Marketing with ML – Part II

Marketing with ML – Part II

Part 2: Churn Rates, Dynamic Pricing and Customer Analysis

The evolving role of machine learning in marketing is transforming how businesses work – it is now an indispensable tool in remaining competitive. Here’s the second part:

Consider the case of US e-commerce giant Ebay:

To engage with its millions of subscribers, the company has to send over a 100 million emails with striking subject lines in each – something that may prove rather tedious for human writers. However, with expertise from UK SaaS platform Phrasee, they were able to generate engaging subject lines which would both not trigger spam filters as well as be aligned with the Brand’s voice.

The success was palpable too: a 31% rise in clicks and about 16% rise in open rates. This, an otherwise daunting task, could now be completed within minutes, and at scale, using machine learning.

When incorporated with prediction analytics and a personalisation model, product recommendation models lead to a boost in conversion rates, average order values and several other key metrics. Research has found that targeted offers made using historical data can push revenues up by almost 25%.

Churning Customers

Although Churn Rate Prediction, i.e., the rate at which customers stop doing business with a company over a given time period, can work rather well without ML models as well, a “dose of intelligence goes a long way toward perfecting the ability to leverage reliable information about customers, which can then be used to strengthen customer retention and marketing strategies, such as churn rates and offer timing,” opine experts at VentureBeat. Low churn rates mean happier customers.

Churn rate models provide insights and work as a gauge for business clarity, customer satisfaction and competitor analysis. “To do this effectively, however, the ML model requires access to some highly specific predictive data, such as recent purchase history or average order value. With this in hand, the model is able to analyse and classify clients according to their propensity to remain engaged.”

Customer analysis is especially important in this regard, vital to a wide range of marketing functions. Descriptive analysis is used in this regard to ensure organisations define segmentations at a granular level, down to the nuances of customer behaviour. VentureBeat writes: “prescriptive analytics can leverage these insights to speed up and simplify the creation of new models and launch A/B tests to assist in churn rate or even lifetime value (LTV) analyses.

ML brings equally powerful tools to the popular RFM (Recency, Frequency, Monetary Value) analyses that drive many marketing strategies these days. At both speed and scale, ML vastly improves the ability to quantitatively rank and group customers to develop targeted marketing campaigns. This is particularly effective for email-based outreach campaigns, with organisations gaining the ability to time emails to generate maximum site traffic and limiting offers to those most likely to engage them.”

Dynamic Pricing

With consumers becoming increasingly precise and sensitive in the post-pandemic era, dynamic pricing allows businesses to optimise on special promotions such as sales or discounts in order to balance their financial structures. There are three major aspects to keep in mind in this regard: competitor action, expenses to maintain a desired ROI and supply-demand fluctuations.

The most effective in the above is supply-demand prediction, carried out through clustering and regression techniques to graph out relevant data – season, geographies, prior sales, etc. – to produce a predictive outcome. In this way, VentureBeat writes, “pricing models are built on data, not hunches, although marketing executives can always establish limits as they see fit, including not reducing prices at all.”

ML not only performs several critical functions with greater efficiency; they have also proved to be more efficient as well – provided the modelling is appropriate and the quality of training data is high. In e-commerce environments, an ROI on ML functions can be seen in a range of about open to four years.

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