AI-Driven Customer Analytics: Using Data From Existing Tools

mariefincher
  • May 25, 2022
  • 4 mins
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Advances in technology and marketing allow us to drive our products and strategies to their maximum potential, but have we reached that maximum potential just yet? Well, it seems that the bar has been slightly raised recently, once AI and machine learning models have started becoming increasingly accessible throughout the last few years.
The vast majority of B2C companies heavily rely on a myriad of tools, ranging from E-commerce systems, customer support tools, point of sale software, and so forth. Is it possible to aggregate this information, so that we can use AI-Driven Customer Analytics?

Following customer footsteps

Businesses of various profiles and sizes have become obsessed with data. And rightly so — the data our customers leave behind, as actual users of our services or mere observers of our ads, provides us with insight on their marketing decisions. By using this information, businesses can better calibrate their services to the needs of the end customer.
As the tools that collect information on the users’ behavior online become increasingly complex and sophisticated, so does the businesses’ capability to implement changes based on the footsteps their users leave on their sites.

Data and its impact

So far businesses were collecting data and addressing issues locally. What do you do once you’ve created a high-end design, but users don’t convert? You A/B test various call to actions, you look into Google Analytics for the time your potential customers spend on the page, you look at the bounce rate, and a host of other variables specific to the users’ behavior online.
Furthermore, it seems that integrating data ai-driven customer analytics is now slowly becoming the new standard for the current market. A body of research suggests that over three-quarters of companies who identify as advanced in their integration of technology, business goals and analytics have stated that they occupy a higher market position.

The vast majority of the tools businesses use to optimize the users’ experience with their services and websites generate enormous amounts of raw data, which isn’t put to work afterward.
“It is as if they’re sitting on goldmines without knowing that they have access to incredibly valuable information or they don’t have the right tools to extract insight from it.“ — Rod Johnson, CEO at Trust My Paper.

Machine learning (ML) in marketing

ML is a rapidly expanding field that has shown impressive results in a broad spectrum of niches and industries in a brief amount of time. What Machine Learning introduced was the ability to collect insight on patterns in large amounts of data that are either inaccessible to humans, or would take copious amounts of time to go through (and arguably with much lower precision).
Here’s a list of applications that Machine Learning has in marketing and fields adjacent to it:

  • Identify fraudulent activity
  • Decide which ads are most suitable for certain users
  • Develop new pricing models
  • Recognize patterns in large chunks of data
  • Analyze sentiments in text

Ideal Customer Profile — do you have one?

The sudden wave of Machine Learning in marketing has fulfilled all the necessary preconditions for Customer Data Platforms (CDP) to emerge, a multimillion-dollar industry, which is to reach the $1 billion mark in 2019. These platforms compile information from a broad spectrum of marketing tools, in order to create predictive models of customer behavior, which ensures highly precise segmentation and impeccable personalization.
Marketing specialists are unable to process the massive amounts of data collected by their online tools, due to the fact that earlier-generation approaches are not able to adapt to the continually changing information requirements of the market, which pretty much sets the stage for CDP’s and substantiates their importance in the years to come.
These platforms allow businesses to predict customer behavior, based on the behavior of prior users and customers. This has the capacity to impressively increase sales due to the fact that:
Based on the knowledge extracted from previous site-customer interactions businesses can now understand what marketing actions are more suitable for long-term value.
CDP’s allow the objective chunking of customers into different behavioral groups, based on the way they act on their website or interface, ensured by the analysis of raw data, and not a person’s estimation.
Holistic and intelligent behavior tracking and micro-segmentation
Based on the insight collected from CDP’s, you’ll be able to create high-precision buyer personas. Once your company identifies who their buyer is, you’ll be able to intelligently invest time and effort into tweaking your site, change the product development trajectory so that it becomes more appealing to the end customer.

Conclusion

The time is now. As Customer Data Platforms gain bigger momentum, it is most certainly the right time to look into translating the vast amounts of data to actually valuable insight on your customers’ behavior.

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