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Why are Brands Still Failing at Targeted Marketing?

Why are Brands Still Failing at Targeted Marketing?
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Today’s marketers have more data available to them than ever before. From social media statistics to click through rates (CTRs), email opens to loyalty points, marketers would be hard pressed to argue that they don’t have enough customer information to play with when it comes to building their campaigns. Yet despite all this data, many marketers are still struggling to target their customers effectively.

According to recent research from customer data platform vendor BlueVenn, as many as three in five consumers believe that brands are not targeting them effectively with their marketing efforts. Given both the availability of data and the abundance of analysis and personalization tools, this represents a surprising disconnect between how marketers are collecting customer information and the ways they choose to use it.

Recommended ReadInterview with Anthony Botibol – Group Marketing Director at BlueVenn

So why does this disconnect exist, and what is it that continues to hold targeted marketing back?

Across much of the marketing community, there is still a belief that it is consumers who are fighting against the progression of targeted marketing, particularly an unease with the concept of personalization and targeted outreach, finding it ‘creepy’ or invasive. While it is true that an overuse of personalisation can encourage a backlash from customers, BlueVenn’s research shows that in reality, nearly half (46%) of consumers would like to see more personalization introduced, on the condition that it provides a tangible benefit to them or their online shopping experiences.

Given this growing acceptance among consumers, it’s hard to argue that targeted marketing efforts are falling down. Instead, it seems more likely that failed targeting is down to marketers themselves and how they choose to use customer information.

So, where are marketers going wrong? They have the data available, they have the tools needed to personalize their campaigns, and consumers are open to the idea of individually targeted marketing. What more could they possibly need?

For many brands, the associated issues lie far further up the chain – somewhere between the collection of customer data and the distribution of personalized output. It is here, in the analysis of customer data, that so many brands are encountering issues.

While most marketers feel confident in both their data collection and in their ability to customize that data through personalisation platforms, without effective analysis between these two steps, targeted marketing campaigns will be forever doomed to fail (often before they’ve even started).

Read MoreNew BlueVenn Report: Modern Marketers Awry About Turning into Data Scientists!

Often this comes down to marketers’ inability to develop a consistent Single Customer View. Having collected data on their customers’ various buying habits, many marketers still struggle to tie these behavioral traits together into a consistent narrative. Instead of combining multiple data sources into a clear, consistent image of their end customer, the majority of brands still find themselves working with fragmented, duplicated and incompatible customer data in silos. This issue is complicated further by the wide variety of devices and channels that consumers are using throughout the sales journey (both online and offline).

So how can marketers breach this data analysis gap? While some believe that they must reskill entirely – taking on more of a “data scientist” role – this is not necessary to build an effective targeted campaign. Instead, marketers should focus on building a single database within which all of their customer information can be utilized, analyzed and managed. Through the use of a Customer Data Platform (CDP), brands can help break their customer data out of silos, producing in-depth, cross-device analyses without the need for a data science degree. This is future of targeted marketing – not reskilling but retooling for the big data age.

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