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The alignment problem when applying AI in marketing occurs when managers ask question that does not align with the set objectives. Telecom firms embark on marketing campaigns to retain customers based on the data collected from the market. However, the problem is when the managers fail to deliberate well about the business issue they need to resolve and the appropriate prediction to facilitate the best decision (Ascarza et al., 2021). For instance, they may focus on identifying customers who can potentially defect to the competitors and approaches they use to prevent them from moving. Finding strategies to utilize resources allocated for marketing to minimize churn would best align with customer retention objectives.
Asymmetry problem occurs when the marketing managers using AI prediction fail to recognize that the forecasts can be right or wrong and evaluate the value or costs associated with such projections. It is imperative to understand that while AI predictions may be accurate, it does not necessarily mean they are correct. Therefore, considering the value of correct forecasts and the possible costs of the wrong can help marketing managers to compare the two and implement the most accurate and correct one (Ascarza et al., 2021). This approach can help organizations record positive outcomes for every dollar invested in marketing campaigns.
Aggregation problem occurs when the marketing managers fail to make decisions using more refined AI predictions. Although AI tools can make detailed predictions, the question asked by the marketers determines the forecasts they provide (Ascarza et al., 2021). Decisions made on aggregated predictions are less likely to address issues faced by organizations. They increase the risks of wasting resources and missing critical opportunities. Additionally, marketing managers cannot quantify potential gains based on aggregated AI predictions.
The Way Telecom Firm Measure Success
Telecom firms measure the success of their marketing with the number of targeted customers who renewed their contracts. The completion in the telecommunication industry increases the chances of customers switching to competitors. Therefore, firms in this sector regularly conduct promotional campaigns to retain consumers who are likely to defect. Great customer experience is what clients in the telecom industry demand. However, firms in this sector fail to deliver beyond customers expectations, increasing the risk of having defectors, leading to a loss of revenue (Ascarza et al., 2021). Investment in customer retention campaigns is a strategy implemented by managers in the telecom industry to ensure that none of the existing consumers switch to competing service providers. Measuring the outcome of such campaigns is vital in determining whether they were the success or not.
Nevertheless, assessing the number of the targeted customers who renewed their contracts is not an accurate way of measuring the success of the investment in the retention campaigns. Some customers renew their contracts with the telecom firms even without receiving the promotion. In this regard, the retention campaign would be a waste of invested money(Ascarza et al., 2021). On the other hand, not targeting customers who would have left even after receiving the promotion would have been a success for the firms because no money was spent to persuade them. Therefore, telecom organizations should study their customers and needs before implementing any retention promotional campaigns. This approach is helpful in understanding the needs of every customer and those who are likely to defect or fail to renew their contracts. Additionally, it can allow marketing teams to avoid sending retention promotions to customers who cannot be persuaded by the latter.
The Appropriate Measure of Success
The more appropriate success measure that telecom firms can use is assessing whether customers, those with high churn risks targeted by retention promotions, were persuaded to renew their contracts. Notably, some customers are likely to renew their contracts even without being persuaded by any promotion. Equally, others can leave even after receiving the promotion because they have already decided (Ascarza et al., 2021). Therefore, marketing managers need to be specific when designing and implementing customer retention campaigns to ensure that they only target those at high churn risk. The approach minimizes the wastage of resources and the possibility of missing important opportunities.
The success measure is more appropriate because it can help telecom firms determine the actual profit realized per dollar invested in the promotional campaigns. Marketing campaigns that focus on addressing a specific issue and retaining the high churn risk customers are easier to quantify (Ascarza et al., 2021). The numbers of such customers who are persuaded not to leave are a success, and profits associated with renewing their contracts can be calculated. Additionally, the measure can allow telecom firms always to align AI predictions-based decisions with the business outcomes. Notably, the core business outcomes for every organization are to make a profit and guarantee customer satisfaction. Targeting specific customers is instrumental in learning and fulfilling their specific needs. Consequently, the level of satisfaction due to services provided by the telecom firms is bound to increase(Ascarza et al., 2021). In return, the possibility of defecting to competitors minimizes, they renew their contracts, and the companies retain their revenue generation level. Thus, telecom companies should avoid aggregating their customer retention promotional campaigns and focus only on those at high churn risk.
The Way AI Should Be Used for Marketing Decision Making
The AI should be used for marketing decision-making to allow organizations to target specific customers and measure the actual success of promotions. According to Ascarza et al. (2021), marketers should have a framework comprising of three critical questions. First, what is the marketing problem we are trying to solve? Second, is there any waste or missed opportunity in our current approach? Third, what is causing the waste and missed opportunities? The first question facilitates the definition of the problem at the most granular level, where it is easier to make an appropriate decision or implement interventions. In this regard, marketing managers can decide whether it is necessary or not to send retention promotions to all customers. The second question should allow marketing managers to use the AI to identify waste or missed opportunities in their promotional approaches at the granular level.
The last question helps the managers to pinpoint the sources of waste and missed opportunities and then use the AI to quantify them to help them decide how to allocate resources. Answers to the question should focus on addressing alignment, Asymmetry, and aggregation problems in marketing. The AI predictions should always connect with marketing decisions and business outcomes to resolve the alignment problem by ensuring that retention promotions only reach persuadable customers (Ascarza et al., 2021). Then they should quantity the potential costs of making erroneous predictions than contribute to waste and missed opportunities to address the asymmetry problem. Finally, they should use the AI to perform two analyses to resolve the aggregation issue. The first analysis needs to focus on marketing strategies to eliminate waste and missed opportunities. The second one involves the quantification of potential gains related to AI predictions. Implementation of the framework will allow managers to make optimal marketing decisions.
Reference
Ascarza, E., Ross, M., & Hardie, B. (2021). Why you arent getting more from your marketing AI. Harvard Business Review.
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