The Short and Long Answer is 'Yes'
More than ever before, marketing leaders have access to proven tools and services to ensure digital investments map directly to pipeline and revenue, including the ability to nimbly shift digital spend when they predict a shortfall in returns. By applying a comprehensive view into cross-channel digital marketing—across historical, real-time, and forecasted data performance—marketers can confidently map to business goals in ways they haven’t been able to before. The emergence and legitimacy of Artificial Intelligence (AI) and Machine Learning (ML) technologies now form the backbone of a new view into data analysis and insights, including:
- Unification of disparate digital marketing data into a blended data model
- Deeper insights into campaign performance data—granular ROI insights at the campaign, audience, content, data source, channel and cross-channel attribution levels
- Forecasted engagement and revenue results with seasonality and anomaly alerts
- Optimization recommendations for higher campaign returns with saturation analytics—taking point of diminishing returns into consideration
- Content personalization and journey analytics using statistical models to accurately calculate contribution and influence
A Complete Picture into Data Flow
Facebook, Google Ads, Amazon, and other paid marketing platforms created new and exciting ways to reach audiences, making it easier for marketers to produce and customize targeted ads per customer segment. However, legions of skilled marketing leaders will tell you that silos and walled gardens continue to plague them—keeping them from connecting data in a holistic way and challenging the management of their cross-channel spend. If the goal is to leverage historical, real-time, and forecasted data performance to better meet the needs of target audiences and win more business, a new approach to data is needed.
The diagram below, representing stages of data insight, illustrates how a variety of marketing source data can be blended, analyzed and shared to help marketers make game changing, confident decisions into marketing resource allocation and campaign spend.
Stage 1: Your Audience, Campaign, and Content Data. It’s true that analytics are ineffective if they use incorrect or incomplete data, and manual manipulation using siloed and disparate sources—including spreadsheets—doesn’t work. The prerequisite for advanced marketing analytics is to identify all the performance data from your most important martech systems. This includes all audience, campaign and content data, both historical and as-it-happens—collected into a single unified data asset.
Stage 2: Blended and Analyzed Data. Blending and analyzing data to access current performance benchmarks helps marketers learn and understand the behavior of existing digital marketing programs. Once there is a baseline for program comparison, marketers can begin to apply AI, ML and data science to drive accurate forecasts and intelligent recommendations—optimizing omnichannel campaigns.
Stage 3: Shared Data. Everyone from the CMO, marketing data insights leader, marketing analyst, digital marketing leader and marketing practitioner – all can now access instant and continuous data updates delivered through live dashboards, reports and alerts. Role-based information is delivered with context in a timely way, fueling intelligent actions based on recommendations to shift campaign spend to a proven channel, or shift the day and time of day ad schedules, for example.
conDati’s cloud-based marketing data science as a service platform (DSaaS) unifies data and generates insights and actionable recommendations for how, when, and where to shift budget allocations for higher marketing ROI. These insights can also eliminate any marketing spend currently being wasted. A full campaign data performance view allows an industry-competitive organization to not only drive brand value, but to improve revenues through an innovative, modern, and forward-thinking approach to all their digital marketing spend decisions.