The Scope of Big Data in Driving a Successful Marketing Campaign
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The Scope of Big Data in Driving a Successful Marketing Campaign

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Back in May 2014, a group of people were greeted with congratulatory e-mails from a popular image publishing website, for becoming a parent. The folks were really taken aback by this sweet gesture, however, there was one little problem. They didn’t have a baby! Though the website immediately apologized, the incident still serves as a classic example of horrific marketing “boo-boos”. Many consider marketing as an art nowadays, and it is, beyond doubt, a complicated piece of work, which requires craft as well as graft. Going back to the website’s blunder, it is evident that there was a terrible misinterpretation in the analytical data they received. Today, direct marketing heavily relies upon Big Data analytics to pin down individuals based on their Google queries, browsing history, credit card swipes and so on.

When data analytics go wrong, it can result in huge embarrassment or even worse. But, such instances can be fully averted with the right set of tools and a bit of preparation. Here are a few tips for enterprises to leverage Big Data in devising a smart marketing campaign.

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Never Rely on Broad Data

In the race to increase revenues, companies often ignore the simplest of things, either deliberately or accidentally. Instead of pinpointing their target customer base, companies tend to go for a broad spectrum analysis. The outcome of such broad analytics will always be complicated lists with no particular assumptions. Remember, in direct marketing, the list really matters.

Segmentation of the target audience based on their income brackets, geographical locations, average ages, and ethnicities, will provide enterprises with clearer insights on where to sell and where not. In the past, companies used to make educated guesses on customer demographics and preferences, but today’s modern marketer has a potent weapon in the form of Big Data to sort him out on segmentation.

Furthermore, segmentation can help enterprises identify various other factors which influence a customer’s buying decision like social media trends, store locations, and online shopping preferences. With these massive insights, enterprises can gather a much improved understanding of the target audience’s needs and wants. Isn’t it always better to locate your tree rather than chopping down the entire forest for it?

Real-Time Personalization

Modern marketing has moved into new channels like search, social media, email, mobile, and web, making the process more complex. A successful marketing campaign is one which responds to the customer in any channel at the right time. Timeliness has two-fold benefits for enterprises; it not only provides new customers, but helps retain existing ones as well. Through Big Data, marketers can spot prospective buyers and respond to their queries, across various channels of communication, with appropriate content in real-time.

With Big Data, providing personalized experience across an omni-channel environment is not a big deal by any means. By providing personalized content to prospective customers, enterprises can generate higher conversion rates, longer customer life cycles and greater average order values. Personalized content is not about knowing customers' names; it's also about foreseeing their needs like preferences, availability, and location, and using this information to make precise recommendations.

Make Use of Predictive Analytics

Touted as the next big thing in Big Data, predictive analytics involves the amalgamation of historical, real-time, and third-party data to forecast business trends. In short, predictive analytics will make enterprises pro-active instead of reacting to insights. "Big data, gobs of compute power, and modern tools are making predictive models more efficient, accurate and accessible to enterprises," wrote Forrester analysts Rowan Curran and Mike Gualtieri in the research paper entitled Big Data Predictive Analytics Solutions, Q2 2015.

Predictive analytics helps enterprises identify “high-value” customers based on a sample of existing data, from digital marketing tools, social networks and more. This enables enterprises to focus exclusively on such individuals while unresponsive customers can be removed from the campaign, thereby, keep a tight rein on the marketing expenditure. Eventually, enterprises will witness increase profitability in their business from accurate predictions, improved customer relationship management, and less number of mistakes. A survey has indicated that companies which have employed predictive analytics are already seeing 5-6 percent growth in their profit levels.

Intelligent Retargeting

Nowadays, enterprises are focusing more on retargeting prospective buyers, thanks to data analytics. Companies now provide attractive offers, discounts, and coupons to reconnect with a prospective buyer who browsed through but never purchased. Previously, enterprises conducted retargeting by placing cookies on the user’s device and then using it to track his browsing path. Well, that was a thing of the past. Now, enterprises can reach a prospective buyer even if he/she just navigated through your browser and hasn’t clicked on anything.

For example, a prospect navigates the site of an apparel retailer and reviews pages relating to the latest sneakers. With intelligent retargeting, the retailer has the capability to reconnect with the same individual and a display ad for sneakers or apparels while he/she is visiting other third-party sites. Besides, personalized messages can also be sent to the individual based on his other known attributes like shoe color preference, and so on. This method can be employed to retain existing customers as well.

Big Data can deliver remarkable marketing campaigns; however, the success of the campaign will depend upon the enterprise’s willingness to invest in technologies, processes, and its engagement with customers. Or else, it will turn out to be just another expense. 

See Also: TOP INFLUENCER MARKETING SOLUTION COMPANIES

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