OCTOBER 2022CIOAPPLICATIONSEUROPE.COM9predicting and identifying consumers who are likely to change their future consumption patterns rather than looking at past or present behaviour as proxies of future behaviour. This is where evolutions in ML models will be critical. 3. Transferability of Findings: Deployment of any ML application across the business requires significant budget and resources. Hence, ML capabilities to learn from a defined set of markets, categories to extrapolate to the rest of the business will gain relevance. This capability has been in existence and has been applied for several applications over the years ­ what will evolve is the scale across all ML applications.Convergence: skill sets, business applications 4. The Convergence of Data Science and Econometrics: There has been a resurgence in the budget optimisation applications where econometrics plays a huge role. However, econometrics has challenges that are overcome by using a combination of ML models to drive higher precision, consistency, and granularity. Getting skilled econometricians to apply ML models without losing the past learning will gain more prominence in the coming months as budget optimisation applications move towards the platform as a service solution to accelerate scalability.5. From Continuous Updates to a Dynamic Transfer Learning Closed-Loop System: It is a common practice to continually refine the models with new in-market data to bridge the gap between in-market and validated predictions. However, with the quantum of deviations from the expected trends since the emergence of COVID, the need for a self-correcting closed-loop transfer learning system has become more important. This evolution will require coordination across the multiple models from the different functional units (example ­ Marketing, Supply Chain, Finance, R&D) where the outputs from one model will help course-correct the inputs of another model. This will also help drive the informal co-operation between the different functional units for ML applications.6. The Convergence of Personalisation with Strategic Market Measurement Models: The two sides of measurement models are top-down Marketing Mix Models (MMM) and bottom-up campaign optimisation attribution models. MMMs optimise spends in different marketing levers for strategic planning and drive the ROI (return on investments). Campaign optimisation attribution models enable better personalisation to drive campaign ROI. The two areas are inter-linked but rarely get integrated as a seamless system of each feeding into the other continually. This convergence will be enabled as strategic MMMs move from aggregate measurement to consumer segment level measurement models made possible by data sharing eco-system (between manufacturers and retailers) and segment-specific ML models with sparse segment level data.360 Consumer View Continually: New sources of data and data enrichment7. Enriching Big Data by Learning from Small Data: The ability to project multiple small deterministic data onto big probabilistic data, via propensity models, will gain prominence to increase the depth of first-party datasets beyond online signals alone. This will help create a more predictive basis for activation. This will also help with the emerging era of going beyond identity matching especially with third-party data assets, given the likely challenges in the future.8. New Sources of Passive Behavioural Data: Passive internet-of-things consumer data that has not been leveraged for broader applications at scale will gain prominence but with appropriate data governance. This will vary from energy meter datasets to smart machine data (such as coffee machines, washing machines) which will drive new sources of consumer engagement. Harnessing these datasets will require an ecosystem of data collaboration that may not exist today and the integration of different types of ML models (energy data models and consumer propensity models). 9. Unstructured data as lead indicators of ML models: Whilst the Utopia of continuous learning closed-loop ML model updates may not be achievable for all models in the immediate term due to data and infrastructural challenges, what will gain broader applications is the use of proxies from unstructured data as predictive lead indicators of changes in consumer behaviour metrics in the ML modelsAs the above ML evolutions gain more relevance and drive business impact in organisations, the boundaries will be pushed continually powered by our imagination and advancements in ML capabilities. At the heart of these evolutions is the ability to learn and predict the consumer decision-making process. Without having adequate knowledge of the consumer decision-making process, can a data scientist alone drive such evolutions and applications? Hence, the key task for organizations is to achieve the knowledge convergence of data scientists and consumer insights experts. While data scientists need to think in terms of the consumer decision-making process,the consumer insights experts need to be conversant with the abilities of ML. Machine Learning (ML) can be instrumental in addressing these challenges helping to bring certainty to uncertainty which I have spoken about earlier
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