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How to Survive the Hyper-Personalization Revolution

Your Blueprint for Delivering One-to-One Experiences

The Struggle to Deliver Hyper-Personalization

When it comes to keeping up with consumer expectations of personalization, many brands are lagging behind.

Why is this?

Well, today’s customers are more demanding than ever. They want a customized, hyper-personalized experience across an ever-growing number of digital touchpoints and channels.

However, we find that many brands lack the necessary Customer Data Infrastructure and advanced analytics capabilities to meet these expectations.

Despite the clear business benefits of effective personalization – a study by Deloitte Digital found that personalization leaders saw a 1.5x increase in revenue per customer – the reality is that brands are simply not delivering.

The same study by Deloitte Digital found that 74% of brands don’t have the full range of in-house capabilities and technologies required for effective personalization.

As a result, marketing personalization remains inadequate, leading to significant customer churn. A study by Gartner goes so far as to suggest that brands risk losing up to 38% of their customers due to subpar personalization!

So what can brands do to align their personalization strategies with growing consumer expectations? In his recent webinar at the Snowflake Marketing Data Cloud Forum, Alex Dean, CEO and Co-founder of Snowplow, shared his blueprint for hyper-personalization success. We summarize the blueprint below. 

Let’s dive in.

The Blueprint for Hyper-Personalization Success

For truly effective hyper-personalization, you need to consider the strategic combination of three core elements: Real-time behavioral data, advanced AI/ML modeling, and omnichannel activation.

Real-Time Behavioral Data

Let’s kick things off with our bread and butter – behavioral data.

At the heart of any successful hyper-personalization strategy is the ability to capture and leverage rich, granular customer behavioral data in real-time.

This means you need to go beyond basic web analytics tracking standards. Instead, you need a more sophisticated Customer Data Infrastructure that can collect and unify data from all touchpoints – web, mobile, connected TV, in-store, and more.

We may be biased, but only Customer Data Infrastructure platforms like Snowplow can provide the full context into individual customer journeys and interactions. We call these insights “hyper-transactional” data, which provides highly granular information about your customers.

Most basic web analytics tools capture a dozen or so behavioral events. That’s simply not enough to provide the comprehensive context needed for hyper-personalization.

Snowplow, on the other hand, captures over 140 different entity-based events that cover the entire customer lifecycle – from initial acquisition to conversion, upsell, and retention.

You can then feed this data into a single source of truth like the Snowflake Data Cloud in near real-time (see more about our Snowpipe Streaming service), allowing you to create a single, governed source of truth that your data science and engineering teams can use.

AI/ML Modeling

The second element of the blueprint is, of course, AI/ML modeling.

Once you have your rich, real-time customer data in place, you can begin to apply advanced AI and machine learning techniques to develop a set of sophisticated personalization models.

So how do you do this?

Well, with Snowflake’s features, including Snowpark for ML or Cortex for LLMs, you can create comprehensive Customer 360 profiles that go way beyond basic demographic and transactional data.

By feeding your hyper-transactional Snowplow data into these AI/ML services, you can tackle advanced use cases – from next-best product recommendations to churn propensity.

You can then use these outputs to power personalized offers, content and experiences tailored to each customer.

Omnichannel Activation

The final piece of the hyper-personalization puzzle is the ability to activate these personalized insights and models across the entire customer journey through a seamless omnichannel experience.

By this we mean delivering the right message, offer, or content to your customers at the optimal time across web, mobile, email, SMS, social media, and any other relevant touchpoints.

Sounds easier said than done, right?

But thanks to Snowflake’s storage, processing, and  activation capabilities with a host of best-in-breed MarTech and AdTech tools, it’s easier than ever.

With these features, you can orchestrate a truly personalized, cross-channel customer experience. Let’s look at an example:

You have a new, anonymous website visitor who is shown a targeted ad on TikTok. This then prompts them to visit your website and explore relevant product categories. The Snowplow behavioral data  is then fed into Snowflake, where AI models determine the next best action – perhaps a special offer or a content recommendation to be delivered via your engagement platform of choice.

Once you’ve acquired the customer, you can continuously track their ongoing behavior and engagement and feed it into Snowflake. This allows you to make real-time adjustments to your product recommendations, email campaigns, and other personalized touchpoints to increase conversions, upsells and customer retention. 

The main advantage of this approach is that your optimization and personalization efforts take place not only in the activation phase, but throughout the entire customer journey.

So instead of relying on diminishing returns from optimizing individual channels, marketers can “shift left” and continuously learn from their  behavioral data to deliver more relevant and valuable experiences. Let’s take a look at how this works in practice:

Hyper-Personalization in Action: A Case Study 

One brand that has successfully implemented this hyper-personalization blueprint is DPG Media, a leading European media company.

Working with Snowflake and Snowplow, DPG has been able to power content personalization and recommendation models that have increased customer login rates by 50%.

Key to this was DPG’s ability to ingest Snowplow’s real-time behavioral data into Snowflake, which enabled the company to respond to reader engagement within just 60 seconds of an article being published.

This rapid feedback loop, combined with advanced analytics, has enabled DPG to dynamically personalize its editorial content and achieve significant improvements in user engagement and loyalty.

By centralizing all of its customer data within its Snowflake Data Cloud instance, DPG has also been able to ensure strict GDPR compliance while reducing operational costs by 80% compared to the company’s previous analytics tools.

So there you have it. Brands really can deliver hyper-personalization and see real business impact.

By combining Snowplow’s hyper-transactional data, Snowflake’s AI/ML capabilities, and a strategic focus on omnichannel activation, brands like DPG have been able to deliver truly personalized experiences, resulting in significant business benefits – from improved login rates to increased content engagement.

Take the First Step Toward Hyper-Personalization Success

It’s time for brands to move beyond generic, one-size-fits-all marketing and embrace the role of hyper-personalization in building long-term relationships with their customers

We invite you to start evaluating your current personalization capabilities and identify any gaps in your Customer Data Infrastructure, analytics tools, and omnichannel activation.

Start discussions with your engineering and IT teams, and create a roadmap for implementing a hyper-personalization strategy powered by Snowplow and the Snowflake Data Cloud.

Now is the time to take the leap into the new age of hyper-personalization.

For more examples of our blueprint and a sample questionnaire to discuss with your engineering and IT teams, watch our webinar with Alex Dean, Co-Founder and CEO of Snowplow. 

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Adam Roche
Adam Roche

Communications Lead

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