Today’s consumer landscape is quite saturated. And only those organizations succeed whose marketing teams are able to make their product stand out. Marketers of these organizations usually attribute data and analytics for their success which they use for understanding customer behavior. In the past few years, predictive models, like uplift modeling, have empowered visionary marketers to up their game.
Modern marketers across the globe are increasingly realizing the power of uplift modeling and including it in their suite of machine learning technology. Predictions provided by this prescriptive data technology are used to specifically understand how consumers will respond to any marketing activity. This blog talks about what is prescriptive data, what is uplift modeling and its examples, and how uplift modeling works.
Understanding prescriptive data
Marketers and marketing teams today have access to multiple machine learning and data science tools and techniques. These techniques can be categorized into three groups:
- Descriptive data: It talks about what happened. This data is all about summarizing what we know. For example, Josh bought a new phone last week
- Predictive data: Like the name suggests, predictive data is about what will happen and make predictions about what we do not know. For example, Josh is 60% likely to purchase a new phone next month
- Prescriptive data: Prescriptive data analyzes the past data and makes recommendations based on that data about what our next move should be. For example, Sending him a marketing SMS will increase Josh’s chances of buying a new phone by 20%
Uplift modeling falls into this group.
So, what is uplift modeling?
Uplift modeling is essentially a prescriptive technique that predicts how each consumer might respond to a marketing activity and make marketing recommendations based on the same. This, ultimately, helps marketers target the right audience at the right time and maximize the return-on-investment of their campaigns. To get better acquainted with uplift modeling predictions, there are two inputs that are to be understood:
- Intervention: The marketing action you want to measure the impact of
- Outcome: The conversion metric/event the intervention is supposed to influence
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Uplift modeling example
Here’s an example of uplift modeling that elaborates what’s been explained above.
If you consider conducting a sale and offering discounts (intervention) on your products with the end goal of increasing the number of transactions (outcome). Here, the prediction that might come out on uplift modeling for a customer might be: “The sale & discount will increase the customer’s chances of buying your product by 20%, as compared to if you didn’t offer the discount.”
In the data science community, uplift modeling is an active area of research. However, its practical applicability is gaining steam quickly in the world of marketing. Some companies that use uplift modeling are Wayfair, Uber and Fidelity Investments.
When to use uplift modeling in marketing?
When used for the right marketing activities and for the right products, using uplift modeling can be quite uplifting for organizations. However, uplift modeling needs marketers to run an A/B test. In this test, some randomly selected users receive the intervention you’re considering. Why is this important? Because it gives you an idea if your marketing intervention is worth the investment.
In general, uplifting models are suitable to use when:
- You have robust marketing incentives that you can use. If the intervention you plan on introducing cannot sway customer behavior, there might not be an uplift for your model to find
- The goal is see short-term positive customer behavior. If your goal i to strengthen customer relationships, uplift modeling might not be the best approach to take up
If you’re a startup and don’t feel uplift modeling is the best marketing technology for you, you can check out this blog on effective marketing tools for startups.
Some common uplift modeling algorithms
1. Direct uplift models
This uplift modeling pillar is about algorithms that enable you to model treatment effects directly. The model requires the algorithm, hyper parameter tune, and should be fit to solve for the impact of (t) on (y) given (x).
2. Solo model
In this model, you train one single classifier to forecast the outcome of the intervention. The feature is included as a dummy variable, which indicates whether each customer has received the treatment.
Each customer will be scored twice: First, with the treatment flag value set to 1 and the second, set to 0. The uplift prediction here for Customer A will be defined as the prediction of Customer A with treatment = 1 – Customer A’s prediction with treatment = 0.
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Benefits of uplift modeling
If your short-term marketing goal is to impact customer behavior in the near future and you have the right marketing incentives at your disposal that can help you drive the outcome you desire, then uplift modeling can be beneficial for you.
If, instead of prescriptive analytics, you used descriptive analytics to figure out your campaign targets, you might end up creating a rule: offer 10% discount to any customer who bought our product last week.
Now, these customers have made purchases in the past and there’s a possibility of them doing it again, and a discount could be the nudge they need to make the purchase. However, you’re making 2 assumptions are:
- The customer who has made the purchase is the main target to purchase again, and
- A discount will be enough to get that customer to buy when they wouldn’t otherwise
But there’s a big possibility that some past buyers may now not be interested in buying your product again and communication might only pester them. Some others might purchase again regardless of the discount you’re offering. So, by giving the discount, you might unnecessarily cut into your revenue.
By taking the predictive approach and targeting any customer who is predicted as >80% likely to buy this month, you’ve eliminated the assumptions. The data by uplift modeling will now tell you exactly who might be on the verge of buying.
Conclusion
Uplift modeling is quickly gaining recognition and momentum in the marketing world. Tech giants and renowned companies, like Uber, Wayfair, and Fidelity Investments, rely extensively on Uplift modeling. Various Uplift modeling techniques have already shown promise in driving ROI for marketers, and in the future, progress is most likely to increase as more groundbreaking businesses explore this approach.
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