Creating personalized mobile experiences with third-party data from AWS Data Exchange

By: Colin Marden and Chidambaram Muthappan | June 21st, 2022 

Every day, billions of people interact with their smartphones and personal devices, at home, in the workplace, and on the move. The rate at which mobile internet use is growing has already exceeded desktop growth. Over 90 percent of the global internet population use a mobile device to get online. Within that, 90% of screen time spent on mobile devices is estimated to be spent in mobile applications rather than directly browsing the internet1 . This means that providing an engaging and value-added user experience for your users is critical to creating successful mobile applications.

The benefits for delivering on these engaging and personalized mobile experiences can be lucrative. In fact, your customers may even be asking for it. Here are a few key statistics:

  • 90% of consumers believe that a brand’s ability to personalize their shopping experience impacts the amount they spend/shop with that brand2.
  • 76% of consumers get frustrated when companies do not deliver personalized experiences3.
  • Consumers for whom personalized experiences are “very appealing” are 10x more likely to be a brand’s most valued customer4.

In this blog, we will explore how we can use data from AWS Data Exchange to create personalized mobile experiences. 

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What is personalization?

Personalization is the process of tailoring a service or a product to accommodate specific individuals’ needs or preferences. Many organizations are using personalization today to make their interactions with their consumers more human and personal. The goals driving these are to improve customer satisfaction, drive additional sales, target marketing or advertising, or improve their user engagement or awareness. This could be on a website, mobile application, or physical experience.
Personalization is now a key facet of retail experiences, such as recommendation engines for streaming services or retailers. Consumer expectations have been elevated, and those same expectations now extend to their financial, food, travel, music, and social experiences.
So how can third-party data help with application personalization?

Third-party data for personalization

Companies with customer-centric products and services hold valuable information about their consumers in CRM systems, logs, and customer interactions. This information allows the company to respond to their user and their choices and preferences in a personal and intuitive way.

There are many different types of personalization and customization that might enhance your consumer experience. Typical examples could include:

  • Changing content based on an individual’s characteristics or demographics
  • Triggering specific responses based on user actions
  • Responding to consumer intent, such as responding after a purchase

Mobile application use cases

Targeting: When a user first downloads your application, you have limited information about the individual, their preferences, their status, or their intent. As a provider, you might use prompts to gather that information from your consumer. However, this is self-determined, often open to being skipped, and likely limited in scope. For example, it may elicit negative sentiment asking for age, purchase history, or income.

Here is where third-party data can help by providing you with identity markers, management, and ultimately identity resolution. Using data from providers, such as Infutor Data Solutions, allows you to understand your consumers in real-time and immediately make informed decisions.

Using comprehensive datasets like Infutor’s Total Consumer Insights Standard API, you can accurately resolve a consumer identity through name, address, phone number, or email. Once resolved, you have access to their demographic, location, household, behavioural, purchase and lifestyle information. From there, personalization can be introduced based on the specific consumer and your understanding of your userbase and products. Questions you might be able to answer include:

  • Can we pre-approve loans at that income level?
  • What is the most popular content for that age demographic?
  • Are certain products more or less relevant based on income?
  • Is the user’s country or region of origin important?
  • Do their habits or lifestyle dictate a different experience?

Location awareness: As mobile application users move around the physical world, their preferences and needs change. Information about the individual’s current location provides valuable signals about those preferences, needs, habits and behaviours. As a provider of a mobile application you may already have access to the individual’s location through mobile device’s telemetry. But how are you using that data? What do you know about that individual’s location? And how can you captivate your audience?

Again, here’s where third-party data can help by providing you with unprecedented insight into the consumer’s journey. Using datasets like Foursquare Places of Interest and Visits products, you can measure all the places people go before and after making a purchase. This can help you identify the right audiences to target, the right moments to reach them, and the right messaging to captivate their attention.

With access to the right technology and right data you can tailor the individual’s personal experience to target the right place or moment. The kinds of questions that you can begin to ask with access to rich location-based datasets includes:

  • Can you target offers, discount, and experiences based on physical location?
  • Can you recommend a local service or product, specific to the area?
  • Can you use local consumer trend data to target a onetime offer?
  • Do you need different experiences for different geographies (e.g. language, colour schemes, or A/B testing)?

What is the benefit?

The average application has a short lifetime with studies suggesting that most are deleted within the first 90 days after installation.

With personalization, we are looking to stop user attrition by providing features that drive more engagement, higher retention, and increase brand loyalty. With consumers receiving as many as 10,0005 brand messages per day, it is difficult to differentiate your message from the noise. Personalization is a way for brands to stand out and demonstrate to customers that they understand their needs and wants.

In the year 2000 Amazon founder Jeff Bezos said "If we want to have 20 million customers, then we want to have 20 million 'stores'. ... Our mission is to be the earth's most customer-centric company.".

Demonstrating our commitment to that belief, 20 years later the following statement can be found in Amazon’s Leadership Principles: We aim to be Earth’s most customer centric company. Our mission is to continually raise the bar of the customer experience by using the internet and technology to help consumers find, discover and buy anything, and empower businesses and content creators to maximise their success.

This concept of a store front for every consumer can be seen in the way Amazon provides personalized recommendations on our products. As an organization, we are obsessed with our customers. For that reason we have developed our own machine learning (ML) technology for real-time personalized recommendations.

It is crucial for businesses to help their users discover and engage with more products or content. Because of this, Amazon has taken that ML technology for real-time personalized recommendations and made it available to AWS customers as Amazon Personalize.

Amazon Personalize is an AWS service that uses machine learning algorithms to create recommender systems based on the behavioural data of your customers. The recommender systems are private to your AWS account and based only on the data you provide.

Many services are fast moving with new products and content being continuously added. Amazon Personalize enables customers to include completely new products and fresh content in their usual recommendations. This means the best new products and content is discovered, clicked, purchased, or consumed by end-users more quickly than other recommendation systems.

Combining data and technology

Now let’s consider the combination of 3rd party data and technology with an Infutor use case. In today’s highly competitive marketplace, Infutor offers its customers the ability to improve client solution offerings through targeting.

Infutor partnered with an automotive agency to evaluate the attributes of people who recently purchased a vehicle using current and previous vehicle ownership data. It also considered transaction history, demographics, and other proprietary deterministic data. This data provides a holistic view of consumer intent instead of leveraging only browser or online search behaviour as an indicator of propensity to buy.

Data scientists can build highly predictive, stable models identifying those with similar characteristics that indicate readiness to buy. They can do this by leveraging transactional data, as well as positive and negative behaviors, such as bankruptcy or slow payment history.

With this data and model, an Infutor customer who operates a car sales application might be able to better infer buying signals. As a result, they could be able to proactively personalize content. The content could focus on personal preferences such as a high performance, classic or family car. This could be based on the users own characteristics, attributes or behaviours. These messages can be delivered at the right time, such as when consumers show intent to purchase. These messages could also be shown in the right place, such as when consumers trigger signals like repeatedly browsing a specific product.  

Summing Up

As we highlighted earlier, the opportunity for personalization is huge. Remember 90% of internet consumption is derived from mobile devices and 90% of that activity emanates from mobile applications.

Advances in technology, data collection and analysis have allowed us to view this transition in consumer behaviour from the PC to smart devices in real-time. As part of that evolution, we have witnessed businesses developing capabilities for user tracking and data mining. As a result, businesses are beginning to add user customization and personalization.

Personalization, however, is not as simple as flipping a switch. A few challenges in data and technology persist, including:

  • Not enough data - your data may be anonymous or lack the accuracy or accessibility to make it useful.
  • Attribution – it is difficult to monitor and attribute the customer journey throughout the entire lifecycle with first-party data often siloed and inaccessible.
  • Time and resource - there is a high learning curve for personalization. It may be hard to work out where to start, and it is often an additional priority for a team, rather than dedicated resourcing.

Amazon and AWS Data Exchange supports customers with these challenges through:

  • Diverse massive datasets – AWS Data Exchange helps facilitate rapid access to free and proprietary datasets from many industries to help you fill data gaps.
  • Simplified management – Using modern data architectures, you can centralize your data in one location and bring your tooling to the data.
  • Streamlined integration – AWS Data Exchange makes available pre-configured, cleansed, normalized and auto-updated datasets for ease of use. Once combined with native service integrations, the seamless use of services significantly reduces engineering and automation burdens. 

About the authors:

Colin Marden
Global Account Solution Architect

Chidambaram Muthappan
Specialist Solution Architect – AWS Data Exchange 


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