kentoh - Fotolia


6 customer intent metrics to prevent lead dropouts

Learn about the key metrics marketing teams can use to analyze customer intent, detect leads in danger of dropping out and focus energy on nurturing those leads.

Few frustrations in sales loom as large as lead dropouts -- cultivating a lead for weeks, even months, only to have it slip away. Effective lead management, then, should include a serious effort to identify the customer intent of leads about to drop out and an opportunity to intervene and bolster their chances of conversion.

Most CRM suites accommodate follow-up on dropouts, giving sales the opportunity to woo them back, and most try to properly qualify leads before they are fully pipelined, increasing the chances that they'll turn out well.

But follow-up doesn't mitigate potential lost time and adds the risk of even more time wasted. Better qualification, while always a good idea, does nothing for the many circumstantial events that can derail a good lead.

The should-be lead management feature that isn't

Detecting potential dropouts before they bail should be a state-of-the-art lead management feature by now -- but it isn't.

In most CRM packages, lead scoring includes prioritizing those leads most likely to go the distance. This being the case, the most common sign that leads are candidates for dropout is that they've already dropped out. At that point, it's often too late; they've lost interest or taken their business elsewhere. Salespeople seldom concentrate on possible dropouts until it's too late because the leads were vetted upfront -- typically by the software that detected them to begin with -- and thus they are expected to be prime candidates for a sale. The dropout is an unexpected surprise.

Better qualification, while always a good idea, does nothing for the many circumstantial events that can derail a good lead.

Vigilance in monitoring developing leads to detect imminent dropout isn't as common as it should be; even a small handful of dropouts during a campaign can represent significant loss of time and effort that could have been invested elsewhere.

Analytics -- a dropout flag

How can you detect an imminent dropout before it happens? Since it's predictable, the tool of choice is analytics.

Graphic explaining how to nurture sales leads throughout sales funnel process
Analyzing potential lead dropouts is an essential part of nurturing sales leads.

The exact analytics markers that will give insight into customer intent depend upon many things: the nature of your industry, the typical behaviors of your particular customer population and the product or service they are considering. Not all indicators of potential dropout will apply to all customers or be evident in every campaign.

Dropout detection

The good news here is that most of the analytics required to gauge customer intent are available from sources such as Google Analytics, with just a little tweaking if you'll mainly be tracking page views and other simple metrics. The bad news is, when it's something other than page views, the analytics are much harder to get to.

      • Drop-off in page views. A lead that's keenly interested in the products or services being touted in a campaign will typically make multiple visits to the webpage(s) you've deployed for them. If you configure your analytics to count page hits by visitor and are able to set up notifications based on drop-in hits, you can be alerted ahead of time that the lead needs a contact to bolster interest.
      • Multiple views of pricing. The above is even truer when a lead has viewed the pricing page multiple times. That's generally an indicator that you have a serious customer, because they're doing the purchasing math and seriously comparing you with competitors. A drop-off here is an especially potent warning.
      • Drop-off in information requests. A truly interested lead will typically follow an information request with increasingly specific requests. When the requests become more and more detailed, you know you've got a live one. However, when it stops, the ball is back in your court. Start counting information requests, which can be tracked via specific webpages or dedicated email addresses, and build in notifications when a certain period of time has passed without one.
      • Fluctuation in responsiveness. It's an industry cliché that, when the follow-up calls start taking longer and longer, the lead is fading. But in the age of email, it's harder to maintain awareness that communication is slowing as the time between email increases. This, too, can be tracked as web activity but may require some customization work with DevOps or other IT staff.
      • Changing the channel. If you've achieved a one-on-one dialogue with a lead, there will typically have been a progression from one channel to the next -- from webpage to email to phone call. In some cases, when that progression reverses, it may be an indication of waning interest. If this is the case, your CRM software will be tracking the lead, since it's now in development. Learn how your particular CRM registers this changing of channels, and set up alerts for it.
      • Email text semantic analysis. This is the trickiest of all but may be the best early warning, with the longest lead time to reinforce conversion. If leads have gotten far enough into the pipeline to make information requests and provide insight into their needs, then there is typically an email exchange happening. Email can be analyzed for subtle sentiment clues. Most sentiment analysis software can go beyond simple keyword search and pick up passive linguistic cues of neutral or negative emotion. This can be an indication that the lead is souring and in need of more personal contact.

This may sound like a lot of trouble to go to -- and it is. But the ROI is easy to calculate. Just do a brief survey of your marketing team, and ask how much time and effort goes out the window, week by week, due to lost leads. It's likely you'll quickly conclude that you can get a great return on your investment if you start listening to the customer intent analytics.

Dig Deeper on Marketing and sales

Content Management
Unified Communications
Data Management
Enterprise AI