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Challenges of Creating an Effective Customer Journey

Challenges of Creating an Effective Customer Journey

Providing marketing ROI through conversions and, ultimately, customizing each user journey at scale has been a constant challenge for years. Customers are utilizing multiple channels each day, adding extra touch points and therefore data that needs to be analyzed to architect an effective marketing strategy. Fortunately, planning and proving marketing results has never been easier than it is today. By analyzing and weaving together touch points in a deliberate manner, marketing ROI, and more importantly, conversions, are finally within reach.  Through journey analytics, companies are capturing and acting on “Micro-Moments” that were previously unseen in a proactive and time-sensitive fashion – adding millions to their bottom lines. With all this value, why aren’t all companies doing it? Simple – it’s easier said than done. Here are the top challenges businesses are faced with when trying to architect effective, data-driven customer journeys at scale:

Too much Data!

    • Customers spend hours upon hours a day connected to the internet and through hundreds of different channels none the less. Channels, such as websites, apps, IoT devices etc. are capturing data at exponential and continuous rates. Finding value in this constant barrage of metrics is tough without a well thought out plan.

Data that is analyzed is static and retrospective in nature

    • Regardless of the quantity of data, if you are analyzing even day-old data, the main insights you will find are your missed opportunities. Analyzing is one thing, executing is another. By proactively prescribing actions specific to each user while they are engaged, you can become data-driven.

Silo’d datasets and aggregation efforts are intimidating and tough

    • Users engage with a plethora of different channels – each with their own data sets, and insights are hidden within. Aggregating and modeling this data in an effective and efficient manner can be intimidating.

Specific skill sets are required

    • Even if you are confident in your team’s ability to aggregate, store, and model, this vast array of datasets there are advanced analytic and technical skills that can not be overlooked. Python, R, or SQL are common and essential programming languages that will unlock the true value hidden within the data.

Does this sound similar to problems your organization is facing? Do you have general questions about your Business Intelligence infrastructure that you are looking to get answered? Feel free to fill out the form on the right of this page or contact us as it would be our pleasure to offer guidance through these endeavors. 

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