As marketers or salespeople, we are very much focused on identifying leads, that is potential buyers who showed some level of interest in purchasing our products or services.
But how can we make sure these leads have a business value? One method is to set up a scoring system to differentiate between the good, the bad and the ugly.
Assign points (0-up to X)
Lead scoring is a methodology used to rank prospects against a scale that represents the perceived value each lead represents to the organization.
Here at Expedeon, as the marketing team, we assign to each lead a score that ranges between 0 and 80 points. According to this measuring system, at 80 points a user gets assigned to one of our sales representatives that follows up with an introductory phone call. It’s now the responsibility of the sales team to convert a marketing qualified lead (MQL) into a sale qualified lead (SQL).
The secret of a high ratio SQL/MQL lays in an effective scoring system that identifies the stage at which a website visitor becomes a potential customer. If the MQL value is too low, the user might not be at a buying stage yet, if too high it could damage the chances of closing a sale (aka…losing to a competitor).
The numeric value is meaningless, what counts is defining a threshold score above which, the user is at a buying stage.
The scoring system currently in place assigns:
- 50 points to a guide download
- 1 point to a page visit
- 3 points to an email click
- 1 point to an email open
- 5 points to a custom services page
- 80 points to a direct contact or a brochure download – (the users becomes automatically a marketing qualified lead)
My recommendation would be to start from a high number (such as 100) while readjusting the value of each action to better represent their economic value. Don’t be afraid to lower the scoring threshold to test if marketing qualified leads (MQLs) could be converted into sales qualified leads (SQLs) in a shorter time frame. Finally, keep track of your changes by requesting a constant feedback from the sales team regarding the leads’ quality.
Another key aspect to consider is online behavior. Do purchasers perform specific online activities before converting? For instance, downloading a guide, watching a video or requesting more information? If that was the case, any page that helps conversion should also be reflected in the page scoring system.
How do you do this?
Google Analytics assigns a higher score to pages that contribute to conversion. These pages could reflect a higher score in Pardot. For instance, if a page gets scored 1 point/visit, pages with a higher score could get an additional +2 or +3 points.
Depending on job title, business size, or even social media followers, a lead might have a higher or lower business value. For instance, a lead could have a higher ROI due to a tendency of customers with specific characteristics to make larger orders or become loyal customers.
By looking at the lead’s characteristics what distinguishes a recurrent buyer from an occasional visitor? Is geographical location, job position or age group?
Once you have found what defines your best customers, you can assign a higher grade to prospects who present such characteristics. Grades vary from A (great) to F (poor).
A combination of high scoring and grading should identify your priority leads.
But how can you find common features among your best purchasers?
An easy way to determine purchasers’ features is to generate a segment in Google Analytics to isolate all visitors that performed an online transaction.
How to create a segment:
- On top of a page view, click Add Segment
- Click on New Segment
- Click on Conditions (Under Advanced)
- Select Page from the first drop-down menu
- Select Contains from the second drop-down menu
- In the text-box select your check-out page (example, /checkout)
- Name your new segment (example, Purchasers)
- Click Save
Now, Purchasers can be identified by geography, interests, age, and gender.
Users having a more likely chance to purchase will then be marked with a higher score, while leads coming from a specific geographic and/or demographic with a low purchasing history will be marked with a lower score. The sales team will either not follow up or move them down the priority list.
- Geographic attributes
To determine if the most profitable buyers come from a specific area, apply the Purchasers Segment in the Location View (Audience > Geography > Location). For instance, if most of the purchases come from the United States, by clicking on the US map it is possible to determine which states are the most profitable, and by clicking on the State itself, to visualize from which cities the orders came from.
In addition to scoring these leads higher in Pardot, it’s also good practice to incorporate additional digital strategies, including PPC and remarketing.
If the most profitable companies are located in a specific state or region:
- Generate paid campaigns that are specifically targeting those locations
- Generate an Audience in Google Analytics and use it for retargeting
How to create an Audience:
- Go to Admin
- In property open Audience Definition
- Select Audiences
- Select New Audience
- Import Segment
- Select your new segment (example, Purchasers)
- Select both Google Analytics and AdWords as display platforms
- Name your new Audience (example, Purchasers Audience)
- Click Save
Now, you can target those users by showing them related-products:
- Add the New Audience to a search campaign (use +Audience)
- Generate a Remarketing Display Campaign to target that specific Audience
Re-targeting is a great strategy for cross-selling and up-selling.
- Job position
Information regarding job roles is far trickier to establish in Google Analytics, especially for science-related interests. The best way of figuring out the job role of prospects is to include a (mandatory) job field when a user fills in a form to access free content.
Currently, we have set up Pardot Forms on Progressive. That means that if a user has already downloaded a guide, on their next download they’ll be asked for additional information, such as their job role.
The more complete a data profile is, the easier it will be to send highly tailored information. For instance, academics, a far more budget-conscious type of customer, will be targeted with offers and discounts, while large corporations will receive updates regarding manufacturing and custom services options.
- Age & Gender groups
To determine if the most profitable buyers have an age or gender bias, apply the Purchasers Segment in the Demographics View (Audience > Demographics > Overview). If the most profitable customers come from a specific age group or have a gender bias, you can assign a higher score. For instance, by looking at Purchasers > Age, I found that most profitable users are between 35-45 years of age.
As we do not ask for any personal data such as age and gender, we only use this information to tailor our PPC strategy: we exclude students (below the age of 25) and retired individuals (above the age of 65), while we bid (20%) more on the age group that – based on our data – is most likely to purchase (25-35 years of age).
Based on all the above attributes, users will receive a final grade that is the mean of all their scores. For instance, if a user has an A for geographical location, B for job title, and C for age/gender, their final grade will be a B.
Lead source and offer
A very popular way to attract new leads is to promote offers and discounts. Did customers start purchasing after seeing one of your offers? Did they become first-time buyers after taking advantage of promotional codes? When a promotional campaign ends:
- Report the percentage of new time buyers
- Report the most successful marketing channels
Budget, Authority, Need, and Timeline (BANT)
As the marketing team, we do not rely on BANT to qualify leads whether a prospect is a good fit based on their budget, internal influence, need to purchase and timeline. However, large quote inquiries and bulk orders (B) are prioritized and so are urgent purchasing requests (NT). In all these instances, a same-day contact follows (depending on the time-zone).
Otherwise, it’s the sales team rep in charge of assessing the sale potential of MQLs on the basis of the BANT methodology. If however, an MQL fails to become an SQL, the lead comes back to the marketing team for additional nurturing, through the Engagement Studio.
5. Segment customers based on their purchasing activity – the RFM model
Finally, customers are scored based on the recency and frequency of their purchases. According to this model, if they bought recently, they would get higher points. The same if they bought many times, or if they spent bigger amounts. These three parameters combined, create the Recency – Frequency – Monetary RFM score. This is a comprehensive table from Pluter.
Once you have divided your customers into these groups, you can follow them up with tailored emails. If you don’t have enough manpower to follow up on each segment, focus on your best customers, those with the highest recency, frequency, and monetary score.
But how do you calculate RFM?
First identify Recency (days), Frequency (times), and Monetary Value (CLV) for each customer. Then assign a score from one to five to recency, frequency, and monetary values individually for each customer. For instance, the highest-value customers will have an RFM = 555, and the lowest-value customers an RFM=111.
A common calculation is to assign 5 to people that purchased in the last 24 hours, 4 in the last 3 days, 3 within the current month, 2 for last six months and 1 for everyone else. However, scores should be established based on the business you operate (for instance, for small businesses a purchase of $2K might identify a Champion client). Plus, such ranges should be constantly revised, especially when the business grows.
Once each customer has their RFM calculated, they are assigned to their corresponding customer segment.