Since 2022, carve-out merger and acquisition deal volume has tripled, presenting private equity firms with a prime opportunity to establish a strong foundation of sales growth as they exit their transitional services agreements. Traditionally, private equity investors prioritized quickly transitioning the carve-out asset off the transitional services agreement, potentially jeopardizing the acquisition’s ability to establish a strong foundation for operational excellence and revenue growth. Effective data governance and data management plays a significant role in ensuring that the data captured for an organization is adequate, accurate, timely and properly integrated across their systems.
According to Stephanie Ernest, leader within MorganFranklin Consulting’s Lead to Cash practice, “Many of our private equity clients today are prioritizing timely TSA exits in addition to optimized processes across the business. Their objective is to eliminate cost through workforce efficiencies and increased revenue through access to key analytics and intelligence. From our experience, what differentiates the successful achievement of our client’s deal thesis are those that appreciate the lifecycle of data transformation – first setting the foundation through data management and governance that enables accurate data analytics, automation and effective artificial intelligence and machine learning capabilities.”
Data Challenges Impacting Carve-Out Activity
Many firms undergoing carve-outs are experiencing data issues across their tech stack, impacting their ability to fully leverage the potential of automation, AI and ML opportunities. Data structures are commonly centered around the parent company’s unique needs and create unnecessary complexities and inefficiencies for standalone carve-out businesses. An essential step in achieving data governance lies within simplifying and optimizing data capturing processes based solely on the new entity’s needs.
Data issues often start with the setup and management of the organization’s CRM system. Upstream in the tech stack, CRM systems like Salesforce operate as the backbone for sales and marketing operations. Poor data governance like multiple customer definitions and lack of user adoption can severely limit their effectiveness, while a poorly designed CRM system hampers downstream functions that rely on critical customer and forecasting data.
Issues including lack of standardization, insufficient training, underutilized CRM functionalities and overly complex lead and opportunity forms minimize user adoption, decrease efficiency and prevent establishing an accurate 360-degree customer view. If unaddressed, these data and CRM gaps can undermine the success of carve-out transactions and negatively impact an organization’s decision-making ability, integration capabilities and revenue potential.
Clean Data’s Impact on Carve-Out Success
Organizations with clean data can be agile and decisive, with dashboards available at the touch of a button and board decks pre-populated with critical information. With more accurate and reliable forecasting models, businesses are equipped to evolve and scale and can streamline future CRM initiatives since the data is ready for additional implementations or plug-ins, reducing the need to allocate additional funds or resources towards preparatory work.
To increase top-line growth, companies are now leveraging AI and ML-enabled tools including Einstein, HubSpot and many others. With these tools, firms can generate accurate lead scoring models that increase lead conversions by allowing sales representatives to focus on high-probability deals.
By curating elevated marketing campaigns with actionable insights generated by AI and developing churn prediction models that leverage ML, organizations can identify proactive opportunities that can increase customer retention and satisfaction. Clean data establishes a strong foundation of informed decision-making, operational excellence and revenue opportunities that maximizes AI and ML investments for carve-out success.
Conclusion
In a period of surging carve-out merger and acquisition activity, successful transactions are increasingly characterized by a strategic approach to data transformation. This is an essential step to maximizing the potential of AI and ML opportunities, driving cost-efficiencies and improving sales post-TSA exit.
By proactively addressing these aspects and leveraging AI and ML tools like Einstein for lead qualification and Salesforce for churn prediction models, private equity firms can streamline operations and unlock new avenues for automation and growth opportunities in the competitive landscape of carve-out mergers and acquisitions.
