Use Predictive Analytics to Improve the Customer Experience

by   |   May 19, 2014 5:30 am   |   1 Comments

Steven J. Ramirez of Beyond the Arc

Steven J. Ramirez of Beyond the Arc

To win and retain customers in 2014 and beyond, companies will have to assess their competencies in using data and advanced analytics to develop actionable insights. Data science and predictive analytics can help organizations synthesize data sources across multiple channels to better target the right customer with the right offer at the right time. Advanced segmentation strategies that help to identify niches based on consumer behavior will also significantly boost marketing effectiveness. Companies that deploy these techniques will accelerate past the rest of the pack in developing and deepening customer relationships.

Using techniques from data mining and text mining, predictive analytics lets executives look at historical patterns and make predictions about future behavior for specific individuals. By taking customer data that is held internally and adding what people have said and done, companies can map out what customers are likely to do next.

Deeper Understanding, Better Engagement

Building a stronger customer experience can help you gain a deeper understanding of your customers to more effectively engage them, increasing retention and loyalty. This translates to a more profitable business.

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In a recent customer experience report, the Tempkin Group noted, “Customer experience leaders have a more than 16 percent advantage over laggards in consumers’ willingness to buy more, their reluctance to switch business away and their likelihood to recommend.”

Before a company begins making improvements, the first step is to ensure that everyone understands the context and goals that will generate actionable insights. You must make sure you truly understand your customers’ experience with your business as a whole. Customers see one company. They don’t care if there are different divisions or departments that operate in silos. They want a seamless experience from one touch point to the next.

The best place to start any predictive analytics program is to take the time to define the objectives. Is the goal to decrease regulatory risk? Launch a new product? Reduce customer complaints?

To set the stage for successfully implementing predictive models across an organization, it’s also critically important to involve all relevant stakeholders early. It is important to build buy-in and collaboration, as well as determine whether existing resources will suffice or if there is a need to build new systems. By laying this foundation, executives have a clearer path for putting data-driven models to work.

Collect Data Across All Channels

Only after the objectives are defined should data collection begin. Predicting customer issues requires a palette of data sources and analytical approaches. Companies should collect data from internal sources, social media, and regulatory or government bodies.

Click to enlarge

Click to enlarge

Internal data sources include everything from customer feedback to transactional data. Many companies already collect this information but may struggle with how to analyze and use unstructured data, such as calls to customer support.

Social media is potentially the most important and unfiltered way to gain access into consumers’ thoughts and experiences. An important benefit of establishing a strong social media connection with customers is that they may be more likely to remain loyal to the business and open to growing their relationship. That digital experience is a key part of the overall customer experience. But let’s not forget why people engage in social media: to share their opinions.

Many industries also collect complaints via governmental bodies. In the financial industry, it may be the Consumer Financial Protection Bureau, which publicly releases all consumer complaints made about banks, credit card companies, and lenders. Other industries may need to deal with the Better Business Bureau or other regulatory bodies that collect consumer complaints.

Build a New Generation of Customer Experience Analytics

Instead of looking at the past and reacting to issues as they arise, predictive analytics uses all of the data available to predict how consumers are likely to respond. Once the data has been collected across internal, social, and regulatory sources, companies begin sorting the data to determine where the most valuable information lies.

Analysts can characterize the data and provide preliminary assessments of how suitable each data source is to the desired goal. Once the decision has been made on which sources to leverage, the analytics work begins.

Traditionally, segmentation has been used to divide customers into groups based on their demographics, attitudes, or buying behaviors. This helps in trying to target specific groups with the message that will best resonate with them. Utilizing predictive analytics, previously hidden patterns in the data help organizations generate more in-depth customer segments. The resulting segmentation is more precise and nuanced, and is ultimately based on the likelihood that a consumer will accept a given offer. The result is a win-win situation, as customers are offered more relevant products and services, leading to a more profitable relationship for the company.

Beyond segmentation, there are several other ways that predictive analytics can positively impact success. Advanced customer experience analytics techniques can help companies leverage data for more profitable outcomes across the organization, including:

• Identifying strategies to reduce attrition
• Targeting improvements at key touch points to accelerate issue resolution
• Increasing cross-sell rates with sophisticated customer segmentation
• Boosting the value of your Voice of the Customer program.

Whatever the most important objectives, predictive modeling can help any company transform mountains of customer data into valuable insights that can make a powerful difference for both key customers and the business.

Steven J. Ramirez is CEO of Beyond the Arc, Inc., a firm that combines data science and customer experience strategy to help clients deepen customer relationships and differentiate themselves in the marketplace. The company’s social media data mining helps clients improve their customer experience across products, channels, and touch points. Follow Beyond the Arc on Twitter: @beyondthearc.

Ramirez is presenting a session on this topic titled “Open Your Eyes and Ears: Leveraging Predictive Analytics and Alternative Data Sources to Improve the Customer Experience” at the Predictive Analytics World conference, June 16-19 in Chicago, Ill.

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One Comment

  1. Helder
    Posted October 13, 2016 at 11:49 am | Permalink

    Great article!

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