TDT Analytics: Redefining Customer Engagement via Predictive Analysis
Michael Foliot Founder, President & CEODelivering Assured Results with Predictive Analytics
As an organization that came into being in 2003 and transformed into a Predictive Analytics consulting firm in 2008, TDT Analytics possesses the in-depth know-how to identify and understand the imperative as well as the key pain-points that the clients wish to address and gather insights from. The TDT Analytics team uses a consultative approach with prospects. The team produces a whitepaper that contains a comprehensive assessment of the client’s business objectives, the approach recommended, the methodology to be followed, and the associated deliverables. This is followed by the three Pillars of our Analytics approach. Data acquisition, transformation and cleansing followed by the development of Predictive Analytics model’(s) and subsequent application resulting in the final pillar i.e. outcomes, data visualization and actionable insights.
Through data acquisition, wherein survey data, demographic data, the voice of the customer data, customer inquiry data, or financial data is pulled in from the client’s systems, is aggregated and transformed to evaluate the impact on either the purchasing behavior or the retention behavior of the customer. “The key thing is the availability of data. We help clients find answers to questions like why their marketing campaigns are not working, what will work and what won't, which product or service should they be selling to customer A versus customer B, why is the behavior of the customers changing over time and what actions should they be taking,” explains Foliot.
Trying to sell products to somebody who is not interested in purchasing the same would lead organizations to nowhere
Leveraging High-End Technology Stack with a Consultative Approach
TDT Analytics employs a variety of programming languages for data integration and transformation into a single data flow and creation of an SQL database. These database views are then used in SAS to develop customized Predictive models, such as time series, discriminant, regression, and cluster depending upon the objective of the customer. The insights are compiled in an output file, which is integrated with Business Intelligence (BI) tools in order to enhance client interaction and is finally incorporated into the customer care systems of the client organization. At the end of the procedure, the TDT Analytics team closes the loop by extracting insights gained from the interactions based on business programs for future calibration.
As Foliot explains, “We are not a company that tries to sell products. With our consultative approach, we attempt to penetrate deep into the pain-points and opportunities of our clients and elucidate the right actions in the form of actionable insights that help our clients in the long term.” He believes that the business of an organization cannot grow by adopting a one-time cookie-cutter approach to serving customers. Therefore, TDT Analytics presents a customized approach that focuses on a value-added relationship, assuring comprehensive support to their clients. Besides, the company ensures their clients of staying ahead of the business curve by helping them refreshing their data at regular intervals. TDT Analytics does not make any assumption in terms of the relevance of the data; they leverage accurate predictive analytics to determine which data elements/attributes are relevant to a certain timeline. This helps the clients get an idea of the current customer behavior trends and gain control over the market.
Reaping the Benefits
TDT Analytics serves clients across diverse verticals— from financial services, retail, medical lab facilities, and communication providers. The company has assisted one of the clients to accomplish an enhanced ROI of $2-$3 million with the aid of predictive analytics. In another instance, TDT Analytics was approached by a client in cross-selling products to customers for generating increased revenue. “Trying to sell products to somebody who is not interested in purchasing the same would lead organizations to nowhere,” says Foliot, “Therefore it is necessary to identify and target the customer behavior and interests before attempting to cross-sell any product or service.” The customer enjoyed an increase in profit of 30 percent induced from cross-selling by employing the propensities to buy identified by TDT and developing marketing campaigns and pitching their products to customers.
Currently, TDT Analytics plans to work closely with their BI partners and implement bots to promote natural language processing when communicating with customers. “The use of natural language processing will ensure effective output and consumption of the analyses conducted, considering the amount of data and the involvement of data mining and predictive analysis,” states Foliot.
