Organizations are increasingly investing in predictive data analytics to drive continuous improvement in customer experience (CX) strategies. Today, even more so, with the advancements in AI-guided machine learning solutions to monitor, mine, and surface valuable customer insights. The question becomes: How can companies make customer data actionable to transition from a reactive to a proactive CX approach?
Turning Intent Data into Action
Just having customer data alone is reactionary. Only some organizations can make the mental leap from what they see to how they should respond. The key is making data actionable, so it becomes proactive information. It is only then, when you can put that data in front of the people who need it, and provide next-step guidance on how to use it to take advantage of opportunities or recover from concerns.
Modern customer relationship management (CRM) platforms infused with AI enable organizations to run customer behavior analysis and structure that data into a response that can then automatically execute or propose next-step actions to the consumer of the information.
One example is leveraging CRM to run analytics on invoice information that exists in a company’s enterprise resource planning (ERP) solution to help predict and give actionable guidance to sellers and marketers, such as the purchasing patterns of their customers, to inform recommendations using AI and analytics. This is a proactive CX approach centered around anticipating customers’ needs and deepening connections while optimizing the customer journey reflected in their purchasing behavior.
Leveraging Intent Data to Personalize the Experience
Today more than ever, it’s critical for marketers, sellers, and customer service reps to understand what’s happening in the businesses and leverage customer insights such as purchasing behavior to turn information into revenue. This requires more than just getting the information and showing the analysis, but processing that analysis.
So, now that we have the information, what should we do with it? What actions should we take? How can this customer’s purchasing patterns initiate a marketing campaign theme that can be automatically generated and executed through our marketing automation platform? Does it align with the attributes of other customers like them for greater reach and applicability?
The earlier example of analyzing purchasing patterns also shows if the company is losing market share or if its customers are shifting purchases from one product to another. Customer-facing teams can extract all these details and more, from invoicing data combined with CRM data for actionable intelligence on responding and providing the best experience unique to their customers.
Linking Intent Data to Engagement Scoring
Lead scoring is critical for effective sales and marketing strategies, but its unpredictable nature leaves room for improvement. AI takes the guesswork out of lead scoring and replaces it with science – always updating data based on the measurable activities that drive lead conversion. With AI, lead scoring is based on similarity to historical conversions, while ideal customer profile matching identifies similar leads to a company’s past and current customer bases. This means no more “fishing” to support sales teams with high-quality leads.
In addition, by using customer interactions from sales, marketing, and customer service engagements, organizations can create an overall “engagement score.” Measuring this engagement score over time can provide insights on if your customer engagements are increasing or improving so you can double down and do more, or if they are on the decline, so you can pivot.
All of this data and more is feeding a company’s business process engines to support proactive actions to service customers. The results of doing so are avoiding loss of revenue, increasing share of wallet with customers, and taking advantage of new revenue opportunities quicker.
Detecting Intent with Sentiment Analysis
Whether engaging with a customer in person or via chat or email, companies need to know how to respond empathically to customer conversations in real-time. Sentiment analysis powered by AI is supercharging CRM functions such as sales, marketing, and service interactions with the power of knowing each customer’s and prospect’s emotional state and intent – immediately enhancing the customer conversation.
Empathic AI solutions are becoming increasingly good at using sentiment analysis capabilities to understand the customer better, leveraging a combination of Natural Language Processing (NLP) and AI to surface next-level or next-best action for empathic customer engagement. It provides the predictive insights businesses need to recognize customer emotions and intent with accuracy and precision at scale.
For example, by providing teams with an understanding of this intent, sales, and service personnel can be prompted on the next-best-action to support the customer journey – i.e., to escalate to a supervisor, present a save-the-sale offer, or take an opportunity to upsell. In addition, business professionals can review sentiment data to evaluate overall CX and journey effectiveness – providing the means for continuous improvement in meeting and exceeding customer expectations. By identifying and solving the emotions behind the “why,” companies can better serve the needs of their customers.
Data Quality Matters
Poor data can point to wrong customer identity. Best practices for truly “knowing” your customers and prospects start with ensuring that your underlying data is “clean” and that you have good data hygiene, governance practices, and ongoing data maintenance. This is becoming increasingly important when you consider AI and ChatGPT are used against your own data for information automation, so having a foundation of good data and good practices for curating, maintaining, and cleaning your data is critical. If you have sloppy data, wrong data, or miscoded data, all this automation will just enhance and exacerbate mistakes. And the speed of automation means errors can go wrong faster, which means new checks and balances will be required to keep pace.
The Winning Formula
One thing marketing and sales teams have in common is abundant data from their CRM platform. With advancements in applying predictive analytics and buyer intent data to CRM, marketers, and sellers can transition from a reactive to a proactive CX approach that fuels business growth.
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ABOUT THE AUTHOR
Christian Wettre, Senior Vice President of Sugar Platform at SugarCRM.
Christian Wettre is SVP and general manager of the Sugar Platform at SugarCRM, a customer relationship management software company.