As the Chief Analytics Officer for Earnix, a Tel Aviv-based provider of advanced analytics solutions for the financial services industry, I am focused on how financial services companies can use data and analytics to figure out what their customers really want and how to better interact with them.
Personalization analytics is the key to the future, and financial services companies must rise to a new level of customer engagement that is being defined by such companies as Amazon, Netflix, Apple and Google. They are using advanced analytics to not only understand their customers better, but are able to predict what they will want. All companies in all industries must up their game to reach this new standard of excellence. To achieve this and to really be able to personalize the customer journey, companies must be willing to advance in the following areas:
1. Holistic Data Approach – Know Your Customer Better
Leading financial services companies do an excellent job of building products that leverage their scale of operations and access their vast customer base. Now they must flip that equation and instead of focusing on the similarities among their customers, they will need to focus on what is unique about each of their customers. To be successful in this new environment, they will need to understand individuals and predict what will be the best offer, at the right time and price, through the right channel.
Therefore, it is no longer enough to just utilize product specific and basic demographic information such as name, address, type of account, how long a customer has been with the company, etc. Now companies need to have a holistic view of consumers that considers all of their past interactions with your firm, tracks all of the products they have bought, infer their preferences, explore the use of social media data, etc.
To determine whether a sales proposition is appropriate, you need clean, back-end data to ensure that all customer interactions are relevant. You can then use this data to draw all the relevant information from multiple sources.
To connect all of these dots and get a 360-degree view of your customers, you must use data in a scientific manner.
2. Advanced Analytics – Take the Leap But First Walk
Advanced analytics combined with state-of-the-art technology are the keys to leverage the data, transform it into actionable insights and propositions, enhance your relationships with your customers and drive your company’s business performance.
The main challenge is in finding ways to use the data to build and maintain customer relationships in ways that are different than the past. This relationship has to be personalized, tuned to their specific needs and be supported by a simple user experience. The demand for personalized analytics and customer processes have therefore gained tremendous traction in the financial services industry.
Yet, the implementation of customer centric processes and technology does not happen overnight. A successful implementation necessitates a robust process and technological solution that can cope with a number of specific challenges. These include effective ways of dealing with “Big Data”; lack of in-house analytical sophistication; limited time-to-market capabilities; and ability to measure the impact of various decisions around pricing, propositions, marketing, and more.
I always stress that analytics is a journey: plan ahead, start small, and remember that the most important thing is to start your journey NOW! Rome wasn’t built in a day and neither will your company’s customer data analytics program. But that is not an excuse to put it off and not take the first steps, which should be small ones.
When you start small, you will gain an appreciation of the value of data analytics and what it can provide your organization. And as you start seeing value, it will also be easier to increase your investments in data analytics over time. Or as I like to say: walk, run and then leap. At Earnix, we put this dictum into practice by advising our customers on how to start or take the next step in their the analytical journey commensurate with their readiness, analytical capabilities and sophistication
3. The Next Big Thing: Machine Learning
Considering all this state-of-the art technology and new methodologies can be seriously overwhelming, and difficult to address while still performing in your full-time job. Yet these technologies ensure that the analytics you are using will be not only relevant today but also in the future; keeping you at the forefront of the industry that is increasingly becoming more competitive.
Machine Learning is one such advancement that is worth embracing now. It is center stage in almost every analytical conference and is becoming increasingly popular in top financial service industry conferences. While presenting and attending at the Casulty Actuarial Society (CAS) RPM conference this spring, I heard a lot of hallway discussion about machine learning. Machine learning focuses on a series of statistical & computational modeling techniques, many of which are geared toward predictive modeling. This modeling relies on computer programing to develop, test and refine algorithms with the least amount of human intervention. Machine learning can help you leverage data and promote your customer centric journey.
A recent Earnix survey, “Machine Learning – Growing, Promising, Challenging,” found that there is enormous potential for insurers in this area. In fact, the survey of nearly 200 global insurance professionals from companies that provide personal lines coverage found that machine learning is now being used by 60 per cent of those working for insurance companies with over 5,000 employees.
When asked about the main benefits their organizations have realized from machine learning, 57 percent responded “greater analytical accuracy” as one of the top three. Survey respondents also reported that the most significant promise and expected benefits of machine learning are “greater automation, productivity and cost savings.”
Machine learning will not be replacing financial risk modeling executives or business analysts/actuaries in financial services any time soon. Instead it will augment the work you and your colleagues do resulting in greater efficiency, accuracy, and ultimately improve business results as well as deepen exiting customer relationships. At Earnix, machine learning continues to be at the center of our analytical research and during our upcoming Analytics & Innovation Summit to be held in London, we will discuss how financial institutions can effectively utilize machine learning to drive business performance & customer engagement.
The path to customer centricity is within your reach, but your company should start transitioning from a product-centered approach to a customer-centric model through scientific data and along the journey should also remove the siloes. By doing this, you can create meaningful cross-channel experiences for your customers—experiences that provide customers with targeted, just-in-time offerings in an effective matter. Using these technologies and techniques is the key to driving the personal offering and enabling your organization to rise to the new bar being set across all industries.