There are also two types of output created by AaaS: output to humans, such as dashboards and alerts, and output to machines, such as recommendations served up directly to a website when a customer visits it. Due to the breadth of potential AaaS applications offer, the actionable outcomes may be very diverse.
One example is demand forecasting, where AaaS enables retailers to determine what merchandise to stock in each of their stores based on electronic POS data, as well as external market data. A food retailer in Japan, operating hundreds of outlets, is using real-time analytics to make subtle but important adjustments on a daily basis. The retailer has integrated its sales forecasting database with additional information sources such as weather forecasts, holiday schedules, company marketing campaigns and product launches to help calculate optimum levels of inventory and guide production and distribution.
Risk-based analytics is yet another possibility. For example, banks are using AaaS to identify which customer segments are likely to default on credit card or mortgage repayments. Insurers are also using such tools to better identify fraud risks.
In China, an online retailer engaged external data scientists for a customer segmentation project. Real-time insights from the project led to successful tactics for cross selling and up selling. They were also able to make better strategic decisions with their advertising approach. Due to higher conversion rates and higher-value baskets for each customer segment, the project resulted in an increase in sales of more than 160 percent for the e-commerce retailer.
Speeding the analytics journey to ROI
Analytics as a Service also offers great flexibility in an operational context. Some business will want to have their own analytics teams working with the tools provided. Others may choose to outsource the whole process, with the provider designing, building and operating the system, and even working with the business to implement the actions identified as necessary to deliver the desired outcome.
In practice, most businesses will sit somewhere in the middle, and many companies will want to have enough in-house expertise to lead analytics, even if they do not want to do all of the execution work.
Despite the various approaches of deploying AaaS, at the end of the day, organisations need to remain mindful that speed is of the essence - taking too long to build the capabilities increases the risk of having market share taken by competitors.
Getting the full returns on investments in analytics is a journey that leads from issues, to insight, to decisions, to action and ultimately to visible and measureable outcomes. Accenture Analytics experts believe that incremental value is to be gained at every step of this journey, and that organisations should set clear goals for each step they climb, for example focusing on near-term value so their analytics efforts are self-funded within the first twelve months.