The statement ‘predictive lead scoring and analytics made easy’ seems counterintuitive. How can lead scoring and analytics be easy? If it was easy wouldn’t everyone be doing it?
According to a marketing effectiveness study by The Lenskold and Pedowitz Groups, 68% of successful marketers cite lead scoring as most responsible for improving revenue contribution. Additionally, the Pareto Principle says that about 80% of your sales comes from the top 20% of your customers. These statistics should give you pause.
Think about it. What if you could find new prospects that look like your best customers and discover new opportunities among people you already know? Simply put, wouldn’t you want to find prospects that have the same characteristics as those in the top 20% of your customers? Having these actionable insights can dramatically impact your sales and marketing strategies, resulting in increased revenue. This is empowering and valuable information.
Let’s take it a step further with an example:
A luxury retail organization is developing a strategy for their holiday marketing campaign. Specifically, they are planning to host an instore event and want to ensure they are inviting the right people so that they get the most out of the dollars spent. They also want the event to be enticing so both customers and prospects attend.
The organization analyzed their top customers residing within a 30 mile radius of the store location to develop a customer profile. Surprise! These customers have an affinity for equestrian and tend to drive a Lexus. Just those two data points alone influence their marketing plan. What about partnering on the event with Lexus or an equestrian-focused organization? Additionally, the retailer can now identify prospects who fit this customer profile and have the same interests, increasing the likelihood that they will be interested in attending the event.
Going forward, the organization can now score new prospects and customers to determine how to market to them going forward.
Bonus – this information was identified in minutes and doesn’t require a data scientist. How’s that for actionable?
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