AI Revolution in Sales & Marketing and Purchase Functions

AI, or GenAI, is often regarded as a corporate game-changer with high upfront capital costs and a long-lasting tool to personalize, predict, and differentiate. Businesses are integrating AI within their processes to experience positive outcomes. AI is a value advantage for large data-led business units, i.e., sales, marketing, and purchase.

Representatives can obtain critical selling plans with AI. These views can be applied to interact with customers and handle objections confidently. Further, AI may fine-tune the outside and inside selling pipeline with priority tags.

Marketing is another function where AI will have a significant impact. Firms can calculate on-demand marketing returns using AI, as it can identify profitable clients with potentially high lifetime value.

Procurement is yet another area where AI is proving to be invaluable. AI facilitates resource right-sizing, vendor rationalization, and cost-saving opportunities within purchase activity.

Key areas where AI can have a major influence are 1) sales pipelines, marketing returns, and 2) supplier contracts. Let’s deep-dive.

1. AI can positively impact sales pipeline and marketing returns.

Businesses utilizing AI in campaigns may increase consumer activity by 30% and increase marketing returns by 20%.- Boston Consulting Group 

Are you aware that sales teams spend over 35% of their time on administrative tasks? For instance, sales reps manually follow up with prospects. Sales and marketing teams use channels and campaign data to investigate client needs and campaign impact on revenues. However, both processes could be more proactive, leading to inefficiencies and errors.

a) AI can make sales & marketing tasks more accurate and agile in the following ways:

Conversion: AI enables real-time updates to client data and evaluates clients based on their potential to convert.

Prediction: With AI’s ability to study time-series data, companies can proactively anticipate upcoming trends and modify selling actions.

Validation: AI can identify at-risk deals by reviewing customer relationship records, helping the sales force focus on areas where they can be most effective.

Personalization: By providing nuances in buying behavior, AI aids sales reps in fine-tuning their outreach efforts and enhancing consultative selling.

Risk-mitigation: AI helps reduce selling and retaining risks by highlighting deals that may not close and scheduling follow-ups for at-risk clients.

Optimization: AI tracks marketing returns across multiple channels, thus streamlining campaigns and budget allocations using real-time performance data.

AI may alter firms through enhanced client interaction, increased representatives’ productivity, improved returns on marketing, and more effective resource management.

• Salesforce Einstein increased qualified leads for businesses by 30% by enabling them to focus on AI-ranked opportunities.
• With its AI-powered features, HubSpot helps sales teams manage customer relationships more efficiently, cutting administrative time by 20-25%.
• A B2B SaaS company reported increased client satisfaction using AI-driven tools to customize products based on customer interactions.
• An electronic commerce company saw an 18% increase in marketing returns through AI-enhanced advertising across social media channels.

b) Enhanced sales data using AI:

• Collect and Pre-process Data: Utilize historical sales and customer data, cleaning it through normalization and outlier detection techniques.
• Design Specifications: Identify key variables that forecast future events, such as social and personal interactions and previous spending history.
• Choose and Train Model: Choose among models like classification, regression, k-means, random forest, or time series tailored to the data’s complexity.
• Validate Results: Assess the model’s accuracy with precision-recall and RMSE (Root Mean Square Error) metrics.

Some AI Tools for Sales: Python, Tensor Flow, PyTorch, XGBoost, Azure Machine Learning, AWS Sagemaker, Gong.io, X.AI, Adobe Sensei, and Google’s AI campaign tool.

c) Applying AI to marketing data: 

• Journey Mapping: Identify and outline every point of contact in a customer’s buying process to analyze how marketing influences their decisions at each stage.
• Integrate Signals: Aggregate data from various sources (social media and email campaigns) into a single, normalized dataset for analysis.
• Attribution Analysis: Employ multi-touch attribution models to effectively distribute returns across different marketing channels.
• Expense Optimization: With accurate and instant data, AI dynamically adjusts campaigns based on audience targeting and advertising spend.

Sample AI tools to estimate marketing returns with AI and GenAI (Generative AI): Google Analytics 360, Adobe Analytics, SpaCy, NLTK, Optimizely, Google Optimize, Marketo/HubSpot.

2. Using AI to manage supplier contracts

AI in procurement can reduce operational expenditure by up to 30% through better supplier management and predictive analytics. – Gartner

Tasks such as comparing and shortlisting vendors, analyzing contracts, and negotiating prices are all part of procurement, and they can be error-prone with Excel spreadsheets, missing possibilities for cost control while purchasing. Market turbulence can cause supply chain disruptions, such as when a supplier fails to meet deadlines or quality standards or when unfavorable contract terms are overlooked.

a) What can AI do in procurement?

• Prediction: AI can track pricing trends and automate contract analysis, helping businesses make data-driven decisions.
• Recommendation: Machine learning provides supplier credit scores that can suggest optimal vendors based on past performance.
• Prevention: By anticipating supply chain interruptions, proactive risk management becomes possible.
• Efficiency: Generative AI can draft contract terms and analyze supplier data to provide negotiation insights.

AI can augment supplier relationships, cut procurement expenses, speed up contract negotiations, and enhance risk mitigation.

• One electronics manufacturer reduced supply chain risks by 15% using AI for supplier performance tracking.
•  A consumer goods company leveraged AI to forecast market prices, allowing for better contract negotiations.

b) How AI can help procurement tasks:

• Spend Analysis: AI groups expenditure data to identify high-cost categories, revealing opportunities for cost savings.
• Supplier Risk Management: Machine learning models evaluate supplier performance based on various criteria, helping in vendor selection.
• Contract Optimization: Natural Language Processing (NLP) techniques analyze contracts to highlight unfavorable clauses and ensure compliance.

The moment has arrived for companies looking to expand and integrate AI-powered solutions into their daily operations. As we advance, companies need to ensure that AI is included in sales, marketing, and procurement to enhance the explainability, ethicality, accuracy, and responsibility of intelligent algorithms.

Whether in sales, purchase, or marketing, concentrating on data-driven functions and analytical solutions is a great place to start when deploying AI/GenAI solutions that offer real-time decision insights, increase prediction efficiency, and reduce bias and errors.

Reference Sources:

1.   https://www.engati.com/blog/8-ways-ai-will-change-the-future-of-marketing
2.   https://dataconomy.com/2023/05/17/artificial-intelligence-in-sales-101/
3.   https://www.crossrivertherapy.com/research/artificial-intelligence-statistics
4.   https://10web.io/blog/ai-in-marketing-examples/
5.   https://www.sprintzeal.com/blog/how-ai-has-impacted-consumer-buying-behaviour

Anirban Parua

Anirban is a domain expert on Solvecube. He is a Senior Director with 20 years of experience in strategy, M&A, and digital transformation. He has delivered over 70 projects across nine industries and eight countries, with expertise in program management, process excellence, climate change, cybersecurity, and AI, and has published articles on these topics.