Leveraging LIFT ROI for Telcos: Balancing the Short and Long-Term Advertising

In the Telecommunications industry, effective marketing is crucial for customer acquisition, retention, and overall growth. MMM or what we call LIFT ROI here at Kantar, is a powerful tool to measure the impact of various marketing activities and optimize the allocation of marketing resources. 

This leadership piece explores how Telcos use LIFT ROI to balance short-term and long-term advertising effects, the importance of granularity in media analysis, and how to transform historical insights into predictions using Bayesian methods, all which Kantar has proven expertise in.

LIFT ROI involves statistical analysis to estimate the impact of different marketing tactics on sales and other performance metrics. For Telcos, we can dissect the contributions of various channels such as TV, digital, print, out-of-home, and even non-media factors such as promotions and economic conditions.

- Telcos possess vast amounts of data from multiple sources from customer interactions, transactional data, and external data such as economic indicators. Integrating these data streams is the first step in building an effective marketing mix.
- The use of regression analysis, Telcos can quantify the impact of each marketing channel. This involves historical sales data, marketing spend data, and control variables to account for external factors.

Balancing Short-Term and Long-Term Advertising Effects
A key challenge in marketing is balancing short-term sales activation with long-term brand building. Short-term effects are immediate responses to marketing efforts, such as spikes in sales following a promotional campaign. LIFT ROI captures these effects by analyzing high-frequency data (e.g., weekly, or monthly.) Whereas long-term effects are gradual changes in brand equity, customer loyalty, and market share. Long-term effects are typically harder to measure but are crucial for sustained growth. Kantar also incorporates brand tracking studies and other longitudinal data to estimate these effects.

The Value of Granularity in Media Analysis
Granularity refers to the level of detail in the data used for modeling. In the context of LIFT ROI, higher granularity allows for more precise insights.

- By analyzing individual media channels and even sub-channels (e.g. different TV networks or digital platforms), Telcos can identify which specific touchpoints drive the most value.
- Granular data allows for customer segmentation analysis, enabling telcos to tailor their marketing strategies to different customer groups based on demographics, usage patterns, and preferences.
- Using daily or weekly data can help capture the immediate effects of campaigns and understand seasonality patterns.

Turning Historical Insights into Predictions Using Bayesian Methods

Traditional MMM uses frequentist approaches, but Bayesian methods (embedded in LIFT ROI) offer significant advantages in making predictions and handling uncertainty.
- Bayesian Hierarchical Models: These models allow telcos to incorporate prior knowledge and hierarchical structures (e.g. different regions or customer segments.) This approach provides more robust estimates, especially with limited data.
- Incorporating Uncertainty: Bayesian methods naturally incorporate uncertainty in predictions. This is crucial for decision-making under uncertainty, providing telcos with probabilistic forecasts rather than single-point estimates.
- Updating with New Data: Bayesian models can be updated with new data, allowing telcos to continuously refine their predictions and adapt to changing market conditions.

Top 5 Practical Steps for Telcos to consider in this space:

1) Start with a comprehensive data collection strategy, integrating data from all relevant sources to ensure both data quality and consistency.
2) Develop and validate LIFT ROI models using both frequentist and Bayesian approaches. This should include short-term and long-term effect modelling, incorporating granular media data.
3) Use the model outputs to guide marketing strategies. For instance, allocate more budget to high-performing channels and tailor messaging for different customer segments.
4) Implement a system for continuous data collection and model updating. This allows the models to remain relevant and accurate over time.
5) Engage stakeholders from marketing, finance, and strategy to ensure the insights from LIFT ROI are effectively used to drive business decisions.

LIFT ROI, enhanced with Bayesian methods and granular data analysis, offers Telcos a robust framework for optimizing marketing efforts. By balancing short-term and long-term effects, Telcos can ensure sustainable growth and a stronger competitive position. Embracing these advanced analytical techniques can transform historical data into actionable insights, leading to more informed and effective marketing strategies.

For more information about Kantar’s LIFT ROI and how it can help you unlock your investments and potential please contact Pamela.goodman@kantar.com.