Machines are Predicting What You’ll Buy
Your favorite retailer already knows what you’re going to buy next.
Customer spending data is a goldmine of intelligent insight. Typically, a company will look at how you spend to determine how you are likely to spend in the future, and thus, how to target their marketing to your likely purchases.
All of this is made possible through predictive analytics.
Targeting Your Consumers
It’s important to note that most predictions assume that consumers spend based on their interests. Companies consider financial behaviors like the types of products customers are purchasing, when they purchase, and where they are spending their money.
Having access to this information allows companies to improve their value by personalizing products and delivering them to you when you most likely need them.
A classic case study of this is the story of Target predicting a teenage pregnancy.
Target was sending the teenager coupons for baby items based on a change in browsing behaviors. Target knew she was algorithmically more likely to be a pregnant shopper. Her father angrily called and complained that his daughter was not pregnant, only to sheepishly call back when the algorithms proved more accurate than his knowledge.
Getting married, buying a new house or getting pregnant is when the consumer’s usual routine breaks. At that point, retailers need to adjust.
To account for this, retailers have statisticians like Andrew Pole, who created what he called a “pregnancy-prediction model” for Target.
This model was built from an existing database associated with their customer ID numbers, which included their historical transactions through credit cards, coupons used, opt-in survey responses, emails, demographics, and various other types of data.
Using these data, the system can predict which women are most likely to be pregnant, and Target can start placing ads and offers for maternity products to those customers, while not making it intrusive. The act of following a consumer’s spending pattern does not occur solely on the retail floor.
A Broader Field
Recently, the University of Texas at Dallas published a study that shows how companies can target consumers based on the amount of money they have already spent elsewhere.
This model requires a new kind of methodology — instead of looking at how a consumer buys at their own stores, companies can now examine share-of-wallet, or the amount of a consumer’s spending in a defined product category that a business captures.
For instance, if a customer spends a total of $100 on toiletries every month, with $60 of that amount at Walmart and the other $40 at Target, the share-of-wallet for each company would then be 60% and 40% respectively.
This method makes sense as it also looks at a consumer’s total size of wallet, or how much they can afford for a specific product category alone.
If a consumer has a wallet size of $100 for toiletries per month compared to those who can only afford $50 for the same products, then Target and Walmart would want to put more efforts into the former customer.
The same goes for banks. A customer can either decide to put 100% of his or her savings into a bank, or split them at 50/50, or whatever they’d like. If a bank can entice the customer with a certain wallet size into putting more of their savings into that bank’s system, it subsequently gains a higher level of profit.
However, getting competitive data can be challenging as it isn’t always available.
Finding the Data
Without accessible data, companies can turn to predictive models to gain an estimation of their customers’ spending.
Most of the data feeding into these models originates from past surveys or information aggregators, as well as historical transactions and approximated customer average income within a demographic or geographic segment.
In addition, interrelationships between product spending behaviors can also be used to facilitate cross-promotional efforts. If customers tend to spend more on soaps after buying shampoos, but not quite the reverse, then companies might want to promote shampoos more heavily.
Using consumer data for retail is one thing, but aggregating life patterns is quite another.
Banks in this modern era are in a tremendous position of power to understand, predict and exploit customer trends.
Applying Predictive Analytics
There is a lot of value that a bank can derive from your monthly bank statements. For example, Kasisto developed KAI, an AI-based conversational platform with an “expertise” in finance. KAI helps consumers manage money and track expenses through simple messaging.
The bot can also show you bargains that you might be interested in based on your financial spending history and geography.
Furthermore, even the banks that you are connecting with can tap into your data and offer additional banking services when it makes sense.
By combing through your statements, credit scores, reviews, and many other streams of resources, banks can start offering deals and saving options that not only match your interests, but also know when you need them most.
Banks, retailers, and companies have retained data streams that have long gone untapped.
With artificial intelligence, machine learning, and predictive analytics advancements, companies can now put these data into use. This data not only increases profits, but also benefits their customers.