Saying “Marketing has changed” is perhaps understating the incredible dynamism of this age of always-on customer engagement. Marketing is evolving everyday in a more apposite expression. Gone is the trusted linear progression, the funnel if you will, where the customer homes in on his purchase decision as he advances from product awareness to conversion. He reaches a buying decision in a much more dynamic and iterative way. He relies on multiple sources to pick and narrow his options, is both expressive and explosive about his experiences with a product. He doesn’t easily fall prey to the fancy-shmancy of one way messaging. What he downright digs to the bone is a product/brand experience that is highly personalized, responsive, and relevant to his needs and expectations (lets add ‘authentic’ to that list). And once he has had a taste of this just- for-him-sweet loving, he expects the same courting, comforting, compelling experience from every brand he interacts with.

The bottom line is that the customer craves (rather demands) personalization and relevance at every touch point, which means that the marketers must always be receptive and responsive to the customer’s needs and behaviors. This requires a strategic tandem play of advanced marketing analytics to observe the signals of customer intent and a response program designed to use those signal to drive relevant engagement across customer touch points.

And to design responsive, relevant marketing programs, you need to develop a positive data culture in your organization. Once you integrate data at the centre of your strategy, the seams of your program to strengthen, reflected in a McKinsey Survey that showed companies that use customer–centric marketing analytics are twice as likely to make above-average profits, are nine times more likely to have greater customer loyalty, and are 23 times more likely to enjoy success at new-customer acquisition.

The game today is about being able to spot and predict customer intent based on the data around key metrics, and advanced analytics that are key to finding, collecting and making sense of that data to deliver pinpoint personalization.

Using Analytics to spot the Intent
You need to set well-designed triggers to alarm you of the first purchase intent to close in on a customer to make the kill. These triggers could be anything from product queries on social media to downloading a piece of sponsored content to signing up for a coupon. Analytics help you spot and respond to these signals, and steer the customer’s attention to your brand.

In most scenarios though, identifying intent can be less straightforward than spotting one trigger being pulled. Consumers may develop the intent to purchase more gradually through everyday interactions with the brand across channels and touch points, from stumbling on it on a social media conversation, or coming across as an ad somewhere. You could also notice if the customer’s behaviour is changing, if he is maybe looking to settle into a new area, buy a new car, or having a child which can lead to changes in purchase behavior across the spectrum of unrelated categories. In this case, you can leverage advanced analytics to “sense” mushrooming interest to get a head start on the course of directing the customers to a positive purchase decision.

Using analytics to predict the Intent
Being able to spot intent is critical to success, but what’s absolutely game changing is being able to anticipate customer intent. The whole BI and advanced analytics industry is based on this promise and premise. They basically gather customer data from across touch points and channels, to help you derive insights on his preferences and behaviors. They help you predict his next move, like when he is most likely to purchase a product. A lot of companies are boosting their revenues by leveraging advanced analytic systems that let them in on what product a customer might like to buy next, and when.

But I think the importance of data in today’s marketing also puts the onus on the marketer to incentivize the customer for volunteering to share data. For instance, some health monitoring wearables recommend relevant products based on the data that they collect of a user’s workout. And doling out promotional coupons in exchange of customer data never goes out of fashion. Your ability to extract, collect and leverage data will always give you the competitive edge in the market.

To be truly responsive and drive relevance in every interaction with the customer, you need to build analytics to core strategy, create systems and content to react quickly to relevant signals when they arise. You can use Analytics to generate heat maps of a customer’s decision journey to identifying opportunities around your brand across the digital space. You can segment the heat maps at a granular level for a customer segment based on a broad range of criteria, like behavior, demographics, location, age, buying stage etc. And then based on this detailed data on chosen customer segments, you can develop a rich set of personalized messages, to drive better, more valuable interactions.

Research shows that putting analytics at the center of your personalization efforts can increase the ROI on marketing spend by five to eight times and lift sales by upto 10 percent or more. Amazon is the glaring proof of the power of advanced analytics and relevant messaging.

In the end, marketing is about being responsive to customer intent, being able to quickly react in a relevant and profitable way. How well you are able to drive customer relevance has profound implications on having the edge in the marketplace and securing longer-term profitability.