While we all can agree on the importance of data-driven marketing, the question remains: are companies using the right data? A recent Datamation opinion piece suggests that too many marketing organizations rely on data primarily for backward-looking analysis—for example, measuring performance rather than building analytics dashboards that drive upcoming campaigns or inform strategic planning.
If this sounds familiar, take a look at the examples below for tips on how to kick your data-driven marketing campaigns into high gear:
1. Knowing Your Target Audience
Combining design thinking methods and data science gives marketers a holistic understanding of different consumer profiles, behaviors, and needs. Using data science, companies can first analyze and segment consumers by demographic characteristics, geographic information, product use, and behavioral characteristics. From there, design thinking processes can be used to analyze these consumer patterns, identify the most relevant factors for each, and create personas. This combination design and data science framework ultimately informs better, more targeted campaigns for key customer personas.
2. Predicting Customer Lifetime Value
Analytics and machine learning can help companies determine the customer lifetime value (CLV) and optimize acquisition costs accordingly. With a firm understanding of the CLV, companies can evaluate how much to invest in a customer based on the potential return. In addition, this approach allows marketers to assess the various strategies and levels of investment worth making for each customer profile to acquire new customers with a higher CLV.
3. Building an Effective Propensity Model
Most marketing organizations still rely on a one-size-fits-all approach to engage leads. Data analytics can modernize this approach with propensity modeling, which can predict the likelihood that prospects and customers will perform certain actions. In order to be effective, a propensity model must be dynamic and able to adapt to changes with time. Companies can address this by automating data pipelines and processes to retrain the model on a regular basis. It’s also critical that the model be scalable so as not to be abandoned after the first use in a single campaign.
4. Monitoring and Acting on Consumer Sentiments
Data analytics, coupled with natural language processing (NLP), enables companies to mine the treasure trove of consumer sentiments across social channels. Using machine learning, marketers can classify sentiments as negative, neutral, or positive, and act more efficiently on customer feedback and obtain insights to improve the overall customer experience.
5. Driving Intelligent Automation
Another key use case is drawing on AI to improve the efficiency and performance of repetitive tasks. Automation can increase revenue by improving marketers’ ability to make better predictions and making content creation and delivery more efficient with marketing automation tools.
For more on how analytics, AI, and other trends are reshaping the marketing industry, take a look at this recent Forbes piece.