analytics in sales lift

Predictive Analytics in Action: Implementing Strategies for Dramatic Sales Lift

According to Gartner, the business intelligence market has grown 9% per year surging to an estimated worth of $81 Billion in 2014. Taking these statistics into consideration, there is a significant opportunity for businesses to ignite revenue growth 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 in the past and web 2.0 and social media influencers are the reality. Every consumer now has an abundance of information available at their fingertips at all times, thus making competition high and attention span low. Brands are now faced with the tough challenge of standing out above all of the noise to drive both sales and brand loyalty. This is becoming increasingly complex.

To stay competitive, brands must take an analytical and predictive approach to better anticipate the consumer behavior on an individualized 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 will enable companies to capture the attention of the audience quicker and reduce the total cost per acquisition. The solution is broken down into three parts that can work together to manage a fully engaged experience:

  1. Demand Creation stimulates a lead to take action by leveraging predictive algorithms that customize key marketing elements, such as web content, resulting in dramatic conversion lifts and an optimized cost per sale.
  2. Demand Activation appends CRM data and publically 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.
  3. Demand Conversion removes the guesswork of contacting leads by determining the best dialing strategy. Conversion based routing and predictive analytics predicts the right message, right product, right incentive and right channel to effectively convert a lead through the call center.

Strong growth, sales and customer loyalty are often areas that a CMO is tasked to measure and correlate with their campaigns.  With the abundance of customer attributes and evolving channels available, how does a CMO optimize their 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 ROI’s that are 75% more effective than campaigns not utilizing 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 onsite 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 deliver the best content, products, and promotions that will best engage the user into becoming 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 up-selling opportunities customized to each individual user.


By appending CRM and publically available data, a lead can now be segmented and scored 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 to the top, drastically increasing conversion rates.

CASE STUDY BRIEF: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 was based on an analysis in the consistencies in a 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 <Read Full>


With the use of predictive technology integrated into the call center, a brand can now route a call to the best call center agent (conversion based routing) that has the highest probability of converting based on the common behavioral attributes. This allows for multiple attributes to be used including gender, location, product type, and CRM data. This process increases the speed and efficiency of a company’s sales pipeline by analyzing the prospect database to determine which customers are the 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 to 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.

Match the caller with the right agent, right message and right product set to better convert

By capitalizing on the power of predictive analytics, a performance increase averaging 120% 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.


Published by

Pete Schmitt

Peter Schmitt is the Chief Strategy and Innovation Officer of Dialog Direct, where he provides strategy formulation and execution across multiple business units. Pete has a passion for leading ideas through to commercialization, which led him to achieve a successful track record for growing start-ups, managing turnarounds and leading large corporate transformations.

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