Measuring Trade Promotion Effectiveness and ROI of Promotional Campaigns using Machine Learning

Measuring Trade Promotion Effectiveness and ROI of Promotional Campaigns using Machine Learning

As per Nielsen’s studies, 55% of the trade promotion budget fails to increase the company’s market share or the category growth of its brand. There are certain metrics to measure trade promotion effectiveness, ignoring which cost businesses nearly 800% of returns on their marketing budget. If promotional activities do not fetch the desired results, companies need to return to the drawing board to start afresh with their strategies. Needless to say, many marketing heads struggle to find the missing piece in the promotion puzzle. 

A simple tip: one should begin by identifying the granular metrics used adaptively for a win-win bargain and optimize promotion as per demand. Let’s evaluate the key metrics for improving marketing strategies and measuring promotion effectiveness.

Important Metrics for Measuring Promotion Effectiveness

In order for your promotional campaigns to work as per your expectations, you need to measure the promotion effectiveness of past campaigns and resultant sales. Such insights are only available when you have certain metrics to measure; a few of which are elucidated below: –

Consider Empirical Data as Metrics for Measurement

Various factors impact the success of any promotional campaign; considering them for further evaluation can help businesses mint money with effective marketing strategies. Hence, careful granular data analysis can shed light on factors that boost or drop sales numbers. There are multiple sources from which such insights can be drawn effectively, such as: –

Tracking performance on a daily, weekly, and monthly basis to identify the change in trends early or account for ambiguous factors, like a change in weather, that can directly impact sales. Such data can help businesses take measures accordingly to ward off any hurdles during campaigns and ensure the effectiveness of promotional activities as per expectations.

Frequent visits from representatives for measuring promotional effectiveness can easily arrest out-of-stock situations at retailers’ end before they occur. Simply put, if your retailers are running out of stock during campaign weeks, your brand stands to lose potential buyers attracted by the campaigns. But reps maintaining healthy communication with the retailers are better positioned to identify such discrepancies before they occur, thereby saving your business from losing customers or brand reputation.

Measure shelf space to keep the flow of customers to your retail outlets steady. Believe it or not, extra shelf space and extra stocks are enough to draw your customers’ attention away from the competitors.

Track and Compare Competitor Market Share

A simple question – why do businesses spend dollars running promotional campaigns? 

Firstly, to spread knowledge about the availability of certain products in the market matching the ongoing demand.

Secondly, to attract potential and prospective buyers.

Thirdly, to expand the market for your products.

Unless owners have zero knowledge about the market share of their business and that of their competitors, creating effective campaigns will result in zero output; forget about measuring the promotion effectiveness. Hence, tracking market share and comparing it with that of the other players is to smoke out competition from the market.

Measure Category Growth

Category growth is as important as the company’s market share. A campaign also works by expanding the market of your products and bringing in a new category of buyers that was never your targeted demography. For instance, a brand selling skincare products does not necessarily target female buyers only. They introduce a new line of beauty products for men to draw them as prospective buyers. Category growth simply means creating demand in the market for new product lines.

Prioritize PoS Data

Businesses often focus on tracking shipment data. How many of your products are sitting in the storage or on the shelves of the retail outlets is not enough to measure promotion effectiveness. Rather, how many products are leaving the store in the hands of your buyers should be the focus area for analyzing campaign effectiveness.

Analyze Incremental Sales as Marketing ROI 

Tracking PoS data also helps understand the additional sales that occurred following promotion campaigns. Monitoring incremental sales against expected figures can help you understand how much extra business your company has made due to a promotion.

The metrics mentioned above are crucial to measuring trade promotion effectiveness only when such insights are available in real time and shared just when needed. Manual tracking and measuring of such metrics are time and labor-intensive. And, before the insights reach your hand, you have already lost a good amount of time and business to your competitors. 

Many businesses today resort to tech-based solutions to speed up the data extracting process and make strategies on real-time inputs to arrest the valuable time lost. 

Measuring Promotion Effectiveness with Machine Learning

If we consider consumer goods companies as an example, statistics suggest that over 500 billion dollars are invested by them in promotion campaigns annually. If the campaigns fail to generate the desired outcome, all the efforts, time, and labor are completely wasted. Hence, measuring trade promotion effectiveness is paramount – something we have already covered earlier on.

The main challenge arises when sufficient data to analyze the effectiveness is unavailable in real-time. This is where new-age technology solutions like AI and ML can make a striking difference.

Many software solutions are present in the market today to address the challenge mentioned above. These platforms leverage machine learning and artificial intelligence to help businesses understand, measure and enhance promotion effectiveness. These tech-based tools offer the following features: –

Data Management: Data is the foundation for making game-changing strategies. Timely availability of granular insights can make all the difference, even while planning promotion campaigns. A tech-based software solution powered by ML capabilities accommodates data acquisition, cleansing, enriching, and harmonizing data across the supply chain. Based on such inputs, campaigns can be planned, executed, and measured for effectiveness.

Casual Modeling: Machine learning-based casual modeling understands promotion performance and identifies empirical factors silently impacting the overall effectiveness of promotions.

Simulation and Prediction Modeling: Both of these consider various ‘what-if’ scenarios to adequately understand how campaigns will perform when given a specific scenario. Companies can effectively plan the most optimal promotion to run under user-defined constraints based on the input.

Benefits of Using ML Tools in Measuring Promotion Effectiveness

Using such advanced technology aims to gain optimum visibility into factors silently influencing campaign performances. Those insights are further utilized for a recommendation of optimized promotion parameters.

The following are a handful of benefits businesses can enjoy with these tools: –

  • Gaining insights into promotion performance drivers
  • Evaluating promotion effectiveness and ROI
  • Optimizing campaigns based on real-time data

In a nutshell, promotion effectiveness helps enterprises measure, evaluate, and fully understand the effectiveness of trade promotions. But, in the absence of real-time insights, creating optimized promotions will remain a distant dream. Organizations will spend billions of dollars on campaigns that are incapable of generating enough business and adequate returns on investment. With the help of tech-based software solutions, the hindrance of data unavailability can be aptly addressed, accurate analysis across multiple metrics, like spend ratio, and volume uplift, amongst others, can be easily managed, and creating more optimized promotions will be possible.


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