In the age of Big Data, heavy hitters like Netflix, Amazon, and Walmart have championed predictive analytics as a major tool for guiding strategies and making smarter, more cost-effective decisions for the future. Predictive analytics have helped these and countless other businesses forecast the likelihood of future outcomes by processing historical customer data through powerful analytical software.
28% of businesses currently use predictive analytics to inform decision-making through forecasting. With the adoption of predictive analytics only expected to grow in the years to come, businesses not taking advantage of their own data increasingly stand to risk losing ground to prediction-powered competitors. The bottom line? Any business focused on growth, cost savings, and increased operational efficiency should integrate predictive analytics into their business strategy to harness the substantial power of prediction before their competitors do.
What Is Predictive Analytics?
Predictive analytics calculates the likelihood of outcomes by marrying historical data with powerful computer modeling, data analysis, and machine learning. Predictive analysis helps businesses anticipate the likely results of strategy changes with data-driven forecasts. A curious product marketing manager might use predictive analytics to determine the likelihood of a successful product rollout or explore how changing a feature within the product interface might affect the customer’s likelihood to convert. Used correctly, a company committed to predictive analysis can experiment with new ways to drive customer conversions and sales numbers while minimizing the risk associated with exploring new methods and strategies.
Using data to predict future events may seem like a plot device from a science fiction novel, but the mathematical components pre-date modern computing. Within the past few decades, several advancements have made it possible for businesses to use predictive analytics to make forecasts of their own:
- Computers became faster, more powerful, and more affordable for commercial use
- Software innovations made analysis more accessible to the modern business
- Companies began to collect vast amounts of data from their customers that was previously unattainable—an essential factor in optimizing predictions
Thanks to these digital advancements, companies have more intimate insight than ever before on the past behaviors of their customers. With an immense volume of customer data streaming in through website usage, product ordering, and more, predictions only stand to increase their already formidable accuracy as the era of Big Data marches on.
Guide Marketing Strategies With Data-Driven Predictions
Predictions are often used in the marketing world to better focus spend on higher likelihood positive outcomes. Data, computer learning, and statistical modeling produce quantifiable marketing results, with predictions built using Amplitude Recommend producing between 5% and 20% lift compared to a behavioral cohort. There are three chief use cases for predictions as a means of improving marketing campaigns:
Determining who to include in your campaign
Including customers with a low likelihood of conversion is a waste of money and effort that could otherwise be spent appealing to those more likely to convert. Amplitude Recommend avoids this pitfall by allowing marketers to predict which users have the highest likelihood for conversion. Once this group is identified, you can then build a cohort consisting only of these high value customers for use in your marketing campaign.
Determining what incentive to offer
Offering an incentive to your customers may boost conversions, but how do you know that you’re not offering discounts to customers who would have signed up without one? In this case, predictions can be used to eliminate high likelihood customers from incentivized campaigns. By doing so, marketers avoid giving away at a discount for what high likelihood users were likely to pay full price.
Determining what content customers see
Personalization is the name of the game in the Big Data Age, and predictions are what power personalized marketing campaigns. In fact, targeted customization has become an expectation for many consumers, with 90% of customers finding personalized marketing at least somewhat appealing. Savvy marketers create predictive cohorts consisting of users who prefer a certain product or feature and build campaigns specifically featuring those same preferred products to produce greater results.
Analyze and Anticipate Customer Behavior To Avoid Churn
Predictive marketing analytics is especially helpful in running “what if?” scenarios involving customer retention. Specifically, various aspects of the customer experience can be tweaked or altered within a predictive model to determine the optimum customer experience. For example, by running a predictive analysis, a product marketer doesn’t have to wonder whether sending a personalized message to a high-value prospect during a free trial will hurt or help conversion.
Personalization comes in handy when a prediction anticipates a customer’s needs ahead of said customer realizing they have any. Perhaps most famously, streaming services such as Netflix and Spotify and retailers such as Amazon make product suggestions to their customers based on a combination of previous choices and the choices of a similar cohort, increasing both the consumer experience and sales numbers.
The use for recommendations extends beyond the realm of streaming services and ecommerce sites. Chik-fil-A recently used Amplitude-built predictions to alter how menu items within their app appear to specific users based on past behaviors and purchases. This provided a way for customers to purchase what they want without having to scour the UI for it and minimize friction at the purchase stage.
Sometimes, the insights gleaned from predictions can inform product and service development. Netflix greenlit its wildly popular House of Cards show after predictions suggested a David Fincher-helmed, Kevin Spacey-led remake of the original British show would be a hit with viewers. Netflix was able to take a $100 million dollar risk and craft a product that was attractive to its existing customer base and new subscribers—all by banking on the reliability of their predictive models.
These reliable, data-driven predictions can help identify friction in the customer experience before the customer experiences them. If a product marketer noticed a significant usage drop-off within a few weeks of signing up for a music streaming service, the business could create scenarios with altered variables to identify the highest likelihood of disruption in the customer’s experience.
A streaming service looking to identify users at high risk for churn could use Amplitude Recommend to run predictions based on:
- The length of the cohort’s tenure
- The date of their last stream or download
- The historical frequency of downloads
- Comparisons to other cohorts who signed up around the same date
Once predictive analyses help to identify high-risk customers, businesses can target them with cost-effective and personalized retention efforts.
Predict and Prepare for Changes in Demand
More accurately forecasting product demand using predictive analytics can help minimize over or understocking and their associated costs. Any retailer in 2021 basing stocking decisions exclusively on 2020’s holiday season numbers would likely find themselves woefully understocked in the face of differing environmental circumstances. On the flip side of the coin, a retailer anticipating pre-pandemic sales figures may wind up overstocking their shelves. Instead of guesswork, businesses can instead use a model created by predictive analytical software to anticipate a likely scenario rooted in historical data and powered by computer calculations.
Furthermore, forecasting demand can create more realistic expectations for scheduling, reducing the chance that a particular business will be under or overstaffed during any given period. Predictive analytics help Walmart identify peak times at their pharmacies to maximize staffing efficiency and shorten prescription fill times.
While poorly anticipated customer demand can create supply and staffing issues for physical businesses, online retailers and SaaS businesses face bottlenecks of their own with overtaxed servers and digital infrastructure. Predictive analyses can be used by such companies to bolster their services and provide appropriate IT and customer resources ahead of a predicted surge in demand.
Plan for Potential Futures
Businesses with their eyes fixed firmly on past performance limit potential future growth opportunities. Company-wide adoption of predictive analytics can lead to happier and more engaged customers as well as a more attractive bottom line—benefits early adopters are already capitalizing on. While businesses can find an immediate lift from integrating predictive analytics into existing operations, the benefits aren’t limited to the here and now. Predictive analytics platforms like Amplitude certainly help predict probable outcomes for a singular product or cohort of customers, but its chief benefit lies in its ability to aid businesses in choosing the potential futures they see for themselves.
