CEO & Founder, conDati
Tell us about your roadmap to building the conDati Marketing Analytics platform?
conDati Marketing Analytics blends siloed data from multiple MarTech systems to deliver a single, unified data asset. That data asset is processed, analyzed, modeled, projected and visualized to provide complete visibility into the financial performance of digital campaigns. conDati is the first truly modern Marketing Analytics platform, providing a real-time understanding of campaign performance to marketing leaders and practitioners and replacing spreadsheet-based tracking metrics for marketing departments of all sizes. This first release delivers on three important value propositions:
- Completely eliminates manual data assembly and spreadsheet reporting.
- Provides visibility into the financial performance of marketing campaigns through a set of interactive visual dashboards, alerts and reports.
- Wraps anomaly detection models around every marketing campaign to watch and alerts the marketing team when revenue, cost or volume metrics diverge from predicted ranges.
How do you benefit marketing teams? What are the key features of your Digital Marketing Analytics product?
Every marketing team we meet suffers from a lack of critical campaign financial performance information — and they are very aware of this fact. conDati eliminates manual effort and provides the information to make spend and mix choices that are based on hard data that is constantly updated.
How do you leverage data management and customer data platforms to refine your analytics?
Our focus is the financial performance of marketing campaigns. We do not collect PII information. We are not a customer data platform.
Our solution leverages a cloud database service from Snowflake. In our opinion, Snowflake delivers the best performance and the lowest overall cost of any database offering available today.
How do you see the transformation of analytics technologies with maturity in data science and AI/ML?
Every business process and application will be transformed in the next 5-10 years by AI/ML. Anomaly detection — as a primary use case — is made possible by machine learning modeling. In our case, we use a technique called Bayesian Structural Time Series Modelling. Historical time series data is used to predict the future. Those predictions are then used to define a range of expected results in the future. This range then defines the boundaries for alerting. And this produces far fewer false positives than threshold or rate of change alerting.
Digital marketing is complex, high volume and costly activity. Complete, timely data put into context by modeling and what-if forecasting is in everyone’s future.
To what extent could you improve marketing campaign outcomes with conDati analytics? How does it fit into the social media analytics stack?
There is an old joke told in marketing circles, “We know that 50% of our digital marketing programs are working; we just don’t know which ones.” That doesn’t have to be true going forward. Visibility combined with modelling and what-if forecasting will be a game-changer for our customers. Decisions based on data will replace blind guessing in spending decisions, resulting in increased results and reduced costs. Constantly available data will support faster decisions made more frequently to take advantage of cost arbitrage opportunities.
Thanks for chatting with us, Ken.
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