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Break Down These Four Omni-channel Silos with Experience Analytics

The idea of breaking down silos in business is nothing new. In fact, esteemed architect Frank Lloyd Wright helped popularize the idea of an open office arrangement because he "believed the design would democratize the workplace by tearing down walls both literally and socially." Despite many workplaces eschewing rooms and cubicles for open offices, the problem of silos in business remains.

Today, we're going to talk about the four different types of silos that hamper many companies and explore how a strategic approach to experience analytics can help them overcome the barriers.

What is a business silo, exactly?  According to Business Enterprise Mapping "The silo mentality occurs when different departments and hierarchy levels in your organization fail to share ideas and information to others outside their interpersonal bubble. The name for these communication gaps was inspired by the idea of a farm silo, which stores, isolates and seals in each of the farm’s outputs in its place."

Sure, if you're trying to keep your corn separated from the wheat, a silo is great. But in business, those silos are hampering a multitude of strategic goals.

One of those goals includes transforming your company from a multi-channel to an omni-channel experience. Most companies have multiple channels through which they sell and support customers, but without tearing down the barriers between the four types of business silos, an omni-channel experience that creates a "seamless experience and consistent messaging across each of these channels" is impossible.

Whether omni-channel is your end goal or not, the idea of providing more transparency around customer and employee experiences is is omni-present in most companies. The increased number of channels and specialized teams to manage those experiences means the risk of silos has increased alongside the rise of the digital transformation. And with the digital transformation comes the (exponential) increase in experience data. By not managing this data and uncovering insights from it, companies are not only putting the customer experience at risk, but also creating an environment that makes complying with emerging data and privacy regulations burdensome.

The symptoms of business silos are more far-reaching than just an inability to deliver a world-class customer experience.  The problems can also include employees that are unfamiliar with other teams and their goals; it can result in an "us vs them" mentality that shuts down the free flow of ideas; it can lead to disengaged employees; and, lastly, it can lead to task duplication when no one knows what other groups are doing.

How Experience Analytics Can Tear Down the Silos

There are four main types of silos that business face today: organizational/operational silos, technology silos, knowledge/insight silos, and experience silos. If you're really into silo-busting and want a granular approach to the concept, here's a great article from Customer Think that goes into ten different CX silos.  For now, let's stick with these four and briefly examine each one.

     Organizational and Operational Silos

Open offices might have been intended to remove the barriers between different organizations within a business, but divide between groups remains a persistent problem. Organizational silos occur when teams and department don't have shared goals in place with plans for working across the divide.

     Technology Silos

The average enterprise uses 1,295 cloud services today. That's a staggering number of technologies to manage individually, let alone get them to work together. The challenge of technology silos is often comes down to resources-- IT teams only have so much bandwidth to manage integrations-- but it can also be cultural, as teams don't want to share info or complicate a system that works on its own.

      Knowledge and Insight Silos

Organizational and technology silos often result in knowledge silos. Data that's held by one team might not get shared out; when you multiply that across the hundreds of technologies that make up the CX tech stack, it quickly becomes apparent that no one in the organization is working from a shared understanding of the customer experience. The lack of shared knowledge and insights means that work is being duplicated and insights remain hidden or underutilized.

      Experience Silos

Customers and employees alike might face experience silos. Experience silos are what happen when the other types of silos are entrenched in the company and result in competing experiences of a brand. Interactions in the contact center should reflect the same brand experience and values as an app-based interaction, for example. Fixing the experience silo is often the end goal for leaders trying to improve the company's culture around silos.

There are many strategies and technologies that companies will need to adopt to address the problem of business silos. But one of the most powerful ways to handle the most common underlying problem behind these silos-- too much data and too few insights-- is by prioritizing experience analytics.  

When it comes to the unique problem of too much data and too few insights, the right experience analytics platform powered by Artificial Intelligence can provide a spark that helps prove the value of tearing down organizational and technological silos. Simply shuffling data from one group to another isn't enough: teams need data to be synthesized and insights to be readily available so they can focus on actions.

Too much data is not going to stop being a problem. That's why Artificial Intelligence is such an important development for analytics platforms that are aimed at helping tear down business silos. The volume of data to analyze and the constantly evolving nature of the customer experience requires a next-gen AI approach that can learn on the fly as data streams in.

But any experience analytics needs to not be another silo itself. To do that, look for an experience analytics platform that is:

  1. Vendor neutral: Can it use data from any system you have and does it allow you to own your data and insights after its in the platform?
  2. Business-user friendly: Data science skills are a finite resource; to engage multiple teams and data sources across the organization, the technology needs to be used a wide variety of users.
  3. Automated and ready-to-go: The 24/7 nature of business today means you need an analytics platform that can keep up with the constant influx of data.  
  4. Customized to your needs: Black-box algorithms and one-size-fits-all models may not provide the insights that matter to your company.  
  5. What goes in must come out: Any analytics platform is not an endpoint; insights need to be easy to share and actions should be clear based on those insights.

At Stratifyd, we've developed the next generation of  AI-powered Experience Analytics Platforms that companies are using to take the burden of manual analytics off their teams and proactively surface hidden experience signals and themes 24/7. See how we're helping them stay ahead of the customer and their competition with a demo today.

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