5 Ways of Promotion Optimization Using AI and Big Data
Trade promotions are a huge expense for the consumer goods industry. In spite of large spends, it is seen that many times the promotion does not achieve the desired results or break even. This is why trade promotion optimization has a significant impact on revenue generation. According to the Promotion Optimization Institute’s 2019 State of the Industry Report, consumer packaged goods (CPG) companies spend anywhere between 11% and 27% of revenue on trade promotions. Interestingly, half of all consumer products purchased are bought on promotion. No wonder, the marketing and promotional strategies continue to be fueled by price-driven promotions.
What is Trade Promotion Optimization?
Trade promotion optimization (TPO) is a data-driven process by which brands can minimize spend while increasing return on investment (ROI) on their trade promotions. It involves utilizing integrated goals, factoring in promotion and supply constraints and deploying predictive analytics to continuously improve trade promotion strategies and results.
Challenges to Trade Promotion Optimization
CPG companies face increasing trade spend owing to pressure from retailers and competitors. With 20% to 30% revenues being spent on trade promotions, it is necessary to evaluate if the spend is helping in achieving the company objective of maximizing ROI. There are several challenges which come in the way of promotion optimization, prominent among them being:
- Data Availability Challenge
Organizations are unable to measure the effectiveness of a trade promotion optimization program due to lack of sufficient data, inefficient data harmonization and predictive intelligence. There is a dearth of data-backed optimization of upcoming promotions. Further decisions are made either based on past experience or gut feeling. Even for those organizations which employ data brokers, a lack of sufficient data will not convey the bigger picture.
- Data Not in Proper Form and Shape
Decision-makers are having to pull out data from multiple systems, compile information from dashboards, spreadsheets and many use sub-optimal analytics. This complicates the process and the result is inaccurate and insufficient.
- Extreme Dependance on the IT Team
The help of the IT/MIS team has to be sought every time the statistics and figures need to be compiled. This is a time-consuming process.
- Incomplete Analysis
Data is generated from internal sources such as point of sales data, results from prior marketing schemes as well as external sources such as industry reports, competitor campaigns, weather reports and so on. Therefore, much of the analysis is based on forecasting how a certain action may work out.
Many of the trade promotional tools miss out in-depth analysis of internal and external data and provide just a general analysis which may not offer actionable insights to generate ROI.
Retail Predictive Analytics
In the light of the drawbacks mentioned, it becomes imperative for organizations to employ promotion optimization software. Supported by retail predictive analytics, such software can provide accurate insight and support a forward-thinking strategy.
The methodology employed must adhere to the following:
- Data from all data sources must be collected
- Data thus collected should be in the correct format
- It should be used and customized with minimal training and intervention
- It should be able to gather information from past promotions, measure effectiveness and provide suitable recommendations and forecasts for future promotions
Trade promotion optimization is becoming a necessity for promotion planning. It therefore becomes necessary to adhere to a scientific approach in order to maximize profit, reduce loss, optimize customer experience and enhance the competitive edge of the company.
Technological advances in data and analytics are reshaping the trade industry in a big way today. The use of AI, Big Data and Machine Learning (ML) is paving the way for successful promotion optimization.
5 Ways to Predict Promotion Success with ML, AI, and Big Data
Industry leaders today prioritize analytics initiatives within their organization to bridge the gap between data availability and actionable insight. With the incorporation of AI technologies such as big data, ML and predictive analytics, a goal-driven analysis which is key to promotion optimization is possible.
The following methods of promotion optimization utilize AI and Big Data successfully:
1. AI Chatbots
Many of the trade promotion software do not appeal to the end user due to the complex navigation flow in the system. Integrating AI chatbots into the software helps the users to understand promotions better and help in TPO.
2. Measurement of Real-Time Success with AI and ML
Retailers and CPG companies can use advanced analytics platforms to analyze campaigns and advertising spend in real time. Organizations can assess the success rate of their campaigns with respect to customer expectations and re-orient for better results. AI and ML also help companies understand potential outcomes for different scenarios. Previously companies were reliant on weekly or monthly reports to evaluate the success of the promotion campaigns. With real-time data and its analysis, it is possible to tweak the promotion campaigns and make better decisions.
3. Personalized Trade Promotions with ML
The efficiency and performance of trade promotions can be greatly enhanced when they are personalized. Promotional analytics help to determine optimal target audience, communication time, communication channel and so on. It also helps in personalization during an online sale on e-commerce websites as well as in store sales. The choices offered to customers will be based on numbers and data from previous sales and browsing history with the help of retail predictive analytics.
4. Performance prediction with ML
Some customers have a high lifetime value. This value is generated by calculating how much they spend on your products, their paying history and how many times they've purchased from the same organization. Identifying such customers is the hardest thing to do in marketing. This is where big data and ML help. They help to gather data from the buying history of the customer and compile it in a way such that it can be used for performance prediction. It can further enable companies to formulate successful promotional campaigns and prolong the history and the relationship of the customer with the company.
5. Post Event Analysis
The performance of promotional activities is crucial to forecasting growth in sales and increased market share. Several companies are predicting promotion success with ML to harness the large amounts of data generated around their customers, market trends and the company’s performance. This allows the company to enhance their promotional activities in a way that is beneficial to the growth of the company.
The science of trade promotion optimization is steadily getting more complex with the availability of increasing amounts of data. Data insights are the key to gaining a competitive advantage among today’s savvy and discerning customers. Recent advances in AI and ML have resulted in sophisticated platforms that can not only handle disparate data but also analyze it all efficiently and quickly with the goal of building trade promotions that will outperform all previous ones and those of competitors.
Get in touch with our experts to understand how AI, big data and performance prediction ML can transform your trade promotion campaigns.