Lead generation comes in all forms, from the old standby of print and email list rentals, to emerging and evolving online search strategies. For offline lists – the focus of this article – there are two types: active and compiled lists. After explaining the differences between the two, we’ll focus on compiled lists and best practices to improve lead generation results from prospecting efforts.
Active lists reflect recent (i.e., less than 12 months ago) activity such as magazine subscribers, event attendees, responders or buyers. These are often contact-level lists. Buy small samples from each list to test response before investing in a bigger campaign. Check that there is sufficient quantity of names available for a larger rental later, and be sure to take into account any subtractions such as do-not-mails, geographic limitations, etc.
You can select desirable attributes relevant to your targeting needs, such as contact title and decision-making authority. But caution: although selects can produce more targeted prospects, they dramatically lower the available universe of names. Selects may also not be indicative of response until you have tested the list several times.
Compiled lists are a broad compilation of individual or company records. Due to the extensive number of records gathered, you will often have a number of selects to choose from. In this case, you will want to use the appropriate selects to target the best leads for your product or service.
Many selects such geography or industry may be obvious choices. Beyond that, deeper selects — and the various combinations of selects — could have strong correlations to response, but these relationships may be difficult to detect until you test. Or to the contrary, they may lower performance because of unforeseen interactions. The conundrum is, these deeper selects, and combinations of them, have potential for significant increases in response, thus improving your prospecting and lead generation investments.
How can analytics help you overcome these issues? There’s a few steps you can take:
Step 1: “Look Alike” Profile
To improve performance of selects, start with “look alike” models. These models compare your best customers to the general list universe, and they identify attributes that “over-index” — meaning that’s where your best customers are found in higher proportion than the general universe. The results will surface a combination of selects that will yield the maximum response.
These models can typically yield an approximate 20% better lead generation response. While this is a nice upgrade, continue to overlay selects that have traditionally worked for you. Test the model against your existing selection criteria both through back-testing and live in a campaign.
One crucial element of success – as well as risk – is how you define your “best customer.” Is this your most responsive customer to a single campaign? Or the most valuable customer after 12 months? For example, let’s say you choose to profile customers in the top 20% of spend as your best customers. The problem with this definition is it ignores how long it took for those customers to become valuable, and does not distinguish between “one large order and done” customer versus one that took ten years to get there. In short, this has little to do with how you acquired that customer – it brings no understanding to how to improve prospecting performance.
Step 2: Multiple Outcome Comparison
One way to address the risk that you’d be off target with a single “look alike” definition is to create multiple definitions to cover a broad range of outcomes, and build and test several models. Start with outcomes that are immediate, such as leads that downloaded product information or set an appointment. You can also evaluate long-term outcomes such as placement of five orders or spending $5,000 in the first year since acquisition, or customer purchase across three product categories.
Other outcomes could be narrow, such as a prospect downloaded a white paper or requested a quote. You can also try niche outcomes such as highest first order amount, or purchases of specific service, warranty or higher margin product.
Analytics can test all of these long-term and narrower outcomes at the same time, and can tell you the most valuable methods of prospecting.
A successful prospecting campaign should not only generate the best immediate response, but contribute the most to your franchise value over time. While it might seem like an arduous task to evaluate numerous outcomes, this is the very path to high-performance prospecting. While solutions are available including Valgen’s to make this process simple through automatic iterative modeling, with careful planning, you can start by testing a few outcomes manually, and evolve over time to a more intensive analytical process if you want more detailed insights.
Step 3: Response Cohort Analysis
A prospect that responds to your campaign by downloading online product information, requesting a quote, accepting an appointment or even placing an order is only one piece of the puzzle. You must ensure that the value proposition that brought the customer to you in the first place is reinforced, through both the circumstances of the purchase itself and their on-boarding experience with you. That’s where the effects of offer, channel and sales process becomes important.
Going beyond multiple outcome comparison, you also want to capture key information about how prospects are converting to customers. This is what we mean by response cohort – assigning the newly-acquired prospect to a segment containing a unique combination of factors related to the response such as the offer, channel, salesperson’s level of experience and even number of contacts made before first sale. Ensure that you capture this type of data, so you have the ability to refine the quantitative and qualitative aspects of your lead generation process to improve results over time.
Analytics + Sales Operations = Powerful Prospect-to-Customer Success
After you have settled on the analytic approach to take, it should be paired with the most suitable operational tactics. This will help you validate the analytics, and improve your prospecting processes based on the feedback from your analysis. For example, you may find that with high-scoring prospects, making two additional touches within a month could yield an exponential conversion rate. This finding could only be uncovered by in-depth analytical efforts. We will cover this in another article here soon.