Personalization Marketing Marketing

Why is personalization so difficult?


By Kevin Sieck, Director of Digital Marketing Analytics

February 20, 2019 | 5 min read

Imagine being visited by a traveling merchant who sells fabrics, furnishings, spices, dry fruits and other goods. The moment the merchant steps into your home, he begins gathering data in his mind. As he scans the room and chats with you, he gets to know you as an individual — discerning your likes, dislikes, and preferences. What quickly emerges is a mental ‘algorithm’ that enables him to make precise product recommendations and provide you with a satisfying sales experience.

Why is personalization so difficult?

Today, marketers can and should optimize and scale that mental algorithm exponentially using advanced analytics. The need to mimic the human, in-person process and give customers a personalized experience has never been more pressing.

It’s well established that, when done correctly, personalization advances customer relationships and drives sales. According to Gartner, 90% of brands will practice at least one form of marketing personalization by 2020. And although most marketers invest time and resources into personalization, many remain unsatisfied with their current efforts and lack confidence in their future ability to implement successful personalization initiatives.

Truth be told, personalization is hard.

AI and machine learning hold the promise of optimizing personalization, but using them well (or at all) is still more goal than reality for many marketers. Most still face multiple systemic organizational and technology roadblocks that create barriers to success. Much of this centers around data access and organization, as advanced personalization hinges on the ability to harness, analyze, and react to data in real time. Many marketers believe they lack adequate amounts of data for granular personalization — especially compared to companies like Amazon or Netflix. But often it’s not the amount of data available that’s the issue; it’s real-time access to it that’s the problem.

Data is usually siloed across teams and systems with no simple way to unify it. Compounding the problem is the fact that businesses often lack the internal experts with relevant skills needed to properly manage personalization efforts (e.g. data scientists for AI solutions, personalization specialist to oversee more advanced strategies). Even those marketers with access to ample amounts of data often lack the content required for the kind of personalized experiences that many consumers today demand.

Despite those obstacles, the new normal is for brands to try and engage consumers with personalized, relevant content at all points of their purchasing journey.

Today, the most engaging personalization starts with a 360-degree understanding of the consumer, something that can only come from unifying data sources and having an insights-driven approach to the customer journey. Most enterprises have access to large volumes of data and must be prepared to link it so they can act in real time in response to the data they’re receiving.

The changing landscape of personalization

The reality is, all marketers must advance their personalization to keep up with consumer demands. Today’s marketers must know exactly how, where, and when to reach their consumers for optimal effectiveness. AI will increasingly play a role in this.

Just as the old-world merchant gained an understanding of his customer by analyzing all the available information, modern brands must do the same. But instead of getting to know customers through chatting, body language, and visual cues, marketers today can use technologies that bridge data silos, such as a customer data platform, and then analytics tools such as AI and machine learning, to gain real-time insight from myriad channels. These unified insights will allow marketers to deliver truly personalized experiences to each consumer.

In addition to using AI for advanced personalization, marketers can use it to anticipate consumers’ needs using predictive analytics and trend-spotting through social data. AI also allows for advanced modelling of consumer behavior using a mix of quantitative and qualitative data points.

Consequently, AI is fast becoming a crucial tool in providing timely, relevant, and personalized messaging for individual consumers. Gartnerhad predicted that global business value derived from AI would total $1.2 trillionin 2018 alone, so this is not something marketers can afford to ignore. And AI will become even more critical as new data sources — from wearables, to beacons, to facial recognition technology — generate even more valuable insight.

It’s clear that AI and machine learning will continue to change the game for the future of personalization. Like the traveling merchant who uses all he knows about each person to make recommendations, AI will not only help marketers optimize personalization for individual consumers, it also will enable them to scale that personalization to millions of customers at each consumer’s individual moment of truth.

Kevin Sieck is director of digital marketing analytics for LatentView Analytics

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