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Intelligent messaging, big data & machine learning: A powerful combination for multi-channel marketers

Consumers engage with brands from multiple devices, at varying times throughout their day, for varying reasons. Brand marketers must reach each customer with the most relevant content at the right time. This content, whether it is a message, promotion, or information, must be delivered over the channel that is optimal for that particular customer. However, this is not an easy task for marketers.

  • Will an email or text message reach one consumer segment more effectively than another?
  • If an app user ignores an initial text message about a pending opportunity or promotion, how should the follow-up message be targeted or re-targeted?
  • If a hotel guest begins a destination search on the web and shifts activities to a smartphone, which channel is the ideal avenue for a marketer's follow-up message or offer of help?

These multi-channel challenges are intertwined with what Gartner describes as the merging of offline and online marketing, with 61 per cent of leading marketing executives saying their digital marketing budgets will rise in 2016. Boosts in spending, of course, bring higher expectations for marketers to link their efforts to tangible results and revenue, according to the same Gartner survey.

The solution can be found in "big data," which refers to data sets which are too large to be easily processed or analysed. However, if this data can be explored and leveraged effectively, it can create valuable, detailed and tangible insights into consumers' activities, preferences and known behaviors. Some marketers call this personalisation, but OtherLevels frames the concept as "intelligent messaging” - a technique powered by machine learning that enables marketers to effectively leverage the vast amount of data about their customers.

Multi-channel marketers: Focus on results, not the mechanics, with intelligent messaging

Big data can help marketers to build customer profiles that contain extensive intelligence about each individual customer’s interaction with the brand as well as their digital habits. For example, data might tell a marketer that a particular consumer is most likely to respond to messages delivered on a mobile phone during early-morning hours from a push notification sent to a mobile app, while another customer might be a late-evening online shopper who uses a laptop and is most likely to respond to emails. Marketers armed with data will automatically know a consumer's time zone, preferred language and operating system, as well as known preferences identified from previous transactions or loyalty program memberships. Then, when this data is paired with the proper machine learning techniques, predictive intelligence can be applied to recommend to the marketer the optimal strategy for reaching each customer.

For example, marketers can build campaigns based on details from the customer’s previous interactions such as frequency and method of engagement, time of day and location. Once the initial communication is sent according to this intelligent strategy, the subsequent real-time collection of data from the campaign enables the marketer to prepare retargeting messages that can be updated with new and relevant content that takes the customer to the next logical step in his or her conversation with the brand.

Essentially, with the powerful combination of big data and machine learning, marketers' strategies for interacting with users and customers can become more and more intelligent as the customer relationship develops, because they are based on better, richer data and analytics that logically and automatically inform the next round of communication. With each interaction, more data is collected, and the brand learns more about the user, driving even more effective predictive analysis around the optimal messaging strategy for that individual, including a determination of which channel is most appropriate. With so many options for brand engagement (emails, push notifications, in-app messages, interstitials (pop-ups), rich inbox messages, browser push messages, text messages, and alerts), any assistance with automating the decision-making process can significantly improve the effectiveness of marketers’ campaigns.

A data-informed, intelligent messaging approach can deliver significant improvements to a company’s marketing metrics. A 2015 OtherLevels Retail Messaging Study found that well-timed app messages can boost lift by 28 per cent and increase engagement by 22 per cent, and that relevant content can increase shoppers' views and clicks by almost 20 per cent. Marketers who leverage intelligent messaging can focus on compelling content, their users’ experience, and ultimately their ROI, instead of getting bogged down with the mechanics of message delivery,

Brendan O'Kane, managing director and chief executive officer of OtherLevels

Image Credit: Shutterstock/Carlos Amarillo