04/28/2021 | News release | Distributed by Public on 04/28/2021 08:02

Uncovering the Value of Individuals Through Client Relationship Management Systems

The human capital theme spans specific features such as employee history, relationships and connections, job listings, compensation, and corporate governance, to name a few. Content associated with an individual can be used for more mature-but no less critical-in-house workplace applications such as Client Relationship Management (CRM) systems. In this article, we'll focus on this business workflow.

Introduction

The CRM domain of human capital data is an environment where externally sourced data is often used in conjunction with internally collected data to support sales and marketing activities as well as lead generation.

Traditionally used by organizations as a place to store contact details and meeting summaries, they also use CRM applications to enhance their business development and prospecting activities, with the goal of driving new business and add-on sales. Today's CRM tools bring additional sophistication and functionality that enable sales representatives to uncover opportunities that otherwise may have been overlooked. We will first introduce the CRM market as a whole and then observe its rapid growth in the software industry.

The CRM Market

CRM is now the biggest software market in the world with rapidly increasing growth. The current estimated market size of approximately $32 billion is on course to more than double by 2023. One of the key factors behind this growth is the need for mobile and remote accessibility, a feature that has soared in demand in parallel with the COVID-19 pandemic. Companies need access to customer data in real time, through both mobile and cloud-hosted solutions.

Companies that have embraced mobile and cloud-hosted technologies have seen a huge improvement in the adoption of CRM software by their employees (and their subsequent ability to reach sales targets). Increased dependence on mobile technology has improved productivity, as companies are now able to encourage their employees to adopt and use the software. An Innoppl report finds that of companies using a mobile CRM, 65% are achieving their sales quotas, as opposed to a 22% attainment level for those exploiting non-mobile platforms.

CRM Lead Management

It is widely accepted that just about all companies need to generate 'homegrown' sales leads to grow their business, rather than purely relying on inbound inquiries. CRM lead management can be defined as the process of capturing leads and tracking their activities and behavior, as well as qualifying the leads, giving them constant attention to make them 'sales ready,' and then passing them over to the sales team.

The suite of tools and strategies required for effective lead management is the focus of CRM software systems, an industry serving as a progressively critical pillar of modern business success. In a digital era where content channels are constantly diversifying, acquiring new customers is the holy grail of growth. At the same time, CRM systems are a vital tool for businesses to reduce overhead costs and operate on margins that would otherwise be untenable without software-as-a-service (SaaS) solutions.

All of this is in the scope of lead management aligning itself further with the broader marketing and business operations of companies. There is also a significant shift toward lead management functionality being part of a larger CRM or marketing solution. By leveraging agile products, businesses are free to automate ideas and operations in a matter of minutes. However, the real power behind CRM systems is derived from the evolution of artificial intelligence (AI) and machine learning (ML).

Data Mining and Advanced Analytics

Data footprints produced by consumers and businesses across the internet's business-to-consumer and business-to-business landscape are highly actionable but require discerning the signal from mountains of noise. That's where AI and ML have made a measurable impact on lead generation.

Today, companies of all sizes across all industries leverage the power of AI for sales forecasting and analytics, and to curate big data into actionable information for sales teams. It is fully anticipated that AI and ML will eventually drive all business intelligence software, particularly around lead management and generation.

As the digital world develops, businesses will need to adapt to remain relevant. CRM systems have provided the support corporate enterprises need to excel in an environment of increasing market competition. The parallel evolution of advanced lead generation tools in CRM systems is poised to remain key in growth acceleration.

The CRM Data Backbone and Source Integration

Core person-related data acts as the critical pillar to successful CRM implementations and is without doubt the most important feature due to the necessity of such content being both highly timely and accurate. As a result, corporates managing complex CRM environments may only have relatively basic requirements from their data vendors. These requirements can be as simple as having top-level personal information (e.g., name and current job title) alongside high fill rates for related contact details such as email addresses and direct/mobile lines.

Companies seeking enhanced CRM sophistication features will look for a comprehensive data management backbone that draws connections between disparate individuals and corporations through granular symbology linkages. The potential breadth of organizations reached via a backbone such as this is key. As new business relationships are more likely to be found in the small and medium-sized enterprise (SME) space, access to private company information-especially around emerging technologies and geographical markets-may provide a significantly enriched landscape from which to harvest.

Enriching CRM displays with more discrete person- or role-specific data can provide greater insights into individuals beyond just their basic name and contact information. At the same time, if aggregated up to the entity level, such content can provide views of how successful the source organization is in reaching the key decision makers (CEOs/CTOs) or specialists (quant analysts/research scientists) within their target.

Additionally, businesses should ensure that top-level entity data is available in their CRM solutions; this will provide the initial entry or screening point for establishing communications with target organizations. Content such as corporate hierarchies, market data (for public companies), business descriptions, and industry lines are consequently highly desirable markers.

In environments with multiple sources of proprietary and external data, the CRM application should provide content management modules that enable system operators to implement and maintain levels of data item control. The application should also allow administrators to establish certain logic around data overwriting/updating rules and select source precedence for overlapping data fields.

Users of CRM applications can be diverse and range from sales managers and administrators to bankers. However, all will be looking to extract enriched data from such an application in their typical daily workflow. From a banking-specific point of view as opposed to direct sales, users will commonly look to discover and subsequently establish connections with corporate board members. They will also create more sophisticated views (by aggregating financial data) to aid in the prediction or targeting of merger and acquisition (M&A) deals, and also to support complex private equity and investment banking deals and transactions.

Conclusion

Leveraging a decision-making framework following the critical components discussed above eliminates the guesswork and unneeded burden of evaluating every data provider in the market. Additionally, discerning which data use or discovery path (following a buy, build, or blend approach) will best suit your primary workflow need(s) should subsequently streamline what can be a time-consuming process.

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