Data is everywhere. Whether it’s web traffic, buyer behavior, or social media engagement, there are numbers for everything. In the Data Never Sleeps report, DOMO estimates that 1.7MB of data will be generated for every person per second in 2020. Many popular analytics platforms help to process and understand the massive amounts of data being generated; however, not all analytics are the same. While most analytics report and assess what has already occurred, predictive analytics uses data from the past to predict the future.
What Is Predictive Analytics?
According to SAS, predictive analytics is “the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to provide the best assessment of what will happen in the future.” Another definition, focusing on a marketing perspective, states that, “predictive marketing relies on data science to come up with accurate predictions for making marketing decisions and to plan marketing strategies that are most likely to succeed in the future.”
Like all analytics, predictive analytics is only as good as the data sets it’s based on — if the raw data isn’t available, even the best algorithms won’t be effective. Below are six ways predictive analytics can be a critical tool for accelerating growth in companies that have a robust data set.
6 Ways Predictive Analytics Can Accelerate Growth
Accurate prediction of customer behavior
Before analytics, predicting customer behavior was often a guessing game. Marketers relied primarily on their intuition to predict how a given customer or group of customers would respond to an action. For example, would a 10% discount encourage new customers to buy the product, or would it merely reduce profits among existing customers? The only way to accurately answer a question like that was to experiment.
Predictive analytics gives marketers better tools than intuition-based guessing or random experimentation. By applying advanced algorithms to massive amounts of data on past consumer behavior, they can predict how customers will respond to various actions and even recommend custom actions for target consumer groups.
Content may be king in the digital world, but content fatigue is a real challenge that marketers must overcome to be effective. In today’s saturated platforms, it’s not enough to simply produce content; it’s important to be able to target potential customers with content that best matches their unique needs, pain points, and interests.
Predictive analytics makes it possible to target content distribution and deliver the right content to the right potential consumer. Not only that, but analytics can help determine the optimal frequency and even the right time of day for email marketing delivery and social media posts based on the past responses of similar customers. They can also create recommendations for custom frequency and timing for each lead or customer. Unlike traditional segmentation approaches that group similar customers, predictive analytics combined with marketing automation can help companies develop the personalization holy grail: a segment of one — each customer getting unique treatment perfectly tailored to their preferences.
Marketers often focus on generating new leads and overlook the enormous potential in their low-hanging fruit — the customers who are already on their sites. Selling new products and services to existing customers, known as cross-selling or upselling, is another area in which predictive analytics can drive growth. Predictive analytics uses large data sets to assess what products a given customer might like based on his or her past purchases or interactions on the same or similar sites, along with the previous purchases of similar customers. Websites can then proactively display those items in intelligent recommendations. Amazon piloted this strategy for upselling and cross-selling with its “you might also like” feature. The approach has been so effective that most e-commerce websites and platforms include it today.
This strategy is also commonly used by native advertising platforms and content recommendation systems such as Netflix and Amazon. Past online behavior is used to create interest profiles for internet users, which enables the ad platform to serve targeted ads based on the specific user’s demographics, location, and interest data in real-time. These smart content recommendations tend to achieve higher clickthrough rates since the content that is shown is more relevant, useful, and attractive to the user. Advertisers and marketers can use native ad platforms with advanced interest targeting to attract new audiences, retarget to existing audiences, and drive engagement and conversions.
Not all leads and customers are created equal. One lead may never convert, and another may purchase a product after one email blast. One customer may buy an inexpensive item and never return to the site, whereas others will become loyal returning customers or brand ambassadors.
Predictive analytics can help with lead and customer segmentation and scoring, evaluating where each lead is in the purchasing funnel, and predicting the lifetime value of a specific lead or customer to the company. Predictive analytics can also assess the predicted churn rate — how many people are expected to drop out of the sales funnel or cancel their subscriptions to services in order to create accurate long-term sales predictions.
Using predictive analytics can also help optimize campaign placement and buying. This is already widely used in programmatic ad buying and other advanced approaches to digital ads. As the technology matures, it will get even more powerful, able to put the right ad in front of the right customer at the right time, anywhere on the internet.
Dynamic pricing is a type of predictive analytics that allows e-commerce sites to adjust product prices in real-time to increase the likelihood of purchase. Dynamic pricing platforms analyze a variety of variables, including market demand, consumer behavior, competition prices, available inventories, financial targets, and sometimes even the weather to predict the highest price at which a given customer is likely to buy.
This can be a critical tool for nurturing leads and ensuring they don’t move to a competitor due to a small price difference. It can also ensure that brands don’t leave money on the table by underpricing products and services when consumers are likely to pay a higher price — such as when a competitor runs out of stock.
Matching influencers with products
Influencer marketing is extremely popular and can provide enormous opportunities. However, many marketers make the mistake of thinking that bigger is better — the more followers an influencer has, the more effective they’ll be at promoting a product. This is not always true. If a product targets a niche market, marketers can often achieve better results working with an influencer in that specific field than with general influencers with a bigger following. Even if a niche influencer has a following of only a few thousand, or even a few hundred, those followers are more likely to convert to higher quality leads than the general population. Moreover, smaller niche influencers charge less for their endorsements, giving marketers a better return on their investment than endorsements from influencers with larger followings.
Predictive analytics can help marketers accurately identify where their potential customers are online and which influencers they follow. This is vital information for selecting and partnering with the right influencers, whether they are mega influencers or micro-influencers for a specific brand or product.
No More Guesswork — Only Growth
Predictive analytics is a critical tool that takes much of the guesswork out of marketing. There are numerous ways that predictive analytics can be used by marketers today to boost performance, attract customers, and accelerate growth. By creating predictions based on massive amounts of data, marketers can effectively anticipate the likely behaviors of their audiences in the future, which is vital to strategizing and planning digital marketing campaigns.
One thing is clear, however — using predictive analytics requires having access to good data. As advanced analytics techniques begin to play a bigger and bigger role in planning and executing campaigns, brands will need to realign internal resources to make sure they have the people and tools to get the right data at the right time. It may take an investment in a data science program or better collection tools, but the results will be well worth it.