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How machine learning and IoT increase customer lifetime value

Any business looking to implement emerging technologies has one primary goal: to generate revenue. Deploying advanced digital technologies into business functions is no small undertaking; it requires a large financial investment, a reskilling of the workforce and a cleaning of vast amounts of data to ensure it’s prepared to be analyzed. Simply put, if you take this on, you want to see the return.

However, there’s a fundamental problem with the approach many companies are taking with machine learning. They are using it to superficially enhance the customer experience, but stop short of transforming it into a true revenue-generating engine.

Take fast-food companies, for example. Many are racing to introduce AI-powered menu boards that will recommend add-on items based on the current selection, restaurant traffic or conditions such as weather or time of day. While this is a fantastic upsell opportunity, this could turn out to be more of a novelty than a mission-critical system that will boost the bottom line.

Personalization for a purpose

For AI and machine learning to ascend as top revenue-generating engines for the business, advanced analytic models must be embedded across the complete customer lifecycle. Without analytics that illustrate the why and the how, the technology does little more than scratch the surface of possibilities.

Personalization for personalization’s sake — such as seeing your name on a menu board — accomplishes little more than making the customer feel special in the moment. It is vastly different than using advanced analytics and IoT to personalize the experience for a segment of one in real-time based on their behaviors, interests and future intent.

Consider AI-powered chatbots that help customers resolve simple issues. The customer might be pleasantly surprised by the user-friendly experience, but a single interaction will not translate into direct and measurable revenue gains. Why? Because it’s restricted to one channel.

Data siloes inhibit chatbots and allow them to only go so far. Perhaps the bot can recommend a product that the individual later purchases, but if the data is siloed by channel, the brand will not have visibility into the entire customer journey. This significantly clouds any direct revenue impact and depresses the value of the advanced technology; a one-off sale is not akin to direct revenue lift.

According to Accenture, 91% of consumers are more likely to shop with brands that provide relevant offers and recommendations. Personalization also drives retention, which astute brands know is more profitable than acquisition.

Unlock channel constraints to realize new revenue streams

To produce a revenue lift that moves the needle, advanced technologies must automate intelligence to dynamically engage with customers across every touchpoint available.

Additionally, self-training models should be built on unified customer profiles that capture data from every source in the moment. Embedded intelligence — supported by a complete view of the customer — has the power to recommend a next-best-action to a segment of one that is far more likely to end in conversion.

This is transformative. Traditional engagement strategies are bound by channels, making it very difficult to know how a customer moves throughout a dynamic journey. Say an email is sent to a segment of customers, with success measured by click rates and conversions. If a customer who doesn’t respond then shows up anonymously on the website, without any link between the email and the cookie, the customer will likely receive an inconsistent message and end their journey prematurely.

Or, take the example of a company that sells coffee pods. With a traditional model, customers buy a subscription to deliver coffee on a regular basis. However, this model ignores actual consumption and results in either a backlog of coffee or the customer running out before the next subscription arrives.

With a connected brewer that understands consumer behavior, the brand can deliver the right pods when they’re needed. With an added layer of machine learning, the brewer can begin to understand more sophisticated factors as well, such as day of the week or time of year or even the weather– and deliver taste appropriate product accordingly.

Using embedded advanced analytic capabilities unbound by channels and data siloes, businesses will know in real-time everything there is to know about the customer. The intelligence is in knowing the right question to ask and confirming easily with the customer then and there. Frictionless relationships win revenue and loyalty in interactive IoT.

By consistently feeding data collected by IoT channels into machine learning models, businesses can essentially predict what the customer will do next.

Optimize models to deliver real business value

Advanced digital optimization ensures a consistent, personalized message and journey for a customer regardless of channel or any other variable.

Embedded AI and IoT allow businesses to use models built on their customers’ data to automatically recommend the next-best-action on each stage of the customer journey based on business goals. Further, simulation engines constantly watch models and will move new models into production that predict better outcomes based on predetermined metrics. This results in reduced operational expenses, increased productivity, improved personalization at scale and increased customer lifetime value.

Automated, embedded intelligence enables hundreds or thousands of models to run concurrently, all with a single-minded purpose of exploiting revenue opportunities according to any metric the business proposes. The resulting personalized and differentiated customer-facing experience empowers businesses to monetize customer data and truly impact the bottom line.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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