The future is uncertain. That’s what we’ve all been taught.
But the truth is that the future can often be predicted, and we have become a lot better at it—mainly due to technological advancements. For example, meteorologists today can accurately forecast the weather three days out as well as a one day forecast in the 1980s. This is known as predictive analytics, and using this data-driven process offers phenomenal and largely untapped marketing opportunities. In fact, Forbes recently wrote that using a similar model “to retail holds unrealized potential.” With predictive analytics, you reach consumers with the right message, at the right time, in the right place.
The simplest examples of predictive analytics in the consumer space are the product suggestions you see with companies like Amazon, or the song or movie suggestions with services such as Netflix or Spotify. As Blake Morgan of Forbes explains, “A customer’s actions, such as watching a certain show or skipping certain songs, impacts the next recommendations they’ll receive. Things change quickly based on customer feedback and preferences so brands can capture what customers want at that exact moment.”
Predictive analytics begins with data. The clearer and more in-depth of a customer profile you create, the better the predictive analytics will be. Collecting and organizing a user’s shopping history, search history, age, birthday, location, and other data points allow the retailer to create a predictive model (with the help of machine learning, which you can learn about here) to help forecast the needs of a potential customer. With these advanced analytics, retailers can effectively project risk and opportunities. The second part of predictive analytics is decision optimization that determines which action derives the best possible outcome. In terms of marketing, this means implementing deeply personalized campaigns, which offers suggestions when you’re likely to run out of a product, or a coupon when you’re likely to walk by a store.
To give a real world example, let’s look at a case study from Lineate. Foreknow, a company specializing in food and beverage, aimed to supply predictive customer insight to their clients in order to send geo-targeted offers based on a customer’s movements. “Foreknow analyzes past location data to determine when users are most likely to use a special offer. For example, Foreknow can determine the best time to send a coupon to a customer who regularly passes a coffee shop at 9 AM–ensuring the customer actually stops in.”
For this to work, Foreknow utilized Lineate’s DataSwitch technology. With DataSwitch’s three modules (Audience Manager, Campaign Manager and Publisher Manager), Foreknow was able to accomplish their goal of creating a successful geo-targeting marketing tool by having a system that tracks disparate data points and using their audience manager to implement personalized marketing campaigns that sends the right messaging at the right time.
This was done in the span of just six weeks. “Aside from ongoing talks with Speedway, Wahoo, and Slapfish, Foreknow is now supporting a major restaurant chain with 992 stores across the country earning $1.8B in annual revenue.”
And predictive analytics is not just used in personalized marketing. It is also used to set prices, improve supply chains, and anticipate demand, not just of goods, but also of staff. For instance, Walmart uses predictive analytics are used to “anticipate demand at certain hours and determine how many associates are needed at the counters.”
It doesn’t take a fortune-teller to know that predictive analytics will drive how and when retail companies interact with their customers, make pricing and supply chain decisions, and organize staff. And the more data that is collected, systematized, and processed, the more inventive and effective your predictive actions will be. Lineate can help build predictive analytics algorithms with its own Data Orchestration tool, DataSwitch. If you would like to schedule a demo with DataSwitch, reach out on our contact us page.