Predictive Analytics refers to the use of historical data, statistical models and techniques and artificial intelligence to predict what may happen in the future – often also assigning a probability percentage to possible events in the future. Predictive Analytics is used in many areas of business, including:
- Project management: Predictive Analysis can be used to reduce cost and time overruns, and to improve team output in complex projects. This can be done by using data from past projects to help improve time estimates, cost estimates and team performances.
- Customer interface: With businesses – especially B2C businesses – increasingly going online to interact with customers, Predictive Analysis can predict probable glitches in the customer interface, thus enabling companies to give customers a smoother online experience, ultimately leading to greater customer satisfaction and customer retention.
- Production: Predictive Analytics can help everyone, from farmers to multi-product factories, to improve their production by identifying blockages in the production process and predicting how changes in various processes will improve output.
Having seen how Predictive Analytics helps in various business functions, it is time now to tackle Predictive Customer Analytics. As the name suggests, this form of analytics uses historical customer data as well as empirical market data to predict how customers will behave in the future. This enables the companies using it to pre-empt customer churn, enhance customer service and retention and also to gain new customers at competitive costs.
Here are seven reasons to invest in Predictive Customer Analytics:
- Securing Competitive Advantage: Predictive Customer Analytics enables businesses to not only analyze their own customer interactions in the past but also competitor behavior and what prompts those who don’t become customers to go to a competitor. By analyzing customer aspirations and behavior, Predictive Customer Analytics can suggest ways in which a company can gain and retain competitive advantage.
- Managing Customer Expectations: Increased competition means increased customer expectations. Predictive Customer Analytics enables a company to identify such expectations well in advance and take steps to address these expectations. For example, in an increasingly online interface world, customers may expect businesses to offer smoother online interfaces. If a company has no online interface at all, it might disappoint customers, who will then migrate to a competitor.
- Identifying New Business Opportunities: By analyzing past customer data and factoring in business environmental realities, Predictive Customer Analytics can help companies identify new business opportunities, thus enabling them to expand their customer base significantly. What’s more, Predictive Customer Analytics can also provide a tentative blueprint for catering to the new customer bases.
- Improving Product Quality: Customers, through their behavior, provide valuable insights into product quality. The problem with customer surveys is that respondents often answer survey questions based on what they think the surveyor wants to know. This can lead to wildly inaccurate data and therefore incorrect analysis and prediction. Customer behavior itself, however, is a very accurate indicator of customer preferences and can therefore help improve the quality of the products the company offers.
- Micro Segmentation: So far, companies relied on broad segmentation of customers, based on factors like age, educational qualifications, marital status, annual income, etc. Predictive Customer Analytics enables companies to break down these segments into micro segments, factoring in a huge amount of data that it has gathered from existing customers, competitors and the general business environment. This enables the company to tweak its offering to these micro segments, ensuring that each existing customers gets exactly what he / she wants and potential customers get attracted to the company’s offering.
- Disaster Prevention: The loss of a customer – even one single customer – must be treated as a disaster in this competitive business environment. Customer churn is often a cascading effect, with customers taking friends, family and social media contacts along with them to a competitor. In a globally inter-connected world, this can certainly spell disaster to a company. Large chunks of customers can disappear overnight, severely hurting the bottom line. Predictive Customer Analytics can prevent such disastrous churn by warning the company well in advance, as well as suggesting steps to take to prevent the disaster.
- Cutting Costs: Profitable companies would rather cut costs than increase prices. A price increase can lead to customer churn, losing the company valuable customers. Retaining customers is always a better strategy than seeking new customers, which has proved to be an expensive affair. Predictive Customer Analytics provides insights that can help the company reduce costs, often through seemingly insignificant changes in design, packaging, supply chain management, etc. For example, a customer who buys exclusively online won’t particularly care about packaging, as long as the product reaches him / her undamaged. This gives a company a huge cost-cutting advantage if it eliminates printing cost on the package.
The success of a company in a fiercely competitive global market boils down to a seemingly simple but actually highly complex tactic – using data effectively. Companies that use customer data and environmental data effectively with lead the race. Those that don’t use Predictive Customer Analytics are already lagging, according to several studies by leading analysts.