Lead Scoring_Set Yourself Up for Success

I’m just going to come right out and say it: We really should separate.

No, I don’t mean it like that. I mean separate as in … pick the wheat from the chaff. Cull the proverbial sheep from the goats. Pick and choose. Winnow and glean.

As marketers, we need to prioritize the good leads over the bad. When it comes to pursuing prospective customers, it pays to spend some time determining who makes the grade. The modern marketing department can’t afford to waste time chasing after the wrong prospects. Budgets are tight, marketing staffers are overworked, and marketing managers have their hands full running the show.

But, by taking the time to set up a lead scoring system, you can assign points to potential prospects, target the attributes most often associated with serious customers, and more easily identify which people to further invest time and resources in.

As Matt Heinz, President of Heinz Marketing, explains it: “By assigning relative point values to various customer activities and traits, you can better rank your leads and focus your marketing efforts where they will be most successful.” It sounds ideal — but it’s not a paint-by-numbers exercise. A well-designed system takes time, research, and cross-departmental teamwork to develop.

Heinz provides an eight‒step checklist for implementing a lead scoring system. TOPO’s Craig Rosenberg also offers his advice and top tips for augmenting your lead scoring method.

1. Create and embrace your target buyer persona(s).

“The first step to any marketing program is to identify your target buyer. When I advise organizations on this first step, they often chuckle as if it is obvious. Actually, it is a very important exercise. Scoring is the act of deciding whom to spend time on, and when to spend it. Also identify the people you don’t want to talk to. Scoring is often broken up into two categories: psychographic and demographic. With regard to demographic factors, there are types of buyer personas that are a waste of time, and negatives scores should help sales avoid them. A simple example of this is the company that does not sell to consultants. If a consultant is identified, they should be scored away from sales. Psychographic scoring is the process of scoring prospects based on their activity, such as downloading whitepapers. One common negative scoring activity is when prospects hit the careers page, as they are often a waste of time.” (Rosenberg)

2. Know your customers’ buying cycle and buying signals.

“Customers need your products, but not necessarily all the time. Let’s say you’re selling office supplies, and you know that a customer places an order every three months. You can use that information to rank their likelihood of buying based on when they placed their last order. If your records indicate a seasonal bump — for instance, from a tax preparation firm that makes an extra purchase in the spring, you can also use that information. Tracking past purchases will also tell you whether a customer typically makes an extra purchase at certain times of year, or simply increases the size of a regular purchase.” (Heinz)

3. Score each step, activity, or buying signal based on its relative value.

“Not all data segments are equal, so it’s important to accurately rate their relative importance. Let’s take another look at your tax preparation customer, again assuming a three-month buying cycle. One month after their last purchase, their buying cycle might score them 20 points; 40 points at two months, and at three months — when they’re running low on supplies — they score 60 points. Another customer buys every four months, so they might advance in 15-point increments each month. Both customers are ‘ready to buy’ at 60 points, and you can accurately compare the two buying cycles.

“Other data segments might accumulate points over time. One visit to your website might indicate curiosity or price-shopping, so you might score that at 10 points. But as customers accumulate points from repeated website visits, you’ll be able to differentiate between customers who are just looking and customers who are looking to buy. A request for a quote might be worth 30 points all on its own, because the customer has specifically indicated their interest.

“But what if a customer is making extra website visits when their buying cycle doesn’t indicate that they’re ready for another purchase yet? This is where lead scoring is especially useful. If a company is growing and needs to buy from you more often, their buying cycle might not tell you that, but combining it with other signals for a total score will. A customer three months into a four-month cycle has only accumulated 45 points from the calendar, but extra website visits or a quote request might tell you they are ready to make their next purchase early.” (Heinz)

4. Create three to five segments to start (don’t overdo it).

“Data is useful, but too much data can let less relevant data obscure more important information. In our examples, we’ve seen how effective lead scoring can be using only three data segments: buying cycle, website visits, and quote requests. It’s possible to add more data — as much as we want about any metric we care to look at — but doing so defeats the purpose of lead scoring, which is to extract and use the most relevant data about your customers. Start out by picking just a few essential data segments. You can always add more if you need to.” (Heinz)

5. Use anecdotal data initially.

“You will not have enough data when you start, so you will need to make your best guess. Sales is a terrific resource for this data. Lead scoring is often at its most optimized after a year of data, so just get started and your scoring will get better over time. In the meantime, stay a bit broader than you want to be; don’t rule too many leads out, because over time, the data you collect will allow you to make better scoring decisions going forward.” (Rosenberg)

6. Get complete alignment with sales.

“You’ve set up your system, but marketing isn’t the same as closing the deal. Your sales staff will also need to use the system to guide their actions, and (depending on the data segments you chose) may also be making inputs to the system based on their interactions with customers. It’s important that everyone understand the data segments, their relative importance, how to assign scores, and what action to take based on scores. If they don’t, data — or customers — might slip through the cracks.” (Heinz)

7. Build a set of specific next steps.

“Using the data you’re already using to score your leads, you can customize call scripts and email templates for use at certain benchmarks. For instance, mentioning a customer’s three-month buying cycle during a call can convey a sense of personal service that enhances your relationship with the customer. Your website might be able to automatically use an email template to respond to quote requests or a certain number of visits.” (Heinz)

8. Track behavior, and adjust scores and tactics accordingly.

“Like any other strategy, follow-up is vital to accurate lead scoring. Make sure the data segments you’ve chosen accurately predict customer behavior. If customers are reaching the ‘buy score’ but not buying, you’ll need to either raise the benchmark or adjust how many points customers accumulate from each segment. If customers stop visiting your website after preliminary contacts, that might indicate a problem with those contacts, and you should adjust accordingly.” (Heinz)

In Closing

By better ranking your leads and targeting the attributes most often associated with serious customers, you can focus your marketing efforts where they will be most successful. Concentrate your initial efforts on buyer personas, buying cycles and buying signals, and anecdotal data. As you make progress, leverage data to determine the steps to take based on specific benchmarks. Be sure to track behavior and adjust your system according to real-world prospect and customer behavior.

Investing the time to set up a lead scoring system is well worth the effort. You’ll find selectivity and separation pays off, big time.