By Anand Jain
Shoppers will spend over $200 billion from their smartphones in 2019. By 2022, that number will more than double. That’s a huge market, but many companies aren’t taking full advantage of it. Because marketing to the modern consumer is hard.
Today’s shoppers are a highly dynamic group. With more access to information than ever before, they can research, try, buy and change their minds about products in no time at all. Companies have to adapt. Those that can predict consumer behaviour and intent have a big advantage over those who can’t, and that translates directly into increased revenue.
That’s where predictive analytics comes in. It is the process of taking data and predicting what a person or group of people are likely to do in the future — digital fortune telling, but backed by solid data science.
Companies have been using predictive analytics for years, but the field is growing quickly and presents loads of exciting new opportunities. More about those in a moment. First, let’s take a moment to look at how some companies are already using predictive analytics to beat the market.
One of the most high-profile examples of predictive analytics is Netflix’s prediction algorithm. It’s a simple concept: using information on what you have watched, the ratings you have given, and other behaviour metrics, Netflix recommends movies and shows that you might like. It seems like a trivial thing, but it is big business. That’s why Netflix announced a $1 million prize in 2009 to anyone who could improve the prediction algorithm by just 10%. And you can bet that even a small improvement is worth a lot more than a million dollars.
Here’s another example you are probably familiar with: Amazon’s ‘suggestions’. Sign in and you will see a dozen examples — including ‘Books you may like’, ‘Video: Recommended for you’, ‘Your recently viewed items and featured recommendations’ and ‘More items to explore’ — all of which are created by predictive analytics.
Amazon doesn’t just collect data on what you buy; it records what you click on, what you look at on other sites, where your orders are delivered, and more. The company is confident it will know what you want before you order it (as outlined in a patent for anticipatory shipping).
As more customers shop from their phones, mobile predictive analytics will become increasingly important. Yes, mobile-focussed companies can use predictive analytics in ways similar to Netflix and Amazon, but there are even more possibilities.
Consider a music subscription company, for example. One of the most important factors in its success is churn — how many people cancel their subscription each month.
Predictive analytics can assign users a score based on how likely they are to cancel their subscriptions. That enables marketing teams to deliver highly tailored campaigns addressing specific issues a user is facing.
Predictive algorithms have the capability to go beyond what a user is likely to buy. By collecting data on location, apps will know where a user is likely to be, which opens up a wide range of possibilities. Imagine walking into a store and immediately getting a text message with a coupon code specifically for that store; or getting a mobile notification suggesting restaurants you might like when you arrive in a new city.
Identifying, collecting and putting this information to use will scale up to an entirely new level when we enter the 5G era. And marketing tools like these will become much more common.
Companies are already using mobile predictive analytics to solidify their businesses. As more consumers start to buy products from their phones, the importance of those analytics will only increase.
The author is co-founder and CSO, CleverTap