weeding with sc oring 1 · predictive lead scoring uses data points on prospect intent to augment...
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Weeding Out Junk Leads With Predictive Lead scOring 1
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Weeding Out Junk Leads With Predictive Lead scOring 2
Bad leads cost a staggering amount of money. An average sales rep wastes a whopping $218,000 each year pursuing the wrong prospects. That’s according to the Altify Buyer/Seller Value Index, which also says that futile sales meetings cost companies $519 billion in the U.S. alone.
There’s a lot of reasons why we’re seeing such an alarming level of lost opportunities and misspent resources. But most of this boils down to not having the ability to tell apart good leads from bad ones.
Here’s a familiar situation: A lead fills out a form on your website and downloads your whitepaper. The contact details look good, and the prospect’s topic choice indicates she’s likely a key member of the buying team. Everything about this lead so far is saying “qualified” in big bold letters. But how “qualified” is this prospect exactly?
When leads first enter your pipeline, how do you separate the ones that will most likely turn into your customers versus those who probably need a little nudge versus those who are simply random passers-by?
Lead scoring helps you answer this question more methodically by letting you assign points or ranking to each lead in your funnel based on how well she meets a given set of criteria.
intrOductiOn
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Weeding Out Junk Leads With Predictive Lead scOring 3
What is lead scoring andwhy does it matter?
A lead score is a number or label that tells you how sales-ready a given lead is at a given time. Typically, lead scores are computed based on metrics that show how much a prospect is interested in your business, how well the target company fits your solution, and how far along the buying journey the lead is.
Having a well-defined lead scoring system reduces the guesswork out of your workflow. When you have a bunch of leads you’re trying to manage, lead scores help you identify and prioritize which ones are worth handing off to your sales team and which ones need a bit more nurturing, as well as those who really shouldn’t be part of your opportunity set at all.
When done right, lead scoring works so well that an overwhelming 86% of marketers in the B2B space consider it as a key component in their lead generation strategy. When asked how lead scoring specifically benefits their marketing efforts, around 74% of B2B marketers cite improved focus on prospects with the highest likelihood of turning into their customers.
B2B companies also credit having a well-run lead scoring process as an important factor in helping them bump up sales productivity, conversion rates, and ROI. MarketingSherpa reports that companies that do lead scoring right achieve a lead generation ROI that’s nearly 1.5 times higher than that of businesses without a lead scoring program in place.
86%consider lead scoring as a key component in lead generation strategy
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Weeding Out Junk Leads With Predictive Lead scOring 4
Will lead scoring workfor you, too?
Before you get fully sold on lead scoring, though, there’s a couple of things that need to be ironed out first before signing up. The pre-flight checklist usually involves deciding whether you have a genuine need for lead scoring, the right amount and kind of data, and the processes needed to tie everything seamlessly together.
It’s crucial you carefully go over this initial step as thoroughly as possible so that things will run smoothly later on. In fact, the success of your entire lead scoring project might just hinge on getting it right at this early stage. According to the Demand Gen 2016 Lead Scoring Survey Report, about half of marketers admit their lead scoring initiatives need improvement, while only 20% rate their lead scoring programs as “highly effective.”
“Nearly half of those surveyed admitted their lead scoring initiatives need improvement, and fewer than 2 in 10 ranked their programs as highly effective.”
- Demand Gen Report
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Weeding Out Junk Leads With Predictive Lead scOring 5
Lead scoring is a data-intensive process—even at the setup phase. If you’ve already clearly defined what your “qualified lead” is exactly supposed to be, then you need to comb through your CRM for data on prospect wins/losses and a number of other fields that quantify or describe your target leads’ attributes. Depending on who you ask, these can cover a lot of qualification items but typically include explicit (fit) and implicit (interest) data.
Lead scoring is just a single component of your lead generation efforts. It will only function once all the other pieces fall into place. To work as intended, a lead scoring system needs (at the very least) a definition of a qualified lead that both marketing and sales agree on. A lead scoring model also requires that you’ve already identified your funnel (i.e., the series of steps a lead has to go through before conversion), meaning that some level of marketing automation also has to be up and running already.
Do you have enough data?
Do you have the right processes in place? If not, do you have the resources to put them together?
MarketingSherpa says that if your answer to this is an emphatic “yes,” then you probably don’t need to have a lead scoring program in place. This can indicate that you’re handling a relatively low volume of leads and/or a short sales cycle. Otherwise, you should probably start giving lead scoring some serious thought.
To help you find out whether or not there’s a strong business case for adopting a lead scoring program, here are some of the key questions you should be asking:
Can you easily tell which leads deserve sales outreach and which leads need to spend a little more time with marketing?1
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Should you choose traditional or predictive lead scoring?
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Weeding Out Junk Leads With Predictive Lead scOring 7
Now that you’ve decided that lead scoring is something you can use, there’s one more crucial decision to make. Your lead scoring model can follow either the traditional or the predictive approach.
Traditional Lead Scoring
Under traditional lead scoring, lead quality is determined based on a set of demographic, engagement, and behavior criteria chosen and weighted (ideally) by your marketing and sales teams. Values are assigned for each of the attributes, and the results add up to that particular prospect’s lead score.
A traditional lead scoring model that’s based on the classic BANT system, for example, might let you assign more points to a prospect that’s a C-level executive and deduct points if her company falls outside a specified industry. It’s entirely up to the marketing and sales people that put your lead scoring system together to choose which attributes to include as well as how these characteristics are weighted.
This is where the main problem of using traditional lead scoring arises. Traditional lead scoring relies too much on the arbitrary choices and assumptions that the people designing the model make. Unless your lead scoring team really knows what it’s doing, traditional systems can be prone to human error and bias. In addition, coming up with traditional lead scoring models tends to be a heavily manual and static process, making them very difficult to modify and adapt.
shOuLd yOu chOOse traditiOnaL Or Predictive Lead scOring?
Traditional Lead Scoring Model
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Weeding Out Junk Leads With Predictive Lead scOring 8
Predictive Lead Scoring
Enter predictive lead scoring. Predictive lead scoring makes use of algorithms to estimate the probability that a lead converts based on some relevant prospect attributes. Leads are then classified or ranked according to whether the computed probabilities fall within or outside some threshold values.
Predictive lead scoring uses data points on prospect intent to augment the fit and interest attributes, so that a fuller prospect profile is available to gauge lead quality.
Predictive lead scoring differs from the traditional approach in that predictive models are 100% data-driven. Predictive lead scores are based on vast amounts of data analyzed using a machine learning algorithm. The attributes to include as well as how each factor is weighted are completely determined by the dataset and the algorithm. In other words, traditional lead scoring reduces the amount of guesswork involved in qualifying leads, while predictive lead scoring eliminates guesswork completely.
Predictive lead scoring models are also more robust than their traditional counterparts, which means they’re better able to capture prospect activity and behavior that make a lead qualified. In addition, predictive lead scoring systems are more dynamic than traditional lead scoring since predictive models can easily be modified by training on another dataset or applying a different algorithm.
That’s not to say predictive lead scoring is not without its drawbacks. The biggest hurdle for most organizations getting started is the sheer amount of data needed to build a reasonably reliable predictive lead scoring model. Again, predictive models mine data not only on fit and interest but on intent as well.
shOuLd yOu chOOse traditiOnaL Or Predictive Lead scOring?
Predictive Analyticsradius.com
Datainternal & external
Statisticsto find patterns
Predictionsdiscovered patterns
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Weeding Out Junk Leads With Predictive Lead scOring 9
How exactly does predictive lead scoring work?
Sirius Decisions has uncovered some pretty telling adoption statistics for predictive lead scoring. Small and medium-sized businesses have embraced predictive lead scoring in greater numbers than large companies, with about 83% of B2B organizations using predictive lead scoring having annual revenues of less than $250 million. Also, almost all users find value in predictive models, since 98% of users say they’re planning to renew their predictive lead scoring purchase. All these point to one thing: predictive lead scoring works. Here’s how.
Many of the algorithms that power predictive lead scoring systems are the very same machine learning models that data scientists use in such tasks as image recognition (tagging if a photo is of a cat or a dog), natural language processing (clickbait auto-detection), and other AI applications (such as self-driving cars).
What we’re doing in predictive lead scoring is training (or teaching) a machine to correctly identify whether or not a given lead in your marketing database would turn into a customer by showing it actual examples of prospects that have and haven’t become buyers.
To do this, we first gather data on past wins/losses (known as labels) and attributes that indicate fit, interest, and intent (known as features). An algorithm (such as logistic regression, k-means clustering, or deep learning) then crunches the numbers to find relationships between the label and the features (i.e., whether a certain value of an attribute increases the chances that a lead becomes a customer). The algorithm quantifies this relationship by assigning weights or coefficients that define how a given feature affects what value the label takes.
That’s pretty much what goes on under the hood and, if this isn’t clear to you yet, that’s okay. Predictive lead scoring is baked into many commercially-available marketing automation and CRM platforms that you really don’t have to worry about every part that keeps the whole thing running.
Predictive lead scoring is training a machine to correctly identify whether or not a given lead in your marketing database would
turn into a customer.
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Weeding Out Junk Leads With Predictive Lead scOring 10
What are the things you shouldkeep in mind?
Of course, that doesn’t mean you can just start weeding out bad leads with predictive lead scoring right out of the box. As we’ve talked a little bit about earlier, lead scoring systems need to meet a number of prerequisites to work properly, and the requirements for predictive lead scoring (particularly in terms of data) can be a bit more demanding.
Not only do you need a sufficiently large dataset for training the algorithm to classify leads, but the data (for both training and actual deployment) needs to be granular enough so that lead scores are based on patterns that accurately predict outcomes.
The main takeaway that you should glean from everything we’ve covered in this post is that predictive lead scoring is a tool to help you do your job better.
The keyword here is “tool” and, at the end of the day, it’s going to be you who’ll get the job done.
consider lead scoring as a key component in lead generation strategy