Does Predictive Scoring Work in Sales?

Can sales tech improve your prospecting success? Learn the pros and cons of using predictive analytics for lead scoring as a modern sales professional.

November 29, 2017

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Wouldn’t it be nice if you could look at a list of leads and immediately intuit which ones were most likely to convert? New sales technology just might give us that capability. 

Using predictive analytics, we can now foretell with a fairly high rate of confidence what actions prospects will take, how they will react to outreach, and whether they are likely to buy. One study suggested that high-performing sales teams are four times more likely to use predictive analytics.

Here’s what you should know about the latest innovation helping sales teams drive better results.

What are Predictive Analytics?

Predictive analytics combines data mining and algorithms based on machine learning (aka artificial intelligence) to plow through vast quantities of data and detect patterns. From these patterns, it will predict the likelihood of future events.

For relatable examples of how this works, consider the likes of Amazon and Netflix, with their abilities to mysteriously determine what you’ll want to buy or watch next. The secret behind this sorcery is in predictive analytics. Your organization can call upon these same powers to more accurately score leads.

How It Works

First predictive analytics combs through all the information about prospects and customers that you’ve collected in your CRM and marketing automation databases. Then it scours the web to unearth more insights about these contacts and accounts by correlating what’s in your database(s) with other known information. For instance, a contact’s email address will likely show up on social media profiles and perhaps even in a company press release. A company name could show up in a database showing the technologies it has implemented.

Predictive analytics might, for instance, surface the fact that prospects in a certain size company that has implemented an on-premises ERP system are three times more likely to buy your product compared to those not fitting that description. Additionally, predictive analytics could detect that once these prospects are customers, they also tend to purchase an add-on service that doubles the average annual revenue associated with these accounts.

In essence, predictive lead scoring makes it possible to actually predict which lead attributes matter most, and then prioritize the leads that match that criteria.

Results Can Vary

Compare this to traditional lead scoring, relying on rules defined by marketing and sales teams. Conventional methods usually take into account a prospect’s fit (i.e., company size or contact title) and behavior criteria (e.g., content downloads, email clicks, and demos viewed). Marketing and sales need to assign a weight to each factor that contributes to the overall score.

How one decides whether it’s more valuable that someone is in a certain department or downloaded a certain content asset is often a guessing game. So, unless your teams have done a bang-up job of truly nailing the definition and scoring equation, the results can be based on faulty assumptions. Let’s not forget: this type of scoring calls upon a much smaller universe of data about each prospect (compared to predictive lead scoring). Combined, these flaws in traditional lead scoring can generate misleading results.  

It should be noted, though, that even predictive scores can differ depending on the analytics software you use. The final outcome depends how the data was integrated, modeled, and scored. Not all tools are alike when it comes to grouping, filtering, analyzing, and scoring the data. Obviously, software that considers fewer or lower-quality data sources a will spit out less reliable results. As the saying goes: garbage in, garbage out.

The Benefits of Predictive Scoring

By helping to prioritize the most promising leads in an automated and objective way, predictive scoring eliminates some manual work and the finger-pointing between marketing and sales. It’s no longer a matter of whether someone properly and accurately weighted the qualifying lead characteristics. This helps minimize questions about the trustworthiness of leads and the time spent chasing low-quality ones.

Plenty of research shows that the winning vendors are often those that respond first to a hot prospect, making it critical to reach out at the right time. Using predictive scoring, your team will know which leads to call first, boosting your deal conversion rate.

Moreover, machine-learning algorithms get smarter over time. So as your predictive analytics software analyzes more data – and gets a better idea of what is meaningful to your organization – it can predict with greater accuracy.

The Pitfalls of Predictive Analytics

You might be wondering what you have to lose by going with predictive analytics. As we alluded earlier, just like any other data-hungry tool, predictive lead scoring is only as good as the information you feed it. If you don’t have enough contacts and sales-related information in your database, the software will struggle to find meaningful correlations. Plus, predictive analytics tools can be pricey.

How to Get the Most from Predictive Scoring

If you decide this software makes sense for your organization, you can take steps to optimize the results. Make sure it’s drawing data from contact- and account-rich sources like LinkedIn. This will help ensure you’re considering trigger events like new funding, a recent move, or new hires in a certain department.

By combining both contact- and account-level attributes, you’ll get a more complete picture of all the buying signals. That means your sales professionals become smarter sellers, helping propel your account-based sales efforts to new heights.  

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