Solution
TCS Interactive
Humanizing Technology to Create Iconic Experiences
Highlights
- Contextualized/personalized customer experiences may satisfy customers today, but it may not be enough to retain customers and drive sustained business value tomorrow.
- A ‘superforecaster’ engine can help companies with real-time decisioning for evolving scenarios planned and unplanned in a hyper-personalized environment.
- Superforecaster will allow marketers to not only have better control of the marketing narrative, but also optimize marketing spend and drive better ROI.
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Why superforecaster
“Technology changes exponentially; organizations change logarithmically.” As this article points out, this is a dynamic that marketing technologists wrestle with every day.
It’s especially true about the way AI technology is progressing faster than organizations can absorb its implication. And it has a direct impact on how enterprises deliver customer experience, which needs to be contextual as well as real time. With surveys like this one indicating that 55% of buyers will switch brands to get real-time superior service experiences and another one saying that acquiring a new customer is up to 25 times more expensive than retaining an existing one, the question is: Will organizations sustain business value by just delivering personalized experiences or will it require a ‘superforecaster’ engine to deliver real-time decisioning for evolving scenarios in a hyper-personalized environment?
Predicting the unknown
A ‘superforecaster’ is what we call a marketing technology (martech) platform’s capability to make accurate predictions about customer expectations and responses.
It will allow precise decisioning for unplanned scenarios as well as for expected outcomes.
Superforecasting requires translating data into insights and using scenario planning to accurately predict what’s next. Consider a financial institution aiming to predict stock market movements. Superforecasters in this context would be individuals or teams who meticulously study historical stock data, economic indicators, news, and market sentiment to make precise predictions about the future performance of specific stocks or the market. They might not focus on personalization but on providing accurate forecasts to inform investment decisions.
Superforecasters often work with longer time horizons, aiming to provide insights that guide marketing strategies over months and years.
Developing superforecasting powers is crucial for companies because it will enable real-time experiences that will make them stand apart from the rest. So, what can companies do to win and keep customers new and old? They can start by transitioning from rule-based martech tools to AI-driven forecasting of customer needs.
Transforming martech
Reinventing martech platforms as superforecasters will involve integrating advanced predictive analytics, data science, and AI-driven insights to improve marketing and sales strategies, and the decision-making process.
Reinventing martech platforms for precise decisioning
The innovative concept of superforecasting seeks to revolutionize martech platform decision-making by fostering a closed ecosystem where the synergy of art and science is made possible by integrating various platforms including:
Data lake or CRM for management of data.
Customer data platform to build customer insights.
Real-time interaction engine to help make smarter decisions.
Let’s take a look at how each of these elements help make marketing organizations more innovative and drive greater results.
Data lake or CRM for management of data
Data and analysis: Varied and massive amounts of data will serve as the foundation block for a superforecaster. Customer profile data from various data sources including CRM, data lakes, lakehouses, and data warehouses will be mastered in the customer data platform.
Predictive modeling: Using predictive modeling techniques, martech platforms can forecast customer behavior, market shifts, and campaign outcomes. This will allow marketers to anticipate changes and plan their strategies accordingly.
Customer data platform to build customer insights
Real-time insights: A martech-focused customer data platform will provide real-time insights into customer data, audience engagement, and real-time customer behavior. This will allow marketers to quickly adjust their tactics based on accurate and up-to-date information.
Analytics: The martech-focused customer data platform can be designed to dynamically respond to changing market conditions more effectively by predicting which channels, campaigns, or segments are likely to perform best.
Real-time interaction engine for smarter decisions
Scenario planning: A real-time interaction engine could facilitate scenario planning by simulating different outcomes based on various inputs both in-plan and off-plan. Marketers can virtually test different strategies before implementing them in the real world.
Real-time decisioning: Using artificial intelligence and machine learning, the platform can make in-depth customer decisions, understanding preferences, emotions, and buying behavior. This information can be used for automated decisions.
Continuous learning: The real-time interaction engine could use machine learning to continuously improve its forecasting accuracy over time, refining predictions based on historical data and new insights.
Essentially, reinventing martech platforms as a superforecasting platform will allow marketers to make proactive engagements with customers based on AI-driven decisions, predict market shifts, and optimize marketing strategies. The integration of these superforecasting capabilities in martech will increase the role of marketing in driving innovation and business success.
From idea to reality
A superforecaster will not just be limited to predictions or forecasts.
A recipe for superforecasting
The objective is to create an ecosystem where business value is centered around developing a delightful customer experience just when a customer wants it. Bringing to life a superforecaster that enables this will require use of continuous learning and a combination of rigorous methodologies, AI-driven analysis, and insights that make forecasts more accurate and effective, some of which are:
- Probabilistic decisioning: Scenario planning is a crucial aspect of a superforecaster and probabilistic decisioning based on different scenarios will allow assigning probabilities to various outcomes rather than relying on binary yes-or-no predictions. This nuanced approach acknowledges uncertainty and provides a more accurate reflection of potential outcomes.
- Granular analysis: Superforecasters will depend not only on customer data, but also on synthetic data to granularly analyze different datasets and problem scenarios. Superforecasters break down complex predictions into smaller, more manageable components, allowing for more accurate assessment and prediction of individual factors that contribute to the overall outcome.
- Bayesian analysis: Data is generated at a rapid pace and superforecasters must include new datasets and insights to refine the forecast in a continuous manner. Superforecasters must include analysis processes like Bayesian analysis to adjust predictions as new insights become available. This method allows them to continuously refine their forecasts based on the latest data and insights.
- Diverse analysis: Bias and ethical traps are common concerns around superforecasting. Incorporating insights from a diverse group of experts and analysts as well as historical data and past events to identify patterns and form future forecasts can help. The collective intelligence helps to mitigate biases and errors, resulting in more well-rounded predictions.
- Quantitative analysis: Superforecasters utilize quantitative models and statistical analysis to make forecasting more objective and evidence-based. Quantitative models can work with pre-trained models to optimize the cost and efforts related to deep analysis. The quantitative analysis approach will help in reducing subjective biases and enhancing prediction accuracy.
- Cognitive flexibility: The ability to adapt to new insights based on the performance of predictions, and prevailing market conditions will allow superforecasters to deliver better and more effective forecasting. This adaptable approach allows them to adjust predictions when new insights challenge initial assumptions.
Superforecasters can be a game changer for the marketing industry. The success and viability of superforecasters will depend on how the industry opts for AI and effective use of the technology across a long data supply chain within an organization. Systematic approaches, data analysis, diverse inputs, and a commitment to continuous improvement will make way for superforecasters and will give an opportunity for the industry to look beyond just real-time enablement of data and insights.
