Putting Lead Fatigue to Bed

By understanding customer behavior and the potential drivers behind those actions, brands can better assess which leads may convert and which will likely fail as they work to hone their targeting efforts and build strong, profitable relationships.

Though they may not have a yellow brick road, or a pair of ruby slippers, marketers and salespeople may be well on their way to leaving lead fatigue in a field of poppies. Thanks to predictive analytics, such professionals are on the path to discovering they've had the power to target, generate, and convert leads right under their noses all along.

Over the past few years, customer data has become more imperative than ever for organizations to make real-time, relevant decisions to engage with customers and successfully convert prospects into leads. However, as Big Data begins to dominate, companies are having an increasingly difficult time parsing the information in order to extract patterns and feedback. But, with the introduction of predictive analytics and enablement tools, companies have the opportunity to not only tap this data for actionable insight, but also look to this information for possible clues as to which leads will prove fruitful and which customers are likely to tune out the brand's messaging.

According to Karla Blalock, COO at PointClear, predictive analytics allow companies to quickly identify the ideal prospect so they may shift their efforts to spend more time with that segment. Just as intelligence comes into the organization, such insight must then be funneled back into the targeting process in order to successfully remain relevant and aware of developing trends.

"If companies come to understand that their most likely buyer, for example, is an existing female customer, age 35-40, who lives in a zip code with above average disposable income and who usually starts her buying journey when prompted by an email offering 20 percent or greater savings, then marketers can easily use this information to narrow their target and develop personalized marketing communications that bring more like-buyers into their sales funnel," says Dan Smith, vice president of product at Outsell.

Predictive and descriptive models, Smith adds, allow sales teams to recognize which factor or combination of factors are most indicative of intent to buy, while enabling them to categorize buyers so as to separate the brand loyal from the brand agnostic, and the price-sensitive from the quality-sensitive shopper. Understanding these potential drivers empowers businesses to focus sales' time on the prospects who exhibit valuable behaviors. Because most companies already have access to the data necessary for predictive modeling, they need only identify or hire people with the skills necessary to gather and organize the data, and build the subsequent models.

With predictive capabilities, companies can then make educated decisions with greater speed and take quicker action as needed. Though these patterns may not always pan out, this strategy enables sales teams to keep moving forward, not stand still or fall behind the competition. Salespeople separate the buyers from the lookers-the immediate buyers from people who may purchase later-so they may focus their time on the most valuable near-term buyers, while establishing marketing programs that help move the later-term buyers through the purchase funnel without the need for salesperson involvement. Inevitably, such actions result in higher lead conversion rates, therefore translating into higher sales and faster revenue growth.

"If a company can successfully capture every customer touchpoint, as well as all attributes, the data can illuminate both explicit and implicit drivers behind customer behavior," says Justin Klapprodt, director of strategy and insight at Quaero. "If consumption goes up or down substantially for a customer who has yielded fairly static performance, chances are there's something in the customer footprint that explains the change."

More than anything, predictive analytics have the power to reveal unique sets of attributes that contain buying signals throughout the customer experience. Thousands of variables can trigger sales, and patterns, above all else, enable the prediction of customer intent to buy and sales-readiness. From credit card companies that can spot the signs of potential default, to toymakers that can gauge the next hot trend, predictive models allow businesses to pinpoint relevant interests to increase and encourage continued engagement. Offering the right information at the right time can be an invaluable asset and competitive advantage.

"Marketing data is too often in silos," says Brian Kardon, CMO and Lattice Engines. "CRM, Web analytics, and marketing automation are independent internal systems, and unstructured information exists on the public Web and social networks. In order to analyze this data to obtain a more complete view of their customers and prospects, companies need to connect these sources of disparate data, bringing multiple streams into one consumable output. To do this, companies can either employ a team of data scientists or take advantage of data-driven applications that connect disparate data through APIs and back-end algorithms to uncover previously hidden behavior patterns and insights."

By watching the patterns made apparent by predictive analytics, companies can target and pursue the right prospects and the most promising leads, spending time on the deals most likely to close.