predictive analytics for customer service
Understanding Predictive Analytics

According to Gartner, the business intelligence market is growing at an annual rate of 9 percent, surging to an estimated worth of $81 billion by 2014. With that growth comes significant opportunities for businesses to ignite their own revenue by applying predictive technologies to anticipate consumer behavior. Thanks to Web 2.0, the factors that influence a buyer’s decisions are shifting. Traditional ad spending on billboards and newspaper space is a thing of the past, and Web 2.0 and social media influencers are the new reality. Every consumer has an abundance of information at his or her fingertips at all times, making competition high and attention span low. Brands are faced with the challenge of standing out above all of the noise to drive both sales and brand loyalty. This is becoming an increasingly complex task.

To stay competitive, brands must take an analytical and predictive approach to better anticipate consumer behavior on an individual basis. Predictive analytics helps marketers to be dynamic and relevant by giving them the knowledge to present the right offer at the right time via the right channel, all based on what will best motivate the consumer to act. This allows companies to capture the attention of the audience quicker and reduce the total cost per acquisition. The predictive approach can be broken down into three parts that can work together to manage a fully engaged experience:

Demand activation appends CRM data and publicly available data to predict a lead’s likelihood to purchase, down to the preferred price and product type. Leads are scored and prioritized to convert high-value prospects quicker.

Demand conversion removes the guesswork of contacting leads by determining the best dialing strategy. Conversion-based routing and predictive analytics predict the right message, product, incentive, and channel to effectively convert a lead through the call center.

Demand Creation

Growth, sales, and customer loyalty are often areas that CMOs are tasked to measure and correlate with their campaigns. With the abundance of customer attributes and evolving channels available, how does a CMO optimize marketing data to attract customers and drive higher levels of performance?

New technologies that analyze Web behavior and predict the best way to engage are becoming the strategic differentiator of forward-thinking brands, often resulting in campaign ROIs that are as much as 75 percent higher than those not using predictive technology.

Demand creation through predictive engagement empowers a brand to turn online consumer behavior into customer intelligence that can be acted upon in real time. Predictive engagement Web technologies maximize consumer engagement and conversion through predictive algorithms that optimize the cost per sale. This is done by dynamically customizing key marketing elements of Web, mobile, and social media pages for every visitor, resulting in dramatic on-site conversion lifts. Visitors to your site(s) tell you a lot about themselves through every Web interaction. Predictive engagement technologies utilize anonymous environmental, behavioral, social, and third-party data, learning what variables are the most engaging for specific individuals or segments of your audience. The learning is ongoing and delivers real-time optimized Web experiences to provide the best content, products, and promotions to engage the user to become a buyer. Through a shopping cart analysis, a brand can even determine what a customer is most likely to buy next, allowing for effective cross-selling and upselling opportunities customized to each user.

Demand Activation

By appending CRM and publicly available data, a company can segment and score a lead according to defined attributes. Predictive lead scoring can be tied into a brand’s existing lead data to predict which attributes will most likely result in a profitable sale and which are less likely to close. The data scoring can even predict the preferred price and product type that will entice a particular lead to convert. By analyzing and scoring your lead lists to create a picture of the “ideal buyer,” your engagement center can now prioritize the leads that are predicted to close, drastically increasing conversion rates.

Consider this situation: An auto insurance company that markets to high-risk drivers hired a call center outsourcer to sell more auto insurance policies for the company. To sell smarter and more efficiently, the outsourcer provided statistical data on which lead types were more likely to convert, along with which lead sources sold the most quality leads based on conversion ratios. Through the use of a predictive lead scoring technology, the outsourcer was able to predict what the attributes of the ideal buyer were based on an analysis of the consistencies in buyers’ attributes. The leads with these converting attributes were prioritized to the top of the call list to ensure the first sale. Before analytics, the auto company had to talk to an average of 62 leads to get one sale; after analytics, that number was nearly cut in half, with a lead per sale ratio of 32:1.

Demand Conversion

With the use of predictive technology in the call center, a company can now route a call to the call center agent with the highest probability of converting based on common behavioral attributes, also known as conversion-based routing. This allows for the consideration of multiple attributes, including gender, location, product type, and CRM data. This process increases the efficiency of a company’s sales pipeline by analyzing the prospect database to determine which customers are of greatest value and will close the fastest. Removing the guesswork reduces acquisition costs when trying to contact leads by determining the best dialing strategy. Through predictive technology tools, all historical data, including attempts, contacts, call outcomes, feedback, and recorded conversations, can be captured in real time for each lead to give clients a complete view of interactions. This information helps determine which lead sources to buy from again. Reported information is then put back into the artificial intelligence engine to further analyze and predict the value of a lead.

By capitalizing on the power of predictive analytics, a performance increase averaging 120 percent is not uncommon. Success depends on numerous factors, though, including the accuracy of the data that is used to train the model and the scale of the program that enables multiple paths.