With the breadth of options now available to consumers, customer journey is more important than ever. Indeed, in a recent survey by Salesforce, 88% of top marketing teams said they believe customer journey to be critical to the success of their overall marketing strategy. The range of channels customers use to interact with organizations is now hugely varied, and keeping track of them all is a job in itself. More important, however, is ensuring that customers engage with them, and that each step drives them towards both purchase and an enduring relationship.
To do this, organizations must have a deep level of understanding of their customers’ behaviors and how they differ between each channel - where they drop off, which route they take to purchase point, and so forth. They must also employ a high degree of personalization to ensure they remain engaged. In a recent study conducted by Econsultancy in association with IBM, 76% of respondents said they expect companies to understand their needs, 80% that brands do not recognize them as individuals, and just 35% that communications they receive from their favorite brands is relevant to them.
Companies have, for a number of years, been looking at data for this, examining historical data for patterns that they can use to segment their audience and target them with materials accordingly. By analyzing and monitoring cross-channel paths using free software as simple and easy to understand as Google Analytics, companies can identify vital insights about groups of people who exhibit similar patterns of behavior and recognize opportunities that may once have gone undetected.
Predictive analytics takes this one step further. Using predictive analytics helps to determine customers’ next move, the probability of churn, and interest in certain product or offer. It essentially enables the personalization of customer journeys in response to events as they occur in real-time, and actions can be put in place to react to them, whether this be emailing them with a certain promotion or ensuring that they are pushed to the front of the queue in a live chat.
One company who has employed predictive analytics successfully throughout the customer journey is online auctioneer eBay. For example, if someone abandons their shopping cart before purchase, an email can be sent gently reminding the customer that it’s still in there. They also use data to identify interesting events that indicate what customers are likely to care about and personalize the search results and deals that appear to them across both the site itself and social media. This does not even require for a product to have been in their search history. If their browsing histories are similar to other people who have bought a certain product, eBay will be given to understand that the user will also likely be interested in it too and promote to them accordingly. They can also gain understand what they want from that product. If the algorithm was to determine that you’re the kind of person to be interested in, say shoes, it will also determine the style you’ll likely be looking for, the brand, and how much you’re willing to spend based on metrics like household income and buying history.
There are, however, pitfalls, and marketers must ensure they do not go too far. As you move from channel to channel and you’re presented with the same advert for shoes, it can feel like you’re being stalked by a pair of Nikes. Speaking to us recently, Kuntal Goradia, Customer Experience & Digital Analytics at PayPal, noted that: ‘Customer analytics industry is empowered with rich information about not only their browsing journey but also their habits, including what places they visit, where they like to eat, where they shop, how much walking/running they do per day, how many hours they sleep per day, who we hang out with….. so many of us use Yelp, Open table, Uber, Lyft, Amazon, Fitbit, Apple Watch, and Facebook, and we can't imagine our lives without a smart phone. Most of these companies use data to create and improve the products that are now an integral part of our lives. But with the power comes responsibility and unfortunately some companies exploit it. Our governing laws are not keeping up with the speed of innovation. As a consumer, we must take precautionary steps on what we share online and as business leaders, we need to keep pushing the regulations that protect the consumers.’
Douglas Daly, Senior Manager of Data Science at Capital One, agreed, noting that, ’As long as the data is used to service the customer, deep analytics is a good thing. However, great care must be taken to ensure the insights are secured and contacts with the customer do not result in embarrassment or worse.’ Both are correct, but these are not considerations that should put anyone off optimizing customer journey with data, it is just something to be aware of. Without data, customer journey will be a mess.