Enhancing CRM - Data to Insights

The Sales function is becoming less transactional and the focus is shifting to establishing deeper and more meaningful client relationships. Sales teams rely heavily on acquiring relevant and timely insights to generate and convert leads. A deeper look into customer data and their behavior can provide insights that can lead to an enhanced client experience through a better understanding of client needs, more frequent and customized client communication, and proactive identification and resolution of potential issues.

This has led to an increase in the proliferation of CRM systems at an exponential rate. Various estimates project the CRM market size between US$45-55bn in 2021. In an increasingly competitive environment, organizations are investing heavily in CRM systems and business intelligence teams and tools to help:

• Target the most promising leads
• Shorten the sales cycle time
• Increase the conversion rate
• Mine existing clients to increase the share of wallet and enhance profitability

However, in the real-world, CRM systems are not maintained properly, resulting in poor quality and incomplete datasets. This results in a vicious cycle, wherein the data quality deteriorates as data entry is a tedious process for sales, and with the sales team’s lack of positive experience with the CRM output, they are further disincentivized to invest their time in maintaining a CRM. This leads to organizations ending up with heavy-duty CRM systems, which provide incomplete customer records and no insights.

Key Challenges for CRM Systems

• Lack of user adoption: Low user-adoption rates driven by inertia and resistance towards learning new technology affects the efficacy of a CRM system. Even existing users are not aware of all the capabilities a CRM system offers and, in most cases, use just 40-50% of the available features.
• Breakdown in the data entry process: CRM systems are not maintained and monitored actively. Critical information from client interactions is not entered into the CRM given the mundane nature of the task. This limits even the most basic functionality of the CRM (to maintain customer records), while all potential for predictive insights is lost.
• Incomplete data, and redundant, inconsistent records: With a lack of validation at the time of data entry, duplicate entries are created and data is entered in an inconsistent format by different users. This makes it difficult and time-consuming to filter information for useful insights.
• Multiple systems: Data is maintained across multiple systems, which leads to issues in sourcing consistent and timely information.

Using CRM for Insights and Analytics

Organizations need to think about their CRM system not as a database for client records, but as a system that can provide targeted insights and recommend actions to the sales team. A system augmented with automation and machine learning can identify trends, prioritize leads, predict customer needs, and deepen client relationships.

AI and machine learning provide a tremendous opportunity to overcome the challenges that organizations face with their CRM systems and unleash the full potential of existing CRM systems. Some key opportunities for leveraging machine learning include:

• Automate low-value adding tasks, including data entry, transcribing meeting/call notes, and eliminating human error
• Enhance the quality of data by deduping records on a periodic basis
• Automate audit of CRM data
• Improve accuracy of sales forecasts by analyzing historical data provided by the sales team and combining it with external factors and client response
• Predictive scoring of leads to prioritize leads with the highest conversion probability
• Use unstructured data captured in notes from client meetings to generate a better understanding of the sales landscape, including the behavior of the sales team and client preferences
• Detect and flag anomalies in client behavior and enable proactive action to resolve customer issues