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Why Customer Churn Should Not Be Your Only Use for Predictive Analytics

Pratibha SalwanIn this special guest feature, Pratibha Salwan, Senior Vice President of Digital Services, at NIIT Technologies, discusses how predictive analytics helps companies reduce customer churn, personalize content and predict major internal company shifts and directly drive business success. Pratibha Salwan is an experienced executive leader who leads the digital service practice in the Americas for NIIT Technologies. Her experience includes over 22 years of pioneering experience in the information technology outsourcing industry. For over 8 years, she managed Travel & Transportation accounts for NIIT Technologies in the Americas, leading a team of hunters and farmers. She then headed NIIT Tech’s group on sales excellence in the Americas, responsible for streamlining sales operations and continues to be directly responsible for leading transformational and strategic opportunities. In her current role, she is also responsible for all projects digital including transformation, enablement, experience and analytics.

Recently, I have been facing quite a quandary – in my discussions about the uses of predictive analytics, I have heard multiple companies talk extensively about using technology and their extensive data set to anticipate when a customer will jump ship for a competitor.

While anticipating and preventing customer defection is certainly valuable, I find myself perplexed by why companies are not embracing other uses of predictive analytics to revolutionize business in general. With the vast array of data being collected today, businesses could and should be using the information to predict more than just customers leaving.

For many companies it seems like they are waiting for the technology to evolve. Companies employing this strategy will only be left in the dust. So where should a company start? We recommend starting with a few, focused uses such as:

  • Understanding what marketing campaigns will work with specific customers – By tracking attributes of customers and prospects along with reactions to previous marketing campaigns, companies can determine which customers will respond to a specific campaign. With this information, companies can send out targeted coupons or offers that will more likely attract people to come into the store than the traditional “spray and pray” method.
  • Providing more accurate prices for service rates – For many companies, data sets were reviewed and updated irregularly, which meant companies could not analyze how a price change impacted sales in great detail. With predictive analytics and a consistently updated data set, companies can anticipate how their bottom line will be affected if they increase or decrease prices. For example, they will be able to predict how many customers will no longer frequent a restaurant if they increase the price of breakfast 10%.
  • Customizing options for each customer – For companies that offer a service with a variety of options – such as airlines – companies can create a personalized sales experience. For example, if data is effectively tracked, an airline can know which customers will want more legroom, flight times, and prices, which can help an airline proactively offer these options to the customer and, theoretically, increase the likelihood of the customer buying the ticket.

In the coming years predictive analytics will continue to evolve and will significantly impact how every businesses operates – any company that doesn’t embrace these tools will lose out on revenue opportunities and face an escalating exposure to systemic market, brand and economic risk.

 

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