Why Marketing Mix Modeling is Key to Ecommerce KPI Optimization?

Seasoned marketers have been familiar with the marketing mix modeling (MMM) framework for decades. Back in the 1950s, when promotion was restricted to radio, television, newspaper, magazine, and billboard advertisements, marketing mix modeling was one of the best KPI tracking tools to estimate the impact of these advertisement campaigns on an organization's overall ROI.

As marketing evolved and new digital channels like LinkedIn, Facebook, and Google emerged, it was time for new-age marketers to try advanced data analytics methods like multi-touch attribution (MTA).

However, despite being an effective technique, MTA is unsuitable for growing ecommerce businesses. Firstly, MTA's capability is restricted to only three digital channels at a time for ad performance tracking. Secondly, MTA uses individual user-level data to track ad metrics, and smartphone manufacturing brands like Apple have introduced strict guidelines against user-level data sharing.

This is why ecommerce marketers use a refined and more evolved version of marketing mix models. Marketing mix modeling performs multi-channel KPI tracking through statistical methods and capitalizes on aggregate data like overall sales, store distribution, seasonality, inflation, and more. 

That's why adopting marketing mix modeling for ecommerce KPI tracking is important, and in this article, we will explain this more comprehensively.

The Role of Marketing Mix Modeling in Ecommerce

Marketing mix modeling helps ecommerce retailers estimate how marketing ad spending, the price of products, the assortment of various items, and other promotional strategies impact the store's overall sales.

Optimizing Ad Spending

Trained marketing mix models combined with statistical techniques like saturation curves can help optimize ad spending. The benefits of using marketing mix modeling for optimized ad spending include:

• A trained marketing mix model leverages the relationship between media strategies and dependent variables like market trends and seasonality to capture how a campaign performs over time. As a result, ecommerce marketers can identify the point of diminishing return for each campaign and plan ad spending accordingly. Thus, there is no additional spending beyond that point of saturation.

• With the combination of saturation curves and mix models, marketers can analyze historical data to predict future ad spending and visualize the impact of ad spending on the outcomes. This, again, guides marketers to allocate ad budgets efficiently.

Price Optimization

Price elasticity is critical for ecommerce stores as it explains how the demand for a specific product changes due to the change in its price. Marketing mix modeling offers insights into price elasticity. As an outcome, marketers predict how a changed pricing strategy impacts a product's overall sales and adjust the pricing plans accordingly.

Product Placement & Assortment

Marketing mix models focus on identifying the ideal variables to generate maximum revenue from marketing operations. Some key variables include product, distribution channels, package size, customers' location, timing, and so on.

Let's take the example of Ikea. The company primarily sells furniture and home decoration items through their website and offline stores. With the help of marketing mix modeling, it can identify which distribution channels, location, and package size can be more profitable for it in the long run.

Promotion Effectiveness

Marketing mix models help marketers to measure the power of various advertising campaigns in a business. The models rely on different dependent and independent datasets to identify how these campaigns impact overall sales and their roles in driving conversion for an ecommerce business.

Suppose you have just started your ecommerce store journey and are trying multiple promotional techniques like paid ad campaigns, social media marketing, search engine optimization (SEO), and so on. Due to budget constraints, you must pick any of these channels to invest in. Based on historic performance, you can use marketing mix modeling to predict which marketing channel will be most profitable for you.

Neil Roy, VP of Marketing at Lifesight, talked about what sort of macro-level questions MMM can answer for ecommerce brands

Important Ecommerce KPIs for Media Mix Modeling

Below are some of the most important ecommerce KPIs that you can track through a marketing mix model:

• Revenue incl. VAT before returns (Rev. incl. VAT pre-returns): This KPI represents total sales revenue, including value added tax (VAT), before deducting factors like returns or refunds in an ecommerce business transaction.

By tracking this KPI, ecommerce stores identify accurate pricing, promotion, and product strategies and enhance precision in the marketing mix model.

• Revenue excl. VAT after returns (Rev. excl. VAT post-returns): This KPI refers to total ecommerce sales after deducting returned items It provides insights into ROI calculations and helps marketers with strategic decision-making.

• Revenue from customers who did not accept analytics cookies (No-cookie Rev.): Ecommerce sales generated without cookies are called no-cookie revenue and it determines which marketing channels and strategies will be most effective for customers who value privacy?

• Gross Margin after returns (GM post-returns): When the cost of goods sold is deducted from the revenue, the gross margin after returns is calculated. Ecommerce and DTC companies can gauge profitability based on GM-post return rate.

• Customer Lifetime Value (CLV): An ecommerce business's CLV is the revenue they expect from customers throughout their relationship. Marketing budgets can be allocated more effectively when marketers understand CLV as it predicts long-term profitability from each customer.

• Customer Acquisition Cost (CAC): There are costs related to marketing, advertising, paying the sales team, and so on. By monitoring and contrasting CAC with CLV, ecommerce retailers evaluate the efficacy of their marketing channels.

• Return on Ad Spend (ROAS): This KPI determines the revenue from advertising campaigns for an ecommerce store relative to the expense of those ads. Tracking ROAS data offers valuable insights into how online advertising affects overall sales.

• Advertising Cost of Sale (ACOS): ACOS is the ratio of advertising spending to total revenue generated from advertisements.

• Cost Per Acquisition (CPA): CPA is the overall cost of obtaining a new customer. Businesses determine the most profitable marketing channels for campaign execution by tracking CPA in marketing mix models.
  
  

Ecommerce stores can estimate the least expensive acquisition strategies by comparing CPA across several channels, including social networking platforms, search ads, and email marketing.

For example, MMM model shows the share in ad spend and return for each paid channel which would enable marketers to identify if channel is underperforming or over performing and redistribute ad budgets accordingly

Learn more about acquisition costs here.

• Average Order Value (AOV): It refers to the typical purchase amounts made by customers on a website during a given time frame. Customers who spend more money per transaction have greater AOVs, which boosts overall revenue.

Challenges Ecommerce Businesses Face When Tracking KPIs

For large ecommerce businesses with multiple product lines, KPI tracking in fixed time frames is a significant challenge.

Let's consider Nike, which has three broad product segments—shoes, apparel, and equipment. For each of these segments, Nike offers multiple product lines. Multiple KPIs will be tracked for each business segment to understand which product lines perform the best, which promotional campaigns acquire the highest return, which ad channels generate the highest revenue, and so on.

It is impossible to track all these KPIs manually as several challenges may occur, like missing out on critical data, creating consolidated reports, and tracking online and offline campaigns simultaneously.

In this section, we are explaining these challenges in more depth:

Data Overload and missing out on Critical Information

Ecommerce stores generate large volumes of data every day. These data types include sales data, in-store data, in-app data, shopper data, product data, and so on. If you are a marketer responsible for analyzing these vast datasets, suffering through data overload is normal.

While marketers often rely on tools like Google Analytics (GA) for data analysis, it is common to miss critical datasets because GA doesn't capture conversions from users who don't accept cookies.

Real-time Monitoring and Reporting

As an ecommerce marketer, even though you navigate through the data overload and find a way to collate all required ecommerce KPI datasets in one place, another problem still needs to be solved. You need to find a way to monitor these raw datasets in real time and create advanced visual reports for stakeholders to decode the underlying insights from these datasets.

Offline Data Tracking becomes Difficult

Digital channels might be dominating the world of ecommerce, but specific offline marketing channels like billboards, newspaper advertisements, and television ads still contribute to the success of an ecommerce store.

Modern MTA tools cannot track ecommerce KPIs for offline channels as these tools need user-level data for KPI tracking. Therefore, ecommerce brands often end up not focusing on high-potential offline channels.

Learn more about the common data challenges related to marketing mix models here.

How does MMM Solve These Challenges?

Lifesight's Marketing Mix Model helps ecommerce stores measure the accurate performance of their online and offline marketing campaigns with an automated marketing mix model. Here's how.

Seamless integration and continuous calibration

Lifesight's automated marketing mix models can integrate with multiple data sources, regardless of platform type. It integrates with GA, Shopify, Google Ads Manager, Facebook Business, LinkedIn, Instagram, TikTok, and other data sources where ecommerce stores usually run ad campaigns.

Marketers can easily access all data types and formats and merge critical datasets related to sales, products, and customers with their web analytics KPIs.

Additionally, Lifesight's marketing mix model auto-integrates and calibrates with your existing attribution models to generate marketing mix modeling data feeds and retain models with just one click.

Consolidated Real-Time Reporting

Lifesight's Marketing Mix Model can automatically integrate with diverse data sources to develop a consolidated overview of multiple ecommerce KPIs. By combining Lifesight's MMM with various data connectors, ecommerce stores generate standardized data schemas, pipelines, and outputs.

That means marketers can collect KPIs from multiple sources and convert them into simple formats that are easy to analyze. This model also helps you eliminate broken datasets and inconsistencies across various channels to create intuitive dashboards.

You can also integrate datasets from GA to make your reports even more in-depth. The best part is that you generate these real-time reports within a few minutes with the help of Lifesight's automation features and avoid manual interventions.

Seamless Offline Campaign Measurement

Lifesight empowers ecommerce stores with self-service data collection regarding offline campaign performance measurement. You can download Lifesight's templates and checklists to collect and add inputs related to offline campaigns.

Make sure to input the datasets based on Lifesight's guidelines so that the MMM captures required datasets and analyzes the effectiveness of offline campaigns on overall sales.

Final Thoughts

If a large part of your marketing responsibilities is tracking ad campaigns, identifying insights from those campaigns, and forming strategies for future business goals, you must learn to leverage marketing mix models well.

Learning to track the ecommerce KPIs with your marketing mix model will help you create real-time, consolidated marketing reports by auto-integrating with multiple data sources. Use these reports to convince your C-suite on how the marketing strategies will impact overall sales.

Get started with Lifesight's automated marketing mix model to explore ecommerce KPI tracking further. Book a demo!