Lower Marketing Costs: Using New A.I. Technologies to Replicate Amazon’s Success

by | Nov 5, 2023 | Marketing, Lead Generation, Marketing Strategy

Lower Marketing Costs: It Begins with Budget Allocation

Effectively allocating marketing budgets across the various channels is one of the biggest challenges a company can face when trying to lower marketing costs. Navigating complex multi-touch marketing landscapes to effectively engage customers and drive conversions is an ideal outcome most businesses still wrestle with. This article will explore the pivotal role of A.I. in dissecting customer behavior across diverse online channels in a way that enables for budget allocation controls and improved returns. Drawing insights from Amazon’s marketing strategy, we’ll delve into the sophisticated methods employed by companies to optimize marketing campaigns and lower marketing costs using A.I. technology.

More importantly, we’ll talk about how even small businesses with limited budgets can begin implementing these strategies to maximize their returns. Through a detailed analysis of the Amazon case study and an exploration of broader industry trends, we’ll shed light on the significance of A.I. and machine learning as a key differentiator for local businesses.


Lower Marketing Costs: The Amazon Example

The digital marketplace is inundated with data, making it imperative for businesses to employ advanced analytical tools to extract meaningful insights. The challenge is, making use of all of that data.

Amazon is clearly one of the most successful companies in the world, and its success is due. in large part, to its effective use of technology to fully understand the behaviors of their customers (and prospective customers) by tracking marketing touchpoints and engagements across all channels, including paid search, social media marketing, email marketing, and direct mail.

Once Amazon has collected this data, it uses it to create a customer journey map for each customer, showing which marketing channels they interacted with before making a purchase. This information allows Amazon to see which channels are most effective at driving conversions and to optimize its budget spend accordingly.


Lower Marketing Costs: The Importance of Customer Mapping

A.I. technologies facilitate the creation of intricate customer journey maps, offering in-depth insights into the touchpoints influencing purchase decisions. It provides a detailed and holistic view of customers’ interactions with a brand, helping businesses identify touchpoints, pain points, and opportunities for improvement.

The process of customer journey mapping begins with gathering data and insights about customer behavior, preferences, and expectations. This information is then used to create a visual representation of the customer’s path, highlighting key interactions across various channels such as websites, social media, email, and in-store visits. By mapping out these touchpoints, businesses can better-understand the customer’s emotions, motivations, and challenges at each stage of their purchasing decisions.

Customer journey mapping serves several essential purposes. Firstly, it enhances customer understanding by allowing businesses to step into their customers’ shoes and see the brand from their perspective. This empathetic approach helps in designing more personalized and meaningful experiences tailored to meet customer needs.

Secondly, it helps in identifying pain points and bottlenecks in the customer journey. By pinpointing areas where customers disengage with the brand, businesses can implement targeted improvements, leading to enhanced customer satisfaction and loyalty.

Furthermore, customer journey mapping is an iterative process. As customer behaviors and market dynamics evolve, businesses need to update and refine their journey maps to stay relevant and competitive. By continuously monitoring and adapting their customer journey maps, businesses can stay responsive to changing customer expectations and preferences, ensuring long-term customer satisfaction and loyalty.

Historically, this process of data analysis has been an incredibly manual one. Recent developments in A.I. and machine learning have enabled companies to automate the process, creating customer journey maps, with corresponding campaign updates, in real time.


Lower Marketing Costs: The Amazon Echo Case Study

One example of how Amazon used its customer journey data to optimize its marketing budget coincided with the launch of their Echo smart speaker. When Amazon first launched the Echo, it was not selling very well. Amazon used its customer journey data to track how customers were interacting with its Echo marketing campaigns. The data showed that many customers were interested in the Echo, but they were not sure what to do with it, as search queries for how to use a smart speaker spiked.

Based on this information, Amazon decided to focus its Echo marketing campaigns on educating customers about the Echo’s features and benefits. Amazon also created new Echo tutorials and videos to help customers get started with their new devices.

In addition, they were able to deploy these new elements of their marketing across the channels that demonstrated the highest conversion metrics. Equally important, this enabled a degree of personalized marketing that was able to speak, specifically to the individual consumer’s wants and needs.  From email to social media, PPC advertising and display ads, every consumer interaction was measured and compared against the cost of that interaction. Campaign elements that secured the lowest interaction costs received the largest percentage of the budget.

As a result of these changes, Amazon’s Echo sales increased significantly. Amazon was able to attribute this increase in sales to its customer journey data, which helped it to optimize its Echo marketing campaigns.


Lower Marketing Costs: Predictive Analytics and Campaign Optimization to Find New Opportunities

Predictive analytics becomes a powerful byproduct of this process. By tracking consumer behavior in such a granular way, predictive analytics enables businesses to foresee future trends and customer interests, providing invaluable insights that can shape strategic decision making.

Amazon is a perfect example of these strategic decisions in action. When they launched Amazon Web Services in 2006, it was considered a significant and unexpected move. At that time, Amazon was primarily known as an e-commerce company, and its decision to enter the cloud computing market was unexpected for several reasons:

  1. Divergence from Core Business: Amazon’s core business was selling books and other retail products online. Venturing into cloud computing services was a departure from their traditional business model, and the move into a completely different industry was surprising.
  1. Competitive Landscape: In the mid-2000s, the cloud computing market was not as mature as it is today. There were few established players, and the concept of cloud services, especially in the form of Infrastructure as a Service, was relatively new.
  1. Risk and Investment: Building a robust cloud infrastructure requires substantial investments in data centers, networking equipment, and skilled personnel. The risk associated with such a venture was high, especially for a company that had not previously been involved in technology services at such a scale.

In hindsight, AWS’s success can be attributed to its early entry into the market, continuous innovation, and the ability to offer reliable and scalable services to businesses of all sizes. At the time, AWS was created for two reasons: to satisfy their own internal needs, and because through their intensive tracking of consumer behavior and interests, they were able to identify a niche opportunity to launch into a new market.

This is at the core of what predictive analytics enables. By learning from historical data, recognizing intricate relationships between various factors such as customer demographics, purchasing history, online behavior, and market trends, predictive analytics enables businesses to proactively identify potential challenges and opportunities. When it comes to securing lower marketing costs, this level of early insight, especially considering auction-based business models like Google and Meta, can be a huge advantage.

For instance, from a marketing perspective, it can recognize shifts in market demand, allowing companies to adjust their messaging, targeting or timing.  By staying ahead of the curve, businesses can capitalize on emerging trends, effectively marketing to niche audiences or launching into new geographic areas. All of which leads to a level of marketing innovation that decreases costs and powers customer generation.


Lower Marketing Costs: Using New Technologies to Replicate Amazon’s Success

Fortunately, in the years since Amazon launched the Echo, the costs associated with the types of A.I. solutions that enabled that level of customer mapping and budget allocation have decreased significantly. At SaaSQL, we use A.I. to fuel our Dynamic Cross Channel Marketing solutions, which affectively make this type of comprehensive marketing strategy available to businesses with small budgets.

By linking every marketing channel to a single ad delivery source, Dynamic Cross Channel Marketing measures the effectiveness of each channel, dispersing the budget to the media that delivers the lowest customer acquisition costs. Tracking every possible marketing outcome, from phone calls to form fills, e-commerce transactions and foot-traffic, it forces publishes like Google and Meta to compete for their share of local budgets by really performing. 


Lower Marketing Costs: Pulling It All Together

Ultimately, leveraging A.I. in marketing, inspired by Amazon’s successful strategies, enables businesses to navigate complex multi-touch marketing landscapes in a way that can be game-changing. In addition to facilitating precise budget allocation across online channels, enhancing customer experiences and driving conversions, it provides local businesses with unique insights in to new and exciting growth opportunities.

Accessible A.I. solutions, like Dynamic Cross Channel Marketing, level the playing field for businesses, ensuring efficient budget allocation and personalized marketing. Embracing advancements like these empower businesses to thrive, to lower marketing costs and increase market share.

J.W. Martin

About the Author

J.W. Martin is a marketing expert with 25 years experience developing marketing strategy for local businesses. He can be reached at jw.martin@saasql.ai

NOTE: While all articles are written by our team, to provide the most robust and useful reader experience,  SaaSQL uses A.I. / large language models to assist with various aspects of content development. This includes research, sourcing and other content improvements.  

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