Ask any election-night pundit: Confidence in the predictive power of political polling is not especially high right now. Fortunately for marketers, predicting the future is far more straightforward. While polls can be subjective, marketing data is rooted in hard science. The data-assisted insights that go into lead scoring come from attention to definitive signals and carefully tracked histories.
Lead scoring acts as a force multiplier to all your lead generation activities, giving you meaningful answers to vital questions such as:
- Where are your leads in their buying journey?
- Which of your visitors are most likely to convert?
- How can you allocate your marketing resources most effectively?
- When are your leads sales-ready?
No business wants surprises when it comes to lead scoring, so assess your current system and see if it measures up to your organization’s needs.
Data Quality
Programmers have a favorite phrase for what happens when a useful system is fed inaccurate information: “Garbage in, garbage out.” Flaws with your data input will always result in unreliable output. To get a good handle on lead scoring, you and your marketing team need to look at the data you already have and see which demographic, firmographic, and behavioral characteristics correlate to lead value. When you see which of your prospects have historically evolved into customers, you get a clearer picture of the traits they have in common.
Improved Models
Lead scoring software has gotten sophisticated enough to go a step farther and put data into context for you, showing you precisely where your most sales-ready leads are and indicating what you can do to nurture other promising leads. Models are more effective when they draw from a wider range of data sources. Marketing automation systems that integrate your CRM data with the rest of the system, for instance, give you a deeper historical perspective both on individual accounts and on customer behavior as a whole. With that in-depth view, you and your marketing data consultant can refine models until they align with the data and generate valuable predictive insights.
Interpretation
Without effective interpretation of the data you’ve gathered, you have all the pieces of the puzzle and have put them together, but you’ve then turned out the light – the picture’s there, but you can’t see it. Let’s say you see a sudden spike in sales from customers in an industry you don’t typically court. That’s great news in the short term, but to make the most of this new revenue stream, you need to understand who your new customers are and what’s driving them to your sales department. Being able to recognize and nurture new markets is just one way in which your data-driven marketing program turns your lead scoring system into a revenue generator; you can also analyze existing markets to find out how to upsell and cross-sell more effectively or fine-tune your marketing messages for prospects at different stages of their buying journeys.
With quality data, well-designed models, and skilled analysis, you have a lead scoring system you can trust to tell you the truth.
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