The Future is Now: Predictive Analytics Marketing

Personalizing, even individualizing, customer experiences with predictive analytics is now the de facto marketing strategy in the race to stay competitive. However, predictive analytics tools are becoming more sophisticated every day. Machine learning and AI are making the science of using data to predict customer behavior more accurate and far-reaching than ever before. A trend that is only going to continue, at an accelerated pace. Marketers who do not keep up with the advances in predictive analytics are going to have a difficult time playing catch up.

Decoding Customer Buying Behavior

Predictive analytics, aided by machine learning and AI, helps marketers anticipate the buying habits of consumers by decoding past and present buying habits, thus giving greater insight into the customer, their needs and wants, and the ability to project future behavior. For example, if the goal is to increase wallet share, decoding a customer’s current behavior and buying habits can put you in the position to serve up messages based on that behavior and through machine learning perfect those messages over time, to the point where the customer believes you are individualizing the messages – “time to buy more toilet paper?”

Creating A Test and Learn Engine

Predictive analytics coupled with machine learning creates a test and learn engine that requires a fundamental re-architecting of a company’s marketing analytics processes. The definition of predictive analytics is as follows, “The practice of extracting information from existing data sets to determine patterns and predict outcomes and trends.” The goal then is to create a learning ecosystem that connects insights to outcomes as part of a continuous, self-improving cycle. It requires a commitment to iteration, not an absolute success or failure mind-set.

Anytime you use the past to predict what is going to happen tomorrow or the next day, you need to adjust your success metrics. Learning is success. Continuous learning brings greater understanding and great understanding leads to more and more success, over time.

Successfully Using Predictive Analytics Requires Focused Thinking and Modeling  

The insights gained from the use of predictive analytics are only as good as the thinking and modeling that the analysis is based on. Like all data analysis this adage still holds – garbage in, garbage out.

Successful use of predictive analytics demands several actions from marketing.

  1. Be clear about your business objectives
  2. Determine your personalization strategy or approach
  3. Make a clear decision as to what it is you need to know about the customer
  4. Define your model by integrating data discovery and decision making.
    1. Data discovery is about sourcing and combining traditional and behavioral data to uncover meaningful insights about customers (such as their preferences, interests, and needs.)
    2. Automated decision makingis driven by the advanced analytics models that produce propensity scores for each customer or prospect. These scores define the probability of an individual responding to a specific offer or engaging with specific content.
  5. Content distribution is the final step.A good system will use customer and prospect scores to trigger personalized ads and landing pages, and to distribute specific content, offers, or experiences across channels.

To find out how to implement personalization at scale using predictive analytics download our latest eBook, How to Implement Personalization Across All Customer Touchpoints.

About VeraCentra: Marketers everywhere want to use data to implement more modern customer engagement strategies. But there can be many obstacles standing in the way of success. That’s where we come in. VeraCentra provides easy-access Customer Data Hubs. We represent best of breed Cross Channel Engagement Platforms (so marketers get the right fit) and offer the Marketing and Data Services that guarantee speed to value and quick win ROI from technology investments. We deliver these solutions with an unmatched wholehearted approach bringing personalized support, care, and service to every client. That’s why many of our client relationships span more than a decade.

 

Machine Learning and Real-Time Customer Interactions

Customers are interacting with us in real-time and we need to do the same.  We know we need to respond faster, but accessing and analyzing data across channels is cumbersome and painfully slow. And, it just doesn’t cut it in real-time world our customers live in. And the workarounds we put in place to compensate for our silo data are too slow. They make it impossible to deliver the real-time customer interactions needed to drive customer loyalty.

Machine Learning Picks up Where Basic Analytics Fall Short

Making full use of our data is paramount in today’s competitive environment. It is difficult to do so if we are having to create models offline and manually consolidating multiple centers of truth.

Additionally, we need to develop insights from data assets faster and with greater precision, which in turn enables us to develop competitive marketing strategies designed to drive customer loyalty.

With limited time and resources, legacy systems, and multiple partners and channels to contend with machine learning picks up where basic analytics fall short.

How to get started

Delivering relevance at every touch point, in real-time may be too big to bite off all at once. Take a phased approach. Start with getting your data in order. Inventory it and gather it in one central repository for a complete view of your customers.

You need to invest in a Customer Hub to bring order to data organization, cleanliness, and accessibility. Centralizing marketing data across the organization will create the one center of truth you need, pull your teams together, and will do so without having to consolidate partners.

Invest in the right technology to deliver relevance at scale, across all channels and devices. Machine learning can help predict the next step in the customers journey no matter which channel and device — it connects the dots online and offline… from web browsing to face to face interactions.

The Future

Technology, and with it machine learning, is growing and evolving quickly. Creating competitive advantage now relies on having the capability to consolidate our data into one center of truth and leveraging machine learning tools to deliver the real-time customer interactions required to stay ahead.

To find out more about how to advance your real-time capabilities with machine learning download our newly published eBook; The Value of Context, Cross-Channel, and Real-Time Capabilities: Advancing Marketing’s Productivity.

About VeraCentra: Marketers everywhere want to use data to implement more modern customer engagement strategies. But there can be many obstacles standing in the way of success. That’s where we come in. VeraCentra provides easy-access Customer Data Hubs. We represent best of breed Cross Channel Engagement Platforms (so marketers get the right fit) and offer the Marketing and Data Services that guarantee speed to value and quick win ROI from technology investments. We deliver these solutions with an unmatched wholehearted approach bringing personalized support, care, and service to every client. That’s why many of our client relationships span more than a decade.

 

 

Synchronizing Digital and Offline Customer Data

Recent TDWI Research reports that 70% of marketers have “suboptimal ability to integrate online and offline customer data”. I would expect to see this percentage plummet over the next few years. Why?  As more marketers adopt omni-channel approaches, synchronizing digital and offline data – or “onboarding” as it is commonly called – becomes the central component for personalization efforts and achieving the goal of creating a single customer view.  In Winterberry Group’s 2016 The State of Consumer Data Onboarding the US onboarding market is forecast to reach 1 billion by 2020.  This begs the question, with rapid marketer adoption, how much of a competitive advantage goes to those brands that adopt early?

New Technologies Are Advancing Customer Identity Resolution

In the past it was near impossible to get a single customer view, but technologies are advancing and identity resolution techniques are getting better and better.  In the simplest terms, identity resolution is a data management process which searches and analyzes an “identity” between disparate data sets and databases to find a match and/or resolve identities.  But what’s more important is what identity resolution does for customer engagement marketing.  It facilitates our ability to create a single customer view and profile.  It allows us to improve measurement and attribution efforts. It enables us to understand cross channel customer journeys, and allows us to deliver greater personalization at scale. Now that’s a big, big breakthrough!

To learn more about  customer data integration, download VeraCentra’s e-Book;
Five Ways to Kick Customer Engagement into High Gear.

 About VeraCentra: Marketers everywhere want to use data to implement more modern customer engagement strategies. But there can be many obstacles standing in the way of success. That’s where we come in. VeraCentra provides easy-access Customer Data Hubs. We represent best of breed Cross Channel Engagement Platforms (so marketers get the right fit) and offer the Technical and Marketing Services that guarantee speed to value and quick win ROI from technology investments. We deliver these solutions with an unmatched wholehearted approach bringing personalized support, care, and service to every client. That’s why many of our client relationships span more than a decade.  We’ve made it our mission to help marketers advance and thrive.  Learn more about us at veracentra.com.