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Customer Analytics and AI: Better Together

December 8, 2021
· 4 min read

Companies are always looking for better strategies for reaching customers—to deliver better services, products, and value. These days, most seem to understand the importance of AI-driven decision-making for their businesses. But many struggle to turn the data they collect into true, actionable insights that can increase ROI. 

As noted in this report from Forrester®, “four out of five global data and analytics decision makers say that their firms want to become more data-driven and perform more advanced predictive analytics and artificial intelligence projects. Yet most enterprises struggle to capture the full value from data science initiatives on a regular basis.”

Organizations need to have a real-time understanding of customers’ needs and timely strategies for maximizing the value of their data. By using customer analytics derived from data about customer behavior to make key business decisions, businesses can learn which products are relevant to them, and be confident setting prices, choosing locations, creating targeted marketing, and much more. AI improves upon traditional analytical methods by better detecting and understanding the complexities and nuances of the data—from human behavior to finding signal in a sea of information overload.

Combining customer analytics with AI speeds analysis of customer data exponentially and helps companies respond quickly to a changing marketplace. Automation is mostly about efficiency. In traditional data science, it takes a significant amount of time to build a single model for a single use case or SKU. With automation, the process can be scaled and the work democratized across the organization—performed by others in addition to data scientists.

Here are some ways that putting AI to work helps organizations make informed marketing decisions instead of guesses.

Marketing Mix Optimization

Companies often rely on third-party media consulting companies to do studies that recommend the best set of advertising and targeted campaigns to pursue across various channels and media. This process can be time-consuming and costly. It can take weeks to produce answers for just a handful of SKUs and then, by the time the analysis is delivered, the strategy might be out of date. Often, because of the time and cost involved, companies only run these programs a few times a year.

Running automated customer analytics programs—where data is ingested automatically and machine learning models are built with automated tools—is much more cost-effective. And it scales far better to cover the sheer number of products that most companies need.  

Next Best Offer

Once a company has a customer, it’s far less costly to keep them and nurture the relationship than to replace them with a new customer. Knowing what to offer them next is key to keeping them engaged and loyal. By analyzing customer behavior and understanding what kinds of purchases similar customers tend to make, organizations can make the best offers on products and reduce customer churn. This can only be accomplished with advanced analysis that employs AI to analyze and make sense of all of the purchasing patterns the customer exhibits. Traditional statistics simply don’t work on this scale.

Next Best Action

One of the biggest challenges companies face is how to act on their marketing data. If data shows that a customer is engaged, then AI can suggest the next best action to take with them. When there are multiple paths that could be taken after analyzing marketing data, analytics can suggest which product or service is the most appropriate to offer next.

Lifetime Customer Value

Estimating the future value of a customer is one of the most valuable insights customer analysis can provide. It can help companies avoid realizing short-term gain without long-term value. By understanding a customer’s past purchases, it’s possible to target the right people for a product, extending their lifetime value and building loyalty by offering the right product at the right time.

Propensity Modeling

Knowing which generated leads will convert to a purchase makes it possible to understand who will be the most valuable audience for every stage of the sales funnel. This knowledge can help with prioritizing outreach, targeting the hottest leads based on the information gathered about them based on their title, industry, size of their company, previous purchasing history, and other relevant data.

With the DataRobot AI Cloud platform, businesses can tap into the power of augmented intelligence to understand next best offers, lifetime customer value, propensity modeling, and other valuable insights. Customers have quickly answered some of their most pressing marketing, product, and service questions to realize tremendous value for their customers and their bottom line. Contact DataRobot today to unlock the potential of your data with a free demo.

AI in Customer Analytics: Tapping Your Data for Success
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About the author

Value-Driven AI

DataRobot is the leader in Value-Driven AI – a unique and collaborative approach to AI that combines our open AI platform, deep AI expertise and broad use-case implementation to improve how customers run, grow and optimize their business. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with your existing investments in data, applications and business processes, and can be deployed on-prem or in any cloud environment. DataRobot and our partners have a decade of world-class AI expertise collaborating with AI teams (data scientists, business and IT), removing common blockers and developing best practices to successfully navigate projects that result in faster time to value, increased revenue and reduced costs. DataRobot customers include 40% of the Fortune 50, 8 of top 10 US banks, 7 of the top 10 pharmaceutical companies, 7 of the top 10 telcos, 5 of top 10 global manufacturers.

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