Predicting Buyer Intent
In an instant, lead scoring is able to imitate the research habits of the best sales professionals. It measures the expected value of a lead—its likelihood of purchase—by communicating information from a broad array of sources in a short period of time. According to a recent survey conducted for Lattice Engines, a growing number of companies are scoring leads—44 percent do it now, and another 16 percent plan to implement it in the next year. This is no surprise. Companies across the board are adding analytics to their marketing programs for better decision-making. However, most marketers are not confident in these programs—59 percent cite issues with incomplete or inconsistent data on prospects, and 43 percent do not feel they have enough insight into which attributes actually indicate buying behavior.
These hurdles contribute to one of the biggest marketing challenges: Only about 20 percent of leads passed to sales are qualified as real opportunities, according to research from SiriusDecisions. Also, in the survey noted above, nearly 40 percent of marketers indicated that they are looking for more predictive capabilities when planning their marketing initiatives. To improve upon today's lead scoring model and lead quality conundrum, marketers want one thing: greater predictability.
Good news. There are several readily available sources that are common homes to predictive attributes. Here is a look at some with the greatest potential for predicting buyer intent.
Many companies are already sitting on a wealth of information, but lack an idea or process for extracting meaningful insight hidden in their internal systems. Marketing automation and CRM systems are designed to capture and house rich data on both customers and prospects. Information such as email response rates and opens, Web site visits, engagement of content, event attendance, and firmographic data, such as number of employees, is hiding within your marketing automation platform. CRM also stores a bevy of insights, such as past opportunities, product usage data for existing customers, product trials, customer support cases, and the like. Diligent sales and marketing professionals are already taking time to comb through the two databases for this insight and to focus on the leads with the greatest potential value. These attributes become truly predictive when combined with the attributes from external sources.
Many predictive attributes lie outside of a company's typical data warehouses. For example, if a company's credit ranking was recently lowered, it is unlikely to be in the position to make a substantial purchase. It is likely buckling down on costs and pushing system upgrades or new technology purchases into the following year. Government contracts and construction permits and starts could also indicate buyer intent. If a company is remodeling or building new office space, it is more likely to purchase new office furniture, switches, and routers. Or take the use of technology into consideration. If a company has marketing automation in place or a presence of tablets, it may be more likely to purchase additional marketing technology or tools to manage BYOD. The presence of specific job postings can also indicate a readiness to buy, and this could vary greatly based on the industry.
Web Site Information
Additional predictive attributes could be hiding right under your mouse. Web sites, particularly homepages, often house information on buyer intent. The presence of a shopping cart or e-commerce capability is one example, as is the presence of social media icons on a homepage. These attributes could mean that the company is more tech-savvy and open to the purchase of additional software. Similarly, simple data, such as the number of Web pages or the types of keywords used throughout the Web site, may paint a telling picture.
Of course, there is no silver bullet or standard model that every company can apply. That would be too good to be true. And this is not an exhaustive list of every source for predictive attributes. As mentioned earlier, predictive attributes are highly likely to vary from company to company and offering to offering. Many attributes could be concealed in other sources, such as customer support and Web traffic data. And they become most predictive when they work in tandem—providing a true 360-degree holistic customer view to supplement traditional lead scoring—to create a more predictive outlook.
Amanda Maksymiw is the content marketing manager for Lattice Engines, which specializes in delivering data-driven business applications for marketing and sales. She is responsible for setting and managing Lattice's content marketing and social media strategies, including creating, producing, and publishing engaging content.
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