Hoping to Leapfrog the Competition by Connecting Siloed Data and Implementing Machine Learning and Predictive Analytics
The typical large enterprise uses 90 or more marketing and analytics tools, while SMBs and Mid-Market companies employ between 10 and 25 martech tools to manage marketing operations and drive revenue. That’s a lot of systems for any company to manage. The results are dreaded data silos, which at best provide partial insights, and in the worst case overlook critical business goals. In 2018 machine-learning technology promises to deliver easier and more effective ways for marketers to aggregate data from all the silos to create predictive analytics.
Predictive intelligence drives marketing performance. Here are a few key analyses that can be derived by combining data sets from multiple martech tools:
- Optimize ad spending and audience acquisition
- Drive higher conversion rates
- Increase forecasting accuracy and pipeline predictability
- Predict customers’ first purchases, and their lifetime value to the company
- Identify what “normal” looks like, and alert on anomalies
By collecting, blending and storing data from disparate sources, and then applying machine-learning analytics, marketers will be better equipped to individualize communications to their target customers, and to craft differentiated messages for their product portfolios.
Marketing teams need to adopt new solutions to leapfrog their competition and further refine the customer journey. Thoughts to consider for 2018:
- Focus on more contextual channels;
- Embrace personalization, and
- Use data science to develop commercial insights.
conDati is developing analytics-as-a-service to collect, blend, and store data from source systems up and down the martech stack. conDati combines core expertise in massive data collection and integration with advanced data science algorithms to create new insights and analyses in support of business objectives.
