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. Here are six steps to actually implement the system.

1. Know your customer’s 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.

Customers may also have specific buying signals, such as visiting your website more often or requesting a quote. You can identify which signals indicate a higher level of interest and use that information to better tailor your marketing.

2. 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 twenty points; forty points at two months, and at three months – when they’re running low on supplies – they score sixty points. Another customer buys every four months, so they might advance in fifteen point increments each month (fifteen, thirty, forty-five, and sixty). Both customers are “ready to buy” at sixty points, and you can accurately compare the two buying cycles.

Since these two customers are tax preparation firms, you might assign them each an extra ten points in March and April for their “busy season.” If either of them just completed a cycle in March, the extra ten points won’t necessarily put them at their sixty-point “ready to buy”mark. But if their last purchase was in February the extra points tell you that they might be ready sooner than otherwise.

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 ten 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 thirty 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 forty-five points from the calendar, but extra website visits or a quote request might tell you they are ready to make their next purchase early.

You’ll also need to assign actions to different benchmarks. A high point value will mean that a customer is ready to buy, but at lower benchmarks you might use a call or e-mail to maintain a relationship with a customer who will be ready to buy later.

3. Create 3-5 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.

4. 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.

5. Build a set of specific next steps
Using the data you’re already using to score your leads, you can customize call scripts and e-mail 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 e-mail template to respond to quote requests or a certain number of visits.

6. 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.