Predictive Marketing. Modern Marketers will be Data Scientists.
Predictive Marketing is a marketing technique that involves using data analytics to determine which marketing strategies and actions have the highest probability of succeeding.
Predictive Marketing is a marketing technique that involves using data analytics to determine which marketing strategies and actions have the highest probability of succeeding. Usually predictive marketing is the practice of extracting information from existing customer datasets to determine a pattern and predict future outcomes and trends. The foundational principals of database marketing are that you can analyse and score a set of prospects based on their similarity to your actual customers. Now and the tools are becoming more and more accurate, the algorithms more sophisticated, and the results both automated and improving. The growth of predictive marketing is facilitated by:
- Massive Amounts of Data – purchase history, digital behaviour, and demographic data are now available from a multitude of sources.
- The Ubiquity of Access – access streaming data via virtually every tracked and connected resource is providing rich, real-time activity.
- The Simplicity of the Cloud – immense computing power via the cloud, new bid data database technologies with rich and sophisticated algorithms are helping drive innovation in the predictive marketing field.
Business intelligence is about using the data you hold within your company to report on historical trends and current business performance. Predictive analytics goes beyond these backward-facing views and uses the data you already hold in your business to look forwards and tell you what’s going to happen in the future. Predictive Marketing is not a perfect science, but it has the ability to dramatically increase trust, engagement, and conversion between buyers and sellers in the immediate future. And that goes for both marketing campaign results as well as the engagement with your sales team.
Data-driven tools have become more accessible to companies and marketers in recent years, eliminating some of the need for data scientists to interpret data for the practice. And, predictive marketing tools assist companies in using their data specifically to make data-based predictions about how their customers will make purchases, when they will make purchases, and how much they will spend based on their previous behaviours. As the tools continue to evolve, companies are able to utilise automated marketing systems that build models, deploy lead scores, and gain insights in real time.
Benefits of Predictive Marketing
There are several advantages of having such insight into consumers and their behaviours including improving customer engagement and increasing revenue, gaining more sophisticated segmentation of data, identifying campaigns and actions that are better targeted to customers, better utilising marketing budgets, and improving lead scoring.
As consumers become more tech-savvy and brand aware, they are also becoming more cognizant of their options. Whole new avenues, brands, and channels are now available, and traditional retail brands must be able to keep up with these changes. Predictive technology provides the tools necessary to make the leap into personalization, and fully join the next generation of marketing and consumer engagement.
Marketers can leverage predictive technology to:
- Increase revenue and marketing ROI. Consumers are more likely to respond to personalized messages and content specifically tailored to their buying preferences. With predictive technology in place, marketers turn past shopping trends into future buying experiences.
- Create return customers by building a culture of brand loyalty. Customers know when they’re being treated as an individual. When marketing teams invest the time and resources into creating 1-to-1, personalized marketing experiences, the return is brand-loyal, repeat customers who are satisfied with their customer experience.
- Positively affect the customer experience across the entire buying journey. Predictive solutions rely on a huge amount of customer data, which in turn influences a myriad of other marketing strategies, from automation solutions to detailed reporting and insights. Customers are happy to provide personal data in return for the personalized shopping experiences that predictive solutions can readily provide.
- Build true omnichannel customer journeys. Today’s consumers are no longer just engaging with an online brand via a website. Predictive analytics combine the various facets of web, email, and mobile to create holistic shopping experiences that are consistent no matter the channel or platform.
How Does Predictive Marketing Work?
Every brand interaction a consumer has, whether via an e-commerce website, social media, or in-store, is tracked and stored by a marketing platform. Predictive solutions then dig deep into these insights to determine logical next steps and predict actions that specific consumer profiles will take. These analytics inform and catalyse execution across various channels, including email, mobile, and web.
Predictive engines are instrumental in creating and executing personalized marketing strategies. To create truly personalized buying experiences for consumers, they need up-to-the-moment insights into individual audience members, and must then be able to generate and deliver the unique content accordingly. When consumers receive this custom content in near real time, it vastly increases the efficiency of marketing teams’ efforts.
Key Types of Predictive Marketing Solutions
There are multiple types of predictive solutions and use cases, including:
- Predictive Email Recommendations: Predictive solutions can deliver targeted email recommendations to consumers by analysing their past shopping history, email engagement rates, and the historical affinities of similar consumers. Other types of recommendation emails include abandoned cart campaigns, post-purchase campaigns, and abandoned browse campaigns.
- Predictive Web Recommendations: When a consumer visits an online website, predictive analytics can customize views, CTAs, and messaging to create a more personalized shopping or browsing experience. This can include placing certain recommended products above the fold, adding a sidebar with “frequently bought together” items, or even including a recommendation panel similar to what might be seen in an email.
- Predictive Mobile Recommendations: The best part about advanced predictive marketing solutions is that they are all fully developed and rendered for mobile use. Since a growing amount of online shopping is done via mobile devices, it’s important to evaluate predictive technologies that can accurately deliver predictive insights and consumer recommendations via mobile apps or browsers.
- Predictive SEO. Marketers who rely on traditional, reactive SEO often find themselves having to play catch-up. By the time they have all the data they need in order to make an informed decision, that data (and any associated trends) may already be obsolete. Predictive SEO, on the other hand, anticipates trends and other issues before they begin to have an impact, ensuring that sites are optimized to take full advantage of what’s to come.
- Predictive Advertising. As a subset of marketing, advertising can also benefit from a predictive marketing strategy. Predictive advertising uses the marketing data collected from various sources to segment potential clients into very specific interest groups and demographics. It identifies which advertisements would be most successful with which groups, delivering personalised ad content promoting products or services that the specific client might be inclined to buy — even if they have not bought something like it before.
- Predictive Research. Marketing analytics isn’t an exact science. Fortunately, it becomes more accurate as more data is acquired. With each of the marketing campaigns sent out with predictive intelligence, businesses can track what works and what doesn’t for individual customers. The information that they get back makes their overall marketing strategy even more powerful. When combined with effective cross-channel marketing, they can share data across channels and increase accuracy even further.
Examples of Predictive Marketing
How to Use Predictive Marketing Successfully
Now that you better understand what predictive technology is, and how it can impact a brand’s personalized marketing strategy, it’s time to look at how marketers can actually leverage a Predictive Recommendation Engine in day-to-day marketing strategy. Luckily, predictive solutions are for the most part designed to impact every aspect of marketing strategy, making it easy to find a way to incorporate predictive, personalized content into any strategy. Here are just a few ways to incorporate predictive technology in daily marketing operations:
- Personalised recommendations based on a consumer’s shopping preferences.
- Enhanced online shopping experiences that are constantly changing and updating.
- Real-time email campaigns optimized for maximum conversions.
- Customised incentives and promotions designed to increase revenue.
How does it relate to and integrate with Marketing Automation, Marketing Cloud and CRM platforms?
At the moment, predictive platforms, function as a separate technology that integrates with existing Marketing Automation and CRM systems. The integration is simple in concept – the predictive model extracts the data it needs to make its predictions from MA and CRM systems. It returns data in the form of a lead score for each contact, along with varying degrees of information about how the score was calculated. Some predictive platforms also provide a view into the underlying attributes or traits that lie underneath the score. And some predictive platforms take that even a step further and provide you the data itself via contact enrichment services (for your existing leads) or predictive list buying (for net new contacts and accounts).
However, I believe that predictive technology will be woven deeply into Marketing Automation and CRM systems, so that many of the features of these systems become intrinsically predictive. Many vendors are offering are already offering marketing cloud platform, through which marketing teams can build audience profiles by combining data from multiple avenues, from CRM to offline data. Feeding the system appropriate data and tracking behaviour over time builds a behavioural model that allows teams to make data-based decisions in real-time over the long term. Modern marketers, thanks to the technology, can be also data scientists.