So, your customer made a purchase. Great! But what sealed the deal? Will she be back? And did she tell her friends and family about her great experience with your company?
On one hand, answering these kinds of questions has never been easier. Businesses today have so many sources of customer experience (CX) data they can mine for insights: chatbot transcripts, social media posts, call center recordings and store surveillance footage, to name a few.
On the other hand, I’ve seen many organizations struggle to effectively and efficiently glean data-driven insights. Often, we have so much data from so many sources stuck in so many silos, it’s proven humanly impossible to connect all the dots. But making those connections is precisely what enables organizations to understand a customer’s motivations and — most important — how to use that CX to get other consumers to make a purchase.
“Humanly” is the operative word, because it highlights a solution that retailers and other businesses are embracing to overcome this challenge. Only artificial intelligence (AI) can mine all of that structured data (such as browsing and purchase histories) and unstructured data (think tweets and Facebook posts) fast enough and deeply enough to uncover actionable insights. But these are the insights companies need to maximize sales, fend off competitors and boost brand loyalty. That’s why I’m helping more and more businesses implement AI solutions, such as IBM Watson, which can track all of a customer’s interactions with a brand.
Data-Driven Insights at Work
Understanding the customer journey is key because many purchases aren’t impulsive. IBM Watson Customer Experience Analytics lets a business see, for example, when a customer downloaded its mobile app, what he searched for, which products he looked at, whether he checked customer reviews and if he liked the company’s Facebook page. That information then can be aggregated with other customer journeys to identify which step was typically followed by a sale or the point at which shoppers often abandoned their research.
For example, those actions could indicate that a particular product description is incredibly compelling or incredibly confusing, or that frequent Facebook updates spurred followers into purchases. Data-driven insights can also reveal whether customers typically start following the business on social media before downloading its mobile app, or vice versa. This comprehensive understanding is crucial for determining exactly which channels are worthy of additional investment and which ones don’t resonate with consumers. Without AI, managers tend to make such decisions by relying on gut reaction more than evidence.
AI also empowers organizations to leverage data from Internet of Things (IoT) devices, such as connected automobile sensors that detect excessive speed and hard braking. To give some insight into this, traditionally auto insurers have based drivers’ premiums on general information about their location and demographics. Now, insurers can apply AI to vehicle IoT data to understand how individual driving habits affect the likelihood that a policyholder will submit a claim. That, in turn, lets insurance companies offer so-called “pay how you drive” plans. These save insurers money by reducing their risk and reducing churn among the safest drivers, whose habits earn them lower premiums than competitors can offer.
Only AI can deliver those kinds of insights at scale. Just as important, tools such as IBM Watson can deliver them without requiring a staff of AI specialists. That frees up time and money that companies can use to act on those insights and start wowing customers.
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