Pay-Per-Click (PPC) advertising is increasingly data-centric, with a greater emphasis on machine learning, A.I., analytics and optimization. For the technical marketer, the Excel Solver tool presents an opportunity to apply a rigorous mathematical approach to PPC campaign management.
This posts delves into the utilization of Solver for maximizing PPC performance, with a particular focus on the application of portfolio theory in risk management.
Introducing Excel Solver in PPC
Excel’s Solver is a parametric linear and non-linear optimization tool that adjusts decision variables to optimize the objective function within a set of constraints. In the context of PPC, Solver’s capabilities can be used to systematically analyze keyword performance and bid optimization.
Excel Solver is a powerful optimization tool embedded within Microsoft Excel. It allows users to determine the optimal value of a target variable (also known as the objective function) within a prescribed set of constraints and boundaries. Here’s how it works:
- Objective Function: You define an objective, such as maximizing the conversion rate or minimizing cost per acquisition (CPA) in the context of PPC campaigns.
- Decision Variables: You identify the decision variables that Solver will adjust to optimize the objective function. For PPC, these could be the bids for different keywords or budget allocations across campaigns.
- Constraints: You set constraints that the solution must adhere to. These could include budget limits, minimum or maximum bid values, or any other restrictions relevant to your PPC strategy.
- Model Setup: Using historical data and performance metrics, you create a mathematical model in Excel. This model translates your PPC campaign’s operations into a language Solver understands—equations and inequalities.
- Solver Configuration: Within Solver, you configure the parameters, including the cells that contain the objective function, decision variables, and constraints.
- Optimization Process: When you run Solver, it employs mathematical algorithms to iterate through various combinations of decision variable values, seeking the one that best optimizes your objective function while respecting all constraints.
Solver then proposes the optimal solution, which is a set of values for your decision variables. It also provides options to keep this solution or restore the original values if the proposed solution isn’t satisfactory.
Solver is a versatile tool that, when correctly configured, can serve as a decision-making aid across various optimization scenarios.
The Mathematical Model for PPC
A PPC campaign can be conceptualized as a mathematical model where the decision variables are the keyword bids. The objective function could be maximizing the conversion rate or ROI of an ad group. Constraints might include budget limitations or minimum impression share. The model incorporates historical data such as impressions, click-through rates (CTR), and conversion rates to inform the optimization process.
Example Optimization Workflow
Harvesting Data for the Analysis
Platforms like Google Ads, Analytics, and Customer Relationship Management (CRM) systems is indispensable. These tools serve as reservoirs of historical performance data, each offering unique insights that can refine the optimization process.
Google Ads provides detailed bid and conversion metrics, while Analytics can illuminate user behavior and journey nuances.
CRM systems contribute valuable customer lifecycle data, from lead acquisition to conversion.
For technical marketers, the synergy between these platforms and Solver is pivotal. By funneling data from Google Ads into Solver, you can fine-tune bid strategies based on past bid performance and conversion outcomes. Analytics data, when imported, allows Solver to account for user engagement and conversion paths, ensuring that bid adjustments are not only based on final conversions but also on the quality of traffic and user interactions. Meanwhile, CRM data empowers Solver to consider customer value beyond the initial conversion, optimizing for long-term profitability and customer lifetime value.
The integration process typically involves the extraction of data sets from each tool, followed by a transformation phase to align and standardize the data structure suitable for Solver. This may require scripting or the use of ETL (Extract, Transform, Load) software. The final step is the loading of this cohesive data into Solver, where the PPC optimization model resides. The outcome is a data-driven bid strategy that leverages a comprehensive view of past performance across multiple touchpoints.
Data Preparation for Solver: Best Practices
The efficacy of Excel Solver in PPC optimization is contingent upon the quality of the input data. Data preparation, therefore, is a critical step that should be approached with diligence. Here are some best practices for preparing and cleaning PPC data to ensure Solver’s effectiveness:
- Consistency and Standardization: Ensure that data from various sources maintains a consistent format. Standardize metrics like dates, currency, and KPI naming conventions to avoid discrepancies.
- Cleaning and Validation: Remove anomalies and outliers that may skew optimization results. Validate the accuracy of conversion tracking and other key performance metrics to prevent the model from drawing conclusions based on faulty data.
- Granularity and Detail: The level of detail in historical data can significantly impact Solver’s optimization capabilities. High granularity data, such as hourly bid changes and keyword-level performance, can enable a more nuanced and precise optimization model.
- Accuracy and Completeness: Verify that the data is not only accurate but also complete. Missing data points can lead to suboptimal optimization, as Solver relies on a full dataset to calculate the most effective bid strategy.
Model the Data in Excel
Technical marketers can employ Solver by establishing a structured Excel model. The model should accurately represent your PPC objectives, encompassing decision variables, goals, and constraints. Solver’s function is to iterate through potential solutions, guiding you towards an optimal bid strategy within the defined parameter space.
Populate Solver with the critical elements based on the spreadsheet you created:
Here is Solver’s recommendation based on the budget and the keyword performance date you entered, as well as the estimated performance of each keyword:
Tactical Advantages
The tactical advantages of using Solver for PPC are manifold:
- Precision Bidding: Solver utilizes historical performance data to facilitate precision bidding, optimizing for the highest possible conversion rate within the campaign’s constraints.
- Predictive Insights: Unlike basic bid estimations, Solver’s data-driven approach can offer more reliable forecasts based on your specific performance metrics.
- Scenario Analysis: Solver can evaluate various ‘what-if’ scenarios, aiding in strategic decision-making for budget adjustments and bid management.
Expanding on Portfolio Theory
Portfolio theory, in the context of PPC, involves the diversification of keyword bids to mitigate risk. Similar to financial portfolio management, this strategy aims to achieve an optimal balance between risk and return in your keyword investments.
Solver can be instrumental in applying portfolio theory to PPC. By treating keywords as investment assets, technical marketers can optimize the bid strategy across the portfolio to achieve a desired risk-return profile. Solver can identify the efficient frontier in your keyword portfolio, which represents the set of optimal portfolios that offer the highest expected return for a given level of risk. To do this, we simply use Adgroup performance rather than keywords:
A diversified keyword strategy can lead to more stable performance across different market conditions. For example, high-risk, high-reward keywords can be balanced with more stable, lower-cost keywords to ensure consistent performance even when market volatility affects keyword costs and performance.
Addressing the Common Pitfall
Many PPC managers overlook the importance of a thorough mathematical analysis in campaign management. This oversight can lead to suboptimal bidding and missed opportunities for risk reduction and precision targeting.
By integrating Solver into the PPC management process, technical marketers can transcend these limitations, engaging in a more sophisticated, data-driven approach that fully leverages the keyword marketplace’s dynamics.
Solver as a Critical Tool for the Technical Marketer
The Solver add-in is not just a tool for optimization; it’s a critical component for the technical marketer seeking to apply advanced analytical techniques to PPC management. By adopting a mathematical and strategic approach to PPC, you can ensure that your campaigns are not only optimized for current performance but are also resilient to market changes, leading to sustainable success.
