Digital campaigns should drive sales – it’s pragmatic in theory, yet problematic in practice. That’s why innovative marketers with aggressive customer acquisition growth goals are incorporating predictive analytics into their digital marketing toolkit.
Predictive analytics, more specifically predictive modeling, is a tried-and-true offline strategy that has been successfully utilized across multiple industries to predict outcomes (a credit score, for example). Today, this secret weapon in the digital marketing landscape is pivotal in finding new prospective customers who are a great fit for your product or brand, but who might not be showing behavioral signals.
Three Benefits of Adding Predictive Analytics to Your Marketing Strategy
- Identify and target high-value consumers you are missing today
- Precision targeting at scale
- Customized to meet your specific goals
How Predictive Analytics Expands Your Customer Base
Using a sample of your existing customers, predictive analytics engines build an audience by identifying your best prospects — consumers likely to buy, or be a great fit for, your brand or product. The predictive model identifies consumers based on who they are rather than exclusively focusing on a recent behavioral signal, thus exponentially expanding your pool of potential prospects.
With traditional methods of targeting (such as demos or segments), scale is often sacrificed as campaigns become more focused on precision. However, predictive modeling evaluates all available data to classify the relative importance of each data point in identifying your target audience. The resulting formula pinpoints which consumers to target, allowing you to capitalize on both scale and precision. Fueled by the right data, predictive modeling is capable of discovering large reserves of high-value consumers, reaching far beyond what a company can attain with behavioral, contextual or demo targeting.
Tailor Made to Your Goals — the Right Data is Critical
Depending on your marketing objectives, different types of data fuel predictive analytics in different ways. Since predictive models are looking for micro patterns in the data, the more of the right data that’s fed to the engine, the more reliable and effective the output will be. Moreover, because data is leveraged to train the model, it’s imperative that you know what customer type or customer behavior you’re attempting to replicate when you begin.
Knowing the customer type or behavior you want to replicate, the predictive modeling starts with a sample of the consumers you want more of, otherwise known as a seed. The predictive model is then able to create an audience that is tailor made to your business and objectives.
If your objective is to reach consumers with a high propensity to convert, the seed would be a sample of recent converters. If your goal is new customer acquisition, the seed would be a sample of your most valuable customers. In this case, third-party consumer data providing extensive profile insights is the right data for creating the predictive model to understand all the important characteristics that make up your best customers and fuel the analytics for building an audience of best prospects.
Predictive Analytics in Action
Here’s an example of a TruSignal client who exceeded growth goals by implementing predictive analytics as part of their marketing strategy. A home warranty company experienced a monthly sales lift of more than 200% with a custom, High-Value Prospecting solution. Based on the company’s best customers, TruSignal created an audience of new prospects to target through digital channels. Read more about it in this case study.
Your New Go-To Tool for Marketing Success
By providing a way to scale digital campaigns while still adhering to the discipline of a targeted approach, predictive analytics will continue to gain momentum as more marketers aim to exploit digital channels for sales growth. Expand your brand’s message to new customers and create demand efficiently, at scale, and successfully with help from TruSignal.