How Can Analytics Improve the Customer Experience?
Blog: Enterprise Decision Management Blog
Communications Service Providers are competing on customer experience powered by data, advanced analytics and connected decisions. In this third and final article of our series, we take a look at how embedding real-time analytics can lead to higher customer satisfaction.
We have already discussed how CSPs have internal obstacles to overcome with regard to understanding the data at their disposal, turning insight into operational decisions that improve customer satisfaction and designing processes from a customer perspective
According to research by Andrew McAfee and Erik Brynjolfsson of MIT and published in Harvard Business Review (October 2017), “Companies that inject big data and analytics into their operations show productivity rates and profitability that are 5% to 6% higher than their peers.” Embedding advanced analytics into operational and decision processes will increase speed, agility and impact.
This is what we have observed at FICO, working with businesses in multiple industries to use advanced predictive and prescriptive analytics. For example:
- Avis Europe increased their car fleet utilisation by two points, resulting in an economic gain of $19 million, including an improved customer experience by ensuring that the right car is in the right place for the right customer.
- Canadian retailer Loblaws are able to calculate individually appropriate offers for around 10 million loyalty members, translating to 90 million predictive offers per week and a 200% increase in response rates.
- Southwest Airlines, the largest domestic carrier in the US, uses analytics across the complex logistics of profitably running an airline. Optimised decisions have reduced the cost of purchasing fuel by $20 million p/a, increased on-time flight performance by 2% and reduced connection times for passengers and crew by an average of 5 minutes.
These businesses share a desire to really improve the experience for their customers by having the most complete and current picture that data allows and having the ability to act upon that information. Furthermore, having centralised decision centres enables a single source of truth and thus a consistent view of how actions have knock-on impacts across other business areas.
With CSPs having access to incredibly rich data which can be streamed in real time, there is huge scope to utilise this in order to enrich the overall customer experience. Data is fluid and begins to lose value once it has been generated. In the CSP world, data could include location, time of day, type of use — activity which can be used to understand the context of a customer’s behaviour and create insight that enables the right offers or assistance to be offered via the right channel. Similarly, network operations will be able to better predict and mitigate network capacity and maintenance, thereby reducing the risk of network downtime and impact to revenue.
When coupled with machine learning capabilities, the power to learn the “unknown unknowns” from outlier data results in decisions that adapt to shifts as customer behaviour changes. Therefore, the interactions with those customers can be based on up-to-date knowledge and personalised to the context of the situation.
Ultimately, the CSPs that genuinely compete on customer experience through advanced analytics and decision management will focus on better customer outcomes. This is the path to growth.
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