.
“Those types of titles immediately convey a status of power and influence,” says Hulland.
After launching a series of campaigns targeting this audience, however, he realized that he was on the wrong path. While engineering, facilities and IT directors responded well to MonMan’s marketing messaging, early sales conversations would fall through.
“At first, it was very confusing and frustrating,” says Hullard. “The loss of those opportunities was a tough pill to swallow.”
Rather than giving in, Hulland took a step back to figure out what was going wrong. His method of choice was simple – to talk to and research team members at target organizations. He quickly realized that his original assumption, to target executive personas, was wrong. The decision makers and influencers were often subject matter experts or long-time employees.
“The person we should have targeted didn’t care about efficiency, life cycle costs savings and benefits of our product,” says Hulland. “Our actual buyer wanted to know what kind of quality the product had, how it was built and whether it was the equivalent of solutions that he was already using.”
What Hulland’s experience teaches us is the importance of second-guessing our assumptions. Historical sales data – and anecdotal evidence – are often in the eye of the beholder. It’s important to keep exploring the “road not traveled” to determine whether, where and what missed opportunities exist.
Lesson 2: Don’t let the revenue fool you
Morgan O’Mara, relies on data to connect potential customers to document shredding and scanning providers in her company’s network.
“We use all kinds of data to select the right customers, including in-depth analysis of who’s using our website and how,” says O’Mara. “We use multiple sources – such as keyword data from paid search campaigns, content consumption patterns and revenue data from our sales team – to determine which leads are turning into revenue and what type of leads are coming in.”
While working at Shred Nations, O’Mara and her team have learned that prospects tend to fall into two groups: homeowners and businesses.
“We prefer to have businesses in our funnel, and we rely on data to differentiate between these two groups,” explains O’Mara. “We use this information to refine our keyword lists for paid search campaigns.”
Empowered with historical keyword information, O’Mara’s team recently ran a series of business-facing PPC campaigns. At first, the campaigns seemed successful – they were profitable. With further analysis, however, O’Mara and her team realized that these campaigns had veered off course.
“We realized that 85% of these leads were homeowners – the wrong type of customer,” O’Mara says.
What O’Mara learned from this experience was the importance of setting up cross-checks – one of which is for marketers to touch base with sales teams more often.
“This approach helps ensure that we’re making adjustments as quickly as possible,” O’Mara says.
Lesson 3: Don’t miss the empathy mark
Sourabh Mathur, founder at marketing automation company Esanosys, understands the value of an ideal customer profile. That’s why, in an earlier role as head of sales and marketing at an Internet startup, he went to painstaking lengths to create a detailed profile for his company’s target customer: learning and development managers at software companies with more than 2,000 employees.
“We spent a lot of time polishing our email content and doing A/B tests, and we saw decent email open and click rates,” says Mathur.
After refining his campaigns, he deployed them on a larger database. Expecting a substantial volume of leads, he was shocked when the number of enquiries remained stagnant at zero. Wondering what was wrong, he reached out a senior veteran in the learning and development community.
“He bluntly told me that L&D managers would consider our startup’s service a thread,” says Mathur. “I realized from this conversation that we should have targeted executives rather than managers with our service.”
Product-market fit and clearly defined customer personas are the key pillars of a successful business strategy. What we can learn from Mathur’s experience, however, is that we need to separate our targets’ personal objectives from their professional ones. There’s more to an interpersonal connection than the promise of ROI.
When it comes to success with targeting, customer personas are only part of the equation. We need to dig deeper by challenging our assumptions, sanity-checking our data and figuring out what our audiences care about. When we implement these steps, we’ll learn our tough lessons faster.
Image credit: "Amusement Park" by Dean Hochment, Flickr http://ow.ly/ODPcI