The Realization of Personalized Marketing

Posted by Sarah Anderson on Thu, May 07, 2015

2015 could be the year that true personalization finally takes hold, but to achieve it, you’ll need to change the way you think about marketing.

Personalization at scale marketing

Customers expect to be treated as individuals. They require relevancy and prefer having a relationship with a company that keeps track of their preferences, purchases, and correspondence. Customers are ready for personalized marketing, and now, finally, more and more companies are, too.

In 2015, we’ll see rapid growth in the adoption of action-based customer understanding, as evidenced by the increases in marketing budgets for large enterprises. In a recent Gartner survey, just over half of companies surveyed are increasing their marketing budgets. This is because organizations have seen that they are able to personalize the customer experience in new ways and that the results from doing so are significant. Organizations leading this charge are delivering timely, relevant content or offers to their customers and driving increased sales and reduced attrition rates.

The recent ability to apply advanced analytics at scale across millions of customers has allowed marketers to finally turn a corner in personalization. Not long ago, segmentation was the best any company — or customer, for that matter — could hope for. Over time, those segments got smaller and more refined, but they were still segments, and they were still based on what a customer was and not what a customer did. And while some marketing is triggered by actions (sending a catalog after a purchase, for instance), much of it is not done with any detailed understanding of a customer. But now, thanks to what we call the “audit trail of the human experience,” behavior-based analytics has added the ability to focus on behavior over time, allowing marketers to get a long-term view of a customer — and giving them a strong leg up against the competition.  

This long-term view allows marketers to start their efforts with the customer instead of a product or offering. Instead of starting with a large list of customers and breaking it into segments, today’s techniques can now look at one customer and then find similar customers based on behavior and fill in the missing information by making educated guesses and then testing them. It’s not just a new marketing ploy — it’s turning everything we knew about marketing and segmentation on its head.

Take the example below. In the Customer Segments chart on top, you’ll see a segmentation pie chart from 2010. It labels customers based on general behavior analysis and demographics. Each category of shopper has a set of traits. For instance, Mall Maniacs are described as follows:

Ten percent of shoppers in America are not just consumers; they’re “try-sumers.” They like to try new products, stores and styles and connect and interact with preferred brands, and shopping brings enjoyment to them…Hispanic Mosaic USA types index strongly for this segment.

The Predictive Attributes charts below it, however, provides far more detailed and helpful information about a specific customer, and this descriptive information leads to predictive Signals and prescribed next actions. This customer rarely uses coupons (so don’t bother sending him any), but he does have a loyalty card and redeems the points from it. There’s a good chance he’ll be in the store for the next Madden release, but he may not be aware of the latest first-person shooter game. Given his history of impulse-buying video games, today’s solutions can pop up an alert on the cashier’s computer to let this customer know about the latest first-person shooter game and offer double rewards points for purchasing it with his loyalty card.


OS Personalization PieChart SignalCentral GP 2015 V1.3

Such actions can help companies increase customer spend. We’re also noticing that companies are now setting what they call “aspirational spend” or spend goals for their customers. Aspirational spend is the amount of money an analytics model identifies as the customer’s spend potential. It’s based on the frequency of visits, amount of money spent per visit, and types of goods purchased. This information helps marketers prioritize their efforts, improving their effectiveness and efficiency.

Reaching this level of information — and the ability to act on it — has taken years, but companies are now seeing that not only is this possible, but it’s possible at scale. Companies with over 90 million customers are using data science in this way to deliver prescribed actions at the precise moment of contact with a customer, and they’re seeing measurable results.

As more companies find success with these new personalization techniques, other companies are stepping up their efforts to gain the same capabilities and capture more wallet share. We expect these budgets to continue to grow for the next decade as we continue to hone the analytics and execute increasingly more precise predictions and prescriptions.

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Sarah Anderson is the senior manager of marketing communications at Opera Solutions. 

This post originally published at

Topics: Analytics, Marketing