Marketing Recommendations that Maximize Fund Flow

Personalized marketing strategies learned from historical interactions and outcomes in investment management

Marketing strategies in investment management are largely one-size-fits-all

Our client, a large investment management firm, needed a tailored marketing strategy that informs how to best advertise its funds to financial advisors, leveraging its rich history of interactions and outcomes with these advisors.

Traditional approaches to marketing involve developing campaigns based on customer segments. This segmentation is often based on a clustering analysis using demographic information, or predictive models that estimate the success rates of marketing efforts based on customer information.

When considering how best to allocate marketing funds, these approaches fall short of our real goal, which is determining which marketing approach is best for each individual. This is a difficult task, as the historical data only tells us how an individual responded to the marketing approach that was used. Crucially, we do not know what would have happened had a different approach been used, as the data is only observational.

Marketing management tool based on what worked in the past

Unlike traditional machine learning methods, Optimal Prescriptive Trees are uniquely designed to learn optimal strategies directly from observational data. Using these trees as the building block, we built a personalized marketing recommendation engine for the client.
  • Categorize shared patterns under each marketing channel

    Based on large volume of historical interactions data, the algorithm automatically segments customers into cohorts with similar responses to the various marketing channels, which are needed in the optimization step.

  • Meaningful cohort definitions

    Each leaf of the tree creates an explainable segment of customers that have similar responses to prior marketing efforts. The interpretability of the tree allowed the marketing department to validate these segments against their intuition.

Illustrative Optimal Prescriptive Tree for Marketing in Investment Management

Increase in inflow via optimization

The meaningful cohort definitions and predictions are then fed into an optimization engine where it allocates limited marketing resources to maximize expected fund flow under such strategy.

In a pilot study under the recommendation, the client estimated a lift of 8-15% in fund flows using the recommendations without increasing their marketing budget. In addition, the client identified a few cohorts where it can focus its effort on improving fund performance where marketing has limited impact.

Unique Advantage

Why is the Interpretable AI solution unique?

  • Flexibility of Optimal Trees

    Optimal Prescriptive Trees learns a highly accurate rule to predict and recommend at the same time

  • Actionable with validated insights

    The model outputs direct action per customer, which was validated by marketing experts because of the model transparency

  • Working with the lack of counterfactuals

    Even without knowing what happened under alternative marketing channels, the model can learn relevant effects with high accuracy

  • Integrate with formal optimization engine

    Rather than predicting everything and optimizing, the model learns the necessary inputs for the optimization problem directly

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