In 2023, we witnessed a surge in the sales technology market, marked by the emergence of new tools and an increased trend of consolidation. This development has triggered debates about the future direction this landscape will take.

One pressing question revolves around whether companies will persist in investing in individual, specialized solutions, or if there will be a shift towards a more holistic "all-in-one" tool capable of addressing a wide range of needs. The outcome of this debate remains uncertain, and only time will reveal the ultimate configuration of the sales tech ecosystem.

However, what remains crystal clear is that customers have a growing preference for technology that empowers them to boost revenue and address the various gaps that appear throughout the customer journey. The customer journey itself, spanning from lead generation to opportunity development and ultimately revenue generation, is a complex and intricate path.

The tools that manage to provide a unified, clear view of this journey and align teams with clean, comprehensive data are poised for success in the forthcoming era. An intelligence layer, powered by AI, emerges as a pivotal component capable of driving companies closer to their revenue targets.

So, what exactly is an intelligence layer?

It serves as the key that unlocks data, revealing patterns that have long been trapped within databases and applications. This layer possesses the capability to make use of continuously streaming data and provide insights precisely when needed. Moreover, it is intelligent enough to adapt and evolve continuously to stay up to date, thus marrying historical context with incoming data about accounts, opportunities, and prospects. This results in predictive signals that can enhance the value of the customer's buying journey in real-time, transforming it from a reactive to a proactive process.

The sales tech game is increasingly becoming a battle for this system of action. Revenue leaders now inquire: "Which solution can deliver the best insights within a single user interface, serving as both a point of decision and action for our sales representatives?" An intelligence layer answers this question by offering predictive actions that guide teams toward successful deal closures. These insights are invaluable as they ensure that customer-facing teams are consistently focused on high-value accounts with the highest conversion potential.

Nevertheless, the effectiveness of this intelligence layer is intricately linked to the quality of the data it relies on. AI requires meaningful, accurate data to recommend improvements that lead to enhanced revenue outcomes. Thus, a robust data layer forms the foundation for the intelligence layer.

Bad Data = Poor Conversion

Regrettably, many businesses still grapple with data quality issues. A recent survey revealed that only 6% of respondents were highly confident in their data, with 58% attributing data accuracy as the primary obstacle to obtaining quality data. Furthermore, 37% cited poor-quality data as a detriment to conversion rates.

Consequently, revenue leaders must confront pressing questions about their data quality:

  • What will be the source of data when deploying generative AI applications?
  • Can we trust the quality of our data?
  • Is our data sufficiently clean and accurate to provide dependable insights for our business?

For businesses uncertain about the quality of their data, adding even the most advanced generative AI solutions to their tech stack will not improve revenue outcomes; it will merely increase tech-related challenges.

The detrimental effects of incomplete and inaccurate contact data on conversion rates and, consequently, revenue, cannot be overstated. A substantial portion of respondents (37%) identified poor data as a significant contributor to suboptimal conversion rates. Without the right data and insights, demand generation and sales teams face challenges in lead acquisition, lead nurturing, prospect identification, and personalized campaign execution.

This is precisely where the intelligence layer, coupled with AI, becomes indispensable in addressing revenue generation gaps. A strong foundation of accurate, comprehensive, and rich account data is essential for constructing an AI-driven sales organization. Those who fail to invest in this foundation may struggle to keep pace with the dynamic changes in today's sales environment.

Checklist for good and clean data

Quality data, characterized by attributes such as accuracy, automation, completeness, and timeliness, forms the bedrock for making informed decisions. AI models are only as effective as the data they operate on, underscoring the importance of data quality in delivering consistent results.

To evaluate the state of your data quality, consider the following checklist for different stages of the sales funnel:

Top of the funnel:

  • Is your data accurate and timely for maximizing buying group intelligence to opportunity conversion?
  • Does your data enable you to increase your speed-to-lead ratio?
  • Can you identify high-intent leads?
  • Does your data provide actionable visibility into account-based execution for your sales and marketing teams?
  • Do you have access to lost contacts and relationships?
  • Does your data capture partner activities?

Middle of the funnel:

  • Do you have confidence in your pipeline numbers?
  • Can you identify which deals should take priority in the pipeline?
  • Do you possess data about the buying group and their engagement?
  • Can your representatives engage with the right accounts through multithreading?
  • Do you have real-time insights into stagnant deals?
  • Does your data assist sales managers in guiding reps on the next best actions in real-time?
  • Can you gain insights into how a deal is performing relative to similar closed-won deals in your CRM, including outliers?

Bottom of the funnel:

  • Can your data help you identify risks before they impact your revenue?
  • Does your data uncover winning behaviors that can be replicated across customer-facing teams?
  • Does your data reveal coaching opportunities to empower reps to meet their quotas?
  • Can your data facilitate accurate forecasts?
  • Does your data instill confidence in the closure of deals beyond sales reps' intuition?

Renewal and expansion:

  • Does your data provide accurate insights into post-sale or customer success manager activities?
  • Does your data capture partner activities?
  • Can you track engagement with executives and key stakeholders using your customer data?
  • Does your data capture account contacts, activities, and relationships?
  • Do your insights assist in identifying champions and mitigating churn when champions depart?

If any of your responses to these data-related questions are negative, it's imperative to address your data hygiene. Establishing a robust data foundation can help you bridge these gaps.

The Path to Data Excellence

As we've emphasized, data quality is the linchpin of any successful intelligence layer and AI-driven sales organization. To embark on the path to data excellence, organizations should consider implementing the following strategies:

  1. Data Governance: Establish a robust data governance framework that includes clear roles and responsibilities for data management. Define data ownership, access controls, and data stewardship to ensure that data remains accurate and secure throughout its lifecycle.
  2. Data Validation and Enrichment: Invest in data validation and enrichment tools that can cleanse, validate, and enrich your existing datasets. This not only corrects inaccuracies but also adds valuable information to your records, enhancing their usefulness.
  3. Data Integration: Ensure that data from various sources within your organization is integrated seamlessly. Siloed data can lead to inconsistencies and inefficiencies, while well-integrated data provides a holistic view of your customers and prospects.
  4. Continuous Monitoring: Implement a system for ongoing data monitoring and quality checks. Regularly review and update your data, identifying and rectifying any issues as they arise. The goal is to maintain data quality as a dynamic, living process rather than a one-time effort.
  5. Employee Training: Train your teams in data management best practices. Everyone in your organization who interacts with data should be aware of its importance and how to maintain data quality. Consider offering training sessions and resources to enhance data literacy.
  6. Data Quality Metrics: Define and measure key data quality metrics such as accuracy, completeness, and timeliness. Regularly assess your data against these benchmarks and use the insights to drive data improvement initiatives.
  7. Data Hygiene Tools: Implement data hygiene tools that automate the process of identifying and rectifying data quality issues. These tools can help streamline the data maintenance process and reduce the manual workload.
  8. Data Quality Culture: Foster a culture of data quality within your organization. Encourage employees to take responsibility for the quality of the data they work with and to report any issues promptly.

It's important to remember that data quality isn't a one-time task; it's an ongoing commitment to maintaining and improving data accuracy, completeness, and reliability. With data excellence at the core of their operations, businesses can harness the full potential of artificial intelligence and technology, delivering real value to their customers and driving revenue growth.

The essence of the intelligence layer lies in recognizing that, while generative AI is expanding its capabilities, data quality remains paramount in powering the intelligence layer. It's not just about having copious amounts of data; it's about ensuring that the data is trustworthy and reliable, enabling the intelligence layer to fulfill its promises.

In conclusion, prioritize data quality by building a resilient data foundation, as this is the key to unlocking the potential of the intelligence layer.

Make data quality a priority, as with the rise of use of Generative AI it is a critical factor that charges the intelligence layer.

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Sanjay Gala

Sanjay Gala is the Founder and CEO of SMARTe Inc. A serial entreprenuer with extensive experience in international business development, prospecting, sales management, and consulting he share exciting tips and insights about Sales Prospecting.

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All your questions, answered.

Why is Data Quality Important in AI-Driven Sales?

Data quality plays a crucial role in AI-driven sales because the accuracy and reliability of the data directly impact the effectiveness of AI algorithms. High-quality data ensures that AI models make informed predictions, recommendations, and decisions, leading to better sales outcomes and customer experiences.

How Does Poor Data Quality Affect AI-Driven Sales Processes?

Poor data quality can significantly hinder AI-driven sales processes by introducing inaccuracies, biases, and inconsistencies into the predictive models. Inaccurate or incomplete data can lead to flawed insights and recommendations, resulting in suboptimal sales strategies, missed opportunities, and decreased customer satisfaction.

What Steps Can Businesses Take to Ensure Data Quality in AI-Driven Sales Initiatives?

To ensure data quality in AI-driven sales initiatives, businesses can implement several best practices. These include establishing data governance policies, conducting regular data audits and assessments, implementing data cleansing and normalization processes, investing in data validation tools and technologies, and providing training to sales teams on data hygiene and best practices. By prioritizing data quality, businesses can maximize the effectiveness of AI-driven sales efforts and drive better business outcomes.

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