The Life of a Sales Lead and the Power of Predictive Analytics
CSO Insights reports the average sales rep only spends two days a week effectively selling.
Sales reps waste a lot of time on prospects who are unlikely to ever become customers. We call them tire kickers. They show signs of interest but have no real intentions of buying.
This puts your sales reps in a tough spot. They have little way of knowing which leads are hot and which are cold.
Fortunately, advancements in predictive analytics are solving this problem. Marketers are now able to better understand which leads are ready to productively engage with sales teams, and which need more information and instruction before being primed for sales.
To understand this handoff, you need to understand the life of a sales lead.
While the process may vary from organization to organization, depending on the specialization of your sales team or your lead generation methods, the basic process is relatively similar.
Understanding the life of a sales lead will also clearly illustrate how predictive analytics can improve this process, rendering it more cost effective and successful.
Activate the marketplace
The life of a sales lead starts with the placement of external stimuli into the marketplace.
Like bait on the end of a hook, these stimuli are meant to appeal to buyers and incite a reaction.
Once someone takes the bait, either by downloading content, requesting a free trial or engaging in some other way, marketers have one of two approaches they can take.
The first is to reel them in immediately and pass them on to sales. That works if someone is interested right away and marketers feel they can quickly move them through the sales funnel.
The second, however, takes more time. As any good fisherman knows, you can’t always reel in too quickly. Sometimes the hook isn’t set properly or the fish is just nibbling at the bait. If you reel in too quickly, the fish will get scared and swim away.
The same holds true in marketing.
If marketers try to hand prospects off to sales too early in their journey, the prospect will either exit the sales cycle without buying, or will drain sales rep time going through steps in their journey that marketing could and should deliver.
What’s more, if sales reps talk to too many inbound leads that aren’t ready, they may start to ignore inbound leads as a “waste of time.”
The solution here is timing. Marketers might have to lure in prospects with more content and more education before they’re ready for the next stage of the sales cycle.
With more education, prospects become more interested and feel a greater need for whatever product or service you’re selling. At this point, marketers can hand them off to sales, knowing they’re better prepped for the next stage and more likely to become customers.
Living in an exchange economy
Marketing is a two-way street.
We aren’t just handing out content freely. If we are, we’re doing it wrong.
Proper marketing operates under the rules of an exchange economy, where goods and services are offered in return for something of relative value.
In marketing, that usually takes the form of information about a prospect.
An organization might offer an ebook in exchange for a prospect’s name, email and phone number. If they want a free trial, the organization will want even more in return, like information about the prospect’s organization and what his or her role is.
Pulling in external data sources
Acquiring as much information on a prospect as possible is vital for the sales process. Not only does it help sales create tailored messages and pitches to match specific needs, but it also helps marketers understand what types of content are generating leads. This helps them create more compelling content for future prospecting efforts.
However, when it comes to the life of a lead, more information also helps an organization sort potential buyers from tire kickers.
Using information and statistical analysis, organizations can identify trends and try to find common elements that dictate the lifespan of a lead. In other words, which leads are most likely to buy?
One criterion might be someone’s position within an organization.
VPs and managers have purchasing power, whereas sales associates normally don’t. Therefore, the VPs and managers should be sorted higher on a list.
The information gathered in return for marketing materials helps identify the criteria necessary for the sorting process, but it often isn’t enough on its own. It only provides a rough sketch.
In order to color it all in, marketers and sales professionals need more information – often more information than a prospect may be willing to give for that content or free trial offer.
This is where organizations may turn to outside data sources to see what more they can learn about a prospect, their company and industry.
Traditional lead scoring
Most organizations right now are using a very primitive lead scoring process to sort their leads. It essentially involves simple math, gut instinct and maybe a little statistical analysis.
As you would expect, primitive also means unreliable. Many organizations, out of fear of accidentally filtering out important leads, have overcompensated and removed any kind of filtering or lead sorting all together.
Whether or not an organization uses traditional sorting technology, or none at all, they have a problem on their hands. I can almost guarantee their reps are working inefficiently.
Once again, if your reps have no idea which leads are best, they’ll treat them all equally. Time that could be spent on leads more likely to close will be wasted tracking down and trying to contact your worst leads.
Lead scoring with predictive analytics
As a marketer, I want to know how much it costs me for a unit of demand – be it a marketing qualified lead or a marketing-sourced opportunity in the sales pipeline. I also want to know what I can do to lower those costs.
B2B companies spend anywhere from $30 to $200 for each marketing lead generated, according to the 2014 Lead Response Report.
And yet, that same report determined a large portion of these leads is never contacted at all. Of those leads that are contacted, reps only make an average of 1.3 contact attempts before giving up.
That’s a lot of money being thrown out the window.
Predictive analytics technology can help sales organizations wring more value out of their leads.
Predictive analytics software runs algorithms that turn historical and current data into models that predict outcomes and prescribe behavior.
Let’s use an example outside of B2B sales to help explain the power of predictive analytics.
We’re all familiar with Amazon. Whenever we visit the site, make purchases or select specific items, Amazon records that information. It can then recommend certain items you might also be interested in.
That same process can also help organizations overcome the problems associated with traditional lead scoring, because it:
1) Allows reps to be a lot more accurate in determining if the reaction marketers got means a prospect is ready to talk to sales.
2) Redistributes sales efforts and energy, giving higher priority to leads more likely to close.
A lead scoring application that runs on predictive analytics will collect historical information on current and past leads, and using those data points, indicate in real time which of your new incoming leads are most likely to become customers.
With this information, reps can focus more effort on higher-quality leads, instead of wasting time on those not likely to move through the sales pipeline.
BONUS: Your predictive analytics buyer’s guide
To wrap things up, I want to offer a few suggestions to help you as you shop for a predictive analytics product that matches your needs. While there are many predictive lead-scoring solutions, they are not all built equally.
Here are a few things to consider:
Predictive and prescriptive
When considering how to apply analytics, look for solutions that not only predict likelihood to close, but that also prescribe actions, like when and how to contact a prospect.
A good application will score your best leads, then tell your reps which of those high-quality prospects to contact first, based on their likelihood to respond at that particular moment.
Use of macro data
If you’re a Seattle Seahawks fan, you weren’t in a buying mood the Monday following the Super Bowl. That’s because, believe it or not, the outcome of sporting events can influence buying behavior.
There are a lot of other macro data, like weather, stocks and even gas prices, that can influence someone’s likelihood of purchasing. The very best predictive applications incorporate these data points into their algorithms.
The more data you can feed into a predictive model, the more accurate and detailed the predictions will be.
Many predictive applications will tune their algorithms to a specific business, using their individual sales information as a basis for their predictions. That’s helpful, but not optimal.
Instead, look for a service that will tune their product to your needs, but also uses data from other companies to help predict behavior. Imagine how much more accurate a model would be if it were incorporating all sales interactions for hundreds or thousands of organizations, instead of just one.
To see how predictive analytics can improve your sales performance, get the free ebook below.
Free eBook: The Science of Lead Scoring, Prioritization & Sales Success
79% of marketing leads never convert to sales. That means inbound reps waste a lot of time chasing the wrong leads.