Customer is the King – Analyse & Maximize

In the earlier days, an organisation would claim itself being creative, intuitive or a market leader to survive, but not anymore. Right now, when every aspect of the business is under scrutiny and pressure, the CXOs are evaluating each penny invested and wondering when and what they will get back in return for that investment. There is a growing need for quantifiable proof to support each investment decision being made and its returns.

Understanding the customer – his needs, preferences and prioritizing customer service remains the numero uno factor for business profitability. Still not convinced? Below is a list of statistics that will get you thinking:

In today’s world, many organisations ask the question – ‘Where are the customers? How do they buy? What do they like and dislike? What do they want?’. Understanding each customer and the value he brings to the organisation is critical which needs to be measured and tracked periodically to make the most out of that relationship.

Analytically driven customer insights can enrich the customer-organisation relationship and drive key decisions associated with it. It is critical to understand each customer’s real value based on his past and current status, the impact of marketing and servicing costs made and predict the future behaviour of the customer which will in turn drive organisation’s future value. Mathematical and statistical analysis can transform chunks of business, transactional and market data into valuable insights.

Customer analytics are of many types from being as simple as calculating RFM (Recency, Frequency of Monetary) of each customer to complex by using statistical, machine learning algorithms and heuristics. There are multiple sources of data: internal sources such as demographics, transactional, payment patterns and external sources such as bureau and macroeconomic indicators. There is a growing trend to include non-traditional sources of data such as contact center logs, customer surveys, web logs and social media which will enhance customer analytics solution. Using this data can help in understanding the pulse of the customer almost real time, focus on the brand influencers and learn from strong criticism. A Fortune 100 company was able to increase its campaign response rate by 200% through effective use of ‘propensity to respond’ analytics model. A leading CPG company which sponsors football teams was able to understand the relationship between its products vs. fan’s behaviors and identify the social influencers which could impact its brand.

It is very critical to understand the objective of analytics through a thorough business discovery. Additionally, the implementation of the analytical framework can be executed in phases – proof of concept, proof of value followed by complete deployment. Through the use of analytics and design of experiments, meaningful customer segments, behavioral patterns and propensities can be developed which can be used in business decision making. Developing future based models from the integrated data-set can help organisations identify ideas around lead generation, customer acquisition, customer relationship, profit maximization, cost reduction, customer retention and risk mitigation. Amidst the growing variety, volume and velocity of data – the machine learning algorithms are making the systems and applications more cognitive and self-learning.

According to a report from BMO Capital Markets, marketers are spending $50 billion on big data and advanced analytics in hope of improving marketing’s impact on the business. Now this is only from a marketing perspective and the amount is only higher when we add in service management, profitability and retention strategies. This reflects the belief among organisations that big data and advanced analytics can significantly transform business dynamics.

The below steps must be used to make the most of advanced customer analytics:

• Business discovery and questioning the assumptions: This phase is the most significant phase as it lays a foundation for the investments made into analytics. It is important to plan and gather all assumptions and questions that arise through a meticulously planned business discovery process. It is very important to ask the right questions at the beginning of any analytical process and question every assumption in the current state of the business environment. A US based CPG leader wanted to introduce six new SKUs to the market and had the following question – what is the price point that each SKU needs to be launched which will give maximum possible market share?
  This pointed question helped the organisation to target the right data sources, develop pricing models and conduct simulation which developed multiple scenarios by changing pack size, price, promotion and availability. This helped in identifying the best price point for maximum possible market share.
• Make the most of all possible resources: Targeting multiple data sources can enhance customer analytics models and provide for better insights, allowing more accurate views of opportunities and risks. A global pharmaceutical major had recently launched a product for the treatment of auto-immune disease. The executive management wanted to develop an appropriate global marketing plan for the product through a detailed analysis. The customer analytics team developed detailed country level situation analysis for the product based on multiple perspectives such as disease, market and customer. Primary data sources and secondary data sources such as journals, analyst reports and market audit data were used for this project.
• Integration across channels: While driving analytics projects, it is important to focus on integration of outputs and efforts across all customer channels to gain maximum coverage that will help aim, lead, stimulate, track and improve interactions across each customer touch point such as text, speech and web. This will provide for enhanced understanding of the customer and segment behavior, metrics, loyalty and churn over time. A common, scalable and foundational infrastructure with improved analytical tools will come very handy to facilitate fast and dynamic customer engagement. A leading European carrier wanted to identify sales drivers across various distribution channels. Relevant attributes for measurement of effectiveness where identified and analysed. Additionally, all service requests and bookings were closely monitored and checked for revenue, lead time and delivery type. This enabled the carrier to identify top priority channels, enhance service management / sales and facilitate its own website with enhanced features for booking.
• Simplification: Too much of choices and information can be overwhelming for any customer, which is why a process or a dashboard must be simple and user friendly. Else, it will never be used. Simplification can be through elimination of waste and defects, turnaround time reduction, employee engagement and continuous improvement. A leading US bank recognized that a large percentage of its business banking sales and profits was flowing from a small proportion of its customer base. The executive leadership wanted to drive revenues with reduce costs through focused engagement activity. The analytics team conceptualized a predictive analytics framework which adjusts the current value of each customer by incorporating effects of cross-sell of products, growth and attrition in existing relationships. This helped the bank to identify the top 30% of most profitable customers and the factors that drive their relationship with the bank. With this, they developed a detailed engagement plan with seasoned relationship managers who can work with each of these top customers. The relationship managers were assisted with key and precise inputs which helped them engage with their respective customers that eventually drove higher sales and revenues.