What are the most innovative marketing analytics techniques?

Predictive analytics plays a pivotal role in various use cases within the marketing domain. Utilizing Natural Language Processing (NLP)-based sentiment analysis serves as an effective method to assess how a product is perceived in the digital space. Delving deeper by comprehending Net Positive Sentiment and Topics of Conversations provides valuable insights into the factors influencing sentiment. Leveraging these data points facilitates the curation of more impactful content directly related to the primary topics of conversation in the digital space.

AI-based MMM is concurrent innovation version of Attribute modeling. Recently for a beverage firm, we assessed the impact of touch points on sales. Out of 64 odd candidates, we selected 20+ for analysis. We dropped due to poor performance around 10. Of the 30 odd touch points major impactful were price, distribution, TV, FB, YouTube. We took 2 weeks to collect monthly data and took a week to share findings. We did few iterations to ensure model results not only validated from stats perspective but also aligned with ROI from other sources. We used saturation curves to advise optimal per week spend plan. We are currently refreshing the model with new data points. Plan to work with marketing to test few simulations.

Propensity modelling is another innovative marketing analytics technique that many should familiarize themselves with. Essentially, it uses machine learning to forecast event outcomes based on consumer attributes and historical actions. It’s invaluable for predicting a lead’s chance of conversion, anticipating customer churn, or assessing the impact of an email campaign. A/B tests can provide insights, but they’re not always feasible. In cases where risk-averse leadership is hesitant, propensity modeling emerges as a robust, data-driven alternative. Notably, it can even estimate a customer’s value before spending any of your marketing budget on them. But, let’s save that for another day. ??

- Predictive Analytics: This technique leverages historical data and machine learning algorithms to forecast future trends and customer behaviors, enabling businesses to proactively adjust their marketing strategies. - Customer Journey Mapping: By visually mapping the customer journey and analyzing touchpoints, businesses can identify pain points and opportunities for optimization, ultimately improving the overall customer experience. - Sentiment Analysis: Using natural language processing, sentiment analysis gauges public opinion by analyzing social media mentions, reviews, and comments. It helps brands understand customer sentiment and make data-driven decisions.

While all of the above areas are great as concepts, there is always a ton of complexity when it comes to having these in place for an organisation. There are multiple factors to consider including how you martech stack is designed to work and how multiple tools work together in sync along with intricacies of the industry and business you are in. This leads to a unique set of pillars to review as you use these advanced concepts to grow and move up the maturity curve. Always invest in experienced and highly critical people who will look at things in a way that majority cannot and have the willingness to fix them.