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Strategic Sales Management: AI and ML in Digital Transformation of Sales Management

Updated: Jan 24

By: Dr. Carl Harris, Founder & Principal Consultant


January 24, 2024

Strategic sales management is a process where planning, organizing, executing and data analysis are activities to gain optimal sales performance, and includes forecasting, marketing analysis, product segmentation, and territory assignment. All these activities use data gathered through marketing research and promotions, lead generation data and customer service.

Sale management historically has focused on transactions and sales volume. However, today sales management has evolved from operational to a strategic level. Additionally, strategic sales management is customer focused, driven by a mutually beneficial relationship through value creation (Jaskari & Jaskari, 2016). With applications such as Salesforce, which is increasingly harnessing the power of AI, sales management is strategically carried out, with organizing and developing strategies to achieve organizational sales objectives (Orr, 2022). Strategic Sales Management decision making is powered by technology, fueled by data, and advancing analysis with computational accuracy and speed. There is considerable rapid development and tangible application for the use of AI with strategic sales management, because of enormous data, improved analysis power, and novel AI approaches, learning algorithms, and applications (Perifanis & Kitsios, 2023)

Business Intelligence (BI) is where business data input is part of the creation and presentation of user-friendly KPI graphics and reporting. There are three stages of BI evolution or three generations of BI, BI 1.0, BI 2.0 and BI 3.0. The significance of BI and AL and ML is seen as a metaphor, what came first the chicken or the egg. Science has found that a protein found only in the ovary of a chicken is necessary to produce an egg. Hence, the chicken came before the egg. Therefore, BI is the origin of AI, ML, augmented analytics (AA) and other advanced analytic tools.

The first stage of BI-BI 1.0 was primarily an online analytical processing (OLAP) based solution. In this stage to generate a statical report the IT department would run queries for the manager and business user. Stage 2.0 introduced analytics as part of BI, with expansion of data warehousing, enhancements to enterprise data warehousing (EDW) and data integration. Service BI analytics were developed so users could perform many BI tasks and generate their own key performance indicators (KPIs), using ad hoc querying. BI 3.0 is mostly an app-centric strategy, has a collaborative characteristic, and can be used anywhere, at any time and on any device or platform. This generation uses augmented analytics (AA) in the business cycle. AA is the next level of data analytics markets, and businesses are using the technology to differentiate themselves and create strategic advantages. AA allows decision-makers in organizations to create and examine content quickly and independently. It harnesses the power of BI and AI  (Alghamdi & Al-Baity, 2022).

A 2024 Gartner report titled, Leadership Vision for Chief Sales Officers, showed that by 2024 40 percent of enterprises will have embedded conversational AI. By 2025 60 percent of marketing departments will use GenAI. In 2026 there will be a reduction in customer meetings time preparation, with B2B sales organizations adopting GenAI embedded sales technologies, and by 2028 B2B seller’s work will be implemented through conversational user interfaces.

Digital Business Transformation (DBT) is transforming industry by improving business processes and capabilities. DBT promotes new pathways for working and interacting with customers, directly creating new business models. DBT is the practice of using technology to radically improve the firm’s performance of an enterprise (i.e., organizational performance, the functioning of the firm and outcomes of its operations) (Westerman and Bonnet 2015). The integration of AI and data analytics in DBT is powering a powerful transformation in sales organizations, customer service excellence and decision making.

Sales organizations must effectively cope with increasing complexity to survive and prosper. Collaboration, both internal and external, has never been more important for sales success. To continue their critical role in creating, communicating, and delivering customer value, sales organizations must be accountable from economics, ethical, and legal perspective (Ingram, 2004). To this end, AI, ML, and AA play an increasingly significant role. However, AI, ML, AA and DBT technology methodologies are all tools and a great teammate for strategic business planning, decisions, and execution, and yet cannot replace the intrinsic value that human reasoning brings to an organization.

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