Without a doubt, we are living in the golden age of analytics. Most enterprises have implemented transactional applications to automate (and digitise) their core processes such as finance, human resources, supply chain, and customer relationship management. The first wave of analytics took advantage of these digitized processes to get additional insights into data within the four walls of the enterprise. But that is quickly changing – with an increased focus on customer analytics.
Today, companies in every industry are trying to leverage data about their customers to make better business decisions from which customers to target, to which products to offer. In the retail industry, companies are trying to sift through petabytes of buyer information to understand and leverage household loyalty to their brands. In the hotel industry, established leaders are investigating buying patterns and social media to help them guide inventory prices in the age of Airbnb. In the pharmaceutical industry, leaders are analyzing patient journeys using medical claims data to detect and assist patients with the best therapies.
While the benefits of customer analytics are undoubtedly high, getting these customer insights is not easy. There are three main obstacles that companies must overcome.
Customer Analytics Obstacle 1: Access To Data
The first obstacle is getting access to comprehensive customer data. In today’s digital and social world, there is a preponderance of information about your customer if you know where to look and how to bring that information into your customer model.
With the increasing number of digital sources – from the FitBit to Strava to Healthcare applications to Facebook, the bigger challenge for most companies is how to get access to all this information, not if this information exists. And this is the key challenge for most enterprises, as bringing in customer information is a tricky problem.
Most companies have realised that their internal customer tracking applications barely contain 8%-15% of the information they need to understand the customers’ buying preferences. Supplementing this with external data sources is a challenge for every CIO, especially as enterprise data warehouses are usually rigid and sluggish. Trying to adapt data warehousing technologies from the 1990’s to the unstructured and exploding data sources of today has led to countless failures, and has undoubtedly led to the success of cloud-based solutions such as Microsoft (Azure) and Amazon (AWS). Our analysis of the industry shows that companies that integrate cloud-based customer intelligence platforms into their technology stack can integrate and leverage customer data better than their peers.
Customer Analytics Obstacle 2: Knowing Domain
The second obstacle is knowing the customer domain and understanding how they buy. This is a harder problem than it appears to be. How a consumer reacts to retail offers is very different from how a physician diagnoses and treats a patient. So, understanding the domain that the buying decisions are made in, the competitors, the threats and opportunities and the constraints that oversee buying decisions etc. is vital. This is also often an overlooked part of the problem.
Many opportunities are squandered by taking a model that is useful for one segment of the industry and applying it to another vertical altogether. Even in much related problems such as optimising hotel room prices and flight prices, we often have very different approaches to analytics because the customer domain is slightly different. Investing in a deep understanding of the customer buying decision is vital in today’s environment where the information about the customer buying decision is widely available.
Customer Analytics Obstacle 3: The People
The third big obstacle is actually the people that can analyse this information and provide these insights. Sadly, the number of analysts that can leverage statistics to observe patterns in very large data sources, and use these patterns to optimise sales and marketing decisions, is dwindling. A note to the young readers – if you invest in an education in Operations Research, you will be a valuable and rare commodity today and 20 years from now.
This challenge is worsened by the fact that competitive advantage always needs to be developed and honed by skilled analysts – while machine learning is emerging and has much promise, the human element to directing strategy is still dominant and will continue to guide even machine learning algorithms.
We have worked with several leaders that have solved these problems by using the cloud to integrate customer data, by having deep domain knowledge of their customers and the buying environment and by having access to the analytical experts to provide competitive advantage. The learnings from these successes are that analytics and insights in the customer world are hard to get, but great to leverage. Investing in the right technology and human resources pays off in the short term – one company we worked with had a 14X ROI within 12 months.
