Predictive Marketing Can Help Retail Harness the Holiday Hustle

As consumers cross items off of the wish lists of loved ones, businesses across the country hope to boost sales before they close the books on the calendar year. To that end, marketers must recognize their role as revenue generators and focus their efforts on targeted outreach during the final leg of the holiday shopping season.

But the challenge is that consumers' holiday shopping behaviors are evolving. This year, Black Friday sales dropped 10 percent, an alarming trend for retailers preparing for the conclusion of the holiday shopping season. This decrease in consumer participation is especially concerning when you consider that marketers spend millions of dollars on holiday advertising—like Target, which spent $61.6 million in the run-up to 2014’s Black Friday. 

The bright side is that for consumers, holiday shopping has become a marathon rather than a sprint. Shoppers now embrace end-of-year sales through Christmas Eve, giving companies more time to target their marketing efforts and adjust their strategy. 

To drive the greatest return on investment from seasonal advertising and encourage consumers to buy now, marketers must clearly understand their customers' holiday buying behaviors and preferences. Rather than leaving the choice of holiday purchases in the hands of fate, retail marketers can take a page from successful B2B companies and use predictive models to help their companies navigate the holiday shopping season.

Understand Your Data

Most CMOs understand that having access to large quantities of data allows their team to benefit from a myriad of insights into buyer propensity and behavior. This is especially true during the holiday season, when consumers are frequently perusing the Web for holiday gift ideas, leaving a trail of data behind.

In 2014, 78 percent of consumers used the Internet for holiday research. This level of activity results in a daunting amount of data to collect, organize, and analyze.

To quickly make sense of the overwhelming amount of data at their fingertips, it's helpful to understand how to identify and analyze different types of data. Marketing teams can use Big Data to forecast performance of specific stores and predict who is most likely to make what purchases, and at what time. How? By leveraging three key types of data:

• Intent data: Intent data takes into account factors like Web site searches and visits.
• Fit data: Fit data includes descriptors like the potential buyer's age, demographic, and income level.
• Behavior data: Behavior data measures how a potential buyer has interacted with a particular brand's content, such as emails or online catalogs.

By analyzing these three types of data together, marketers can paint a complete picture of buyer propensity at every point of the purchasing funnel, which then allows teams to use personalized outreach to target potential customers at the exact moment when they’re most likely to buy.

Be Proactive

Being a modern marketer is no longer enough. Instead, marketers should strive to become predictive marketers. By using data to take a proactive approach to sales optimization, predictive marketers enable their teams to anticipate customer behavior, putting their outreach one step ahead of consumer action.

With a better understanding of their customers' buying habits and preferences, marketers can anticipate needs and increase response time, giving shoppers a better overall experience. We see this with Amazon predictive shipping, which employs data to anticipate which items are most likely to be purchased in particular areas. Shipping hubs are stocked with goods that local consumers are likely to purchase, greatly reducing delivery time.

In addition to enhancing customer service, using data to guide proactive marketing efforts allows teams to prioritize outreach to certain segments of their target demographic. For example, intent data can help determine which potential customers will wait until Christmas Eve to make their big gift purchases—and in the meantime marketers can focus efforts on procrastination-averse shoppers.

Evaluate Your Progress and Adjust Accordingly

Understanding data and allowing data-driven insights to guide marketing decisions is only valuable if you take the time to evaluate your success. If your marketing team's efforts aren't resulting in the number of desired purchases, it's time to take a step back and problem-solve. Consider hiring an in-house data scientist or investing in predictive marketing applications with a user-friendly dashboard.

The success of your data-driven efforts should be evaluated on a regular basis, and the start of the new year provides the perfect excuse for marketers to take a look at their data capabilities and the success they have (or have not) provided for their team.

In order to drive true ROI for their companies this holiday shopping season, CMOs across all industries have to move away from employing strictly tried-and-true marketing practices and prioritize predictive data analytics. We have seen the market shift away from the once "necessary" Black Friday shopping experience, so it is time to reevaluate the methods CMOs have been employing for so long. Move further into the world of Big Data and real-time analytics and channel it into interactions that deliver true impact—precise targeting, personalization, and meaningful customer engagement. We are living in the age of the customer, and it's up to B2B and B2C marketers to use the right tools and techniques to effectively serve them the right information in order to drive their companies' revenue growth.

Nipul Chokshi is the head of product marketing at Lattice Engines and is responsible for messaging, product positioning, and sales enablement. Prior to Lattice, Nipul built and ran the solutions marketing and sales enablement functions at Yammer, which was acquired by Microsoft in 2012. Before Yammer, Nipul led product management/marketing at Marketo and Merced Systems, which was acquired by NICE Systems. 

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