According to a 2021 study, 46% of marketing decisions are not yet influenced by analytics. Many marketing departments still need days or even weeks to compile reliable data. That is too long to make ad hoc, agile, and valid decisions in the post-pandemic new normal. Rather than making decisions based on trial and observation, all available marketing data needs to be compiled into a single dashboard.
This dashboard enables teams to monitor all KPIs constantly, optimize campaigns across all channels, and proactively identify trends and eliminate anomalies that could negatively affect the marketing campaign’s success. The combination of data from multiple sources and the improvement of cross-channel attribution is paramount to be able to fully understand the market and the customers.
Measure performance in real time with individual data sets
Measuring campaign performance channel by channel is not sufficient. With the increasing number of channels (the web, apps, CRM, social media, sales, paid media and more), it is just not possible to analyze results and to provide a holistic report in real time. Instead of creating dedicated data teams, data can be displayed in real time to meet the needs of each respective marketing team member. The individual data set, supported by AI, enables the individual to respond with agility to any event that requires an adjustment. A good system constantly monitors the results based on classic marketing KPIs, ROI and revenues. Team members can identify underlying negative trends before they have an impact on marketing campaigns, revenues or the business in general.
Augmented analytics allow for a highly proactive approach, applying machine learning to uncover deep insights within potentially vast amounts of data. This leads to a more objective and predictive approach to data discovery, automatically identifying patterns and trends that humans may never uncover. Additionally, this process provides insights into these patterns’ causes and relevance. AI can be used to identify highly specific audience segments, outlining their preferences and pain points, as well as predicting their buying patterns. It unveils bias within data sets stemming from unconscious human preconceptions or flawed data collection techniques, helping to avoid a negative performance impact.
Combine modeling with data analytics for quantitative insights
These meaningful analytics enable marketers to steer campaigns in a granular and revenue-driven style. But do they prove the effects of brand awareness and its conversion into revenue? To demonstrate the ratios between brand awareness, brand sympathy, willingness to buy, marketing campaigns and revenue attribution, teams combine modeling with data analytics. Attribution modeling mirrors the customer journey. It reveals which parts of the journey the customer prefers and which parts need to be enhanced. CMOs can extract the correlation between the multi-channel setup and customer touchpoints and show how they convert.
Many marketing budgets were cut during the pandemic. Thanks to the long-time investment in marketing digitalization, enterprises will be better prepared to manage future crises and make educated decisions about cutbacks. The goal is to be agile and able to re-prioritize quickly. Real-time 360-degree data that reveals the performance of all campaigns across multiple KPIs must be in place. These meaningful analytics provide quantitative insights that enrich and guide marketing team discussions.
By regularly analyzing data and taking action to adjust when needed to drive results, marketers can achieve desired ROI and efficiency. According to our IBM C-Suite study in 2021, only 9% of surveyed C-suite executives create high value from data and have a high level of integration. The most successful organizations will be those that are willing and able to adapt to the disruption caused by data-based decision making. The good news: If they act now, CMOs still have a good chance to surpass their competition.
Source: ibm.com
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