5 critical barriers to omni-channel analytics and how to overcome them

Offline, in-store, mobile, website and many more; there are multiple touchpoints for customers to interact with a brand. Customers expect a positive experience across these channels. Brands are striving hard to provide a superior multi-channel customer experience. However, analyzing customer’s mindset and improving experience requires data and strong analytics capabilities.

Analytics or predictive algorithms help in tailoring customer experiences thereby increasing engagement & reach. However, omni-channel analytics is relatively complex and there might be several barriers to adoption. In this blog, we will walk you through various barriers of omni-channel analytics and suggest measures to overcome them.

According to one of the recent studies by Lewis PR only 41% of the organisations have successfully managed their omni-channel marketing campaigns with accurate analytics and measurement.

Here are 5 roadblocks that hamper brands in achieving their omni-channel analytics goals:

Lack of technological support: The success of omni-channel analytics retrospectively depends on tools for data management. Most organisations are still using non-integrated legacy technologies and  tools that hamper data management and analytics. Tracking the overall conversion across multiple devices and customer journey across touchpoints requires robust data management capabilities with accurate big data tools and technologies.

Inability to refine data and gain insights: Some brands have efficiently leveraged raw data for delivering a seamless shopping experience that generates better brand recall. However, most others succumb to a heap of data sets. They generate huge volumes of data from different channels and touchpoints and fail to make them more actionable. The solution to refine data is accurate and precise data structures. With the help of advanced computing and big data management tools, it becomes easy to refine data and gain insights for data based decision making.

No unified data visualization: Many marketers lack a unified data visualization and charting to get 360 degree analytics and intelligence. Brands with unified charting and dashboarding can access the data easily, track the entire customer journey and derive real-time insights. There are various tools such as Tableau, Chart.js, Dygraphs and HighCharts which can help in omni-channel data visualization leading to better data based decision making.

Missing out on the social signals: Out of the multiple customer touchpoints where they interact, social plays a vital role and is a key enabler for most brands. Missing out on social insights can hamper the larger goal – customer experience. Understanding the brand advocates, share of voice and listening to the social signals is extremely important. Incorporating data from these insights adds a lot of value over website, instore, offline or application interactions. The omni-channel analytics ecosystem remains incomplete without social signals.

Not incorporating the feedback data: It is really great to run predictive analytics and improve customer experience basis this data. However, data sets obtained from customer journeys aren’t enough. Incorporating feedback of individual customers before crafting an omni-channel analytics dashboard and report is important. Missing out on the feedback can hamper accurate omni-channel analytics reporting. A strong feedback mechanism and technology support with crisp forms that are integrated with CRM helps device better omni-channel analytics strategies and in turn drive meaningful insights.

Cross-channel or omni-channel marketing is challenging and analyzing it is even more complex. It requires expertise in tools and technologies to formulate cross channel customer analytics strategy and disseminate the arsenal of information across various channels.