Why Data Analytics Is a Gold Mine for Trade Promotion Optimization
Originally Posted: Consumer goods
One of the biggest criticisms that marketing teams face is that they come up with a budget for campaigns with an ad hoc or gut-based approach.
It is typically based on a few parameters like division between ATL and BTL activities, the number of new products launched typically, social media spends, target geographies, or the increase in sales targets by X% which results in increasing the marketing budget by X/Y%.
The execution is far from this envisaged budget and is mostly reactive in nature. Most of the time the budgets are guess-work, and aren't backed by data analytics, resulting in a waste of marketing dollars. It relates to John Wanamaker saying, "Half the money I spend on advertising is wasted; the trouble is I don't know which half."
So, how can companies make informed decisions when determining marketing budgets? How can companies avoid common pitfalls and misalignments with their overall business strategy and objectives? The answer lies in the details — data analytics can play a pivotal role in effectively utilizing marketing budgets and optimizing marketing spending.
Addressing High-Level Burning Questions With Analytics
Given the competitive nature of the business world, business-as-usual isn’t an option anymore. CPG and FMCG businesses are dealing with rising costs and inflationary pressures, and consumers have to bear the brunt. So, why do trade promotions need to be targeted?
Did you know that CPG companies worldwide invest about 20% of their revenue annually in trade promotions. However, businesses need to build the momentum for technology and analytics capabilities for effective trade promotions.
Trade Promotion Optimization (TPO) for CPG can bring data to life and can enable marketers to plan, budget, present, and execute incentive programs that are established between a manufacturer and a wholesaler/retailer to enhance the sales of specific products. The larger goal for many businesses must be to identify significant increases in effectiveness coupled with reductions in trade promotion spending.
Questions such as, “What should be the price of a product? How can we support dynamic pricing? What promotions should one run? What are the profitable products and channels?, etc. are what businesses should be asking themselves, using analytics to solve these issues.
- Pricing analysis enables organizations to analyze the impact of price on demand and find the optimal price point.
- Promotion analysis helps analyze trade investment levels and develop promotion guidelines. For example, the collection of the right details such as historical data of companies, data from retailers’ sales registers, and third-party data and macroeconomic indicators can help an organization with promotion analysis.
- Profitability analysis helps analyze profitability levels and establish cost component guidelines.
- Further, by using advanced analytics use cases — such as customer segmentation, demand forecasting, price elasticity, shopper behavior through demographics, and social sentiment analysis, as well as market mix modeling — it is possible to arrive at better product categorization and product analysis.
A Deep Dive Into Promotion Analytics
For any organization running promotion programs and campaigns, it is important to analyze and incorporate the impact of promotions to understand the sales forecasts in an efficient manner and analyze which promotion is the most effective.
Currently, a lot of organizations go through complex challenges, such as having several promotion programs running simultaneously with a manual, layered, and time-consuming process. There might also be no effective mechanism to evaluate the promotion impact automatically and, moreover, quantitative analysis of the promotion activities is a complex problem.
In such a scenario, the solutions should involve answering some burning questions such as:
Sales/Demand Forecasting: What will the sales be for a particular level of promotional spend?
Profitability: Who are my profitable customers, channels, and products?
Promotional spend estimation: What will be the promotional spend required to achieve a particular level of sales?
Campaign spend design: How should one allocate promotional spend across store/product/regions to maximize sales under a campaign? What type of promotions should we run and what is the ROI?
Pricing: What should be the price of the product? How can we support dynamic pricing?The approach to counter such challenging situations lies in data collection and validation, modeling processes, visualization to contextualize the problem.
First, it is necessary to validate data availability and sufficiency for modeling, and then prepare data for modeling and optimization. Second, generating candidate models may explain and measure a particular promotion program's impact. Lastly, develop visualizations for the business to provide insights so that better decision making is done.
Understanding Promotion Spend and ROI
With some companies spending over 10% of gross revenue in promotional programs, companies are looking to improve their understanding of promotion spend effectiveness and use that understanding in predictive models during the promotion planning and execution process.
Combining advanced analytics modeling and visualization provides business users with the following:
- Deep insights into profitability, cost, and ROI of the promotions
- Retailer/customer segmentation based on historical and demographical data
- Understanding price sensitivity across different dimensions
- Interactive scenario analysis of promotional spends and impact on the sales
- Insights provided to adjust the promotional spend by campaigns/segments as per changing dynamics of market
- How did you do vs budget, aligning the forecast, and course correcting the spend during the year
Why Data is Your Goldmine
Furthermore, understanding the effect of factors such as festivals, discounts, events, new launches, and campaigns is important and the same can be baked into the existing visualizations. This results in predicting a better tomorrow for the organization. Companies that are successful in turning data into a gold mine of information will excel at using analytics to identify valuable business opportunities to drive decisions and improve ROI, turn insights into products and offers that delight customer, and effectively deliver products to the marketplace.
—Saurabh Singh, Senior Vice President & Head CPG Solutions, Polestar Solutions