How Total Visibility into the Customer Journey Increases Advertising ROI + Lifetime Value
In the marketing area alone, McKinsey estimates that data-driven companies worldwide improve their marketing ROI by 15-20%, which adds up to $150-$200 billion in additional value.
For ecommerce and omnichannel brands alike, keeping up with every element of new business technologies makes the difference between a growing business and one struggling to survive the pending apocalyptic era for retail. When it comes to strategies behind how to move at the speed of your customers’ brand engagement, total visibility into their journey is required to move at the new speed of retail business. That said – when it comes to Big Data, the question does not lie in if it should be used, but rather how can it most effectively be used in the ever-present moment of “now” to meet consumers’ demands.
The necessity of total visibility into the customer journey
The customer journey is a buzzword in and of itself, but marketers need to fully grasp and acknowledge it. It refers to the path the customer takes before becoming a customer, it is like a life-cycle analysis: it starts from the first interaction with your product and service, goes through the decision-making process, the purchase, delivery of product, interaction with the product and support you offer, and up to the point where it churns. The customer journey should be supervised carefully, as any missing link can stop a lead from becoming a customer, and marketers need to instantly see where the problem lies.
According to janrain.com, 74% of customers are frustrated when receiving marketing content that does not relate to their interest. As such, personalization is the key to a successful customer engagement strategy.
As one example, to better understand our customer behavior, the best thing we can do is analyze the stats of every channel used for advertising. We investigate our weblogs and Google Analytics to understand how a customer ended up on our website, the time spent on our pages and see the overall interest in specific products or services, what product they liked, saved, added to cart, deleted from cart etc. We look at our Social Media stats to understand the sentiment, engagement and responsiveness, see who liked a post or reviewed a product. We analyze carefully all the data behind our CRM system, and we investigate our support tickets to see who interacted with our service or product and what type of request they had. However, all this data is decentralized, separated, and even if we do get hints on what could be improved in one of the channels, the problem is that without a proper correlation between all the analytics, we might be missing out on essential information that could be the game changer. That’s where Big Data comes in.
Big Data analytics, the answer to all yet-to-be asked questions
By implementing a Big Data project, you can have all this information, and more, centralized, unified and correlated so that you can get a clear overview over your leads and customers, in a personalized way. The analytical results can reveal entirely new patterns and insights you never knew existed – and aren’t even conceivable with traditional analytics. With entirely new visibility into behavior patterns, marketers know what campaign converted the most leads into customers, and based on this exact information, can focus on those specific campaigns that generate the most leads, and customers.
Moreover, based on the associated information provided by Big Data analytics, marketers can spot what customer behavior is associated with a high chance to churn, and can proactively engage with that market segment to avoid client loss.
For example, one of the most common uses for Big Data is the recommendation engine. Amazon states that more than 50% of its sales come from recommendation. After you purchase a product from Amazon, you will see other similar articles that might be of interest. The ability to show similar products that could be of interest lies in the use of big data analytics to analyze historical data.
ROI challenges with personalized marketing
The most significant challenge with customized marketing is the lack of data and a correct customer profile. According to janrain.com, 74% of customers are frustrated when receiving marketing content that does not relate to their interest. As such, personalization is the key to a successful customer engagement strategy.
With all the data available, especially with a unified data analysis, marketers can do accurate personalized marketing. For example, if a customer is searching for a specific flat screen TV, a retargeted, one to one campaign can be run with “flat screen TV on sale”, thus increasing the chances of purchase, as it is direct marketing to a possible interested customer.
By connecting all of the dots in your data, you can make sense of the effectiveness of the content you publish on Facebook or Twitter through each post, which can then be optimized to the core. By paying attention to real-time data from social networks, such as likes, follows and tweets, you can have a better view of the content and engagement, and provide more relevant information.
For example, you can do sentiment analysis on your Facebook posts, see who is engaging with a conversation on best flat screen TVs, and follow up with them with an e-mail listing the mentioned products, instead of triggering an e-mail with a product that might not be relevant to them.
In a book by McKinsey, “Big Data, Analytics, and the Future of Marketing & Sales,” it is mentioned that personalized marketing can deliver five to eight times the ROI on marketing spend and lift sales 10% or more. Moreover, the same book shows that companies using data-driven personalization can improve marketing ROI by 15% to 20%.’ A study from Forrester said that by matching behavior to products in marketing messages, marketers had seen a 15% revenue lift.
However, note that just by combining all the data into one place is not going to be the ultimate game changer for your business, as we all know there are so many other factors that make a business successful. But by using a data-driven approach, and taking the guesswork out of the equation, you will likely see an improved ROI.