Continuously connected customers with multiple devices and an endless number of interaction touchpoints aren’t easy to engage. They’re on a multi-dimensional journey and can appear to a brand at any time, on any channel.
It’s not surprising, then, that consumers give brands low marks for their ability to deliver an exceptional customer experience. According to a recent Harris Poll survey, only 18 percent of consumers rated brands’ ability to deliver an exceptional experience as excellent.
Even if the data about a customer is well managed, to successfully engage the connected consumer and deliver highly personalized experiences requires advanced analytical tools. Artificial intelligence and machine learning are now being applied by innovative businesses to create real-time, personalized experiences at scale with models that intelligently orchestrate offerings throughout the customer journey.
How to Deploy Effective In-Line Analytics
It’s easy to get caught up in the hype surrounding AI and machine learning, with business leaders chasing shiny objects for an AI application that might have little to do with critical business goals.
When paired with a persistent, real-time, single customer record, AI and automated machine learning platforms can be utilized to meet those business goals, increase revenue and fundamentally change the way brands communication with customers.
In this eWEEK Data Points article, George Corugedo, Chief Technology Officer and co-founder of customer engagement hub maker RedPoint Global, suggests several truths about machine learning that every business leader should keep in mind when thinking about customer records.
Data Point No. 1: Machine learning should drive revenue.
The ultimate goal of machine learning shouldn’t be a flashy, futuristic example but instead a system to drive revenue and results for the business. The result of effective machine learning isn’t likely a robot, chatbot or facial recognition tool – it’s machine learning-driven programs that are embedded behind the scenes, driving intelligent decisions for optimized customer engagement.
Data Point No. 2: Having one model–or even many–is not enough.
Organizations need many models running and working in real time to truly make machine learning work for their needs. For future-forward organizations, intelligence and analysis needs to be embedded, so instead of using one model, multiple in-line analytic models can incrementally adjust and find opportunities for growth. These fleets of ML models can optimize business functions and drive associated revenues.
Data Point No. 3: When applied in silos, machine learning is not as effective.
Today’s consumer is omnichannel. Businesses must forego the traditional channel-specific “batch and blast” approach that sufficed when customer choice was limited and the buying journey followed a mostly straight-line path. Today’s customer journey is dynamic, and the learning applied to the customer relationship should be, as well. Machine learning is particularly well-suited to solving these multidimensional problems.
Data Point No. 4: Analytics are only intelligent when models are up to date.
News flash: Machine-learning models age and can quickly become stale. For this reason, organizations must consistently rebuild and retrain models using today’s data. In fact, models should be developed, updated and even applied in real-time on an ongoing testing basis so that businesses can truly capitalize on the moment of interaction. This is most effective in a closed loop system that continually looks for incremental changes to optimize.
Data Point No. 5: You don’t need to be a data scientist to benefit from machine learning.
When models are configured correctly, they will run 24/7 looking for opportunities within the data, set up and managed by marketers. These systems can be set once and guided to produce the specific business metrics needed. With every record tracked in the system, insights are pulled easily, and the recommendations can be made automatically. Businesses should focus on producing continually updated data and let the automation tools use machine learning to drive greater revenue.
Data Point No. 6: Summary: The power is in your hands.
Machine learning has the power to fully transform an enterprise. Therefore, it’s natural for business leaders to get lost in the hype and lose sight of the real value it can deliver day-to-day. The truth is, the real value of machine learning is that it allows businesses to try new things, amplify creative strengths, reveal new discoveries and enable collaboration across the organization. However, these benefits will only be realized once organizations get past the hype and are willing to walk into the weeds.
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