To remain relevant and competitive in the market, a majority of CPG companies have shifted their focus on deeper analysis of customer insights. While it is hard to predict the behavior, it is imperative that companies capture the change sooner and adjust their strategies accordingly. Digital disruption and CPG marketing analytics has provided new opportunities for CPG companies to communicate and sell products, and build on new opportunities. However, they struggle with reaching out to the right target audience, resulting in high marketing spends with low conversions.
COVID-19 pandemic has further accelerated the need to target the right audience as there has been a significant shift in human behaviour, most of which are deemed to be permanent. One of the major changes was the immediate drift to the online mode of shopping over visiting physical stores. E-commerce websites became the comforting ground, and the trend is only expected to see a significant rise in the coming future.
According to a report, online grocery sales will grow by almost 80% in 2021 compared to 2019, before the pandemic hit. About 40% of consumers are trying online grocery for the first time.
To tap into the right opportunities and ensure an uphill growth, CPG companies need to align their marketing spend to reach a wider and more importantly relevant audience.
This case study covers a detailed approach on how we custom built a recommendation engine for CPG company that assessed all marketing campaigns and recommended ways to optimize the spend in order to achieve improved Return on Advertising Spend (ROAS).
Challenges with Existing Marketing Campaign
The client witnessed an exponential increase in e-commerce sales due to the COVID-19 pandemic. While they had an existing marketing campaign up and running, the spend on the marketing efforts was quite high. The goal was to optimize the marketing spends for a particular product on a major online platform.
There were many challenges that customers faced. To begin with, the current marketing campaign was not streamlined which required constant changes based on rules and heuristics. Further, these campaigns were not properly analyzed, and decisions such as bid pricing and which campaign to run were mostly taken by intuition and past experience. Also, data pulls from various sources such as historical daily budget data, campaign dataset, repeat orders, ratings, and reviews, were manual and not always up to date.
These challenges meant that the outcomes on advertising spend were not up to the mark and were highly priced, requiring an adoption of marketing campaign optimization.
Building Recommendation Engine
To solve the above challenges, we built a recommendation system to optimize sponsored brand ad campaign spends on the online retailer platform. These ML based recommender systems are trained on data such as user behaviour, historical data, and current user activities to create smart systems to provide the best and relevant recommendations.
In this case, the recommendation engine worked on the basis of past purchases. This robust system suggested recommendations and campaign strategy that improved the returns on ad spend for a specific brand. It also suggested the keywords that must be used for product placement while also suggesting the bid price associated with it.
Campaign budget realignment and bid price optimization strategies ensured that the campaign budget is realigned from low-performance campaigns to high-performing campaigns. We also implemented changes in the bidding price for different keywords to optimize and improve returns on advertising spend. Further, design experiments were executed to gather new information.
This resulted in better cost-saving and exploring other areas to invest in while ensuring growth in sales.
With the partial implementation of suggestions and recommendations, the CPG company saw nearly 2% cost-saving due to marketing campaigns optimization efforts. The potential saving on the overall marketing spends was close to $30,000 which was achieved by employing campaign budget realignment and bidding strategies for the client. The solution ensures a regular generation of recommendations, automated solutions, and streamlining marketing operation through regular reports.