Marketers have long leveraged data to understand and improve campaign effectiveness. Over the years, these efforts have gotten more advanced.
First, marketers looked to media mix modeling (MMM). This method of data-driven marketing allowed them to understand the long-term impact that a campaign had on sales, helping guide future campaigns and optimization efforts.
As marketing analytics evolved, marketers advanced to more complex attribution models which moved beyond aggregate data, and toward user-level interactions. These types of models, such as multi-touch attribution (MTA), allowed marketers to understand consumer paths to purchase.
As data-driven marketing has evolved, marketers now have the capability to leverage a new marketing tool: predictive analytics.
Consumers have more choice today than ever before. They are no longer confined to whatever their local store has in stock, they can order what they want, when they want. As a result, competition is fierce among vendors, retailers and service providers. Staying a step ahead of consumer trends and desires is the only way to remain competitive. Predictive analytics allows for this, assisting marketers in understanding consumer behaviors and trends, predicting future shifts, and planning their campaigns accordingly.
Predictive analytics is a form of analysis conducted by leveraging AI and machine learning to combine the insights generated through various datasets, algorithms and models to predict future behaviors. Just like MMM, this analysis looks at historical campaign data and trends along with past user behavior data provided by MTA, as well as additional transactional data. The insights derived from predictive analysis better equip marketers to determine what is likely to happen in the future, to inform effective marketing strategies.
There are three key types of models associated with predictive analytics:
Cluster Models: These algorithms are used for audience segmentation based on past brand engagement, past purchases and demographic data.
Propensity Models: These evaluate a consumer’s likelihood to do something, such as convert, act on an offer or disengage.
Recommendations Filtering: This model evaluates past purchase history to understand where there might be additional sales opportunities.
The information gathered by these models allows marketers to come up with more effective, dynamic media plans that will enhance consumer experience and ROI.
There are several ways that marketers can incorporate predictive analytics into campaigns to improve effectiveness and enhance marketing:
The insights give marketers an understanding of consumer interests based on past interactions. Marketers can segment audiences based on known interests and demographic information. This better equips marketers to serve individuals targeted messaging at the right time on the right device. Ultimately this helps break through constant ad noise — improving consumer experience and brand loyalty.
Marketers can determine where to focus ad spend based on the value the customer presents. Predictive data identifies the channels and times that warrant increased marketing spend and resources as well as where to reduce investments. For example, data for an ice cream brand would likely indicate that marketing efforts in northern temperate areas will yield better results in the summertime, allowing them to save the bulk of the ad spend for summer months.
Predictive analytics also allows marketers to qualify and prioritize leads. Insight into consumer behavior allows marketers to identify ideal audience segments that are closer to conversion. These analytics demonstrates how likely a consumer is to act, allowing marketing teams to devote more attention to those consumers and minimize wasted ad dollars on consumers that will not respond to marketing.
Finally, predictive analytics assist in customer retention efforts as marketers are able to better understand consumers’ needs. This helps with product and service offerings that complement consumer purchase history and interests and also sheds light on cross-sell and upsell opportunities that are not worth exploring.
With so much data available, marketers require advanced marketing tools and measurement capabilities in order to take advantage of predictive analytics.
To predict future trends, marketer need a vast quantity of historical data. This means that as consumers interact with campaigns, move down the sales funnel and ultimately convert, marketers must track each engagement. All of this data must then be correlated and synchronized in order to effectively develop consumer identities. Unified marketing measurement enables predictive analytics by collecting information on market trends, consumer behavior, and online and offline engagements in a centralized way.
Predictive analytics require the integration of multiple measurement models and massive amounts of data. In order to take full advantage of predictive analytics, marketers need an advanced marketing analytics software capable of distilling all of this data into digestible information from which actionable insights can be derived.
AI and machine learning are poised to play a large role in marketing optimization and are considered to be essential features as marketers select omnichannel marketing tools. These tools are essential because they enable marketers to act on insights in real-time, serving dynamic content automatically. Based on predictive analytics, machine learning and AI capabilities can offer dynamic pricing, automated sales forecasts, automated content creation and real-time personalization.
Marketers must always look for new methods to make their marketing campaigns more targeted and effective. Doing so improves marketing ROI, customer experience and customer retention. To stay competitive, today’s data driven marketers are leveraging innovations including predictive analytics through unified marketing measurement, marketing analytics software, AI and machine learning.
© 2019 Marketing Evolution, Inc.