From dirty data to clear value
Understanding the potential value of the usage of data doesn’t automatically lead to value generation from data. When talking about creating value with data, the subject is most likely analytics or AI. Let’s cross the data/analytics/AI topic with customer experience. When looking at a recent publication ‘State of CRM’ (Forrister/Salesforce), the expectation is 79% of organizations will be using AI-powered automation by 2023 to enable easier customer/prospect engagement. Impressive.
AI algorithms produce outcomes based on the data they receive as input. So the quality of the data input is of great importance. And there is a catch, ‘dirty data’ is the most common challenge to overcome when working with data. Recent research (State of CRM) shows that 70% of organizations experience scattered and siloed customer data. Almost 60% know this has a negative impact on customer experience. The impact of a bad customer experience is bad for business. To put this in numbers; 2 out 3 customers will switch to a different brand because of a negative customer experience.
So, if you want to improve customer experience with data, AI and automation, where to begin? When building a data driven, forward looking customer experience platform, using the power of AI there are three main elements that make up the platform:
- Customer data lake / platform
- Intelligent analytics and prediction models
- Process automation flows
1. Customer data lake
Customer data in most organisations is scattered across different systems or clouds. Only 1 in 3 companies have a reliable single customer informaton view, whilst 90% want to have this view. When you unite this data and build 360-degree customer profiles, you are able to maximize the CLV (customer lifetime value) per customer. Therefore the creation of a customer data lake can bring real business value. The data it encompasses can be used to have a more comprehensive view of your customers with backward looking analytics or it can be used to predict customer needs or even prescribe customer service actions. The integration of Salesforce Salescloud, Salesforce Marketing Cloud and Salesforce Service Cloud can provide an ideal basis for a customer data lake or customer data platform.
2. Intelligent analytics and prediction models
When customer data is generally available and accessible in a comprehensive customer data platform or data lake, it’s time to power up the prediction models. Prediction models use statistical techniques, machine learning and data mining to predict and forecast future outcomes based on the available data. Especially the development of low-code or no-code prediction models, for example in Tableau CRM and Einstein have made predictive analytics as part of the bigger AI spectrum, far more accessible for organizations to work with.
3. Process automation flows
When customer data is gathered in a central system and weighed on predefined scoring criteria, you create the ideal basis for AI empowered predictions or AI-driven direct actions. Direct actions take effect via automation rules. With Salesforce Flow Builder or Flow orchestrator you can deliver automation capabilities designed for any business function or industry. It enables companies to build intelligent workflows and integrate data across any system.
No automation without integration
If you want to harvest all the benefits data has to offer, you most likely need to connect to different systems across your organization. You cannot achieve automation without integration; it’s simply impossible to automate things that aren’t even connected. MuleSoft Composer for Salesforce makes it easy to use clicks, not code, to bring data from Salesforce and third-party systems together. And you can automate these integrations to boost productivity.
More on data, AI and automation from a business perspective
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