Using Machine Learning to Drive Deep Personalization with Data: The Salesforce Model

The application of artificial intelligence (AI), known as machine learning, involves generating predictions from data inputs. One of its most surprising strengths is how machine learning (ML) drives the relationship between digitization and customer personalization.

“Digital has bred customer-centricity,” said Neeracha Taychakhoonavudh, Salesforce’s EVP of Global Customer Success and Strategy.

Harnessing digital and data, especially through ML, enables speedy, rich customer relationships and allows companies to move toward hyper-personalization.

“Personalization is the lifeblood of customer loyalty,” Taychakhoonavudh said.

Thanks to ML such as Salesforce Einstein, companies can create highly relevant messaging for the customer journey that improves the experience and increases brand loyalty.

Think of it this way: Machine learning can reinforce a company’s well-established strengths.

For instance, experts recently singled out Salesforce as one of the world’s “most admired brands” in the report “Moving Beyond Trust: Making Customers Trust, Love, and Respect a Brand,” published in MIT Sloan Management Review. The authors used a decade’s worth of research to conclude that Salesforce distinguished itself in terms of easy-to-use software to improve customer service and customer-brand relationships; with machine learning, these signature strengths grow exponentially and are within reach of any company.

Demands of the Data Explosion: Trust, Deduplication, Data Quality

“Everyone wants to go digital faster than ever,” said Neeracha Taychakhoonavudh of Salesforce.

Adding to that push is the sheer size of the recent data explosion: humanity produces the same amount of data as all of mankind up to 2003, every 2 days, according to Harvard University data scientist Matthew Stewart, Ph.D. Whoa!

At this time of explosive growth, data cleansing is more important than ever before. Take duplicates, for example. For businesses taking in data from multiple channels, like call centers, websites, or branch locations, duplicates can pose a major problem. What’s more, any business serious about personalization runs into obstacles when duplicates interfere with customer prospects and existing customer relationships.

“Contact data touches every part of a business,” according to Experian’s The Impact of Bad Contact Data Quality. Lost contacts and communication that misses the mark erode trust and forfeit business opportunities.

More specifically, here are scenarios that can occur with poor data quality:

· Sales reps spend nearly a quarter of their time researching incomplete data.

· Databases with nearly 90 percent incomplete contacts, rendering a bulk of records useless. More than 25% of records are duplicates.

· Marketing messages deemed less-than-relevant, as 54% of customers reported in a Trends in Customer Trust survey.

· Inaccurate customer prospect data, undermining executive decision-making and the customer experience.

· Poor quality data costs businesses around $700 billion a year.

Why Machine Learning is Both Accurate and Liberating for Companies

Machine learning cleanses data continuously through its “active learning” and predictive model algorithms. ML can also detect and leverage patterns in data to weed out duplicates and other poor data quality issues, crunching billions of data points from multiple sources in seconds.

While the ML system Salesforce Einstein is built into the core of the Salesforce ecosystem, one of the only apps on the Salesforce AppExchange to apply machine learning for the deduplication process is DataGroomr. Unlike rule-based deduplication tools — which depend on ongoing human intervention whenever the system encounters a duplication requiring a new rule — DataGroomr’s ML is trained to spot duplicates, without the need for ongoing human intervention. That means it does the work for you and can work for any company’s unique dataset and challenges.

Machine Learning’s Rosy Future

Using the power of machine learning built into their platforms, companies can now look forward to larger benefits tied to machine learning including:

· Quality data to find new customers, increase customer retention, and deepen customer relationships and the understanding of customers.

· Sound predictions using historical data, which will become even more critical in a world with fewer cookies.

· Increased revenue; according to a McKinsey Global Survey published in December 2021, a group of respondents who used AI in the core platform of their business attributed 20% of their earnings pre-tax to AI.

· Meaningful insights and informed decision-making based on correlations that surface between key data points.

“In the new world of data-driven customer engagement, trust is everything,” said Lindsey Finch, Salesforce’s EVP, Global Privacy and Product Legal.

As long as data quality is assured, customers are open to richer personalization. A full 84% of customers say that being treated like a person is very important to winning their business, according to a “Trends in Customer Trust” Salesforce survey.

Much is at stake in the deep personalization movement, but the gains are immeasurable unless of course machine learning is doing the measuring.

Steve Pogrebivsky, the president and co-founder of DataGroomr, is an expert in data and content management systems with over 25 years of experience. Steve has founded several technology companies, including MetaVis Technologies, which built tools for Microsoft Office 365, Salesforce, other cloud-based information systems, and Stelex Corporation, which provided compliance and technical solutions to FDA-regulated organizations. Steve holds a BS in Computer/Electrical Engineering and an MBA in Information Systems from Drexel University. You can follow Steve on Twitter @pogrebs or LinkedIn (www.linkedin.com/in/pogrebs).