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Peter Schmitt: Here are the most important things that predictive analytic can accomplish

Established more than 35 years ago, Dialogue Marketing provides award-winning BPO and customer engagement services that help companies acquire, support and retain customers across multiple channels. By combining proprietary, custom developed technology with passion and a dynamic, innovative culture, Dialogue works with clients to enhance each stage of the customer life cycle.

Its range of products and services, including custom developed CRM systems and predictive analytics, combined with experienced brand advocates allows the company to analyze and engage through the preferences of the customer, maximizing the value of the opportunities presented. Dialogue utilizes its sophisticated analytics and customer interaction strategies to reengineer channel processes that deliver a more effective and integrated customer experience, playing to the strengths of today’s mix of online and traditional service channels.

With more than 1,100 employees, the company’s service offerings include lead generation, customer care, technical support and inside sales to help build long-term enterprise value and lifelong customer relationships.

Rake Narang: How do you define predictive analytics?

Peter Schmitt: Predictive Analytics identifies patterns in the flow of business information that helps a company anticipate the probability of future outcomes. This guides the company to derive better data driven decision making for its customers and for its stakeholders.

Predictive business analytic projects use tools that incorporate technologies that apply mathematically oriented techniques such as neural networks, rule induction, and clustering to discover relationships in the data and make predictions.

About Peter Schmitt

Rake Narang: What are the most important things that predictive analytics can accomplish?

Peter Schmitt: Predictive Analytics offers an organization the ability to optimize the workflow or resources to dramatically improve the net margin of an organization  and the consumer perception towards an organization.  Some tangible metrics that we use to gauge the impact of our predictive processes include:

  • Customer acquisition costs
  • Consumer experience through NPS and C-Sat
  • Consumer buying behavior for increased cross sale and new sale conversion rate
  • Loyalty rate of customers and consumer perception and influence towards an organization through attrition rates

As an example, Dialogue Marketing uses predictive analytics to anticipate the profile of a consumer which allows us to intelligently route a phone call to the agent that is mostly likely to convert based on life style attribute matching. Another important thing that predictive analytics can accomplish is its ability to identify trends or anomalies and automatically trigger events to capitalize or improve on the real time event.  

An entirely different predictive result can be found by using data from social media where we can predict if consumer perception is creating a trend that is either negative or positive and if it requires immediate action.  For example, predictive analytics can isolate product launches to monitor success or failure and in which geocodes or which social influencers are and will have the most significant impact on the product launch success.

Rake Narang: How does predictive analytics work?

Peter Schmitt: At its core, Predictive Analytics is ultimately bound to the business data that a given company generates over time. By capturing the current activities of the company, whether its sales on a website, outcomes of telephone conversations or the content of tweets and blogs, Predictive Analytics enables the company then to perform statistical modeling and data mining operations upon this information, which enables the company to create descriptive, predictive and/or decision based models about its activities.

There are many different approaches, though the two primary application designs processes we use are the following:

First, we build models based on historical sales information. The models look at the consumer information of the buyer vs. those that did not buy. They also look at third party data such as property tax information, consumer interests from magazine subscriptions, housing density in the geo location, etc. The goal is to identify patterns of behavior or personal interests that create influencing factors towards the ideal outcome. The predictive system that we adopt builds groupings or clusters of like individuals that have a similar propensity based on their attributes. The clusters then form a descriptive model of our customers that define their probability of success.We also perform similar clustering techniques on our agents and workflow/campaign management to identify the type of salesperson/agent that is likely to generate a specific outcome during an interaction with a customer.

Once the descriptive model is in place, predictive models are created. As interactions between our customers and our team members occur, these predictive models help us select the appropriate agents, the appropriate products and even the appropriate campaign messaging (conversations, tweets, emails or other customer interactions.

Finally, the system can employ its predictive and decision based models to recommend the best dialing strategies, such as the best time to call, or email. It can also make recommendations on how to best follow-up with a customer: phone call, email, text message, voice mail, etc. After the interaction is completed, the outcome is stored and the descriptive, predictive and decision-based models are then recomputed periodically to further hone the accuracy of the models.

Rake Narang: How is analytics used in the call center to enhance the customer experience?

Peter Schmitt: As described above, analytics is used to enhance the customer experience through its intelligent and predictive routing capabilities. With the technology implemented, we can match agent attributes to those of the caller to ensure that the caller is matched to the agent best fit to their needs. This provides a more personalized and relatable experience to the caller. This can also be done through social media by routing posts with specific keywords and sentiment to the agent best matched and trained in particular areas.

Company: Dialogue Marketing
300 E. Big Beaver, Suite 400, Troy, MI 48083 U.S.A.

Founded in: 1977
CEO: Peter Schmitt
Public or Private: Private
Head Office in Country: United States
Products and Services: Award winning BPO and customer engagement services including customer service, sales, inside sales, lead generation, help desk/tech support, social media customer service.

Key Words: Customer Engagement Center, Proprietary Customer Engagement Technology, Call Center, Remarkable Customer Experience
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