How predictive analytics can boost customer experience
To gain customers and retain the frustrated ones, companies need to evaluate their capabilities in using analytics to develop actionable insights. With customer data increasing by 23% annually through to 2018, it’s no secret that having perception into customer satisfaction issues and the ability to track them proactively is the best cure.
Aware that improving customer experience makes customer analytics a must-have, Customer Experience Management (CEM) is becoming a major talking point in a number of industries, including Telecoms, Financial Services, Retail or Public Sector. Despite the lack of global definition and process, they all seem to understand what CEM’s benefits are. But what are the elements of successfully using analytics to boost customer experience?
Collect data across all channels
Data can be sourced from multiple channels including transactional touch points and social media. This multi-tier approach to data collection is becoming necessary for companies, hoping to gain a comprehensive overview of the customer journey. Facebook, Twitter and other social platforms are growing in importance in CEM as customers use them to share their instant reactions to company services. Integrating analytical tools from CEM into social media monitoring is key in gaining an unfiltered view of customer opinion. Integrating all available channels into CEM will allow its tools to identify widespread behavioural patterns among customers, aiding companies who proactively engage with the customer lifecycle.
Leverage existing data from CRM system
As CRM systems provide a quantitative measurement of customer interactions with a company, they can integrate with CEM to provide a larger and more holistic picture of the customer. CRM is often the foundation of data gathering ventures, thus providing valuable information that CEM systems can analyse. Incorporating CRM into your CEM strategy means information is centralised, accessible and can be surveyed using advanced analytics. By using CRM data from the beginning companies will save time by not collecting masses of new data. Combining these systems will also produce a greater ROI for businesses as customer experience improves.
Analyse unstructured data volumes
In CEM systems there is also an ability to extract information from unstructured data as analytic capabilities develop. Text mining usually comes in two strands: sourcing information from unstructured linguistic or statistical data. As the data volume grows due to the increasing amount of customer touch points, an all-encompassing approach able to handle complicated data mining practises is required. If used effectively this can provide companies a strong competitive edge, identifying previous unevaluated links to attract and retain customers.
There are several other ways that predictive analytics can positively impact customer experience. They can notably help businesses leverage data for more profitable outcomes across the organisation, including identifying strategies to reduce attrition, targeting improvements at key touch points to boost issue resolution and improving the value of your Voice of the Customer programme.
Nurturing the information provided by CEM software into predictive models allows companies to build strategies that are well supported, providing a clear path to revenue growth. The CEM systems are all about building sophisticated paradigms to understand, predict and proactively manage customer relations and interactions.
Analytics will help businesses drive a greater level of customer satisfaction; generate new revenue streams; and help maximise profits. Yet the investment into CEM systems will go to waste if the corporate culture does not embrace this software. Companies need to make sure that all relevant departments feed appropriate intelligence into the system. Once companies have achieved this, CEM strategy can be pursued to its full potential.
The author is Marcin Malinowski, director of UK Business Unit at Outbox