The Point: The world is becoming more volatile, uncertain, complex, and ambiguous than ever before, which makes predicting customer behavior and adapting to changing market conditions more challenging. However, some companies have successfully leveraged AI models to predict outcomes and adjust their marketing and sales efforts, giving them a competitive edge. By analyzing historical consumer behavior data, these firms can predict the likelihood of customers responding positively to marketing campaigns, detect potential churn, and redirect sales efforts when predictions go off track. In effect, they run a large number of digital experiments that help them respond to market changes more quickly than their competitors. In this article, we explore how firms can use AI models to predict customer behavior and adjust their marketing and sales accordingly. We present two case studies that demonstrate how AI models helped a global trading firm and a real estate property developer to adapt to changing market conditions and achieve better business outcomes…Enjoy!
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Why AI Is Changing How We Make Marketing and Sales Decisions
In the analog world, it was challenging to establish a causal link between marketing investments and customer response. However, the digital world has made it easier to build causal links by running a large number of relatively cheap experiments. Firms have the ability to track customer responses at every stage of their journey. These stages include search, click, purchase, and even consumption. This process leads to an exponential increase in the amount of data available to firms. The data provides valuable insights into the customer journey. The insights can be used to improve customer experience and inform business decisions. This data tracking is made possible by technology and data analytics.
Some firms excel in adapting their use of customer data to respond to changing marketing conditions. These firms are faster than others. They can quickly pivot in response to uncertain conditions. These fast-acting firms use AI models to predict outcomes at various stages of the customer journey. For example, they analyze historical consumer behavior data and predict the likelihood of a customer responding favorably to a marketing campaign. This proactive approach to managing customer relationships enables firms to predict which customers are likely to churn and what corrective action can be taken to prevent the customer from defecting. Meanwhile, their competitors react after the customers have already left.
Firms rely on AI feedback to adjust marketing and sales when predictions fail due to external factors. They run digital experiments to respond quickly to market changes and gain a competitive edge. AI tools, while not perfect, can transform decision-making in marketing and sales.
Case Study: A Global Trading Firm
In early 2019, a trading company employed AI-based prediction models to monitor the RFP-based purchasing processes of its clients. The firm focused on quality as the primary criterion for being short-listed, which allowed it to pursue select opportunities.
However, the AI-model predictions made by the firm began to prove incorrect by May 2020. Upon further analysis, it was discovered that delivery-related terms were better indicators of being short-listed by clients. As a result, the company quickly and effectively altered its engagement model globally. Thanks to AI, firm leaders could now anticipate intermediate outcomes in clients’ purchasing processes and quickly adapt the marketing and sales approach to match shifts in the market, rather than relying solely on macroeconomic data or revenue shortfalls after a couple of quarters.
With the help of AI, the trading company was able to adjust to market changes and achieve better results. It promptly changed its global engagement model, aligning sales and marketing strategies with market shifts.
Case Study: A Major Real Estate Property Developer in the UK
In January 2020, a UK real estate developer conducted a study on tenant incentives. The study aimed to find the best way to incentivize tenants in corporate spaces. Their discovery showed that offering a rent-free period for the first few months of the lease was the most effective incentive. The study factored in the low probability of corporate spaces remaining unrented. The findings suggested that offering a rent-free period would attract more tenants, leading to higher occupancy rates. The developer concluded that providing a rent-free period would be the most attractive offer to potential tenants.
The developer and marketing team cooperated for the incentive. Targeted campaigns emphasized the rent-free period’s benefits for business expansion. Increased occupancy and profitability were achieved, establishing the developer as a market leader. Understanding ideal incentives and data-driven insights are crucial in competitive industries like real estate.
The case study emphasizes the significance of comprehending and examining the ideal incentives to draw and retain clients. This is particularly important in fiercely competitive industries such as real estate. Through the use of data-driven insights and collaboration with their marketing team, the developer established an efficient incentive program. The program proved successful, driving business growth and achievement.
In conclusion, the case studies of a global trading firm and a major real estate property developer in the UK demonstrate how AI models can help firms adapt to changing market conditions and achieve better results. Overall, AI models in marketing and sales give firms an edge in a volatile market. It’s uncertain and complex, and the environment is ambiguous. By leveraging data-driven insights and working with their marketing teams, firms can create effective incentive programs that ultimately drive business success.
Sam Palazzolo, Managing Director