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Learn from the ‘right’ data to optimize your marketing

It’s Tuesday morning and you’re running late. The good news is, you’ve been commuting for five years to your job so you know all the options for getting there fast. You quickly decide to drive a slightly longer route that you know usually doesn’t have a lot of traffic at this time of day. Sure enough, you arrive at your office just in time for your first meeting.

You made the right decision on how to get to work because you’ve learned from experience which routes to take on certain days of the week and at what hours of the day. That kind of thinking – discovering patterns in past events to predict future outcomes – is called predictive analytics. It’s what computers in the era of Big Data are doing every day and it’s revolutionizing the way we buy health insurance, find entertainment, and even predict presidential elections.

For digital marketers, predictive analytics promise to change forever the way brands target and interact with customers. Until recently, digital marketing data has mostly looked backwards. Entire campaigns were built around prime time TV ratings, clicks and, more recently, tweets. At a time when marketing to customers is customer-led and no longer campaign-led, predictive analytics help brands to design programs that are forward-looking and highly-targeted.

More data, fresh insights

This level of targeting is made possible by cloud-based data analysis software that just about anyone can leverage – not just highly specialized experts. Marketers can now easily experiment with sophisticated out-of-the-box predictive models, thereby gaining better insights faster through a more holistic analysis of various cross-channel data about user profiles and interactions.

With this new data in hand, marketers can collect and analyze the right measures (e.g., reach, conversion, loyalty) and user attributes (e.g., demographics, location, interests) that are relevant to their objectives. They can now focus their analysis to learn insights about what works and what doesn’t. For instance, with predictive analytics marketers today can:

  • Reduce customer attrition or churn by discovering at-risk customers based on their past activities and profile data. For example, marketers can identify once-loyal customers who have stopped buying from the company, but who are still actively searching and browsing online for similar products. The reasonable assumption here is that those customers are buying their products elsewhere. To lure these customers back, marketers can use coupons or other promotional incentives.
  • Identify cross-selling and upselling opportunities. E-commerce sites like Amazon.com are savvy about recommending additional products based a consumer’s purchase history. These recommendations are often based on the predictive analysis of the online purchase behavior of thousands or millions of consumers.
  • Better direct their targeted marketing. Here, marketers can segment customers into clusters and identify which ones, for example, are likely to be interested in upgrading their mobile phones.

Key considerations

Some Responsys customers are already using predictive analytics to gain better insights from historical data to increase the performance of their cross-channel marketing programs.

For example, an online retailer is using predictive analytics software, in combination with the Responsys Interact Marketing Cloud, to analyze user profile data and behaviors across email, e-commerce and other websites to generate propensity scores. These propensity scores are then used to identify customers who are more likely to purchase a product, and the marketing channels that are most likely to get their attention. A person with a high propensity score, for example, may receive a coupon via email, while a person with a lower propensity score may get a display ad to introduce a new family of products.

In closing, predictive analytics is fast becoming an integral part of every company’s digital marketing program. Here are a few points to keep in mind when incorporating predictive analytics into your digital marketing strategy:

  • Predictive analytics requires the “right” data and models to be able to provide the “right” insights. You should ensure your data is as relevant, clean, fresh and noise-free as possible, and also ask your in-house expert or software vendor to help you choose the right predictive models.
  • Start small and gain confidence from your executives by showing real results and benefits of predictive analytics. Then slowly expand the scope of your data, marketing programs and predictive models to discover bigger and better insights.
  • Use predictive analytics to develop better marketing strategies and use traditional analytics and business intelligence to validate the effectiveness of your strategies.

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