Once again organizations insist that “the customer is king.” They have good reasons to think so. It is evident that customers are willing to search the Web or drive to a specific location for the best deal instead of remaining loyal to one company, and when provoked an increasing number are not slow in complaining about their treatment on social media web sites such as YouTube, Facebook and Twitter. So what can companies do about it? Part of the answer is to get better analytics on your customers that allow you to create new strategies for customer acquisition, retention and up-selling that are based on reliable information.
Many years ago, the phrase “360-degree view of the customer” was coined and promises that companies that had this comprehensive view would create better relationships with their customers and by using his customer relationship management (CRM) software they would reap the rewards of having more loyal customers who would purchase more over their lifetime. The problem was that neither his software nor any other could produce this golden view. My research into customer information management and customer experience management (CEM) highlighted the core issues stopping companies even today from producing this view.
The first is that customer data sits in so many systems in so many formats that it is almost impossible to bring it all together to create a complete view of the customer. Furthermore the users of this information – the business – don’t understand the scale of the problem. My research showed that business people believe customer data largely sits in the company’s CRM and enterprise resource planning (ERP) systems and in lots of spreadsheets that individuals have created because they can’t produce the analysis they need any other way. More IT folks understand the true scale of the problem, recognizing that customer data sits in as many as 22 different types of systems (including CRM, ERP, billing, network management, knowledge databases, customer data marts, e-mail, letters, surveys, call recordings and spreadsheets). This fact highlights another issue, which is the diversity of formats of customer data, many of which are unstructured and so have been hard to access and analyze. All of this complicates the ability to apply analytics to gain visibility and insight to the measurement of customers.
My CEM research showed that as a consequence companies stick with only a few key performance metrics to determine how well they are performing from a customer perspective. Volume of sales made, for example, is easy to find from ERP data, and the number of products held by each customer is easy to find from CRM, but more mature metrics such as lifetime value of customers, cost to serve customers, net promoter scores and even true customer satisfaction scores are much harder to come by because they need data from multiple systems in multiple forms.
To survive in today’s highly competitive marketplace, companies need to change quickly. The good news is that there is now technology to help. Preparation of customer related is easily accomplished with data integration and data quality technologies that can clean customer data and create a single reliable source through use of technological methods like master data management (MDM). Analytics vendors then can focus on applying its technology to generate a range of customer measures, metrics and key indicators that provide visibility to process, performance and risk. Specialized analytic providers can use their techniques against text and speech data and the specific content to truly understand the voice of the customer. Others can take the output of these technologies and combine it with multiple sources of structured data to provide a composite analysis of customer activity. Even further are the advancements from specific vendors that can extract the content from social media channels and sites on the Internet to assess the social interactions of customers about its brands and products.
The advances don’t stop there. Having a historic analysis is one thing, but many tasks such as handling a customer’s complaint in a contact center requires a real-time view, and products have emerged that can support this requirement too. And last but not least some products can use predictive modeling techniques on all this cumulative customer information and present future scenarios based on historic patterns, for example, of customers that are likely to defect in the next six months.
Companies have waited a long time for the 360-degree customer view to increase effectiveness, but at last their wish can come true. To acquire it, however, they must invest in some of these new technologies to start to reap the rewards of having better, more business-related information about their customers.
If you are trying to address your customer analytics, I encourage you to participate in our latest benchmark research. What you learn from the results can help drive improvement in your organization.
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Richard Snow – Global VP & Research Director