Great marketing is backed by data-driven actions that can predictably lead to results. Over time, marketers have introduced sophisticated ways to utilize data to achieve better outcomes. We can capture one of these advancements under the term “predictive marketing”. Before predictive marketing, marketers mostly used data from the past to drive decisions – now they can add future forecasts to improve results.
Collectively, the marketing industry has shifted toward predictive marketing as technological leaps in data science have allowed for more accurate future predictions. This ability to predict how marketing strategies are likely to work and how customers are likely to behave is allowing companies to manage ad spend and drive-up ROI.
Instead of reducing, focus your efforts on strategically allocating ad spend.
What is Predictive Marketing?
All marketing efforts are essentially predictive. Using whatever they can – from historical data to psychological understanding to simple gut feeling – marketers try to predict what campaign elements will be the most effective. Traditionally, this meant relying on decades of precedent, and only when campaigns were finished could their results be analyzed.
Predictive marketing improves this process by utilizing data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Utilizing AI and machine learning, these tools can rapidly analyze big data, identify patterns, and model likely future outcomes. In other words, marketers can predict how audiences will react based on how they’re already reacting. The use of AI in this context is infinitely faster than manually processing and understanding past data.
This makes a world of practical changes available. For example, cluster models are algorithms that can segment audiences based on past brand engagement, past purchases, or demographic information. This kind of segmentation means you can deliver the right message to the right people. This could be applied to messaging shown to streaming audiences or even used to segment email lists. In that case, marketers armed with this information can tailor emails closely to user needs. These more relevant emails see higher open rates, fewer unsubscribes, and better sales conversions.
This kind of focused messaging extends to content recommendations and ad targeting as well. Collaborative filtering, for example, utilizes past behavior and compares it to other customers with similar behavior, leading to predictions about what users may like. This is how streaming services like Netflix come up with recommendations – something that while now accepted and barely noticed, is revolutionary when it comes to user retention. Basically, it means that the service looks very different for each user - as if it were built specifically for them. Imagine a product that simply transforms itself into whatever a customer needs. In essence, that’s what collaborative filtering does.
Similarly, ad targeting efficiency is improved immensely by similar methodologies. The most famous example - the Facebook algorithm - uses sophisticated AI to match user tendencies, creating what are called “lookalike” audiences built from users who have taken certain actions in the past. In essence, the algorithm finds people who are very similar to users who have engaged, allowing marketers to quickly home in on people who are very likely to respond positively to their message. This kind of efficiency has been a game changer for businesses of all sizes, and not just on that platform.
Bottom line: predictive analytics marketing is the hyper-streamlined version of marketing, and smart use of data can reduce cost, drive up conversion rates, and improve customer loyalty, all of which have a tremendous impact on ROI.
Benefits of Predictive Analytics in Marketing
Predictive marketing can lead to immediate transformation in the form of efficiency and accurate messaging, but it also has long-term effects that can make businesses more sustainable and solvent.
Unlike traditional marketing data, which gives useful insight into how markets and customers behave in general, predictive analytics helps marketers gain a deep understanding of their audience segments. This means an opportunity to identify and anticipate customer needs and design strategies that deliver products that address those needs. There’s no guesswork here – the analytics can light a path otherwise well-darkened.
This means predictive analytics marketing helps you gain clarity on current and future customers, build stronger connections with current and potential customers, and uncover which channels your brand will have to most success on.
How Impactful is Predictive Analytics in Marketing?
Since predictive analytics is powered by an assortment of data analytics tools and business intelligence (statistical methodologies), the data you want to capture needs to be considered. Big data comes from a multitude of sources and provides more insight into target audiences; content consumed, at what time of day, device used, location, ads they engaged with, etc.
Over time, data analytics platforms get smarter. Not only are they analyzing historical data, but they are also assessing new datasets as they are being collected in real-time. Predictive analytics can help marketers identify where consumers are engaging to create an omnichannel approach and develop strategies with messaging that deeply resonates with their target audience, which can help improve conversions. As impactful as predictive analytics is you can take this data to the next level with data visualization dashboards. These dashboards are highly visual business intelligence tools that make it easier to ingest and communicate large sets of data to then action on.
Define Your Objective and Goals Prior to Implementation
Like any game-changing tool, predictive marketing isn’t a magic bullet. A few best practices will ensure that you’re applying this methodology in a way that will truly make a difference.
- Clarify your goal. Predictive marketing isn’t a goal or a technology. It’s simply a way of doing things. So don’t fall into the trap of making your goal to implement predictive marketing. Instead, identify a real business objective – ad cost reduction and conversion rate increase for example, that predictive analytics stands to help you accomplish.
- Clarify your process. Predictive marketing is a company-wide approach, rather than a plug-and-play software package or isolated ideology. Once you know what business objective, plan for how you’ll respond to new data and how your organization will utilize predictive analysis to reach your goals.
- Clarify your implementation. Once you know your goal and how your team and systems will position themselves to use predictive data, plan for how you will implement changes based on data. This could be as complicated as integrating an automated, AI-driven collaborative filter to customize a software product, or as simple as preparing copywriters to respond to ad tests that utilize predictive data.
Finally, understand that although there are potential short-term benefits to predictive analytics? marketing, its real power lies in what it can do for you long term. Think of predictive analytics as a marathon rather than a sprint and look at the methodology as an evolution in thinking that you will make better and better use of over time, to build a more stable and effective foundation. Thinking this way, it makes sense to find the right partner to team up with to implement smart predictive marketing strategies.
Learn More About Predictive Marketing Strategies
Predictive marketing is an evolving field but it’s quickly becoming synonymous with marketing in general. To find out more about big data and how you can effectively leverage predictive analytics, contact MNI today.
