Not all leads are created equal
You don’t have to be in this business very long to know the painful truth of that statement. As a media director, my daily challenge is generating quality leads – leads that enroll more efficiently than our competitors’.
Inevitably, the question is: “How do we know a lead’s value?”
What is User Intent?
Before we dive into the question of valuation, let’s back up a moment. Since we are talking about “user intent vs. lead scoring,” we should establish what we mean by “user intent.”
I see the term “user intent” most often referred to when talking about search terms typed by users – specifically the Google algorithm that considers combinations of keywords and subsequent searches. A classic example would be identifying what a person is searching for when they type “new cars for sale” vs. “new car reviews.” The former is ready to shop while the latter is still doing research. This basic sample illustrates what can be understood from how an online user interacts with advertising.
Stated simply: user intent is what the user intended.
One of the reasons we have all learned to hate co-reg is because of the never-ceasing-to-amaze lack of user intent. Of course those users aren’t really interested in furthering their education – they didn’t even see the pre-selected opt-in box at the bottom of the… daily deal du jour (yep, I just wrote that)… registration form.
All right, back to the value of a lead…
Lead Gen Omniscience
In 2011, there are a lot of companies in the online space striving to deliver omniscience. “Lead scoring” cropped up a few years ago as the latest buzzword in lead gen intelligence. Data aggregators like eBureau and Targus launched lead scoring products with education-specific profiles (as well as custom options, of course). Many school groups have even developed their own proprietary lead scoring technology.
Does lead scoring work? Can it look into its crystal ball (consumer databases) and predict the future (propensity to enroll)?
Unfortunately, I can’t answer that question definitively. However, I will share what I do know, and that is…
Lead Scoring: Only as good as your data
Most data doesn’t – or can’t – take into account user intent. No consumer database has information on a given user’s depth and breadth of motivation or where they are in the decision funnel.
If you are doing lead scoring; great. However, be careful to use it to determine how the lead is handled and don’t simply reject a lead based on its score. Leads generated from Google shouldn’t be rejected based on a lead score, wouldn’t you agree? The user intent must be considered. The user behind that Google lead typed in a keyword, hit “search,” and found YOU. WIN.
Consider the Source
When building a media plan, we know that “pull” leads – where the user is “pulled” into engagement while already actively looking for your product or service, i.e., search – convert much better than “push” leads – where we try to engage the user while they are doing something else, i.e., display ads. The difference in user intent between these two examples is obvious:
- Search: user opened a browser and typed a keyword to find a product or service like yours
- Display: user was doing something else – reading the news, replying to email, playing a game online, doing product research, chatting with friends on Facebook – and saw your banner ad
Lead Verification
Separate from lead scoring, we have data verification, which has obvious value. Determining a basic level of legitimacy to avoid wasting resources attempting to work bad data is essential. If you aren’t doing this yourself, please make sure the lead partners with whom you are working are.
Where lead validation is a critical piece of the puzzle, lead scoring can be harmful when not considered properly. (See that bit about not throwing Google leads away above.)
Now, don’t get me wrong…
I am a huge fan of data and demographic targeting. I will never forget the day I was introduced to Nielsen/Claritas’ PRIZM segments – I thought I had died and gone to heaven.
“We can do demographic analysis down to the zip+4 level?!?!” (They have it to the household level now – this was a while ago.)
I gleefully bought many, many direct mail lists based on the top segments from my historical customer database.
The problem we face in the online world is the data we know (or don’t know) about a given user. We have to go with what we know and allocate our resources accordingly:
- What differentiates one 25-year-old female, HHI $45k, ZIP 84111 in a customer service job from another?
- What if you knew one came from Google and the other came from an interstitial on Monster.com?
- What if one was from a banner on the MSN Lifestyle home page and the other from a school-branded email campaign?
From where I sit
At LMP, we have grouped our media sources and placements by how we know they convert to enrollment – on a client-by-client basis, not just in general. Our lead tiers take into account the user’s process to and through our site, potential user intent, and historical data.
This historical data begins with testing new media that shows a high probability of user intent. In addition to the placement, we know that user intent can be heightened before a lead is generated by providing clear information about who and what we are advertising, how the lead process works, and how they will be contacted.
Not all leads are created equal, so smart lead gen requires us to know as much as we can about a lead, as soon as we can. Something about hedging our bets…