Propensity models also called likelihood to buy or response models, are what most people think about with predictive analytics. These models help predict the likelihood of a certain type of customer purchasing behaviour, like whether a customer that is browsing your website is likely to buy something. This helps marketers optimize anything from email send frequency to sales staff time, to money, including discounts.
An example of a company using predictive analytics using the propensity model: Online pet products company Fin & Fur has served pet owners for more than 15 years. It sells many products that customers have to reorder at varying times from three months to 12 months. Like most retailers, Fin & Fur took a one-size-fits-all marketing approach, offering a set calendar of products at set times and promotions to all customers. But not all customers are alike and many are looking to buy at different times of the year.
Using SwiftERM to automatically include predictive analytics, Fin & Fur was able to differentiate products across customers, leading to higher sales and retention without increasing costs. Customers differentiation was incorporated according to their highest likely product choice to purchase. Based on that ranking, Fin & Fur was able to determine which products would obtain the optimal response from each customer and maximize the capture of those returns through Swift’s automatic email process to customers who were already deemed likely to buy. The surgical promotions drove incremental margin from customers who were already motivated to buy and incremental revenue from customers who previously felt no incentive to buy.
Thanks to this and other predictive marketing campaigns, quarterly sales increased by 38 per cent from the year before, profit rose by 24 per cent, and customer retention increased by 14 per cent. Plus, the changes allowed Fin & Fur to more than double its campaign response rates without increasing the marketing or promotional budget by a single dollar.
Propensity models likelihood – response models predictive analytics
To predict which prospects are ready to make their first purchase, a likelihood to buy model evaluates non-transaction customer data, such as how many times a customer clicked on an email or how the customer interacts with your website. These models can also take into account certain demographic data. For example, in consumer marketing, they may compare gender, age, and zip code to other likely buyers. In business marketing, relevant demographics may include industry, job title, and geography. SwifERM automatically appreciated the uniqueness of the visiting customer capturing each and every click made as they navigate throughout the website. Nuance is all.
Here’s how it works: the models compare the pre-purchase behaviour of prospective buyers to the pre-purchase behaviour of thousands or millions of previous customers who ended up buying, comparing attributes like which emails they opened and what products they spent the most time looking at. The prospects that behave most like the previous buyers are tagged as “high-likelihood buyers” and SwiftERM then alters the way it interacts with them to increase the likelihood of closing a sale. Once you’re armed with this data, you have a heightened likelihood of a successful return for each prospective customer.
Predicting the Likelihood to Buy for First-Time Buyers
For consumer marketers, likelihood to buy predictions allows you to decide how much of a discount you might allocate to a certain customer because people who are already more likely to buy won’t need as aggressive of a discount as customers who are less likely to buy. The models then get better over time, as companies collect more data and automatically test whether predictions actually become reality.
For instance, the large European household appliance manufacturer Arcelik maintains a call centre where employees are given a list of customers who are likely to be ready to buy a new washing machine within the next few months. Agents then make calls to these customers with offers such as a year of free detergent with the purchase of a washing machine. The tactic works well for considered purchases, such as refrigerators or cars, and larger-ticket items such as high-end fashion apparel.
A high-end shoe brand provides store associates with lists of customers to call too. The store associates have already developed strong relationships with their customers, but they can be even more successful when armed with predictive analytics. Employees can now see which customers are likely to be interested in a certain style when a new season’s shoe comes out, based on customers’ past behaviour or how similar their purchase habits are to other customers. Employees can then reach out to customers with that information. A call could go something like this: “Hi Joe, it has been a while since we’ve spoken. I just wanted to let you know that there is a new cross-country running shoe I think you might like. It’s similar to the shoes you bought two years ago but in a new material. I have put a pair aside for you in your size. If you have time, perhaps you could stop by on your way home from work to have a look?” Who would not want to receive a call or an email like that from their personal shopper?
As reported by the New York Times and others, President Barack Obama used propensity models, specifically propensity to vote for the Democratic Party, to help him win reelection in 2012. His staff of volunteers could not possibly meet with every voter in the country so the challenge was to find the undecided voters. There was no point spending time or money trying to woo diehard Republicans who would not change their minds anyway, or diehard Democrats who were already likely to vote for Obama. Rather, using propensity models, Obama’s team of data scientists found those voters who were undecided but could still be persuaded. They then focused on finding already strong Obama supporters in the undecided voter’s social circle and asked them to spend time with the undecided voter to explain their views.
Predicting Likelihood to Buy for Repeat Buyers
What good is spending money to acquire new customers if they only buy once and do not return? Based on a customer’s propensity to purchase, it is not only important to predict likelihood to buy for first-time buyers, but it is equally important to predict likelihood to buy for repeat buyers. Your goal is to keep customers coming back time and time again. It is happy and loyal customers who have a large lifetime value, and many customers with a large lifetime value make for large revenues and profits for your company.
Predicting the likelihood to buy for repeat buyers is a lot easier than predicting the likelihood to buy for first-time buyers because there is a lot more information to go on. The likelihood to buy model for repeat purchases evaluates earlier transactions as well as other interactions similar to the model for prospects. However, the added information derived from the first purchase can significantly improve the accuracy of the likelihood to buy model for repeat purchases, as compared to a similar model for prospects. Unlike the first purchase predictions, repeat purchase predictions utilize all interactions of the customer, such as past purchases and returned purchases. That SwiftERM adds data captured on repeat visits satisfies an enormous provision for more accurate returns for each consumer.
Because the replacement and delivery cycles for vendors, deals, services, and products can take a long time, most marketers are hyperfocused on acquiring new customers, rather than getting existing customers to come back, where the likelihood to buy first purchase models take greater importance, and the cost to keep them is infinitely lower.
Predictive models are not the only way to prioritize prospects for business marketers. However, predictive models are by far the most accurate and relatively easy to use.
If you would like to read further articles about the distinction SwiftERM would make to your company, please follow these links to other pertinent articles: