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‘Nomophobia’ is new reality for retailers


Entering 2015, it is a good time for retailers to reflect on the changing state of digital advertising and how the most effective ads are increasingly shaped by smart optimization technologies.One essential fact is driving this broad industry trend: consumers are becoming ever more reliant on their smartphones, tablets and wearable devices. This growing dependence on mobile devices has even ushered in a new term for “nomophobia,” which is the fear of having no mobile contact.

Whether we are nomophobes or not, few of us can stand to be without our precious phones for very long anymore. In other words, we’re rarely more than an arm’s length away from a personalized, location-aware advertising platform. That’s good news for marketers who can master how to make the most of this constant connectivity.

The top marketers are adopting a range of cutting-edge technologies to tap into this growing compulsion for all things mobile. Such technologies include machine learning; big data analytics; persona segmentation; and retargeting. Let’s briefly examine each of these areas as a preview of what’s in store for the New Year.

Machine Learning

Machine learning technologies are a subset of artificial intelligence, an area of computer science in which software systems develop skills normally associated with human intelligence, such as visual perception or the ability to weigh decisions. By crunching very large sets of data, machine learning systems can often come up with highly unintuitive conclusions.

The science of machine learning involves building algorithms that enable computers to “learn” about consumer behaviors and shopping patterns. These patterns are culled from very large data sets over time, revealing how shoppers move through an online “sales funnel.” This funnel includes every step from doing online product research to pricing comparisons, to filling up shopping carts and then making final purchases at checkout.

Big Data Analytics

Retailers can drive greater revenues by optimizing online advertising campaigns through the use of big data analytics. Campaign managers are increasing their bottom line by tracking clickstreams and monitoring the data gathered from call centers and other customer touch points.

Most big data platforms combine a data warehouse with an analytics engine and data-staging technologies that comb through enormous volumes of information. In this way, data from all parts of an organization can be integrated and analyzed to mine for actionable items, such as making a special discount offer at precisely the right moment to grab someone’s attention.

Big data streams are generated by the proliferation of new data sources, including social media feeds, geolocation equipment, and digital sensors. Some advertisers use rewards points to create loyalty programs that provide still more customer information signals. Every consumer interaction with a brand can then be studied to increase user engagement and drive greater sales – be it from social media posts, responses to survey questions, Website requests, or any other online sources.

Persona Segmentation

Marketers are also becoming more sophisticated about separating consumers into distinct groupings known as persona. Each persona type shares similar likes and dislikes, so they are served similar designs and messages within their ads.

By breaking the audience into distinct categories, marketers can provide much deeper levels of personalization. Each persona type is served differently by combining unique ad content with some context. Content includes the messages, the calls to action, the relevant video feeds, etc. Context includes who the user is, when the ad was shown, where the ad was shown, etc. This level of granularity can lead to thousands of different permutations of persona, allowing the brand to establish a highly personalized conversation with each user.


In the past, mobile marketers and app developers mostly focused on acquiring new customers for their products and apps. Recently, many retailers have adopted a strategy for retargeting existing customers with relevant ads on other sites and applications, such as Facebook or Twitter. Retargeting is less costly than doing initial outreach because the core audience is already likely to have some interest in the product or service.

The goal for retargeting is to get people who download an application to come back and use it regularly, or for those who have shopped online to return to the site in search of new bargains.

Retargeting can also involve collecting data on consumers and serving them advertisements for products that are accessories to the products they already own. Data can be collected to find out which products consumers already own. Naturally, the consumer will need other products to further enjoy their existing products.

For example, if somebody purchases a television, they will need complementary products such as cables, DVD players, and subscriptions. Through this retargeting method, advertisers can serve ads that become extremely specific and relevant to the consumer over a period of time, thereby exponentially increasing the probability of a conversion.

Technology Is the Key to Optimization

With the advent of digital campaigns, advertising is no longer a scattershot pursuit in which marketers throw out lots of messages, hoping one will attract the attention of a random buyer. Today the goal involves using advanced technologies to categorize and pinpoint the most likely buyers, and then serving them the most compelling content to increase user engagement over time.

In addition, more advertisers are going back to people who already bought something and offering them new items through retargeting drives. Machine learning systems then analyze shopper patterns to create greater context for consumer engagement going forward.

With consumers becoming more dependent on their mobile devices, there is a unique growth opportunity ahead in 2015. Retailers who adopt these new ad optimization technologies can greatly increase their sales pipelines while also improving customer satisfaction.

Shekhar Deoco-founded EngageClick with Manoj Rajshekar, a fellow student he met in the graduate engineering program at Carnegie Mellon University. The duo launched EngageClick in Palo Alto in 2012.Deo previously worked at NetApp and Cisco where he acquired product development experience in distributed systems, clustered systems, and data storage technologies.

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