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Using Predictive Analytics to Optimize Your Multichannel Marketing Campaigns

Multichannel marketing is a must in the digital age, but executing it effectively is next to impossible without a sophisticated predictive analytics tool.

In the wake of the digital revolution, the typical customer journey has become so complex that the Don Drapers of decades past would hardly recognize it. Especially in the retail space, the most effective marketing campaigns integrate numerous digital channels — email, paid social, paid search, display, ecommerce app push notifications, etc. — with classics like direct mail, out-of-home, and television.

In a perfect world, a retail marketer would be able to drive conversions (and ensure retention) by delivering each customer a highly personalized experience. These experiences would not only be consistent across various channels, but would also be tailored to the unique needs that arise at each stage of the purchasing journey. What’s more, much of this would be automated and fully scalable.

Unfortunately, for many brands, this is little more than a utopian vision of what their marketing should look like. Multichannel marketing is a must in today’s hyper-competitive retail landscape, but there continues to be a significant disconnect between how marketers want to conduct it and how they are actually able to conduct it. The good news: it is possible to bridge this disconnect with the right tools and the right approach to multichannel campaign optimization.

The Challenges of Multichannel Retail Marketing

More often than not, a brand’s multichannel struggles can be attributed to three common marketing challenges: data siloing, data distillation, and actionability.

Data Siloing

Many of the tools retail marketers use on a day-to-day basis are built around channel-level data. For instance, a site personalization tool provides marketers with information related to how a customer interacts with a brand’s website, but it doesn’t include any information related to the promotional emails the customer engages with or the purchases they make from a brick-and-mortar location.

Such channel-centric tools inevitably lead to the privileging of channel-centric metrics, which is why email marketers typically optimize around open rates and unsubscribe rates while brand site managers optimize around conversion rates and bounce rates. Each of these metrics has value, but only represents one piece of the “customer quilt.”

Rather than stitching together multiple channel-centric campaigns, marketers must take a more holistic approach to multichannel optimization, focusing not on individual channels but on individual customers — something that is all but impossible to achieve across a group of siloed marketing teams.

Data Distillation

As we mentioned at the top, the typical customer journey has become remarkably complex. A customer might engage with a retailer’s app, then visit the retailer’s website to look at a pair of pants, and finally visit a brick-and-mortar store to make a purchase. Capturing the nuances of this kind of journey is an essential part of multichannel marketing, but doing so involves an enormous amount of data.

Large, digitally-savvy retailers have literally millions of data points that collectively describe each customer’s engagement with the brand. Sifting through this mountain of data to figure out what really matters — and what doesn’t — is easier said than done, and many marketers end up struggling to see the forest (customer insights) for the trees (individual marketing touchpoints).

Actionability

Even if a brand is capable of gathering, aggregating, and analyzing millions upon millions of customer data points, it’s still incredibly difficult to translate those analyses into concrete marketing actions.

Many retailers have top-notch analytics and/or business intelligence (BI) teams that are quite adept at pulling select data from multiple tools and systems and running it through sophisticated predictive models. Where these teams tend to falter, however, is in keeping these models running continuously at scale and translating the data into insights that marketers can use to shape their actual campaigns. The importance of this actionability often gets lost between the BI and marketing phases of the multichannel optimization process.

Leveling Up with Predictive Analytics

The difficulty of overcoming these challenges has been a major factor in the recent explosion of fully integrated customer analytics platforms. A growing number of retailers are recognizing that specialized vendors are better equipped than they are to lead consistent, results-oriented multichannel marketing campaigns.

What’s more, a predictive analytics platform allows retail marketers to tailor their actions to precise inflection points in a customer’s journey, whether it’s the moment of acquisition (when the customer makes their first purchase), the period of cultivation (when brand loyalty is built), or the moment of reactivation (when the customer returns to the brand after having been lost to churn). A predictive analytics platform helps a brand decide not just what action to take, but also the ideal time to take it.

For instance, once a customer has transitioned into a period of cultivation, retail marketers are going to find the most success in actions designed to facilitate “one- to two-time buyer conversion” — that is, transforming the customer from someone who made a one-off purchase into a brand loyalist. Ideally, this will entail a coordinated multichannel approach that amplifies a brand’s “follow-up” messaging.

The Path to Better Customer Relationships

Ultimately, there’s no better way to build productive customer relationships than by orienting your marketing efforts not only around specific customers, but around specific inflection points in their purchasing journeys. And while personalized marketing comes with a handful of challenges, a sophisticated predictive analytics tool can help any retailer achieve the kind of true multichannel marketing optimization that will set them apart from their competition.

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