Successful demand gen marketers know that lead quality is a key factor in their ability to engage prospects and activate buyers. In a recent study by Marketing Profs, nearly 60% of marketers surveyed indicated quality is the single most critical aspect of the leads they generate. One key indicator of lead quality is accuracy and completeness of data on the lead.
Both marketing and sales operational efficiency are directly impacted by lead data quality because high-quality leads are more likely to convert to customers. Diluting a marketing database with low-quality leads results in wasted time and ineffective communications.
Lead accuracy needs to be managed proactively, whether leads are generated in house or by a third party. Here are six data factors that will create a positive return on your investment in generated leads.
- Lead Freshness. The more recently the lead was generated, the higher the probability they will convert in the next step of the marketing and sales process. That's because the person is more likely to remember their initial interaction, have a current need, and be committed to the blossoming relationship. Leads that are not engaged with expediency – either through inside sales or a nurture program – will move on to another solution if one is not provided in a timely way. It's critical to establish the acceptable interval between a lead being generated and accepted, then enforcing that interval, also known as accelerating lead velocity.
- Digital Body Language. Digital body language shows the interest or intent that a lead has in solving a problem. It is based on pages, websites, and content interaction. Since most buyers don’t contact a company until they are over halfway through the buying process, digital body language has a significant impact on the quality of the lead. Review the digital body language of all generated leads for recent activity within the last 30, 60 or 90 days. Those with more frequent and more recent activity represent higher quality leads.
- Score. A quantitative gauge can indicate how ready and/or likely a lead is to purchase. Lead scoring is widely available, but often not used to its full benefit, which is why it’s included here. Scores can be defined by applying a value to demographic or firmographic information, digital body language and responses to questions at the point of generation. The scoring algorithm should be based on the data points associated with previously generated high-performing leads. Once the algorithm is defined, ensure all newly generated leads have the score included. Then use this score to channel the leads directly to the inside sales organization or to a specific nurture program.
- Data Completeness. Data completeness is the inclusion of required fields as well as the most up-to-date, accurate firmographic and demographic data. The cleaner the data, the more meaningful and personal follow-up interactions can be, and the better the ROI on generated leads. Ensure emails do not hard bounce (a forthcoming Integrate study found that, on average, 4% of prospect provided emails addresses are inactive) or lead to a spam traps. Watch for duplicate leads from the same vendor (15% on average) or names that are bogus. Additionally, third-parties including iProfile and MelissaData can be used to validate the firmographic and demographic data of the generated leads. Dial-testing should also be employed by lead-generation services to ensure all leads are reachable via phone. That's a critical detail for your sales partners.
- Filter Compliance. Program filters are rules that define compliance with data points including geography, industry, company size, job role, competitor/partner lists or account lists and custom questions. These filters can be used to ensure only leads with high probability to convert are generated. They can also be used to manage lead diversity. For example, using three different filters with a lead goal of 50 each helps to spread out the profile of the generated leads. On average 10% of leads processed by Integrate’s data governance software don’t meet filter compliance. Before generating new leads, clearly and concisely define program filters. As new leads are delivered, review them aggressively to ensure compliance.
- Standardized Format. All generated leads should be in a structured format that matches the internal marketing database structure. Standardization of lead data improves lead engagement, lead management and analysis. Ensure all entry points such as registration forms use drop-down list boxes, check boxes, or radio buttons. When receiving externally generated leads, highly customized spreadsheets should be used to ensure standardization of lookup fields including job role, state, industry, and country.
Every year, low data quality costs U.S. businesses more than $600 billion. For marketers, poor lead quality results in missed goals, low conversion ratios, a leaky funnel, and missed sales opportunities. Chasing inaccurate or outdated leads slows processes and wastes limited resources. Ensuring leads are clean and of the highest quality goes a long way toward eradicating these issues. Implementing a structured quality assurance process that has defined guidelines focused on these six data points, as well as a repeatable process to monitor compliance, will positively impact your marketing organization’s ability to meet goals.