Effective lead prioritization is essential. With limited resources and increasing competition, it’s crucial companies focus their efforts on quality leads that are most likely to convert into high value customers.
When done effectively, it can deliver more personalized experiences, improve conversion rates, and enable accurate sales forecasts.
However, some traditional lead prioritization techniques, such as manual lead scoring, have proven to be time-consuming and often yield inaccurate results.
This article unveils modern lead prioritization approaches, including:
- The Pivotal Role of Technology
- The Intersection of Predictive Analytics & Lead Scoring
- The Growing Sophistication of Intent Data
Read on to discover how, by leveraging the power of technology, businesses can streamline their lead prioritization efforts and accurately predict which leads are in-market, sooner.
Reading time: 4 minutes
The Evolution of Lead Prioritization
Lead prioritization systems have come a long way from traditional methods. Restricted access to quality intent data and the technological means to analyze and strategically apply it, has meant lead prioritization tactics have often been subjective or based on a limited view of the buyer journey.
For many businesses this has led to growing frustrations and wasted marketing and sales efforts.
5 common pitfalls associated with traditional techniques include:
- Overreliance on demographic information: Traditional lead prioritization methods often focus heavily on demographics. While demographic data can infer a ‘good fit’ in relation to ideal customer profile (ICP) criteria, it restricts the ability to gauge additional factors such as propensity to purchase and wider account intelligence.
- Inaccurate lead scoring: Out-of-date, incomplete or inaccurate lead scoring criteria can lead to wasted marketing and sales efforts and critically low conversion rates.
- Lack of sales data integration: Disconnect between sales data and lead prioritization efforts can hinder the accuracy and efficiency of lead follow up efforts, including the ability to personalize content.
- Insufficient data analysis: Inadequate data analysis or the limitations of relying solely on first party intent can lead to missed opportunities to prioritize leads displaying in-market tendencies.
- Misalignment between Sales & Marketing efforts: Without strong collaboration between all GTM teams, leads with high potential but requiring more nurturing can be wrongly assigned or dismissed.
Role of Technology in Transforming Lead Prioritization
Technology has played a pivotal role in transforming lead prioritization and overcoming historical pitfalls. Traditional techniques are being replaced by modern lead prioritization systems, utilizing sophisticated AI powered algorithms and machine learning capabilities to analyze vast amounts of data and predict which leads are most likely to convert.
This has revolutionized the way sales and marketing work together, making it easier for teams to manage larger volumes of lead data and share key intelligence across the business. Through accurate AI powered lead scoring and real-time buyer insights, go-to-market teams can identify high intent leads with greater speed and, with access to the right intelligence, convert them into loyal customers.
In addition, data analysis tools have enabled sales teams to efficiently manage and analyze data throughout the lead prioritization process. For example, lead intelligence captured during a campaign flight time, can be synced directly into CRM systems, providing a centralized platform to store, track and evaluate lead information.
This process, ensures the value of lead intelligence is maximized through its accessibility, making it easier for sales teams to manage their lead follow-up process, through prioritizing leads with the highest potential and using buyer signals to tailor more personalized experiences.
Overall, the role of technology in progressing lead prioritization has been hugely significant, transforming lead prioritization from a manual, time-consuming process to an automated, data-driven approach.
The Intersection of Predictive Analytics and Lead Scoring
While there have been many advancements in technology, predictive analytics has emerged as one of the biggest game-changers in lead prioritization. One of the key strengths of predictive analytics is its ability to identify highly compatible leads, who may not show significant signs of engagement but are more likely to convert into loyal customers.
It does this through uncovering implicit data, such as purchase authority and lead behavioral nuances that traditional scoring methods would have missed, to forecast future behavior and anticipate purchase intent.
Machine learning too plays a vital role in predictive analytics, as it enables systems to learn from data, detect patterns, and make predictions based on historical information. Through machine learning, predictive analytic models become more accurate over time, continually refining their scoring criteria and adjusting to changing market dynamics.
These new capabilities mark a significant shift towards a data-driven and proactive approach to lead qualification and prioritization strategies. As new data sources and predictive analytics tools emerge, lead scoring accuracy and predictive lead qualification will further improve, enabling businesses to target the best leads with greater precision. This has a huge impact on the ability to forecast sales performance and increase sales velocity through intercepting the right leads at the right time in their buyer process.
By staying at the forefront of lead scoring best practices such as integration with predictive analaytics and machine learning to enhance lead scoring system automation and real-time lead qualification updates, go-to-market teams can become highly effective at converting leads into revenue.
Preparing for the Future of Lead Prioritization
Key Attributes to Consider in Lead Scoring Models
Staying agile means adapting lead scoring models to mirror market changes effectively. When constructing a lead scoring system, businesses may consider a combination of scoring models based on key attributes that align with their ICP and indicate lead potential.
Here are 4 attributes worth considering:
- Persona Buyer Power: the decision-making authority, influence and level of engagement within the DMU. Leads in senior or managerial roles may possess higher budget authority, making them more qualified prospects.
- Category Intent: helps to predict level of interest in solution category aligned to a specific business. This insight also helps to inform sales follow-up through revealing real-time interest.
- Account Surge: insight into wider account actively beyond first party environments. The aggregation of 3rd party intent signals provides a fuller picture of the buying process.
- Account Penetration: how many leads from a target account have been engaged through campaign life span. This enables teams to gauge marketing’s influence within buying centre.
By considering key attributes such as these, businesses can gain valuable insights into a lead’s potential, fit, and engagement level. These attributes can be used contribute to a more accurate predictive lead scoring system, enabling businesses to prioritize leads, allocate resources efficiently, and target their marketing efforts effectively.
The Growing Urgency for Advanced Intent Strategies
The future landscape of lead prioritization and lead scoring has already been reshaped by AI. In order to remain competitive, it’s crucial businesses have an overall intent strategy that addresses how intent is harvested and strategically applied in order to optimize emerging technology such as predictive analytics.
Modern lead prioritization systems built on data science and machine learning rely on bigger data sets to facilitate continuous learning and optimization. This has sparked a growing sophistication in the collection, management and application of intent data to enable marketers to advance their lead prioritization strategies and overcome limitations of first party intent.
For example, while explicit data such as demographic, can still be used to gauge lead suitability and ICP fit, it provides little indication of purchase intent. And, while implicit behavioral data such as website visits and email opens provide some understanding, given the complexity of modern buyer cycles, this data only gives a fraction of the wider picture.
Modern intent strategies therefore strive to collate a plethora of data points from both first party and third party sources to build rich audience profiles on both an individual lead and account levels.
This can be achieved through strategic partnerships with demand generation providers with access to first party buyer communities and third party intent networks.
Such partners are able to both harvest and process large volumes of data through their proprietary AI supported platforms and translate it into actionable buyer insights, including predictive lead scoring.
While providing go-to-market teams with better insight to tailor their follow-up strategies, demand generation partners with sophisticated tech can also extend their AI capabilities to mapping intent predictively onto customers’ ICP or target account lists (TAL) to identify priority tiers.
This opens up many advanced opportunities for not only evaluating and categorizing leads but for intercepting and engaging target accounts much earlier in their buyer cycle than previously possible.
Having the right intent strategy outlining how quality sources of intent can be accessed at scale, is therefore an essential foundation of modern lead prioritization. With this in place, the significant opportunities offered by merging technology can be fully harnessed.
Final Thoughts
The future of lead prioritization is rapidly evolving and technology-driven. In order to combat accuracy and efficiency issues, traditional techniques are being replaced by highly effective modern techniques that remove human error and enable go-to-market teams to adopt a more agile and precision led approach.
In the realm of B2B lead scoring, companies are now tapping into the potential of predictive analytics to foresee customer actions. By incorporating machine learning algorithms, real-time updates in lead scoring become a reality, revolutionizing how businesses qualify leads dynamically.
By embracing AI-powered techniques and incorporating intent data strategies, businesses can take proactive steps to prepare for the future and reap the many benefits modern lead prioritization approaches bring.
