Outdated Marketing Processes Threaten Revenue, Customer Engagement

daveoflanaganData-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media.

Today’s column is written by Dave O‘Flanagan, co-founder and CEO at Boxever.

Reaching consumers and influencing purchases is harder than ever. Ad blocking, email filtering, shrinking attention spans and the proliferation of devices and channels all present major challenges. But for many organizations, outdated customer marketing and customer intelligence processes are the biggest obstacles when it comes to customer engagement and driving revenue.

Every day, consumers consider investing their hard-earned money in brands, whether it’s buying a new pair of shoes, booking a hotel or purchasing a flight. Typically, the engagement ends as soon as the search or transaction is complete, which creates a massive gap in the relationship and leaves an untapped opportunity to cultivate stronger customer loyalty and generate more sales.

With consumer expectations for stronger, more valuable digital engagements on the rise, brands need to take advantage of every opportunity to foster one-to-one personalized relationships with their customers. The engagement process can’t stop at the search, shopping cart or transaction. Marketers need to follow consumers throughout their entire customer journey – before, during and especially after they make a purchase.

Not only are brands missing an opportunity, but they are making it more difficult to sell in the future. Despite the mega amounts of customer data in their arsenal, brands rarely apply the data effectively and continue to spam consumers with useless mass communications. Consumers are so frustrated with impersonalized ads that many are taking back control, often in the form of ad blocking.

The implementation of these technologies creates roadblocks for marketers who aren’t taking the right approach to customer outreach. Many companies only implement a superficial level of personalization, which doesn’t resonate with most consumers. But ad blockers are making marketers take a closer look at individual customer needs and how to execute extremely relevant, hypertargeted content and communications.

Marketers need to take a smarter approach to tackling customer data. While aggregating traditional demographics and transactional data is a good starting point, it’s crucial to layer behavioral analytics into the analysis. The context of where someone currently is and what they are doing is an essential ingredient in creating a robust, smart view of each customer. The combination of this data forms the basis of sufficient customer intelligence, but with today’s technology, having a holistic, informed view of each customer is only the starting place. The real challenge is in how you leverage the insight.

For decades, marketers have drawn hypotheses about what products and offers consumers want based on their own experiences or intuition. Today, artificial intelligence can help brands predict the future and know what each customer is looking for, sometimes even before the customer knows. In marketing and advertising, it can power computer systems to make decisions, evaluate the results, adjust and learn over time – just like a human could.

For example, if a brand sends an email offer as part of a drip campaign, the computer will recognize which customers opened the message and which customers did not. It could then evaluate which customers went a step farther and started taking actions via the brand’s website, such as utilizing the search function or clicking through to additional resources. If that target group doesn’t end up making a purchase or contacting sales, the machine could tweak the algorithm and send a more targeted communication, offering contextually personalized options based on how and why the machine thinks the individual is engaging with the brand.

Computers can now run tests automatically and refine the process, in near real time, until they get it right. In some cases, the system might slightly change the price point to see if conversion rates increase significantly.

Artificial intelligence can help connect the dots between behavioral data and transactional data. The layers of data mining and machine learning can provide a 360-degree point of view of the customer for the brand by learning how customers react to the recommendations and then readjusting.

Marketing machines can be self-taught, and no longer need a large team of programmers constantly building, analyzing and adjusting deals and offers. Data-mining algorithms can be leveraged to enable systems to see hundreds of millions of transactions being made at once, along with the product search similarities between an individual and a group of customers. Speed is key – the platforms needs to make fast decisions in real time on what networks of people will receive a specific communication, when and across what channel.

Artificial intelligence can help marketers reach consumers in a micromoment by monetizing the results and outputs and ultimately engaging across every channel. Brands can better learn who their customers are, what they want and how to communicate with them and make the best recommendations possible.

Follow Boxever (@Boxever) and AdExchanger (@adexchanger) on Twitter.

 

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