In 2013 fully 83 percent of marketers surveyed by Regalix Research said that a cross-channel marketing strategy is critical to the marketing function. Less than a year before that, research indicated that more than 70 percent of the public preferred cross-channel marketing. In short, cross-channel marketing is essential to digital marketing success.
However, cross-channel marketing is extremely challenging because it demands a skilled application of big data analytics. It is the challenge that causes most cross-channel marketing campaigns to fail.
Analytical, Interpretive and Technical Benefits and Challenges
One major benefit inherent in cross-channel marketing arises from smart use of the big data that are automatically generated as consumers engage in cross-channel interactions. These massive sets of data are extremely useful from a marketing standpoint when they are analyzed computationally because they reveal associations, trends and patterns that afford us insight into which factors influence human behavior.
Big Data Analytics: Getting it Right
Cross-channel interactions generate big data sets, and marketers need to decide which of that customer data is most relevant for optimizing strategies and building great profiles. For example, studying the cross-channel journey consumers can make valuable insights into customer obstacles and help with improving the customer experience.
This is one of the technical challenges of cross-channel marketing: excellent big data analytics is critical to a successful cross-channel marketing campaign. The right analysis can tell marketing professionals why people click or don’t click, the route most conversions take through media platforms, and why customers choose competitors. A related challenge is the ability to understand the behavior between customer and across channels. The ability to see why and how users move between specific channels is not the same as the ability to see overall movement and motives over multiple channels. Part of the cross-channel marketing goal is to see all of these movements as part of a larger pattern.
It is rich, detailed, accurate data that allows any particular brand to edge past competitors. Transforming a company into a customer-centric organization takes high-level data analytics. Businesses can easily keep current customers and acquire new ones when they implement and support best practices for the data management.
Big data gleaned from customers can be used by every member of your team to better understand how your cross-channel customers navigate your business. If you appreciate the insights afforded to you by this big data “map” that your customers provide for you, your customer service, sales and customer loyalty will all profit.
First, look for trends in your big data that indicate when your customers are having a less-than-smooth transition between one channel and another. For example, do you lose more customers between your website and live support steps? If so, it may be because that transition is weaker, repetitive, frustrating or ineffective. The customer journey map for your business should provide you with information to undergird your technical fixes, and like any other behavioral data, it should also be useful to your marketing team.
Second, study your customer data closely to detect channel preference trends. Which pathways lead to the most conversions? Are the stronger pathways linked to some other issue, or are they showing you a weakness in other pathways? For example, a business that knows that 80 percent of shoppers looking for its kind of product begin on their smartphones will expect to see a large, reliable customer path starting with the company’s mobile app. If this is absent, the business should reevaluate the app: is it working well enough for these shoppers or does it need improvement?
Big Data and Data Siloing
Many companies that accumulate big data are experiencing data siloing. Big data works productively when cross-channel strategies and their relevant analytics are integrated into the overall strategies and management of the entire company across all departments. In other words, the larger business plan created by management and executed throughout the company should be consistent with your cross-channel marketing strategy. Because full integration is rare, data can be isolated or siloed in a department while the rest of the business acts without analytical insight.
A related challenge is the task of unifying data gleaned from the multiple customer engagement technologies a business uses. Marketers need a bird’s eye view of a customer that provides them with the whole story. This lack of a unified view means that businesses may be missing the most important insights, failing to act on important insights or even misinterpreting key data and making mistakes.
Big Data Confusion
It isn’t always easy to consistently collect data for marketing purposes. On traditional desktop and laptop websites much of this data comes from cookies. This means that collecting the same data from mobile users is tougher. However, this first-party data is most accurate and valuable to your cross-channel campaign. It’s worthwhile to ensure you can collect it from all users on any device, and failing to pursue this avenue is a common mistake in cross-channel marketing.
Further clouding the issue is unreliable second- and third-party data. Some businesses try to shape their cross-channel campaigns using not only the valuable and accurate first-party data that they produce, but also folding in other data that doesn’t come directly from users. Data from lists and other sources shouldn’t be blended together with your first-party data because it is less accurate; your first-party data is typically almost 100 percent accurate. In comparison, second- and third-party data isn’t even close. Lists and other data sets may be sold as 80 to 90 percent accurate, but this is difficult to verify and a best case scenario. The end result is confusion and dilution of your targeted cross-channel campaign.
Cross-channel Campaign Case Studies: Big Data Failure and Success
An example of this type of fragmented view on data is the case of Tesco, the UK grocery chain that essentially failed in 2014. Tesco was completely committed to basing its marketing strategy on analytics, customer research and loyalty; for Tesco, big data was their distinguishing feature. When Tesco launched its loyalty program in 1995, it changed the industry.
Naturally, the chain’s failure was just as tied to big data in the public eye as its success was. Some critics argued that the failure should be seen as a warning to companies who rely on big data. However, other experts point out that this was not an inherent flaw of reliance on big data – it was a case of poor analytics leading to bad choices.
Tesco’s trouble was poor analytics and siloing as a result: they didn’t use their data to respond to customer demands and market needs and instead they essentially ignored much of the data. For example, they launched the ill-fated Fresh and Easy chain in the US despite their own data which indicated that there was no demand for the store. The operative issue for Tesco was not reliance on big data; it was their failure to glean actionable insights from their big data and connect them to new strategies.
In contrast, the success of Land Rover’s cross-channel marketing strategy to increase digital sales leads has been a major success: online leads now account for 15 percent of their total sales, a major jump, especially for a big ticket item. The company acted on their information from big data analysis that told them that people prefer to shop for cars online, at least initially. Land Rover created a plan for using cross-channel marketing to engage shoppers on all digital devices at every step in the purchasing process. They knew they needed to build leads online even though purchases of big items like cars are typically in person, so their strategy was to focus on homepage masthead and Lightbox ads on digital sites like YouTube. They also focused on mobile accessibility for the comparison shopping experience. The end result was a notable increase in leads that panned out.
Cross-channel marketing done well requires a significant investment in time, planning and revision based on analysis, but the potential revenue benefits make it well worth the effort. The cross-channel marketing strategy is unique because rather than simply using the various channels independently, it is based on understanding and harnessing the interaction of consumers across multiple channels. Marketers can achieve tremendous ROI by focusing their efforts on the cross-channel campaigns with the highest revenue potential and ensuring they know how to handle their data.
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