Traditional data analytics provide historical data that allows companies to examine past trends and sales peaks, and even to try to determine the factors that influenced these changes. The analysis of data is therefore essential to establish accurate forecasts.
However, making assumptions solely on the basis of historical patterns does not allow brands to take into account the different external variables that may have an influence on the final result. For example, a customer may follow the same purchase path as other customers in the past, and you could stop there and convince yourself that if he seems to follow their example, then purchase is guaranteed. But, you would be wrong.
What would happen if this prospect received a more interesting offer from a competitor? What if their needs changed quickly? What if their personal situation shifted and that affected their behavior? By focusing on just one aspect of the customer experience – the purchasing journey – without taking into account the other variables, you put yourself in danger.
Personalization as a Key Factor in the Customer Experience
Because they want to be treated as individuals, customers expect personalized experiences. But, how do you give them what they’re looking for if you focus too much on just one aspect of their buying journey? Succeeding in putting in place an effective customer experience requires taking into account each customer individually. While historical data can help you build this unique, individual profile, it doesn’t:
- Identify when and why the prospect or customer is about to leave you
- Advise you on the actions to be taken put in place
- Evaluate the impact of external parameters on the prospect
Why Business Intelligence Isn’t Enough Anymore
Data analytics is a key component of business intelligence, but the information gathered by historical data analysis is not directly actionable. It simply tell you the history of past actions, based on a number of variables and schemas.
Today’s brands need to drive data-driven decision-making, which requires predictive and prescriptive analytics. With predictive analytics, you can analyze the data to determine the most likely outcome for a given action, and through prescriptive analysis, you can establish hypothetical scenarios to determine which action is most likely to lead to the desired outcome.
Thus, if “Action A” is likely to result in a result that is undesirable, you can stop that action immediately. If, on the contrary, “Action B” is likely to generate the expected results, you can instantly switch to this action and change the outcome.
Real-Time Analytics to Ensure a Successful Customer Experience
In a highly competitive environment, data and insights are the most valuable in the business. Your organization has access to a wealth of customer information, but unless you use this information quickly and efficiently, all of this data is useless.
Having a customer-centric approach requires anticipating your customers’ future needs – based on behavioral patterns, market trends and user experiences – to develop proactive measures that will ensure a personalized and unique customer experience across different channels. In return, the customer will feel understood and valued, and will be more likely to become loyal to your brand.