Data science is playing a significant role in managing customer experience. It has contributed to nearly all areas of the CRM. There are still a number of companies, yet to embrace this technology for enhancing their marketing methodologies. One of the main reasons is the lack of awareness of how data science can help engage customers more effectively, and, moreover, an inability to quantify the potential improvements.
To better understand how data science can make sales and marketing more effective, have a look on the following aspects:
- Unlimited customer data handling in limited time
In most organizations, the challenge is not having too little data; it is having too much. Businesses have immense data on leads, prospects, and customers that it is difficult to organize and use it to prioritize outreach activities. Important data is usually spread across several departments, including marketing, inside sales, and field sales. It flows in from a mix of external sources and databases as well as internal systems spanning all touchpoints and communication channels.
A data science algorithm can help a business make sense of the chaos. It can comb through all of the data sources and digest all the disparate points of data to isolate the right prospect at the right time. It also provides the rich context that is needed to significantly improve the efficiency of the sales team. This includes a rationale or explanation as to why the recommendation was made, as well as contextual data from relevant applications, such as CRM, that enables the user to act on the recommendation with greater intelligence and effectiveness. In other words, targeting a higher quality prospect is important, but to make the salesperson effective, they need a summary of the customer relationship, past purchases, and other elements: all of which can be found through analyzing the data.
How do organizations measure the impact and effectiveness of data science technology when it comes to marketing and sales? The process should involve tracking both the overall effectiveness of algorithms on relevant business outcomes, as well as any improvements in effectiveness when end users follow the system's recommendations. Some key metrics to track include:
The overall increase in net-new customers and the conversion rates of leads prioritized by data-science algorithms and acted on by users.
- The effectiveness of next purchase recommendations for existing customers, including the accuracy of predicted probability that the customer will make another purchase in a certain number of days and the success rate of recommendations by the data science engine.
- The impact of warnings on customer attrition, which involves measuring the attrition rate for customers identified as at-risk (versus the average attrition rate) and further, the overall decrease in attrition rate for customers who were flagged with an early warning and an action was taken by customer retention team to retain them.
- The value of the data science engine becomes even more apparent when compared to a previous environment with no systematic methods to determine the next action for any given customer.
In this case, actions are highly reactive and triggered by customer events or the best guess of sales and marketing personnel. To gauge the true impact of the data science engine, an organization will want to track these same metrics against its previous environment and practices.
Ultimately, data science opens the door to a host of possibilities in managing the customer experience. It gives sales and marketing professionals a way to make sound, data-driven decisions on how to deploy resources and engage prospects and customers in the most effective way eliminating the dependence on best guesses and gut feelings as the guide for many mission-critical decisions.