How to Use Predictive Analytics to Make Future-Forward Advertising Strategies
What if you could see the future?
We’re not talking about a sixth sense. In this article, we’ll cover how brands can use predictive analytics to discover insights about and plan their marketing for the future.
What is Predictive Analytics?
The data analytics experts at SAS define predictive analytics as:
“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 providing the best assessment of what will happen in the future.”
Predictive analytics help brands better identify potential customers and get the right ads in front of the right audience.
When you understand customers’ past buying habits, you can project their future buying habits and make decisions across the marketing mix (and even your entire business) based on those predictions.
What is the difference between predictive and prescriptive analytics?
Predictive analytics and prescriptive analytics can go hand in hand, but they’re not synonymous.
Predictive analytics answers the question, “What could happen?” Predictive analytics uses probability to provide estimates about the likelihood of a future outcome. It offers companies actionable insights based on data.
Prescriptive analytics, on the other hand, answers, “What should we do?” It takes the analysis one step further through optimization and simulation algorithms to suggest one or more possible courses of action courses. That said, prescriptive analysis is more complex than predictive analysis, so fewer brands currently use it in their business planning. We actively partner with our clients to optimize their media performance using simulations and scenario analysis models to inform their decision-making process and answer the question of what they should do in the future.
How to use predictive analytics to plan for the future
Brands can use predictive analytics techniques to identify opportunities to:
- Take advantage of current events
- Increase budget
- Expand to new marketing channels
- Reach new audiences
- Cross-sell and upsell to their most engaged customers
- Exceed past performance metrics
How can predictive analytics help you do all of those things?
Through the power of forecasting.
Forecasting uses time-series analysis to predict future outcomes, and time-series analysis observes patterns over time.
If you’re looking at sales performance and seeing an uptick in Q4 and other months, forecasting uses that data to determine what that sales performance will look like in the next year.
Then, based on those forecasting models, predictive analytics can help you decide on the best course of action for that upcoming year, including which channels to leverage, where to increase or reduce spend, which creative to run, and which customers to target.
Predictive analytics goes beyond traditional forecasting by using more complex technology, such as artificial intelligence (AI) and machine learning, and considering multiple inputs, trends, and fluctuations across your business. While traditional forecasting focuses on limited demand factors, predictive analytics is opportunity-oriented and provides various actionable insights.
At Tinuiti, we use predictive analytics and advanced forecasting models to help brands create the most effective marketing strategy possible.
“We use statistical techniques (data mining, predictive modeling, AI) to understand future outcomes, create deeper insights, recommendations, and what-if scenarios. We compliment the insights team, working on tests and analyses. We expand those capabilities to build predictive analytics and advanced forecasting models. We help brands understand what channels they should be investing in, how they should shift budgets (media mix), creating a high-level view of what channels are driving overall sales and ROI. It also helps create custom multi-channel attribution models to provide a more scientific view of how marketing efforts contribute to sales performance.”
— Annica Nesty, Senior Director of Marketing Sciences at Tinuiti
Predictive analytics case study
Tinuiti worked with an international ecommerce brand to develop a forecasting model that would predict a second-half of the year forecast and optimal media mix, along with a daily forecast to maximize sales and revenue.
To help our client achieve their goals, Tinuiti deployed Media Mix Modeling, a proprietary solution developed to help our client maximize their spend across their digital marketing channels.
This solution used time-series analytics to analyze the last two years of digital marketing and revenue data by market and tactic over this time period.
The ecommerce brand wanted to predict their optimal spend levels for a variety of their existing digital channels, including:
- Paid search
- Paid social
- Display
- Shopping (PLA)
- Affiliate
They also asked Tinuiti to add channels like organic search, direct traffic, referral traffic, and email traffic to the forecasting model.
Tinuiti used predictive analytics techniques to create forecasting models for future spend. These models showed how changes in spend across channels could impact revenue and sales.
The Results
Tinuiti’s forecasting model offered our client a detailed and accurate view of future outcomes. We used the output of the forecasting analysis as inputs to our optimization models across multiple scenarios to inform budget allocation and make prescriptive recommendations to our client of how and where to optimize their spend across digital marketing channels. This included shifting dollars away from social, which historically had been at or near 30%, to search (capturing current demand). Our model also recommended increased investment in affiliate marketing and a decrease in display spend to maximize revenue impact.
Through predictive and prescriptive analysis, our forecasting and optimization models showed the exact media spend needed (by channel) to achieve the targeted 30% YoY revenue goal. And because of the models’ success and accuracy, the client re-engaged Tinuiti to update and leverage the models to plan their US and International business for 2021.
Predictive analytics software and tools
Predictive analytics software and tools use a combination of statistical analysis, algorithms, AI, and machine learning to predict future outcomes based on historical data patterns and trends.
Mobius is Tinuiti’s proprietary marketing intelligence and media activation technology. Mobius unites all of your marketing data in one place, with three distinct products that help unify, optimize, and activate your performance marketing data.
All of Tinuiti’s marketing intelligence products are built on MobiusOS. This cloud-based data platform ingests and synthesizes all of your channel data at scale to provide marketing data, reporting, and insights. Mobius Apps offer data solutions tailored to a brand’s specific business needs, including media mix modeling, cross-channel attribution, and, of course, forecasting and other predictive analytics.
Learn more about how Tinuiti’s forecasting, analytics, and marketing intelligence technology can help close the loop on your marketing performance for good.
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