Segmentación en tiempo real

Crack the Customer Code With Real-Time Segmentation and Predictive Analytics

by Pelin Thorogood |


As retailers and marketing teams head into the summer months in the northern hemisphere, it's time to take a breath and ask, What have we learned about buyers and multichannel marketing in the first half of 2013?

One thing is front and center: Customer relevancy remains the Holy Grail of marketing. A study recently completed by marketing provider Lyris and the Economist Intelligence Unit found personalization was a top strategy of marketing executives surveyed (Mind the Digital Marketing Gap). But almost half of those marketers said lack of capacity to analyze "big data" is their biggest hurdle, behind budget limitations, when attempting to gain insight about customers. That could explain another finding: 70% of buyers surveyed said "attempts at personalization are superficial."

The Lyris/EIU survey underscores some big concerns about customers and how well marketers know them. Yes, buyers leave a trail of breadcrumbs behind them, in the form of data, with every online click or in-store interaction. But mountains of data don't solve a core problem. How do you use that real-time data for true insights into buyer behaviors? Can we identify what causes one consumer to buy and another to leave your site or Facebook page and never return? Can we apply predictive analytics to optimize those insights over time?

The answer to all three of those questions is, of course, "yes!" Marketers need to develop customer personas not just in broad categories but also in highly targeted groups by using rich, multichannel data sets to micro-segment the buyers they hope to reach. The goal: highly targeted and relevant information that delivers the right offers at the right time to the right buyer.

Where does the data come from?

What kind of data supports customer micro-segmentation and predictive analytics as a basis for effectively targeted, personalized campaigns?

Marketers have access to a tremendous amount of information about consumers' search and engagement patterns, demographics, and even social and interest graphs, along with campaign responses.

The content and news feeds we receive on many company websites are based on what we are searching for today, as well as our previous viewing, sharing, and purchase patterns. Search results and online ads are increasingly personalized, relying on information to "increase relevance" for the consumer—and to drive conversions. Social and mobile apps with their opt-in user bases further deliver a wealth of demographic, behavioral, and even location-based data. In addition, we now have the means to identify and target consumers who are not just likely buyers, but likely to spread the word on the value of their purchases to other potential buyers. Users with high virality share their experiences through social requests, Facebook wall posts, and other channels, driving in new users at no incremental cost.

Let's look at it in baseball terms with Tim Zue, the director of business development for the Red Sox baseball franchise and a bona fide data scientist himself.

Zue told Anametrix that the sports franchise makes smart business decisions based on data analytics from surveys, the 37,000-38,000 tickets sold per game, and fan behavior at the park.

He and his team are looking at ways to target fans by behavior and interests in addition to demographics. Family Man Phil, he says, wants the best experience for his family, not necessarily the most expensive seats, while Business Man Bob may be looking for the best seats for weekday evening games.

The goal is to promote loyalty across all customer segments.

Marketing analytics enables right-time marketing

To increase campaign relevancy and effectiveness, you need to discover the direct and indirect relationships among traditional and digital touchpoints.

By analyzing those relationships, marketers can target the most valuable customers for specific products or categories in terms of best potential for purchase, lifetime value, retention, or highest viral factor.

Here are the three key steps...

1. Segment and analyze

Capture real-time, granular website data on response, engagement, and conversion rates from each social and paid campaign. Segment and analyze those data points in real time to identify leading indicators of success. Rapidly assess campaign effectiveness and optimize ad messages, content marketing, or social media tactics on the fly.

2. Contextualize and enrich

Contextualize and enrich real-time data with sales, revenue, and CRM actuals, as well as reference data sources to validate and improve the accuracy of the optimization models. Discover additional relationships that complete buyer segment profiles to improve targeting.

3. Predict and prescribe

Combine real-time and historical data to create an enhanced foundation for running predictive models that reveal the likelihood of future outcomes. And remember, you don't just forecast what might happen. You want the power to create the future you want. That requires access to highly granular data identifying what campaigns and promotions lead to purchases of specific products by buyer segments in a given season.

Correlated by product, location, purchase time, buyer segments or other factors, that data reveals the levers that the marketing team can pivot around to adjust the marketing mix and optimize the likelihood of a desired outcome.

* * *

Yes, marketing analytics should deliver much more than dashboards, which provide only a fleeting view into the state of your marketing. Yes, you need analytics to rapidly diagnose what works and what doesn't, and to enable accurate forecasting and optimization based on these findings.

And,, yes, companies might need to upgrade their underlying analytics capabilities—in the form of people, process, and platform—to engage across these steps. But the efforts will be more than repaid. They will enable the marketer to get closer to that illusive goal of true one-to-one marketing—and achieve relevance in the eye of the consumer.

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Posted in: curva de demanda,demanda,demanda de software,segmentación de clientes