Feature
The Gist
- Predictive analytics in CX. Enhances customer experience across various sectors by accurately predicting customer needs and behaviors.
- Retention and revenue. Significantly improves customer retention and revenue generation through targeted marketing strategies and personalized offers.
- Sales and marketing optimization. Transforms traditional sales and marketing approaches, offering deeper insights and more efficient allocation of resources.
Marketing teams are depending on predictive analytics in the artificial intelligence (AI) field to deliver results in several forms, including email, advertising and deal attribution. Marketers across sectors — such as finance, IT and beauty — employ AI-based prediction models to analyze and target customer types to quantifiably improve customer experience (CX) and push revenue. Several of these companies offer details on their successful adoption of predictive analytics in CX in this set of case studies:
1. Predictive Analytics in Finance: Daiwa Securities
Tokyo-based Daiwa Securities was looking to strengthen its proposals by better understanding its customers, according to a case study. The company was looking to “facilitate advanced customer analysis,” and it set up an AI office in its sales planning department.
Previously, the company’s analytical tools had limitations in processing time and file size, and statistical analysis tools were “not readily adopted due to poor usability,” says Osamu Hasegawa, director of the AI office, Daiwa Securities.
Daiwa Securities worked with the Cary, North Carolina-based analytics software company SAS and its SAS Analytics to create a recommendation support system for sales reps that uses AI to “predict changes for each customer.” The system recommends the best product offers for each customer and best follow-up messages to reduce churn.
For example, the customer relationship management (CRM) interface shows sales reps information on customers who “require support now.” The system also recommends products and information that sales reps can propose to a customer if they call or visit a branch.
“We want our customers to have peace of mind that they can access us, and we’ll be there for them,” Hasegawa says. “There is always a person on the other side, whether in a Daiwa branch or on the phone. Understanding the customer and streamlining their experience with the use of technology, including AI, is essential to our commitment.”
Results:
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2.7x increase in customer purchase rate
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50% reduction in customer departure rate
Related Article: What Is Predictive Analytics? And How It Works
2. Predictive Analytics in Wellness: Hydrant
The New York-based wellness company Hydrant wanted to win back more of its past customers by improving its approach to retention, according to a case study. Hydrant was looking for insights into “which customers were more likely to churn and which past customers could be enticed back.”
To improve its email marketing conversion rates and optimize its budget, Hydrant needed to also improve its customer segmentation for offers as well as insights into customer preferences and their “likelihood of response.”
Hydrant’s email targeting goals were to identify three types of customers: those most likely to place another order; those who would convert from single buyers to subscribers; and those with potential to be won back.
Hydrant worked with the Ramat Gan, Israel-based predictive analytics company Pecan AI to integrate Pecan’s platform across Hydrant’s marketing automation, ecommerce, and data platforms. The predictive analytics tool pulled modeling data from the data platform and fed predictions “back into campaigns” in the marketing automation software.
The predictive analytics churn model analyzed the buying history of “thousands of customers” and their churn likelihood. Hydrant used those predictions to group customers for targeted offers and messages based on their “purchasing potential.”
Hydrant also used a predictive analytics win-back model, which analyzed data on customers who didn’t buy in at least two months — and predicted if they’d buy in 30 days.
The predictions Hydrant incorporated into its email campaigns “informed our marketing efforts, helping us reach out to the right customers and allocate spend in the right places,” says John Sherwin, CEO, Hydrant.
“The models were incredibly accurate in identifying which customers would more likely respond to our offers and make purchases.”
Results:
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260% higher conversion rate on targeted win-back offers vs. control
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310% higher revenue per win-back customer from targeted promotions vs. control
Related Article: Using Predictive Analytics to Improve Customer Retention
3. Predictive Analytics in IT: Automox
The Boulder, Colorado-based IT automation company Automox needed to expand its outbound activity in account-based marketing and sales, according to a case study. The company’s go-to-market strategy was an “inbound-heavy” one, and it “waited for accounts to quality themselves.”
The approach gave Automox “limited visibility” into potential deals and resulted in missed sales opportunities.
Automox worked with the San Francisco-based AI customer prediction platform 6sense as a key part of its outbound strategy. For instance, business development reps looked to the platform for qualified accounts. Data-driven models predicted accounts that were an “ideal fit” as well as accounts in the decision or purchase stage.
The Automox team also uses customer intent data from the platform in outbound marketing campaigns, creating a “holistic” strategy with sales.
The predictive AI platform helps Automox “align with sales by stating anything generated on a 6QA from our outbound team is attributed to this program,” says Laura Partyka, senior manager of account-based marketing, Automox.
Maddie Regis, account-based marketing manager at Automox, says she “couldn’t imagine doing this” without the AI platform.
“It gives you so much more visibility,” Regis says. “Otherwise, you’d be flying blind.”
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Results
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35% increase in sales
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88% increase in number of closed won deals
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17% increase in number of opportunities
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51% of closed won deals at the company
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Nearly 50% of the pipeline generated
Related Article: 3 Ways AI-Powered Predictive Analytics Are Transforming Ecommerce
4. Predictive Analytics in Beauty: Alleyoop
The West Hollywood, California-based beauty brand Alleyoop needed to improve its advertising performance on Facebook as the holiday season approached, according to a case study. The company looked to go beyond relying on its creative, “going broad,” and social algorithms to increase their results.
Alleyoop worked with the New York-based AI prediction platform Black Crow AI to tap into machine learning for ecommerce. Rather than rely “solely” on Facebook data, Alleyoop employed the AI platform to use its first-party data to build targeted customer audiences.
For instance, on every Alleyoop web page, the platform’s machine learning could analyze “over 450 signals” for each web visitor to make real-time predictions on "how likely they were to buy.” Alleyoop could send the audiences created with the predictions to Facebook for retargeting — as well as create lookalike customers for prospecting.
The prediction platform helped Alleyoop develop audiences that are both bigger and more targeted. The company also used the AI tech to predict and develop audiences for another social channel, email and direct mail.
Results:
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50% increase in incremental sales
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55% more Facebook revenue
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10% higher return on ad spend (ROAS)
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200% increase in Facebook retargeting sales
5. Predictive Analytics in the Nonprofit Sector: DonorBureau
The Franklin, Tennessee-based fundraising company DonorBureau wanted to identify “loyal supporters” and their level of giving to see personalized campaigns yield higher donor interactions and outcomes, according to a case study.
DonorBureau faced growing predictive modeling demands, as it worked with “over 900 million mail transactions, 140 million donations, and over 40 million individuals.” The company, without a large team of full-time data scientists, was looking for predictive models that were more effective and accurate.
DonorBureau worked with the Boston-based AI platform DataRobot on predictive analytics. The fundraising company automatically generated more accurate predictive models in a “fraction of the time.” The AI-based technology included software-as-a-service, managed cloud and API components.
The predictions helped DonorBureau clients run campaigns as well as know more about the “science behind fundraising and cause marketing programs.”
“There was no training needed,” says Brian Johnson, VP of product and operations, DonorBureau. “We took it right out of the box and into our Python workflow.
“The only thing now limiting the models we build is our imagination.”
Results:
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10% improvement in prediction model accuracy
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75% reduction in total cost of ownership
