Top 3 reasons your customer experience strategy isn’t living up to expectations

Top 3 reasons your customer experience strategy isn't living up to expectations

Data – and big data, in particular – is a major asset for marketers today. When properly leveraged, customer data opens up any number of possibilities for better brand engagement. For the past several years, we’ve been bombarded with tales of the incredible things companies are supposedly doing with their data. Who hasn’t heard about the strides Amazon has made in customer experience and engagement with the help of advanced analytics, for instance?

With those incredible stories no doubt fresh in their minds, many marketers have set out to create the same almost-preternatural customer experience strategies to drive brand engagement. What some discover, however, is that successfully implementing data-driven engagement strategies is easier said than done.

Some of these initiatives peter out with a whimper while others crash and burn in spectacular fashion. Ultimately, what many of these strategies share in common is that they did not live up to initial expectations. Why is that? Let’s review three of the most likely reasons your customer experience strategy is missing the mark:

  1. Budgetary Issues

It usually becomes abundantly clear to marketers when they embark on a data-driven customer experience strategy that getting the most out of big data doesn’t always come cheap. If they lack the data management infrastructure needed to support big data (think Hadoop clusters and their ilk), implementing that foundation comes at a high cost.

Many marketers simply lack the budget to execute their original vision. In fact, according to a recent CMO Council survey of 265 marketers, more than half cited budget constraints as a main obstacle to their customer engagement strategies.

That being said, budgetary roadblocks don’t necessarily doom a data-driven engagement strategy. They do mean that marketers need to be a little more judicious with the way they fund these projects, though. For instance, data scientists command a very high salary, so hiring analytics experts directly isn’t always feasible or advisable. Instead, companies should consider looking to a consulting firm to fill their knowledge gap without breaking the bank on their payroll.

  1. Organizational Buy-In

Forward-thinking marketers may find that being an innovator within your own company can be awfully lonely. Any kind of change can be met with resistance and skepticism, and it’s not unusual for data-driven customer engagement strategies to fizzle out because only certain teams or individuals would commit to them.

The results of the CMO Council Report bear this out: 30 percent of respondents said their company culture was averse to change, while 43 percent cited an inability to get their organization to become more customer-centric. Ideally, any customer experience strategy should have the full support of corporate executives and other key leadership positions. This isn’t always the case, though. Nearly one-third of responding marketers said their strategies lacked support from company leaders, preventing any kind of culture change.

The kind of shift in mindset needed to make data-driven engagement strategies succeed must come from the top down. The results of data analytics projects may fly in the face of conventional wisdom, and employees who have grown accustomed to doing certain things in certain ways will naturally be hesitant to making a change without a clear impetus to do so. Having the full backing of your senior leadership will cultivate a company-wide directive to embrace data and advanced analytics. That support will remove internal barriers and clear the way for your customer experience strategies to really take off.

  1. Improper Data Foundation

Sometimes even engagement strategies with total organizational buy-in and a healthy budget will fail. In those instances, the culprit is often a poorly laid data foundation. That could include any number of issues, including fragmented engagement systems or data silos preventing project stakeholders from accessing all available information. Collecting data is a challenge of its own, and often implementation strategies focus too much on that one aspect. But even the best data gathering capabilities are useless if the data can’t be properly fed into the marketing and sales automation tools or business intelligence (BI) systems for meaningful processing. Whatever the underlying issue may be, the end result is an inability to turn data into actionable customer insights.

There are many steps that marketers need to take to address this problem. For one, they must identify and knock down data silos and get that information into the hands of analysts and other project members, and into specialized software tools that allow further processing.

Customer Identity and Access Management (CIAM) solutions can help here, providing an easy way to gather customer data into a single, central repository that provides straightforward integration with other systems, such as CRM, BI, or marketing automation solutions. Having the right integrations in place is a must-have item on any customer engagement project to ensure data flow and further processing does not become an obstacle. The variety of marketing software tools used in enterprises is growing steadily, and companies need to make sure they have a cohesive strategy how to utilize the data with their existing technology landscape, which typically includes products and services like Salesforce, Marketo, Oracle Eloqua, Pardot, SAP, or web content management systems like Adobe Experience Manager, WordPress, Drupal, and many others.  

CIAM software offers a unified platform to support data-driven customer experience strategies. With this tool in place, you can streamline brand engagement across all touch points and collect profile, activity and other data along the way. CIAM solutions put your brand in the best position to succeed and improve customer experience.